Amyotropic lateral sclerosis (ALS) is a lethally progressive and irreversible neurodegenerative disease marked by apparent death of motor neurons present in the spinal cord, brain stem and motor cortex. While more and more gene mutants being established for genetic ALS, the vast majority suffer from sporadic ALS (>90%). It has been challenging, thus, to model sporadic ALS which is one reason why the underlying pathophysiology remains elusive and has stalled the development of therapeutic strategies of this progressive motor neuron disease. To further unravel these pathological signaling pathways, human induced pluripotent stem cell (hiPSCs)-derived motor neurons (MNs) from FUS- and SOD1 ALS patients and healthy controls were systematically compared to independent published datasets. Here through this study we created a gene profile of ALS by analyzing the DEGs, the Kyoto encyclopedia of Genes and Genomes (KEGG) pathways, the interactome and the transcription factor profiles (TF) that would identify altered molecular/functional signatures and their interactions at both transcriptional (mRNAs) and translational levels (hub proteins and TFs). Our findings suggest that FUS and SOD1 may develop from dysregulation in several unique pathways and herpes simplex virus (HSV) infection was among the topmost predominant cellular pathways connected to FUS and not to SOD1. In contrast, SOD1 is mainly characterized by alterations in the metabolic pathways and alterations in the neuroactive-ligand–receptor interactions. This suggests that different genetic ALS forms are singular diseases rather than part of a common spectrum. This is important for patient stratification clearly pointing towards the need for individualized medicine approaches in ALS. C9ORF72), superoxide dismutase 1 (SOD1), TAR DNA-binding protein 43 (TARDBP) and mutations in fused in sarcoma protein (FUS), are largely responsible for genetic forms of ALS [SOD1 mutations appear to account for approximately 20% of fALS cases and 1–4% of sALS cases [FUS, encoding a DNA/RNA binding protein, plays a multifunctional role in RNA metabolism  and DNA repair [FUS appear to account collectively for about 5% of fALS cases and up to 1% of sALS cases [Amyotrophic lateral sclerosis (ALS) is a lethally progressive and irreversible neurodegenerative disease characterized by selective loss of motor neurons in the motor cortex, brain stem and spinal cord. Patients usually progress to muscular paralysis leading to death within 2–5 years after the onset of clinical manifestation ,2,3. Abos of ALS . These cs of ALS ,11,12,13LS cases ,15,16. FA repair ,18,19. MLS cases ,22. MostFUS and SOD1 mutation in ALS disease, we have performed a comprehensive gene expression profiling study using microarray hybridization of the iPSC-derived MN models from control individuals and compared with those from FUS-ALS and SOD1-ALS patients. In addition, we further analyzed previously published independent datasets containing the genome-wide RNA profiling of iPSC-derived MN samples from patients with FUS and SOD1 mutations and controls. This microarray dataset (GSE10638) [As the study of sALS and fALS has led to great advances in development of novel therapies, the identification of significantly dysregulated genes and the mechanistic pathways involved in these mechanisms is of the utmost importance. Very few studies, however, have employed interactome-based approaches to identify shared and unique functional interactions among the gene products that could help with distinguishing molecular subtypes in ALS. Furthermore, even fewer reports exist comparing datasets from totally independent labs and different differentiation protocols for motor neuron differentiation to validate such results. To extend our understanding of finding different molecular mechanisms and pathways related to SE10638)  retrieveSE10638)  was screWe analyzed our gene expression datasets of iPSC-derived MNs at DIV 14 of terminal differentiation  difference cutoff ≥ 1.5), in FUS and SOD1 compared with controls, were identified. Analysis of DEGs identified four common genes, including G protein-coupled receptor 50 (GPR50), synaptosome associated protein 23 (SNAP23), NAD(P)H quinone dehydrogenase 1 (NQO1) and nuclear RNA (LOC105376944), shared between the two subtypes  of ALS patients compared to controls, of which 327 overlapping DEGs were identified for both datasets, which were involved in the biological process related to ALS . Analysis returned 16 and 16 DEGs that were shared between FUS and SOD1 types, respectively A, therebTo gain further insights into the biological significance of identified DEGs and to better understand the pathogenic aspects of FUS and SOD1-mediated ALS forms, we performed a GO and pathway enrichment analysis of target genes by querying EnrichR database . In the p < 0.05) with herpes simplex virus infection (HSV), axon guidance and ABC transporters and circadian entrainment as the major altered pathways in FUS-ALS  and biological signatures associated with the DEGs in FUS and SOD1, we performed pathway analysis using the bioinformatics tool EnrichR. In our study, KEGG pathway analysis revealed significant pathways associated  indicated the involvement of genes related to long term depression, synapse-related neuronal functions and metabolic-related processes . SimilarComparisons of FUS- and SOD1-ALS DEGs  in our datasets identified only few common signaling pathways associated with ubiquinone/quinone biosynthesis and SNARE interactions in vesicular transport, whereas the common DEGs (GSE106382) were found to be significantly enriched in amino acid metabolism, p53 signaling pathway and NF-kappa B signaling pathways, indicating that common mechanisms may be involved between these diseases .In addition, comparison analysis between the respective FUS-ALS and SOD1-ALS DEGs across different datasets (present study vs. GSE106382) was also performed because we wanted to focus on common regulatory mechanisms and/or molecular footprints at the pathway level and to better understand the link and the differences encompassed in the development of the two manifestations of these diseases. Interestingly, KEGG pathway analysis of the common DEGs in FUS-ALS revealed significant pathways enriched in HSV infection, metabolism and adherens junction, and most strikingly, “herpes simplex infection” was highlighted as the topmost highly enriched pathway in the network, supporting a potential functional link between HSV infection and FUS-ALS pathogenesis C left. MTo identify key regulators responsible for the observed patterns in FUS and SOD1 gene expression profiling studies, a transcription factor (TF)-target gene interaction analysis was performed using NetworkAnalyst tool . We focuTo get further insights in differentially affected signaling pathways, we went on to perform protein–protein interactome analysis (PPI) in STRING database  to identp-value = < 1.0 × 10−16)  and synaptosome associated protein 23  were highlighted as the topmost highly connected nodes/hub genes among the up- and down-regulated DEGs, respectively. Functional enrichment analysis of DEGs identified in the FUS datasets indicated that genes in these modules were significantly enriched in several pathways, which were related to ErbB signaling (EGFR family of tyrosine kinases), axon guidance/SNARE interactions, TGF-β signaling pathway, cell adhesion and mitogen-activated protein kinase (MAPK) pathways, indicating that these proteins may serve important roles in maintaining the whole interactome.FUS-ALS DEGs  were annotated, matched with the database and used to construct the interactome network. Furthermore, an expanded PPI network/interactome was generated by integrating top 200 known or predicted binding partners to form a tightly connected interactome with 279 nodes representing the proteins and 2517 edges representing the interaction between these proteins (PPI enrichment × 10−16) . Clusterp-value ≤ 1.0 × 10−16, 969 nodes, 6171 edges) with the original seed of 884 annotated DEGs, ribosomal protein L31 (RPL31) was the most highly ranked hub gene revealing the major interconnected cluster among the dysregulated genes  and ribosomal protein S4 Y-Linked 1 followed by phospholipase C beta 1 , RAN binding protein , proteasome 26S subunit, non-ATPase 10 , receptor tyrosine kinase  and mitogen-activated protein kinase 9 . In addition, functional enrichment analysis indicated that these clusters were associated with the ribosome and viral mRNA translation, RNA transport/spliceosome, circadian entrainment, MAPK signaling, Epstein-Barr virus infection and proteasome pathways in FUS-ALS pathogenesis.While the analysis of FUS-ALS DEGs identified in the GSE106382 dataset retrieved many significant clusters in the interactome  of dysregulated genes were significantly enriched in cell cycle, proteasome, SNARE interactions , cell adhesion, PI3K-AKT/MAPK, stress response, ER-Golgi network and mitochondrial mitophagy, consistent with a role for SOD1 in ALS pathogenesis , tyrosine-protein kinase receptor ret , proteasome 26S subunit, non-ATPase 3 , synaptosome associated protein 23  and heat shock protein family A (HSP70) member 2 . Similarly, in the GSE106382 dataset, a total of 1680 annotated genes formed a highly interconnected network (without adding known/predicted interactors) with 1536 nodes connected to 9285 edges. The most highly ranked hub genes among the DEGs were mitogen-activated protein kinase 14 , E1A binding protein P300 , SMAD family member 3 , epidermal growth receptor factor  and protein kinase cAMP-activated catalytic subunit alpha  . In the present study, the unique interactome  . PathwaySNAP23  having a significant number of interacting partners associated with the SNARE interactions in vesicular transport, providing evidence for a conserved functional role of FUS and SOD1 in the vesicle:organelle-mediated membrane trafficking processes during the development of the nervous system , arginine and serine rich coiled-coil 1, RAN binding protein 2 , receptor tyrosine kinases , cytochrome C, somatic  and DExD/H-box helicase 58  were among the pathways with the maximum interactions and were associated with RNA polymerase-based transcription, DNA/RNA metabolism, metabolic pathways, Kaposi’s sarcoma-associated herpesvirus infection and cell adhesion respectively. Of note, SNARE interaction is shared by both datasets  connect within a protein–protein interaction network to identify common pathways for genetic ALS pathophysiology. The PPI  represented in the common network for FUS- and SOD1-ALS identified only one hub gene s system A. Taken s system B. From t compare A,B.p-value = <1.0 × 10−16). A total of 215 nodes (genes/proteins) formed a tightly connected network with 2088 edges (MET), MAPK family signaling cascades (MAPK1/MAPK3), phosphatidylinositol 3-kinase (PIK3CA) and tripartite motif containing 28 (TRIM28) were represented as the most highly ranked hub genes/central nodes showing maximum numbers of interactions with their binding partners and the functional annotations of the genes/proteins associated with the interactome  network enriched in pathways associated with ErbB signaling, neurotrophin and focal adhesion  based on maximum connectivity with the neighboring proteins/genes. Receptor tyrosine kinases such as EGFR/ErbB and RET are involved in numerous neuronal functions and altered pathways associated with the development of neurodegenerative diseases [Finally, we hypothesized that common DEGs across different FUS and SOD1-ALS datasets connect within a protein–protein interaction network to identify common pathways valid across totally independent datasets, thereby increasing the likelihood that they play a central role in the respective ALS pathophysiology. Network analysis of 16 common DEGs across the different FUS datasets (present study vs. GSE106382) identified no significant interactions in the network itself; however, an expanded interactome was generated by adding a total of 200 known or predicted interactors into the network . In this study, we first compared all the DEGs together among FUS- and SOD1-ALS datasets (present study vs. GSE106382) and our analysis showed that gene expression profiles of subjects with FUS and SOD1 (present study) did not overlap with those affected by FUS and SOD1 (GEO dataset) B; howevep < 0.05) between the four FUS and three SOD1 patients vs. four healthy controls using iPSC-derived MNs. A total of only four common DEGs were identified between the FUS and SOD1 samples, and may therefore prove to be critical for neurodegeneration in both diseases and heterogeneity among fALS cases. Furthermore, a comprehensive and systematic screening of FUS and SOD1 transcriptomes utilizing the publicly available microarray data GSE106382 [We have identified a total of 108 and 94 significant DEGs . Put together, the findings mainly highlighted three important results: (i) The existence of heterogeneity across different datasets, which might arise from many reasons, such as the use of different differentiation protocols, different gene arrays, different reprogramming strategies, genetic backgrounds, etc. (ii) Using interaction pathway analysis of first view non-overlapping datasets clearly revealed common pathway involvements. By doing so, our strategy clearly showed that genetic ALS forms do differ more than they resemble each other, suggesting it is not a common disease. Finally, however, comparing datasets within one genetic ALS form revealed central pathways within the respective pathophysiology.Interestingly, the zinc-finger proteins known to have DNA-binding transcription factor activity were found to lead the functional data sets in FUS-ALS. On the contrary, DEGs involved in SOD1-ALS datasets were distributed in cell cycle, metabolism dysfunction, proteolysis, response to stress/stimuli and impaired axonal transport and apoptosis. The in depth analysis of DEGs via GO enrichment analysis and pathway analysis unfolded the presence of unique DEGs across two FUS-ALS gene expression datasets, involved in neuronal developmental and differentiation. In addition, many other intracellular processes also seemed to be involved .Strikingly, the pathway analysis of DEGs in FUS-ALS unfolded significant pathways associated with HSV infection, synaptic vesicle cycle, cell–cell adhesion, MAPK, and PI3-AKT signaling with the HSV infection identified amongst not only the most highly ranked pathways for FUS-ALS but also being clearly upregulated. Of note, a recent report showed that in vitro viral infections can exacerbate FUS-ALS phenotypes in iPSC-derived spinal neurons . More noFUS mutations, which could add to the phenotype of defective DNA damage repair reported previously. [The zinc finger proteins (ZNFs) with the C2H2-type motif predominantly regulating transcriptional programs in neuronal development have now emerged as signatories worthy of investigations in their roles in neurodegeneration. For instance, zinc finger protein Zfp106 interacts with multiple RNA binding proteins, including ALS associated proteins FUS and TDP43 . It appeviously. ,25,61. TMET, SNAP23, PRKG1 and BMP7 were identified, showing the highest degree in all significant clusters , immune system (JAK2) and MAPK/PI3-AKT (MAPK9) signaling incidentally all associated with FUS-ALS. Interestingly, the ribosome was at the core of the interactome network and exhibited the highest degree of connectivity through interacting with ribosomal RNAs. Moreover, a previous study proposed an essential link between RNA stability and neurodegeneration in ALS by illustrating corresponding reductions in mitochondrial components and a compensatory increase in ribosome synthesis using iPSC-derived MNs, showcasing metabolic reliance of the active motor neurons on protein translation and mitochondrial function [Interestingly, interactome analysis based on 884 total DEGs from the GSE106382dataset exposed numerous functional clusters enriched in pathways associated with ribosome, virus transcription/immune system activation, circadian entrainment, the proteasome, the cell cycle, mitochondrial oxidative phosphorylation (OXPHOS) and MAPK and PI3-AKT signaling. In cluster analysis, genes such as function . Owing tSOD1 mutations impair axonal defects through the MAPK kinase family [Contrary to FUS-ALS, totals of 94 and 1680 DEGs corresponding to each dataset in SOD1-ALS (present study and GSE106382) with respect to the controls were identified using bioinformatics analysis. Functional enrichment analysis unraveled the involvement of DEGs of SOD1 MNs with neurological system development and neuropeptide/neurotransmitter activity, suggesting their participation in regulation of genes in the nervous system . Furthere family .The extended analysis of the current study also reflected the sharing of the four DEGs between FUS- and SOD1-ALS, with the leading gene in the PPI analysis being a SNARE protein regulating vesicle trafficking and membrane fusion dynamics, SNAP23. Surprisingly, divergently to our results, the transcriptional profiles from GSE106383 dataset identified 327 DEGs shared between FUS and SOD1-ALS involved with neurodegeneration. Functional analysis of these DEGs demonstrated a series of enriched functional terms associated with transmembrane receptor protein kinase activity, serine/threonine phosphatase activity, ion transport, bone mineralization, cell matrix interaction and nervous system development possibly afflicting neurodegeneration in both diseases. The sharing of important gene clusters between FUS and SOD1-ALS was also vigilant in the PPI network, with the hub genes showing the greatest commonality, justifying shared functions associated with the ribosome, RNA polymerase, circadian entrainment, Kaposi’s sarcoma-associated herpesvirus infection, the cell cycle, mitochondrial metabolic functions, p53 signaling and MAPK signaling pathways, reinforcing their demonstrated functions in ALS. Indeed, aberrant expression and activation of the p38 MAPK pathway in MNs is thought to be important for ALS progression ,65,71. RComparison of FUS- and SOD1-ALS datasets (present study vs. GSE106383) revealed 16 DEGs common to the datasets of FUS- and SOD1-ALS getting significantly visibly associated with biological processes and molecular functions, viz., cell–cell signaling, receptor protein tyrosine/kinase activity, synaptic transmission, cell–cell adhesion and ion binding. Remarkable pathways, including HSV infection and cell adhesion pathways, were shared between FUS-ALS datasets (present study vs. GSE106383) and unambiguously, HSV infection. The pathways were enriched for the FUS-ALS datasets and were consistent with those reported in previous studies in transgenic mice and human samples related to neuroinflammation/immune response in ALS pathogenesis .TRIM28, MET, JAK2, UBC, MAPK1/MAPK3, PLK3CG, GSK3B, PLCG1, ZAP70 and NCOA2 shared between two FUS-ALS datasets, suggesting a critical role of these genes/proteins as potential transcriptomic regulators for FUS-ALS  and has been well documented in a wide range of vital functions, including gene expression, epigenetic mechanisms and DNA repair, which are relevant to different aspects of neurodegeneration [TRIM28 in lytic gene expression during the early primary infection of herpes virus 8 has been identified [TRIM28 and may enhance proliferation or maintenance of neural stem cells [TRIM28 and ZNF568 in transcriptional regulation, apoptosis and DNA repair, follow up mechanistic studies investigating their functional roles will be valuable to understand the implications in FUS-ALS. In contrast to FUS-ALS, analysis of SOD1 DEGs identified a minimal overlap of 16 genes shared across SOD1-ALS datasets (present study vs. GSE106383). In particular, pathway and interactome analysis preceded by GO enrichment analysis revealed a network enriched in pathways associated with synaptic/neuronal functions and metabolism, indicating metabolic dysregulation considered to be a common hallmark of SOD1-ALS. Collectively, commonalities have been exposed between the transcriptional changes seen between FUS- and SOD1-ALS MNs, with disparities observed too in FUS-ALS MNs displaying greater level of changes in genes enriched in MAPK signaling pathway part of signaling signatures in ALS. Dysregulation of protein kinases in MAPK signaling pathway has been shown to contribute to the pathogenesis of several neurological diseases, including ALS [Thus, to sum up, anomalies in protein synthesis machinery and linked ribosomal pathways that signify FUS-ALS pathologies, can in the due course of time, affect RNA processing events in virus–host interactions, eventually causing death of MNs. PPI analysis identified hub genes with highest degrees  FUS-ALS C that maneration ,74,75,76neration ,77. In aneration ,79. Furtentified . Similarem cells . Among iding ALS ,83,84. Hding ALS . Analysiding ALS ,85,86,87ding ALS ,89, but ding ALS ,91, its GPR50, ABCC9, ZNF667 and CALB1) and could prove relevant to several neurodegenerative diseases. In contrast, analysis of TFs in SOD1-ALS datasets (present study vs. GSE106382) identified many significant TFs and their related target genes   with high degrees between two FUS-ALS datasets (present study vsGSE106382) that are linked to prion diseases, transcriptional regulation, viral infection and PPAR signaling pathways. This finding supports the prion-like domain of FUS driving cytoplasmic protein aggregations in ALS ,92. Thesd CDH12) . From thd CDH12) . OverallThus, our comprehensive analysis pinpoints that there are very promising associations with a neurotropic virus enrichment pathway like HSV infection, which is unique to the FUS–ALS, whereas the SOD-ALS is inclined towards the metabolic regulation highlighting differential gene regulations governing genetic predispositions with segregated consequences on biological processes they impact in relation to. Thus, our results open up novel datasets and unexplained transcriptomic targets impacting biological pathways in an idiosyncratic manner, thereby affecting pathogenesis of ALS in corresponding ways.While the present study contributes important insights into the shared genes, pathways and TFs for FUS and SOD1 at the functional level, it would be sensible to highlight the strengths and limitations of the current study. First, our sample was relatively small, purely in silico based and was not integrated map of multiple omic data sets to further analyze complex neurological diseases such as ALS. Although we performed individual normalization for two different datasets independently, the heterogeneity of technical variations in individual studies cannot be removed completely. Further, our analysis on iPSC-derived MN system adapted to artificial cell culture conditions, media, atmospheric oxygen pressure and other possible confounding factors including different microarray platforms, sample preparation/RNA isolation methods, and different clinical strategies based on population studies may bias the results. Therefore, our results should be replicated in other independent and larger cohort of ALS patients-derived MNs but also post mortem tissue analysis, including sporadic forms. Overall, taking the advantage of iPSC model system and by integrating the recent published data (such as the one presented here), our study provides a unique set of data comparing the transcriptomic alterations and/or events found in MNs from FUS and SOD1-ALS patients and controls, revealing conserved signatures/risk factors in viral infection, ribosome/protein translation and cell adhesion processes. From this view, we showed that both genetic and transcriptomic data are useful to guide further investigations to address the impact of virus infection-induced stress granule dynamics in the context of ALS pathology. The conglomeration of microarray technology with simultaneous bioinformatic analysis of the transcriptomic profiles surfaced from RNA data of iPSC-derived MNs from ALS patients has put forth newer molecular signatures and targets to be explored for getting newer insights into ALS pathogenesis and disease progression in parallel.FUS  and in SOD1 (D90A (two clones), R115G (one clone)), which were identified by Sanger sequencing. Three different cell lines from healthy volunteers  and motor neurons (MN) was performed as previously reported  based onTotal RNA was extracted using the RNeasy Kit  according to the manufacturer’s protocol. The precipitated RNA was dissolved in RNase-free water and RNA quality was checked with the Agilent Bioanalyzer . Total RNA (100 ng) was reverse transcribed, in vitro transcribed and reverse transcribed in a second cycle such that the resulting sscDNA could be fragmented and terminally labeled with Biotin using the Ambicon WT Expression Kit (AM4411973)  and the GeneChip Whole Transcript (WT) Terminal Labeling Kit (900671) . Hybridization, staining and scanning of the Affymetrix Human Gene 2.0 ST microarrays (HuGene-2_0-st-v1) were performed according to the Affymetrix GeneChip WT terminal Labeling and Hybridization User Manual (P/N 702808 Rev. 4) . and the data saved in CEL files.FUS, SOD1 or TDP43 mutations, and sALS versus healthy controls, was obtained from iPSC-derived MNs. From this large dataset, we analyzed a subset comprising 4 datasets each for FUS- and SOD1-ALS subtype  compared to 4 healthy controls  that met the inclusion requirements respectively. For gene expression profiling assays, motor neuron populations in neuronal cell culture at approximately 40 days in vitro (DIV) were analyzed in both datasets. We processed and analyzed all the datasets and identified DEGs with p-values < 0.05 and log2 absolute values for fold control FC ≥ 1.5 (see below method).In this study we analyzed 1 independent microarray dataset from ALS patients. Processed gene expression data were utilized with accession number GSE106382  . This general approach to the microarray analysis of Affymetrix expression data is outlined in Partek technical literature. Afterwards, the expression ratios were calculated for each gene and a 1.5 fold change was used as the criterion for significance (FDR < 0.05 and fold change ± 1.5) and exported into an Excel file for further analysis. Similarly, we identified DEGs from the GSE106382 dataset according to the standard procedure like above and analyzed them independently. The flow chart for data analysis is given in Data analysis was performed on Affymetrix CEL files using Partek Genomics Suite 7.0 software  under the “Gene Expression” workflow menu according to the standard pipeline method. Data were normalized and summarized with the robust multi-array average (RMA) method , which ihttps//amp.pham.mssm.edu/Enrich/) [http://www.genome.jp/kegg) pathways [p-value < 0.05 was considered as the cut-off criterion for all enrichment analysis.We performed gene set enrichment analysis via EnrichR (Enrich/)  to identpathways . An adjuhttps://string-db.org/) [p-value < 0.05) and it has been well documented that clustering algorithms were useful for grouping proteins into functional clusters or modules. Therefore, functional clusters were identified using the Markov clustering algorithm (MCL) [To better understand the functional interactions of the DEGs and identify the best candidate genes in ALS disease subtypes, a comprehensive protein–protein interaction (PPI) network of their encoding products was constructed by using the Search Tool for the Retrieval of Interacting Genes/Proteins   and analdb.org/) . The STRhm (MCL)  providedTo gain further insights into the transcription factors (TF) of DEGs, we uploaded the complete list of DEGs into NetworkAnalyst for network analysis. The TF binding site data were predicted by three established TF target prediction databases ,38,39. IP-value < 0.05), and a strict criterion of log2FC ≥ 1.5 or log2FC ≤ −1.5 was used for selection of genes. For the functional enrichment and PPI network analysis, significantly enriched GO terms, pathways and interactomes were identified using a p-value < 0.05 as the cut off value for statistical significance.Raw microarray data were statistically analyzed using PGS software . All of the gene expression samples presented in this study were designed for 3 biological replicates . DEGs with an FDR, using the Benjamini–Hochberg, below 5%, were considered statistically significant (FUS and SOD1 mutations using iPSC-derived MNs from the present study and GSE106382 gene expression profiling datasets. We integrated the common DEGs into various bioinformatics tools to find the enriched GO terms, KEGG pathways, hub genes and TFs. Our findings presented in this study suggest that FUS and SOD1 may develop from dysregulation in several unique pathways and HSV infection was among the topmost predominant cellular pathways connected to FUS and not to SOD1. In contrast, SOD1 is mainly characterized by alterations in the metabolic pathways and alterations in the neuroactive-ligand–receptor interactions. GO terms related to adhesion and signaling were the main functions enriched by the shared DEGs. Furthermore, this study emphasizes the potential of interactome analysis as a dynamic framework to provide new insights into the most significant hub genes underlying the shared pathological signatures between the two diseases. Given the involvement of TFs in viral infection, neuroinflammation and apoptosis, follow up functional studies will be necessary to verify our findings and explore the mechanistic impacts of these candidate TF-genes’ regulation on the outcome of the FUS- and SOD1-ALS disease and might provide potential clues for the development of new therapeutic strategies.In conclusion, the present study provided a comprehensive bioinformatics analysis as a promising approach for the identification of candidate DEGs characterizing different fALS subtypes, thereby revealing key factors and pathway mechanisms common to both diseases and differences that are exclusively highlighted in one of the two entities. We have analyzed transcriptomic changes that occurred in fALS with


Aminoacyl-tRNA synthetase-interacting multifunctional protein 2 (AIMP2) is a non-enzymatic component required for the multi-tRNA synthetase complex. While exon 2 skipping alternatively spliced variant of AIMP2 (AIMP2-DX2) compromises AIMP2 activity and is associated with carcinogenesis, its clinical potential awaits further validation. Here, we found that AIMP2-DX2/AIMP2 expression ratio is strongly correlated with major cancer signaling pathways and poor prognosis, particularly in acute myeloid leukemia (AML). Analysis of a clinical patient cohort revealed that AIMP2-DX2 positive AML patients show decreased overall survival and progression-free survival. We also developed targeted RNA-sequencing and single-molecule RNA-FISH tools to quantitatively analyze AIMP2-DX2/AIMP2 ratios at the single-cell level. By subclassifying hematologic cancer cells based on their AIMP2-DX2/AIMP2 ratios, we found that downregulating AIMP2-DX2 sensitizes cells to anticancer drugs only for a subgroup of cells while it has adverse effects on others. Collectively, our study establishes AIMP2-DX2 as a potential biomarker and a therapeutic target for hematologic cancer. Ku, Kim et al develop a method to analyse the ratio of the alternatively spliced variant of AIMP2 to full length AIMP via single-molecule RNA-FISH. They can subclassify hematologic cancer based on AIMP2-DX2/AIMP2 ratio and find that cells with high AIMP2-DX2 ratio can be sensitized to chemotherapy drugs by depleting AIMP2-DX2. In addition, AIMP2 can also act as a potent tumor suppressor3. In response to DNA damage, a fraction of AIMP2 dissociates from the MSC, and induces apoptosis by binding to p53 and protecting p53 from degradation through ubiquitination by murine double minute 2 (MDM2)4. In addition, AIMP2 augments tumor necrosis factor-α-induced apoptotic signaling and exerts antiproliferative activities in TGF-β and Wnt pathways via distinct working mechanisms6. Given that these pathways are critically implicated in the control of tumorigenesis, AIMP2 is expected to act as a potent tumor suppressor with broad coverage against various types of cancer. In fact, AIMP2 haploid mice showed increased tumor susceptibility compared to the wild-type littermates to carcinogenic treatment, confirming its tumor-suppressive activity in vivo3.Aminoacyl-tRNA synthetase-interacting multifunctional protein 2 (AIMP2) is a component of a macromolecular protein complex consisting of several different aminoacyl-tRNA synthetases (MSC). It is a nonenzymatic auxiliary component required for the integration and stability of this translational complex7. In contrast to AIMP2, which is mainly bound to the MSC, AIMP2-DX2 cannot work as a scaffold for MSC assembly, and thus works as a potent competitor for the tumor-suppressive activities of AIMP27.The full-length AIMP2 transcript consists of four exons, but a small fraction of the pre-mRNA undergoes alternative splicing to produce a variant lacking the second exon (AIMP2-DX2). AIMP2-DX2 protein compromises the tumor-suppressive activity of AIMP2 via competitive binding to p53, but fails to protect p53 from MDM2-mediated ubiquitination8. Moreover, AIMP2-DX2 showed potential as a therapeutic target, since the downregulation of AIMP2-DX2 suppressed the growth of cancer cells and tumors in vivo8. Therefore, quantifying AIMP2-DX2 expression would allow subclassification of cancer patients and identify those who may undergo AIMP2-DX2 targeting treatment. Despite the mounting pieces of evidence, the expression of AIMP2-DX2 and its clinical implications in various types of cancer have not yet been clearly demonstrated.AIMP2-DX2 is receiving increasing attention as an attractive biomarker for diagnosis and prognosis9, but its clinical applicability is questionable. Moreover, molecular beacon fails to examine both AIMP2 and AIMP2-DX2 mRNAs simultaneously in the same group of cells. Considering the competitive situation of AIMP2-DX2 and AIMP2 in carcinogenesis, simultaneous quantitation of the two variants is expected to provide a more relevant marker for accurate assessment of patient samples.The clinical application of AIMP2-DX2 has been limited due to the lack of a detection technique that allows a quantitative assessment of the AIMP2-DX2/AIMP2 expression ratio. Currently, the primary experimental approach relies on PCR amplification and examining the size difference between the two splicing variants through electrophoresis, which cannot be applied to analyze patient samples. Molecular beacon-based detection technique has been developed10. However, as DNA does not provide information on gene expression, in particular those of alternatively spliced RNA variants, RNA-ISH is an alternative approach to investigate mRNA expressions. In addition, RNA-ISH allows analysis at a single-cell level with minimal sample disruption, which makes it an attractive clinical tool. Moreover, using multiplex single-molecule fluorescence ISH (smFISH), expression levels of both AIMP2 and its splicing variant AIMP2-DX2 mRNAs can be quantified and compared together in the same cells.In situ hybridization (ISH) uses nucleic acid probes that are complementary to the target DNA/RNA sequences to detect and visualize the target. Clinically, DNA-ISH has been widely used to visualize DNA pathogenic variants or chromosomal structuresIn the present study, we investigated the significance and clinical implications of AIMP2-DX2 by analyzing samples from the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) database, and validated results with a clinical patient cohort of acute myeloid leukemia (AML), which was found to have the most significant association with AIMP2-DX2/AIMP2 expression ratio in terms of cancer signaling pathways. The potential of AIMP2-DX2 as a key regulator of major cancer signaling pathways in AML was further analyzed using AML cell line, which further supported our analysis of public databases. Moreover, to satisfy the unmet clinical need for a quantitative assessment of the AIMP2-DX2 expression ratio at the single-cell level, we developed an RNA-smFISH-based image analysis tool to measure AIMP2-DX2/AIMP2 expression ratio. Our image analysis showed good concordance with targeted RNA-sequencing, an alternative method to quantitate the AIMP2-DX2/AIMP2 expression ratio. Applying our image-based quantification tool, we subclassified seven hematologic cancers based on their AIMP2-DX2/AIMP2 expression ratios. More importantly, we showed that targeting AIMP2-DX2 expression in those cells with a high AIMP2-DX2/AIMP2 expression ratio could sensitize cells to anticancer drugs. To our surprise, we also found that targeting AIMP2-DX2 in cells with a low AIMP2-DX2/AIMP2 expression ratio had adverse effects and made cells more resistant to anticancer drugs. Collectively, our work provides the development of tools for quantitative assessment of alternatively spliced variants and clinical implications of AIMP2-DX2, as a potential biomarker and therapeutic target in hematologic cancer.11. We first designed smFISH ZZ probe pairs that targeted only the exon 2 and tagged them as channel 1 (C1). We designed another set of ZZ probe pairs that recognized exons 1, 3, and 4, and tagged them as channel 2 (C2). By simultaneously hybridizing and amplifying these two sets of probes, we could visualize AIMP2 mRNAs in one color , while another color can be designated to visualize both AIMP2 and AIMP2-DX2 mRNAs , and 46.1% were male  set analysis, 10 out of 13 predefined major cancer pathways were shown to correlate with the AIMP2-DX2/AIMP2 expression ratio to different degrees and directions among 14 cancer types Fig. , while tQ1) of the AIMP2-DX2/AIMP2 expression ratio in AML, a Kaplan–Meier curve for the overall survival (OS) showed that patients with an AIMP2-DX2/AIMP2 expression ratio ≥Q1 tended to exhibit poor OS  compared to those with an AIMP2-DX2/AIMP2 expression ratio <Q1  can still act as a potent inhibitor of AIMP2. Two-sample t statistics estimated by the generally applicable gene-set enrichment (GAGE) method for 16 AML patients with AIMP2-DX2/AIMP2 ratios ≥0.04 and 6 AML patients with AIMP2-DX2/AIMP2 ratios <0.04 are summarized in Fig. q values. The most differentially expressed pathway by AIMP2-DX2/AIMP2 expression ratio was the mitogen-activated protein kinase (MAPK) signaling pathway , and the female to male ratio was 10:9. Using a cutoff ratio of 0.04, which was the first quartile value  and progression-free survival  rates according to AIMP2-DX2 positivity determined by RT-PCR . Adjustment by age and MRC risk group gave an HR for OS of 2.48 . Similarly, the AIMP2-DX2-positive group showed a worse PFS  months) compared to the AIMP2-DX2-negative group  months; HR, 2.59; 95% CI, 1.32–5.11; P = 0.006). The HR for PFS, adjusted by age, and MRC risk group, was 2.71 .In this cohort of AML patients, there were statistically significant differences in the OS , and hepatocellular carcinoma (log-rank P = 0.24). For these cancer types, although statistically insignificant, the AIMP2-DX2/AIMP2 expression ratio showed a tendency toward increasing along with the stage. Additional studies with a larger patient number will be able to delineate clinical implications of the AIMP2-DX2/AIMP2 expression ratio in these cancers. In contrast, such a prognostic value and correlation with the stage were not evident in other cancer types. Analysis of lung adenocarcinoma is shown as an example in Supplementary Fig. The implication of the AIMP2-DX2/AIMP2 expression ratio for OS and tumor-node-metastasis stage was further investigated in other cancer types  and its correlation to shorter survival in patients, with either de novo or secondary AML33. Indeed, treating AML cells with AZ960, a JAK2 inhibitor, induced apoptosis, indicating the importance of JAK-STAT signaling. In the future, investigation on the regulation of MAPK and JAK-STAT signaling systems by AIMP2-DX2 may provide a better understanding of tumorigenesis of AML.According to our analysis, AIMP2-DX2 expression ratio is positively correlated with MAPK and JAK-STAT signaling pathways in AML. This is consistent with previous studies that showed the importance of these pathways in AML. It has been shown that JNK contributes to drug resistance and hyperleukocytosis in AMLQ1 displayed worse survival outcomes  using another R package, gage40. Candidate genes involved in each canonical pathway were taken from the KEGG (Kyoto Encyclopedia of Genes and Genomics) pathway database 41. Among various pathways, the analysis was focused on the ten major cancer pathways.After processing with HTSeq-count, the RNA-sequencing data of the ICGC/TCGA database were analyzed for DEG set analysis. DEGs by AIMP2-DX2/AIMP2 expression ratio were identified using an R package, 39. DEG set analysis was carried out by using the GAGE method40, and the test statistics across samples were summarized at a pathway level with an FDR q value. Because tumorigenesis is controlled by AIMP2 in a dosage-dependent manner3, and AIMP2-DX2 competitively inhibits the activity of AIMP2, the AIMP2-DX2/AIMP2 expression ratio was assumed to affect the expression of gene sets in a dose-dependent manner. Accordingly, the AIMP2-DX2/AIMP2 expression ratio was considered as a continuous variable in the DEG set analysis. Differences in continuous variables between two groups in a clinical validation cohort were analyzed by the unpaired t test or the Mann–Whitney U test, if appropriate. Survival analysis was performed using the Kaplan–Meier method and compared using a log-rank test. The Cox proportional hazard regression model was applied to determine the HR for AIMP2-DX2 positivity with respect to OS and PFS. For all statistical analyses, P values < 0.05 or FDR q values < 0.10 were considered statistically significant. All statistical analyses were carried out using R version 3.3.1 (http://www.r-project.org). For cell studies, statistical analysis was performed with the Microsoft Excel 2016 software. Statistical significance was determined by using one-tailed t test with unequal variance. All data are presented as mean ± s.e.m. as indicated in the figure legends. P value < 0.05 was considered as statistically significant . The sample size n is presented in the figure legends.The negative binomial generalized linear model was applied for identifying genes differentially expressed by AIMP2-DX2/AIMP2 expression ratio42. Data regarding patient demographics and survival outcomes were obtained by a review of medical records. OS was defined as the duration from diagnosis to death from any cause, and PFS was defined as the time from diagnosis until relapse or death from any cause. Patients who were alive were censored at the date of the last contact. This protocol was approved by the Seoul National University Hospital Institutional Review Board . This study was conducted in accordance with the Declaration of Helsinki provisions.A total of 51 adult AML patients, who agreed to donate bone marrow samples for research after giving informed consent, were included in the clinical validation cohort. Bone marrow samples of these patients at the time of diagnosis were collected using PAXgene Blood RNA tubes . The AIMP2 and AIMP2-DX2 mRNA levels were assessed by RT-PCR. Patients were divided into two groups according to the positivity of AIMP2-DX2. The diagnosis of AML was based on the WHO criteria, and AML was classified according to the FAB classification system. Patients were stratified into three risk groups based on cytogenetic and molecular analyses of bone marrow samples according to the refined MRC criteriaThe cDNA EcoDry Premix  was used to convert RNAs to cDNAs, following the manufacturer’s recommendations. The primer pairs were designed for full-length AIMP2 and AIMP2-DX2, separately. For full-length AIMP2, the forward primer was designed to target the splicing junction between exons 1 and 2, while the forward primer for AIMP2-DX2 was designed to target the splicing junction between exons 1 and 3, skipping exon 2. A common reverse primer targeting exon 3 of the AIMP2 was used. In addition, one more primer pair was designed to target exons 1 and 3 to amplify both full-length AIMP2 (1078 bp) and AIMP2-DX2 (871 bp). Primer sequences for RT-PCR used in this study are provided in Supplementary Table AIMP2 gene. Library construction was performed using the Ion AmpliSeq Library Kit 2.0 , and library templates were prepared and barcoded for sequencing using the Ion OneTouch System, as per manufacturer’s instructions. Sequencing reads were processed using the Ion Torrent Suite Software v 4.0.2 (Life Technologies). Demultiplexed samples were assessed for sequencing quality, and high-quality reads were mapped to the complete hg19 human genome . Expression levels of full-length AIMP2 and AIMP2-DX2 mRNAs were estimated by samtools43. Reads having junctions between exons 1 and 2 (chr7:6009499-6015145) were defined as the full-length AIMP2, while those having junctions between exons 1 and 3 (chr7:6009499-6017814) were defined as AIMP2-DX2. In terms of a CIGAR string, reads corresponding to AIMP2-DX2 had a longer length of skipped lesion than those of wild-type AIMP2.Targeted RNA-sequencing was performed using the Ion AmpliSeq targeting the 2 humidified incubator. Cell lines were purchased from American Type Culture Collection , Deutsche Sammlung von Mikroorganismen und Zellkulturen , and Korean Cell Line Bank . SNU-536MM was internally established at Seoul National University Hospital using a bone marrow sample of multiple myeloma patients. Each culture media  was supplemented with heat-inactivated fetal bovine serum, penicillin/streptomycin, sodium pyruvate, and L-glutamine . All of the suspension and adherent cells were cultured and stabilized at least for 24 h before using them for experiments. All cell lines were authenticated by short tandem repeat (STR) profiling and were tested regularly for mycoplasma contamination. According to the International Cell Line Authentication Committee register (https://iclac.org/), cross contamination has been reported in HS-Sultan cells where no authentic stock is known. We used HS-Sultan cell line authenticated by ATCC, using STR profiling analysis.Seventeen cancer cell lines were cultured under recommended conditions  targets exon 2 of AIMP2, while Hs-AIMP2-E1E3E4 (cat. no. 451471) targets exons 1, 3, and 4 of AIMP2 and AIMP2-DX2 mRNAs. These two probes were labeled with C1 and C2, respectively, and the multiplex detection kit was used to visualize them simultaneously.RNA-smFISH was performed according to the RNAscope Multiplex Fluorescent Assay  protocol with an additional step to attach suspension cells on to a microscope slide. Cells were washed with phosphate-buffered saline (PBS) and fixed in 4% paraformaldehyde  for 1 h at room temperature. Fixed cells were washed two times with PBS, and resuspended with 70% EtOH and kept overnight at 4 °C to permeabilize. Cells were spread on a microscope slide and attached on to the slide by drying it at room temperature for 40 min. This step was omitted when adherent cells were used. Cells were then dehydrated by a subsequent wash with 50, 70, and 100% EtOH. Dehydrated cells were treated with Protease K  for 30 min at 40 °C. Cells were then hybridized with AIMP2 probes (cat. no. 444981 and 451474) for 2 h at 40 °C. Subsequent signal amplification steps were performed by treating cells with AMP1-FL, AMP2-FL, AMP3-FL, and AMP4-FL reagents of the RNAscope detection kit (cat. no. 323382). Cells were incubated in the DAPI solution for 1 min at room temperature to counterstain the nuclei. Mounting solution  was applied, and Zeiss confocal microscope  with a 40× objective (NA = 1.20) was used for imaging.RNA-smFISH images were analyzed with a program coded using MATLAB  v2014b image processing tool. Images were split into two single color channels (red and green) using an intensity threshold, and the two-channel images were analyzed simultaneously. The noise was identified based on the object size and removed from the image. Individual objects from the two channels were identified, and their pixel areas were measured. The ratio of red/green, which reflects the expression ratio of AIMP2-DX2/AIMP2, was calculated by dividing the average pixel areas from the red channel to that of the green channel, as shown below:Therefore,For the single-cell analysis, the blue channel was used for single-cell detection. Connected pixels of the blue channel were identified using an area threshold and labeled as a nucleus. Red or green signals were assigned to the nearest nucleus based on the distance to the center of each nucleus. The red-to-green ratio was calculated by dividing the pixel area from the red channel to that of the green channel for every cell.siRNAs were transfected to adherent cells using Lipofectamine 3000  following the manufacturer’s protocol. For suspension cells, the Neon electroporation system  was used. siRNAs were purchased from Bioneer Korea Inc. or Genolution, and their sequences are provided in Supplementary Table For qPCR, purified RNA was treated with DNase I (Takara) and reverse-transcribed using RevertAid reverse transcriptase (Thermo Fisher Scientific) with random hexamer primers. Synthesized cDNA was amplified by SensiFAST SYBR Lo-Rox Kit (Bioline) and analyzed. Primers used in this study are provided in Supplementary Table Total cell lysates were prepared by sonication in the lysis buffer  supplemented with protease inhibitor cocktail III  and phosphatase inhibitor cocktail I (AG Scientific). A total of 30–50 μg of protein sample was separated on an SDS–PAGE gel and transferred to a PVDF membrane using the Amersham semidry transfer system. Primary antibodies used in this study are: AIMP2 , AIMP2-DX2 , GAPDH , and following antibodies were purchased from Cell Signaling Technology: p-p53 (9284 T), PUMA (12450 T), and TUBB (86298 S). All antibodies were used with 1:1000 dilution except for AIMP2 and AIMP2-DX2, which were used with 1:500 dilution and 1:15000 dilution, respectively.2 incubator. Cells were then pelleted by centrifugation at 1500 × g for 5 min, and the supernatant was carefully removed. Cells were resuspended in 100 µL of dimethylsulfoxide. The absorbance was measured at 545 nm using a Varioskan Lux microplate reader (Thermo Fisher Scientific).MTT assay was performed to measure the cell viability. A total of 100 µL of cell suspension was transferred into a 96-well plate. A total of 20 µL of MTT solution  was added, and cells were incubated for 3.5 h at 37 °C in a humidified 5% COFurther information on research design is available in the Supplementary informationPeer Review fileDescription of additional supplementary filesSupplementary data 1Supplementary data 2Supplementary data 3Supplementary data 4Reporting summary


Several monosaccharides constitute naturally occurring glycans, but it is uncertain whether they constitute a universal set like the alphabets of proteins and DNA. Based on the available experimental observations, it is hypothesized herein that the glycan alphabet is not universal. Data on the presence/absence of pathways for the biosynthesis of 55 monosaccharides in 12 939 completely sequenced archaeal and bacterial genomes are presented in support of this hypothesis. Pathways were identified by searching for homologues of biosynthesis pathway enzymes. Substantial variations were observed in the set of monosaccharides used by organisms belonging to the same phylum, genera and even species. Monosaccharides were grouped as common, less common and rare based on their prevalence in Archaea and Bacteria. It was observed that fewer enzymes are sufficient to biosynthesize monosaccharides in the common group. It appears that the common group originated before the formation of the three domains of life. In contrast, the rare group is confined to a few species in a few phyla, suggesting that these monosaccharides evolved much later. Fold conservation, as observed in aminotransferases and SDR (short-chain dehydrogenase reductase) superfamily members involved in monosaccharide biosynthesis, suggests neo- and sub-functionalization of genes led to the formation of the rare group monosaccharides. The non-universality of the glycan alphabet begets questions about the role of different monosaccharides in determining an organism’s fitness. Python script and associated files used in this manuscript can be found here: https://github.com/jayaasrivastava/GlycopathDBThe curated set of proteins used in this study, with domain assignments, is listed in the Supplementary Excel file (supplementary_data.xlsx), available with the online version of this article. The corresponding 396 references with evidence of experimental characterization are included in the Supplementary Material. The results of the genome scan, which include predictions of monosaccharides as well as the biosynthesis pathway enzymes, is available at blastp. This revealed that the glycan alphabet is highly variable; in fact, significant differences are found even among different strains of a species. Possible implications of this variability may be significant in understanding the evolution of Archaea and Bacteria in diverse and competitive environments. Factors that drive the choice of monosaccharides used by an organism need to be investigated, and will be of interest in understanding host–pathogen interactions. Additionally, the knowledge of the glycan alphabet can be employed for structural characterization/validation of glycans inferred using MS. Knowledge of unique monosaccharides and biosynthetic enzymes can also be used to identify novel drug targets against human pathogens.Carbohydrates, nucleic acids and proteins are important classes of biological macromolecules. The universality of DNA, RNA and protein alphabets has been established beyond doubt. However, the universality of the glycan alphabet is unknown, primarily because of the challenges associated with the elucidation of glycan structures. This has precluded a comprehensive investigation of the glycan alphabet. To address this challenge, we have identified the prevalence of 55 monosaccharide biosynthesis pathways in 12 939 completely sequenced archaeal and bacterial genomes by searching for homologues of biosynthesis pathway enzymes using hidden Markov model profiles, and in a few cases Living organisms show enormous diversity in organization, size, morphology, habitat, etc., but are unified by the highly conserved processes of central dogma: replication, transcription and translation. The enormous diversity seen in life forms is encoded by DNA and decoded primarily by proteins. Both DNA and proteins use the same set of building blocks  in all organisms; yet, they store the requisite information by merely varying (i) the set/subset of building blocks used, (ii) the number of times each building block is used and (iii) the sequence in which the building blocks are linked (collectively referred to as the ‘sequence’) . The infd and l), size (five to nine carbon atoms), ring type , and type and extent of modification . Some pairs of monosaccharides differ from each other merely in the configuration of carbon atoms. The sequence (as defined above) of monosaccharides brings about diversity even in the primary structure of glycans. DNA and protein are linear polymers and the linkage type that connects monomers remains the same throughout. In contrast, glycans can be branched and have alternative isomeric linkages  [Monosaccharides show a lot more structural variation than amino acids in terms of the enantiomeric forms (both d so on) , as welld so on) , a featuMonosaccharides are viewed as the third alphabet of life . How larO-acetylation). Monosaccharide derivatives so obtained are not considered in the present study. Enzymes catalysing one or more steps of the biosynthesis pathway are not characterized experimentally for some of the monosaccharides. Such monosaccharides were not considered in this study.In this study, it is hypothesized that the glycan alphabet is NOT universal, i.e. different organisms use different sets of monosaccharides. This is in contrast to DNA, RNA and proteins. This hypothesis is put forward based on the observations that >60 monosaccharides are found in living systems, the database of glycan structures shows differential usage of monosaccharides and several serotypes differ from each other in the monosaccharides they use. Results obtained by mining whole-genome sequences of 303 Archaea and 12 636 Bacteria are presented herein in support of this hypothesis. Monosaccharides considered in this study are nucleotide-activated moieties that are utilized by glycosyltransferases (GTs) in the biosynthesis of glycans. Subsequent to such a GT-catalysed transfer, monosaccharides may be modified  were installed and used locally. Default values were used for all parameters except when stated otherwise. Word size was set to two for blastp to prioritize global alignments over local alignments. Thresholds for hidden Markov model (HMM) profiles were set based on the best one domain bit score rather than E values, since the former is independent of database size.Protein sequences and 3D structures were obtained from UniProt and PDB (Table S1) databases. Completely sequenced genomes of 303 Archaea and 12 636 Bacteria were obtained from the National Center for Biotechnology Information (NCBI) RefSeq database. These genomes are spread across 3384 species belonging to 1194 genera . Gene neighbourhood was analysed using feature tables taken from NCBI for the respective genomes. blastp queries when the number of enzymes characterized experimentally was not sufficient for an HMM profile (Supplementary_data.xlsx: worksheet2). In-house Python scripts were used to scan genomes to identify homologues. The presence of a homologue for each and every enzyme of the biosynthetic pathway of a monosaccharide was taken as evidence of the utilization of this monosaccharide by the organism. However, absence of a homologue for even one enzyme of the pathway was interpreted as the absence of the corresponding monosaccharide from the organism’s glycan alphabet.Pathways for the biosynthesis of 55 monosaccharides have been elucidated to date . Fructofuranose-6-phosphate and sedoheptulose-7-phosphate are intermediates in the glycolytic pathways, viz. the Embden–Meyerhof pathway and the pentose phosphate pathway, respectively, and these enzymes were not considered for the search. Pathways for biosynthesis of UDP-Glc2NAc and GDP-mannose have been considered separately, since Glc2NAc and mannose are glycan building blocks as well as intermediates in the biosynthesis of several other monosaccharides. Hence, biosynthesis steps of UDP-Glc2NAc and GDP-mannose were excluded from those of their derivatives. An additional pathway for UDP-glucose biosynthesis was considered to analyse its ubiquity, since UDP-glucose is part of both anabolic and catabolic pathways. The biosynthesis of CMP-Leg5Ac7Ac starting from An HMM profile was generated for each step of a biosynthesis pathway except where mentioned otherwise. Profiles were generated in two steps (Flowchart S1). The extended dataset was created to account for sequence divergence. In some cases, no additional sequences satisfying the aforementioned criteria were found; hence, there is no extended dataset. Each profile was given an annotation based on the enzyme activities of proteins that were used to generate the profile and an identifier of the format GPExxxxx; here GPE stands for Glycosylation Pathway Enzyme and xxxxx is a unique 5-digit number (Supplementary_data.xlsx: worksheet1).Thresholds for HMM profiles were set as described below (profile-wise details are given in Supplementary_data.xlsx: worksheet1).The TrEMBL database was used to generate ROC curves. Several of the TrEMBL entries have been assigned molecular functions electronically based on UniRule and SAAS (Table S1). It is assumed that these annotations are correct while generating ROC curves. True positives, false positives and false negatives were identified by comparing TrEMBL annotations with profile annotations.Members of some enzyme families differ in their molecular function, while retaining significant global sequence similarity, e.g. C4- and C3-aminotransferases. Consequently, annotations of several TrEMBL sequences belonging to such families are incomplete, e.g. DegT/DnrJ/EryC1/StrS aminotransferase family protein. In such cases, bit score scatter-plots were used to set thresholds . Scatter plots were also used to set threshold in case of hydrolysing and non-hydrolysing NDP-Hex2NAc C2 epimerases, since many TrEMBL hits are just annotated as NDP-Hex2NAc C2 epimerases.expT or Textend was used as the threshold for some profiles for one of two reasons. (i) The sequences used to generate the profile were a subset of the sequences used to generate another profile; the latter set of enzymes has broader substrate specificity than those of the former set. For instance, sequences used for generating GPE02430  and GPE02530  are homologues, but the former set has narrow specificity. extendT was set as threshold for GPE02430, as lowering the threshold would make this profile less specific. (ii) For some profiles, such as GPE50010 (nucleotide sugar formyltransferase), very few TrEMBL entries that score < expT had been assigned molecular function; hence, a ROC curve could not be generated.hmmer  resulted in 2693 hits with matching annotation and their scores ranged continuously from 705 to 303 bits and then from 57 to 41 bits. It was not possible to generate a ROC curve because of this discontinuity. Hence, 303 bits was set as the threshold.Scanning the TrEMBL database with GPE00530 (glucose-1-phosphate uridylyltransferase family 2) using the default threshold of Escherichia coli UXS [Many sequence homologues catalyse the ‘same’ reaction, but with (slightly) different substrate specificities. Sequence changes that confer such differential specificities are subtle and often unknown. HMM profiles of such families lack the ability to discriminate between sequences with varying substrate specificities. Two products, a major product and a minor product, are formed in certain enzyme catalysed reactions . It is poli UXS . Both thSome steps are associated with more than one profile for one of two reasons. (i) Non-orthologous enzymes are known to catalyse the same reaction, e.g. phosphomannoisomerases. (ii) Two or more profiles are generated, one with narrow and the other(s) with broad substrate specificity. Enzymes used for the former are a subset of enzymes used for the latter type of profiles, e.g. aminotransferases. The process flow adopted to assign annotation for a sequence that satisfies thresholds for more than one profile is shown in Flowchart S2.blastp searches. The default values were set to be ≥90 % query coverage and ≥30 % sequence similarity. However, these values were upwardly revised when query sequences belonged to homologous families that were functionally divergent (Supplementary_data.xlsx: worksheet2). Specifically, similarity and coverage cut-offs were revised by performing an all-against-all blastp search of all experimentally characterized sequences of monosaccharide biosynthesis pathways.HMM profiles were generated only when four or more experimentally characterized enzymes were available (two exceptions are discussed below). Global alignment and sequence similarity were used as the criteria to infer homology based on Bacillus cereus PdeG (Q81A42_1–328) is a retaining UDP-Glc2NAc 4,6-dehydratase [ydratase . It sharblastp approach was used instead of generating an HMM profile. This was because a suitable bit score threshold could not be assigned, which, in turn, was because several of the TrEMBL entries obtained as hits were annotated as CMP-N-acetylneuraminic acid synthetase or equivalent (for hydrolase), or O-acetyltransferase or equivalent (for acetyltransferase).Four experimentally characterized enzymes are known for each of these two families. However, the blastp thresholds were chosen with higher stringency and assignment of substrate(s) and product(s) was made conservatively by manually curating false positives and false negatives from the Swiss-Prot database, details of which are given below.Any description of the molecular function of a protein is stratified and includes specifying the type of reaction catalysed, substrate(s) used, etc. A vast majority of sequences conceptually translated from genome sequences are assigned molecular function based on sequence homology to experimentally characterized proteins. Even though experimental validation is available for only a small fraction of proteins due to practical constraints, such studies have shown that homology-based assignments are generally valid, and deviations typically pertain to the extent of substrate specificity, metal ion dependency and such. Nevertheless, caution is warranted with increasing sequence divergence and one has to be on the lookout for homologues that have acquired new molecular functions as a result of mutation of a handful of key residues . In view of this, in the present study, HMM and Both GDP-rhamnose and GDP-6-deoxytalose were assigned as products of the same pathway, because their biosynthesis proceeds through the same pathway with the exception of the last step being catalysed by homologous C4-reductases. It is not possible to infer whether product specificity of enzymes in this family is absolute or partial, i.e. one is a major product and the other a minor product, due to inadequate experimental data. An identical situation is seen in the pathways for the biosynthesis of CDP-cillose and CDP-cereose, and for CDP-abequose and CDP-paratose. In view of this, prevalence data will be the same for the two monosaccharides of a pair (Supplementary_data.xlsx: worksheet3).l-Fuc2NAc, l-Qui2NAc or Man2NAc3NAcA pathway are also present.Non-hydrolysing NDP-Hex2NAc C2-epimerases (GPE02030) are part of biosynthesis pathways of different monosaccharides. The extent of substrate specificity of the experimentally characterized members of this family is not known, since not all enzymes have been assayed using all possible substrates. In the literature, substrate specificity has been assigned based on the genomic context, and the same approach has been followed in the present study as well. For example, hits for the GPE02030 profile are treated as Man2NAc synthesis pathway enzymes, unless other enzymes of l-Rha2NAc → l-Qui2NAc, (ii) l-rhamnose → 6-deoxy-l-talose, (iii) fucose → fucofuranose, (iv) paratose → tyvelose, (v) galactose → galactofuranose, (vi) GlcA → GalA, (vii) l-Ara4N → l-Ara4NFo, (viii) Per → Per4Ac, (ix) Man2NAc → Man2NAcA, (x) Glc2NAcA → Gal2NAcA and (xi) Bac2Ac4Ac → Leg5Ac7Ac.Some monosaccharides are precursors for other monosaccharides; hence, genomes predicted to have the pathway for the latter monosaccharide will also have the precursor monosaccharide. The following are the precursor–final product monosaccharide pairs encountered in this study: (i) l-arabinose is an extension of the pathway for the synthesis of xylose. However, most genomes predicted to have the xylose pathway also have the l-arabinose pathway. This is because UDP-sugar C4-epimerase family members (GPE02230) catalyse C4-epimerization of glucose, GlcA, Glc2NAc, Glc2NacA and xylose. Assigning substrate specificity solely based on sequence similarity is not possible. The challenge is compounded by the fact that some of these enzymes show broad substrate specificity, while the rest are only specific to a single substrate. Not all enzymes have been assayed for all potential substrates.The pathway for the synthesis of E. coli 14EC033). Just 1 and 2 monosaccharides are used by 188 and 117 species, respectively. Glucose, galactose and mannose, and their 2-N-acetyl  and uronic acid  derivatives are the most prevalent besides l-rhamnose, as the biosynthesis pathways for these monosaccharides are found in >50 % of genomes (The number of monosaccharides used by different species is significantly different  and is i genomes . These mN-acetyltransferase (Hex2NAc from a hexose), C4-epimerase  and C6-dehydrogenase (uronic acid) belonging to the short-chain dehydrogenase reductase (SDR) superfamily, non-hydrolysing C2-epimerase (Glc2NAc to Man2NAc), mutase (6-P to 1-P) and isomerase (pyranose to furanose) , amidotransferase and re group .Occurrence of the common group of monosaccharides in all three domains of life points to their presence early on during evolution. Neo- and sub-functionalization of horizontally acquired and duplicated genes during the course of evolution have been widely reported . It is eE. coli, Pseudomonas aeruginosa and Campylobacter jejuni , and show very little variation in the monosaccharides they use. Both are capsule-producing opportunistic pathogens, suggesting that they might bring about antigenic variation by variations in linkage types, branching, etc. [Mycobacterium tuberculosis, Brucella melitensis, Brucella abortus or Neisseria gonorrhoeae, all of which are human intracellular pathogens, also show insignificant variation. It is possible that different strains of a pathogen are a part of distinct microbiomes and microbial interactions within the biome/with the host determine the glycan alphabet of the organism. Availability of additional characteristics, such as phenotypic data and temporal variations in glycan structures, is critical for understanding the presence/absence of strain-specific variations.Remarkably, variations in the size of the glycan alphabet are significant even at the strain level . Strain- jejuni , possiblng, etc. , even wiThermotogae, suggesting that pathways for their biosynthesis are lost in this phylum. A similar conclusion is drawn for the absence of l-fucose and l-colitose in the TACK  group. Most of the rare group sugars are limited to a very few species in a few phyla (l-glycero-β-d-manno-heptose (ADP-linked) are found only in Gammaproteobacteria, a class that comprises several pathogens. The other three heptoses, which are GDP-linked, are absent in Gammaproteobacteria. Recently, it was found that Helicobacter pylori, belonging to the class Epsilonproteobacteria, synthesizes ADP-glycero-β-d-manno-heptose for activating the NF-κβ pathway in human epithelial cells [blastp queries and not by HMM profiles. In the present study, this pathway turned out to be a false negative because of the high stringency set for blastp thresholds. In view of this, it is possible that such sugars which appear restricted to a few phyla are also found in others.Not all sugars of the common group  are founew phyla . For insal cells . This paMycoplasma) and endosymbionts (e.g. Ehrlichia sp. and Orientia sp.), because the biosynthesis pathways are completely absent. Mollicutes lack cell walls [Buchnera aphidicola: 13 of the 25 strains have the pathway for the biosynthesis of UDP-Glc2NAc, 7 have a partial pathway and 5 do not encode any genes of this pathway. Pathways for none of the other monosaccharides are found in this organism. Pathways are incomplete, i.e. enzymes catalysing one or more steps of the pathway are absent in some organisms. Some species of Mycoplasma, Ureaplasma and Spiroplasma lack mannose-1-phosphate guanylyltransferase, because of which GDP-mannose is not biosynthesized. GlmU, which converts Glc2N-1-phosphate to UDP-Glc2NAc, is absent in Chlamydia sp. However, Glc2N is found in the lipopolysaccharide of Chlamydia trachomatis [None of the monosaccharides are biosynthesized by some mollicutes , Orientia tsutsugamushi (7 strains) and Chlamydia spp. (143 strains) are obligate intracellular bacteria. O. tsutsugamushi does not contain pathways for the biosynthesis of any of the monosaccharides. This is in consonance with the finding that it does not contain extracellular polysaccharides [Rickettsia species have pathways for the biosynthesis of Man2NAc, l-Qui2NAc and l-Rha2NAc. l-Rha2NAc is the immediate precursor for l-Qui2NAc . Rickettsia are known to use Man2NAc and l-Qui2NAc but not l-Rha2NAc [l-Rha2NAc is just an intermediate in these organisms. The pathway for the biosynthesis of UDP-Glc2NAc, precursor for these Hex2NAcs, is absent, suggesting partial dependence on the host (human). Notably, genes for the biosynthesis of Man2NAc and l-Qui2NAc have not been reported so far in humans, which explains why Rickettsia have retained these pathways . Both Rickettsia and Orientia belong to the same order, Rickettsiales. Symptoms caused by these two are similar [Rickettsia spp. continue to use certain monosaccharides while diverging from O. tsutsugamushi [Rickettsia use ticks as vectors, whereas Orientia use mites [Rickettsia akari, the only rickettsial species that uses mites as vectors and contains pathways for Man2NAc and l-Qui2NAc biosynthesis, has been proposed to be placed as a separate group, because its genotypic and phenotypic characteristics are intermediate to those of Orientia and Rickettsia [charides . Ricket-Rha2NAc , indicat similar . In spitgamushi , which use mites ? Ricketkettsia .Spirochaetes contain this monosaccharide. Further analysis revealed the loss of the first (GlmS) or last (GlmU) enzyme of the pathway in several of their genomes. This pattern suggests that organisms of thse phyla are in the process of losing the UDP-Glc2NAc pathway. Incidentally, some of these genomes do contain its derivatives. They include host-associated organisms such as Bacteriodes fragilis, Flavobacterium sp., Tannerella forsythia, Akkermansia muciniphila, Bifidobacterium bifidum, Leptospira interrogans, etc., suggesting that they obtain Glc2NAc from their microenvironment. However, a few free-living organisms that contain derivatives of UDP-Glc2NAc but not UDP-Glc2NAc were also identified. For instance, GlmU is not present in Arcticibacterium luteifluviistationis (arctic surface seawater) and its C-terminus (acetyltransferase domain) is absent in Chlorobaculum limnaeum (freshwater). Nonetheless, both organisms contain the UDP-l-Qui2NAc pathway cluster.UDP-Glc2NAc is the precursor for the biosynthesis of several monosaccharides . However, pathways for its biosynthesis are absent in ~10 % of the genomes excluding endosymbionts. None of the organisms in the FCB  group and N-acetyl derivatives of fucosamine  and of quinovosamine (6-deoxyglucosamine) are found in living systems. The N-acetyl group is present at C2, C3 or C4 position in these isomers. Only a few organisms use more than one of these three isomers  and l-Fuc2NAc (in O177:H21) are present as part of the colanic acid cluster. Four genomes (strains) of Pseudomonas orientalis use Qui4NAc, Qui2NAc and l-Qui2NAc; genes required for the biosynthesis of these three monosaccharides are all in the same genomic neighbourhood.Both enantiomers of a few monosaccharides are reported in natural glycans. The two enantiomers may or may not be biosynthesized from the same precursor, and may be linked to different nucleotides (Table S2). The present analysis shows that both enantiomers are found in only a small number of organisms, in specific genera, class or phyla . Three i isomers . One sucEuryarchaeota contain a higher number of monosaccharides than the TACK group. This could be suggestive of lateral gene transfer events with bacterial members, as members of Euryarchaeota, particularly methanogens, coexist with other organisms in microbiomes [Most of the rare group monosaccharides are absent in Archaea. Members of robiomes  and haverobiomes . It is probiomes . This isblastp queries are from Archaea. The pathway for the biosynthesis of TDP-/dTDP-l-rhamnose has four enzymes, viz. RmlA, RmlB, RmlC and RmlD. Of these, only RmlB could not be found by HMM profile in Saccharolobus sp., Desulfurococcus sp. and Sulfolobus sp., leading to the conclusion that l-rhamnose is absent in these organisms. Analysis of the neighbourhood of RmlA, RmlC and RmlD revealed a sequence that could potentially be RmlB, since it retains conserved residues of this family. This sequence could not be captured by the profile-based search due to stringent thresholds (=400 bits) . Potential RmlB sequences of these organisms score 300–350 bits. This observation suggests that the pathway exists in these organisms, but was not identified due to the stringency of the threshold. However, this is in contrast to other cases of absence of monosaccharides, wherein none of the proteins of a pathway in the genome score even the default bit score of hmmer (i.e. 10 bits).Apart from these possibilities, methodological limitations may have resulted in the apparent absence of monosaccharides in Archaea. Only 4–5 % of the 789 sequences used for generating HMM profiles or as l-Rhamnose and Qui4NAc are biosynthesized as both UDP- and TDP-/dTDP-derivatives . However, the TDP-/dTDP-pathways are found in Archaea and Bacteria, but not the UDP-pathways. TDP-/dTDP-6-deoxy-l-talose is biosynthesized via reduction of TDP-/dTDP-4-keto-l-rhamnose or C4 epimerization of TDP-/dTDP-l-rhamnose . The former pathway occurs in 141 genomes belonging to multiple phyla, and notably in Pseudomonas sp., Streptococcus sp. and Streptomyces sp. The latter pathway is found in 255 genomes belonging to Proteobacteria and Terrabacteria, and notably in Burkholderia sp., Mycobacterium sp. and Xanthomonas oryzae. N,N′-Diacetyl legionaminic acid can be biosynthesized either from the UDP-route or GDP-route . The latter pathway is found in 93 of 96 genomes of Campylobacter jejuni, whereas the former is found in 10 other genomes primarily belonging to Bacteroidetes/Chlorobi.3+; thus, promoting its bioavailability to marine phytoplankton for primary production [E. coli was shown to confer resistance to T7 phage [The importance of glycans, especially in Archaea and Bacteria, is well documented. Establishing the specific role of glycans and studying structure–function relationships is largely hindered by factors such as the non-availability of high-throughput sequencing methods, inadequate information as to which genes are involved in non-template driven biosynthesis, phase variation  and microduction  and agaioduction . MutatioT7 phage . Thus, oAlphaproteobacteria and Actinobacteria, respectively; the bacterial carbohydrate structure database had no Neu5Ac-containing glycan from organisms belonging to this class/phylum [l-Rhamnose and l-fucose are found in 16 % of Deltaproteobacteria and Epsilonproteobacteria genomes and in 25 % of Actinobacteria genomes. However, very few l-rhamnose- and l-fucose-containing glycans from these classes/phyla are deposited in the database, leading to the inference that these are rare sugars in these classes/phyla. This is indicative that monosaccharide usage based on an analysis of experimentally characterized glycans can at best give a partial picture.Genes encoding enzymes for the biosynthesis of Neu5Ac are found in 5 and 0.6% genomes of s/phylum . l-RhamnCampylobacter jejuni PglB, an oligosaccharyltransferase, since it can transfer glycans that have Glc2NAc, Gal2NAc or Fuc2NAc also at the reducing end [N-glycans. l-Rhamnose, Neu5Ac, l-Qui2NAc, Man2NAc and l-Ara4N are not used by Leptospira biflexa (a non-pathogen), but are used by L. interrogans (a pathogen). It is tempting to infer that these monosaccharides impart virulence to the latter, but analysis of monosaccharides used by E. coli strains belonging to multiple pathotypes  did not reveal any relationship between monosaccharides and their phenotype. Tyvelose, paratose and abequose are 3,6-dideoxy sugars that belong to the rare group. These are found primarily in Salmonella enterica, Yersinia pestis and Yersinia pseudotuberculosis. These are present in the O-antigen of Y. pseudotuberculosis [Y. pestis, closely related to and derived from Y. pseudotuberculosis, lacks O-antigen (rough phenotype) due to the silencing of the O-antigen cluster [Yersinia enterocolitica, also an enteric pathogen like Y. pseudotuberculosis, does not contain these monosaccharides. Hence, the role of these 3,6-dideoxy sugars in the O-antigen of Y. pseudotuberculosis does not seem to be related to enteropathogenicity.Rare group monosaccharides are those that are found only in a few species, genera and phyla. Reasons for acquiring rare group sugars can at best be speculative. For instance, Bac2Ac4Ac occurs at the reducing end of glycans N- and O-linked to proteins , but thecing end . Perhapsculosis . Y. pes cluster . Yersinl-Rhamnose, mannose and l-Pse5ac7Ac are found in Bacillus cereus, Bacillus mycoides and Bacillus thuringiensis, but not in Bacillus subtilis, Bacillus amyloliquefaciens, Bacillus licheniformis, Bacillus velezensis and Bacillus vallismortis. Such differences potentially may be exploited towards taxonomic identification, provided that these patterns hold true after analysis of a larger number of strains from each of these species. Enzymes synthesizing monosaccharides that are exclusive to a pathogen vis-à-vis its host can be identified as potential drug targets. An illustrative example is the non-hydrolysing C2 epimerase: it mediates the synthesis of UDP-Man2NAc, UDP-l-Qui2NAc, UDP-l-Fuc2NAc and UDP-Man2NAc3NAc, and is found in 60 % of the archaeal+bacterial genomes, but not in humans . It has already been reported that inhibitors of this enzyme are effective against meticillin-resistant Staphylococcus aureus and a few other bacteria [Besides answering the question of the universality of the glycan alphabet, this study also has led to certain beneficial outcomes. bacteria . Based obacteria , knowledThus, glycans show the least evolutionary conservation among the three macromolecules  . Owing tClick here for additional data file.Click here for additional data file.


Since similar complex diseases are much alike in clinical symptoms, patients are easily misdiagnosed and mistreated. It is crucial to accurately predict the disease status and identify markers with high sensitivity and specificity for classifying similar complex diseases. Many approaches incorporating network information have been put forward to predict outcomes, but they are not robust because of their low reproducibility. Several pathway-based methods are robust and functionally interpretable. However, few methods characterize the disease-specific states of single samples from the perspective of pathways. In this study, we propose a novel framework, Pathway Activation for Single Sample (PASS), which utilizes the pathway information in a single sample way to better recognize the differences between two similar complex diseases. PASS can mainly be divided into two parts: for each pathway, the extent of perturbation of edges and the statistic difference of genes caused by a single disease sample are quantified; then, a novel method, named as an AUCpath, is applied to evaluate the pathway activation for single samples from the perspective of genes and their interactions. We have applied PASS to two main types of inflammatory bowel disease (IBD) and widely verified the characteristics of PASS. For a new patient, PASS features can be used as the indicators or potential pathway biomarkers to precisely diagnose complex diseases, discover significant features with interpretability and explore changes in the biological mechanisms of diseases. Complex diseases threaten human health and life quality. Similar complex diseases make the early diagnosis of patients more difficult due to similar clinical symptoms. Therefore, mining effective biological information to accurately discriminate between similar complex diseases has become the most important research area of biomedicine. In the previous research, several methods based on a single biological network, such as the metabolic network, regulatory network, or protein–protein interaction (PPI) network, have been put forward to aid in disease prediction, diagnosis, prognosis, and so on . NeverthSince genes generally take effect synergistically by forming functional modules, inferring features related to disease classification at the functional level can effectively ameliorate the adverse effects of heterogeneity and obtain more reproducible markers. Some methods utilize Gene Ontology  to diffeA series of methods for disease classification integrate pathway information from the Molecular Signatures Database (MSigDB)  or KyotoBy analyzing the high-dimensional information of expression data and the differential distribution  of a single patient against a given number of normal samples , we propWe applied PASS to classify ulcerative colitis (UC) and Crohn's disease (CD) . UC and We compare our method with seven network-based, GO-based, and pathway-based methods, respectively, and obtain prominent performance against these methods. In addition, our experimental results showed that our method can elucidate the molecular mechanism of UC and CD and has the potential to identify biomarkers with functional interpretability.We downloaded two pediatric datasets and three adult datasets from the Gene Expression Omnibus (GEO) , namely In order to maintain the consistency of data and reduce the impact of noise, we selected data from the same anatomical site and patients under the same conditions. We excluded samples of CD patients during treatment for GSE9686 and samples of Crohn's ileitis for GSE16879. We mapped probes to gene ID using files provided by the corresponding platforms, discarded probes corresponding to multiple genes, and chose the median when multiple probes were mapped to the same gene to eliminate the influence of measurement errors. Only genes detected in all datasets can be used for the downstream analysis. As a result, there were 11242 genes included in all five datasets. From the KEGG database, all human pathways were downloaded using the KEGGgraph package . A totalPathway-based features are more robust while maintaining biological interpretability and tend to be small in number, which can prevent overfitting. In this study, we introduced a new method, called PASS, to evaluate the state of each known pathway. PASS defined the state of a pathway from the aspect of genes and regulatory links. Although it was difficult to analyze the regulatory links in the pathway for each patient, the sample-specific network (SSN) analysis provided a feasible and effective way to mine the different regulatory patterns for each patient.In this study, we first constructed a fully connected network for each pathway. For each dataset, we analyzed the condition-specific state for each disease sample based on the pathway and thus assessed the PASS features. The schematic diagram of our framework is shown in n healthy samples to calculate the Pearson correlation coefficient (PCC) of each pair of genes as background value of the corresponding edge, denoted as nPCC. nPCC is defined as follows:For each fully connected network, we used a group of x1 and x2 are the expression profiles of a pair of genes that correspond to an edge, and E represents the operator of mathematical expectation.where nPCC+1. After that, the difference between background and interference values for the edges in each fully connected network could be quantified, which is represented as ΔnPCC . The difference was derived from the influence of the newly added disease sample, thus it can reflect the specific characteristics of this single sample. Statistically, ΔnPCC obeys the volcano distribution. Therefore, the significance of ΔnPCC can be estimated by the hypothesis test Z-test. Z-value is calculated as follows:Next, a single disease sample was added to the set of the normal samples, and the new PCC was calculated and denoted as The statistical difference of genes between single disease sample and normal samples in the expression level was calculated by fold change:b represents the expression value of gene ix in the individual disease sample and ix over the n healthy samples.where Based on the single sample analysis, we used AUCpath to estimate the activation of a pathway, which can evaluate the enrichment of an attention set as an area under the receiving operating characteristic curve (AUC) according to the ranking of all objects in a fully connected network. There were two sets, called the attention set and the background set. The attention set contained the subset of objects we considered as important, while the background set contained all the possible objects except important objects. We described the states of pathways from the aspect of genes and regulatory links.From the perspective of edges, the input was the Z-value of all edges in each fully connected network, and the output was the activation of each pathway. The scoring approach was divided into two steps. First, the edges that exist in the pathway were regarded as an attention set , and the artificially added edges (in the step of the construction of fully connected network) were considered as the background set . Then, all edges in each fully connected network were ranked in ascending order of their Z-values. Second, AUC was applied to evaluate whether edges in a pathway are enriched in the top ranking, and we thus regarded the AUC value as the quantitative indicator of pathway activation. It is defined as follows:irank represents the ranked position of the i-th edge of the attention set, m represents the number of edges in the attention set, and n is the number of edges in the background set.where Besides, considering that genes were also critical for mining effective information, we calculated the pathway activation from the perspective of genes. We first obtained all genes in pathways. For each pathway, genes on it were regarded as an attention set, and other genes were considered as the background set. Then, we assessed the enrichment of genes in the attention set as AUC based on the ranking of all genes, whereby all genes were ranked in ascending order according to their fold change between a single disease sample and normal samples.After the evaluation of pathway activation from both nodes and edges, we obtained a matrix with PASS scores for pathways and patients.http://string-db.org/) for NetRank, stSVM and CNS, and collected biological processes (BP) terms of Gene Ontology (GO) (http://www.geneontology.org/) for CNS.We built a comprehensive scheme to demonstrate the performance of our approach for distinguishing two similar diseases as well as compare them with other state-of-the-art feature engineering methods. We selected seven representative methods from three aspects: network-based, GO-based and pathway-based methods, that is, NetRank , stSVM , comparaNetRank  is a modCNS  is a fraFor genes on each pathway, PCA  compressWe used the random forest classifier to verify the classification results and applied three-fold cross-validation considering the small sample size of several datasets. For unbiased evaluation, we repeated these experiments for a total of 500 times for the entire datasets. The results of eight methods are shown as ROC curves and AUC corresponding to the ROC in In order to validate the effectiveness of PASS features, we analyzed the differential pathways according to the PASS index. The p-value was calculated using two-sample t-test for the five datasets. Epstein-Barr virus infection pathway, and others are shown in Supplementary We analyzed pathways that were differentially expressed  on all the datasets  in differential expression pathways. DAGs relevant to UC and CD were collected from DisGeNET , and a hN is the number of genes in all pathways, M is the number of DAGs, n is the number of genes in the differential pathways, and m is the number of DAGs enriched in the differential pathways.where log10. We compared the statistical significance of the enrichment of DAGs in the significant differential pathways identified by PASS index with other pathway-based indexes  on five IBD datasets. The results show that our PASS had the more discriminative power and was more stable than other seven methods. Besides, the PASS index can capture more differential expressed pathways with biological interpretability, which indicates that our PASS feature can widen the gap between UC and CD and aid researchers in comprehending the pathogenesis of these two similar complex diseases.Our method can be applied to the classification of two similar diseases and has improved classification accuracy compared to seven state-of-the-art methods. However, due to the complexity and difficulty of similar complex diseases, there is still a space for improvement in the discriminative power. The performance of the PASS method relies on the all human pathway data and the topology of pathways, and more complete pathway information can better reveal the biological processes within cells and the statistic difference between a single disease sample and healthy samples calculated by our method can be also more accurate. With the rapid development of human interaction databases, we believe that the completer and more accurate pathway information could help to further improve the diagnosis of UC and CD.Publicly available datasets were analyzed in this study. This data can be found at Gene Expression Omnibus .XL and RZ conceived and designed the experiments. XL and XC performed the experiments and analyzed the data. XL wrote the paper. ML, JX, F-XW, and JW supervised the experiments and reviewed the manuscript.This work was supported in part by the National Natural Science Foundation of China , the 111 Project (No. B18059), the Hunan Provincial Innovation Foundation For Postgraduate (CX20190123), and the Hunan Provincial Natural Science Foundation of China (Grant No. 2018JJ3568).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.