Synovitis in osteoarthritis (OA) is a very common condition. However, its underlying mechanism is still not well understood. This study aimed to explore the molecular mechanisms of synovitis in OA. The gene expression profile GSE82107 (downloaded from the Gene Expression Omnibus database) included 10 synovial tissues of the OA patients and 7 synovial tissues of healthy people. Subsequently, differentially expressed gene (DEG) analysis, GO (gene ontology) enrichment analysis, pathway analysis, pathway network analysis, and gene signal network analysis were performed using Gene-Cloud of Biotechnology Information (GCBI). A total of 1,941 DEGs consisting of 1,471 upregulated genes and 470 downregulated genes were determined. Genes such as PSMG3, LRP12 MIA-RAB4B, ETHE1, SFXN1, DAZAP1, RABEP2, and C9orf16 were significantly regulated in synovitis of OA. In particular, the MAPK signalling pathway, apoptosis, and pathways in cancer played the most important roles in the pathway network. The relationships between these pathways were also analysed. Genes such as NRAS, SPHK2, FOS, CXCR4, PLD1, GNAI2, and PLA2G4F were strongly implicated in synovitis of OA. In summary, this study indicated that several molecular mechanisms were implicated in the development and progression of synovitis in OA, thus improving our understanding of OA and offering molecular targets for future therapeutic advances. It is well established that OA is not only a single cartilage lesion but also a lesion involving the entire articular cartilage, subchondral bone, and synovial membrane3.In terms of joint disease, osteoarthritis (OA) is the most common form of arthritis and is linked to a high rate of disability. Characterized by the degeneration and destruction of the arthrodial cartilage and hyperosteogeny, the predilection age group of OA is middle age5. In particular, they even consider synovial inflammation a secondary change in the pathogenesis of osteoarthritis6. In addition, the intensity of research in recent years has revealed that the inflammatory mediators produced by synovial inflammation of joints can act not only on articular cartilage but also on some cytokines and proteases that regulate the metabolism of articular cartilage7. On the one hand, synovial inflammation leads to changes in articular cartilage structures and destroys and degrades the cartilage matrix. On the other hand, synovial inflammation exerts a physicochemical action on some cytokines and proteases, accelerating the process of joint degeneration8.For a long time, researchers who studied the pathogenesis mechanism of OA have focused on the pathological changes of articular cartilage and chondrocytes and have not paid much attention to the inflammatory changes of the synovium9. These enzymes and cytokines impair the articular cartilage, leading to the discharge of damage-associated molecular patterns that also have pro-inflammatory functions, producing a perpetuating loop that causes chronic low-grade inflammation11. This inflammation is indicated to contribute to disease progression and disease phenotype12. Studying the fundamental pathological processes in the OA synovium would help in obtaining a treatment for this disease.In OA, the synovium, which is the tissue that lines the joint capsule, frequently reveals inflammation signs, such as those of macrophages that infiltrate the synovium. Pro-inflammatory cytokines such as TNFα and IL1β are produced by these macrophages, activating fibroblast-like synoviocytes to release matrix-degrading enzymes and extra cytokines 13. Then, we identified the differentially expressed genes (DEGs) between OA and HC samples to explore the molecular mechanisms of synovitis in osteoarthritis. In addition, we performed GO enrichment analysis, pathway analysis, pathway network analysis, and gene signal network analysis. This study findings may play significant roles in the genesis of OA and may serve as potential biomarkers in both the prognosis and diagnosis of OA.In the present study, we downloaded the microarray data of accession number GSE82107, which included 10 synovial tissues of osteoarthritis patients (OA) and 7 synovial tissues of healthy controls (HC)14. The significance of DEG identification is Q < 0.05, P < 0.05 and a fold change >1.2. After data preprocessing, a total of 1,941 genes were determined to be differentially expressed in the OA samples compared to the HC samples. Among these DEGs, 470 genes were downregulated, and 1,471 were upregulated. The result is displayed in the volcano plot and the dendrogram -anchor biosynthesis, protein processing in endoplasmic reticulum, and N-Glycan biosynthesis. In the pathway network analysis, a total of 56 pathways, including 37 upregulated pathways and 19 up/downregulated pathways, were identified. We identified 156 relationships between all determined pathways Fig. . In the These results indicated that the degree values of the MAPK signalling pathway, apoptosis, and cancer pathways were the highest. The nature of their relationships remains unknown. Based on the information listed in the GCBI, the cancer pathways were upstream of the MAPK signalling pathway, which was in turn upstream of the apoptosis pathway. In other words, the pathways downstream of the cancer signalling pathways were the apoptosis and MAPK signalling pathways, while the pathways upstream of the apoptosis signalling pathway were the cancer and MAPK signalling pathways.The gene signal network analysis constructs the network of gene interactions, based on the interaction between the proteins and the proteins in KEGG database. In the gene signal network analysis, a total of 257 hub nodes (hub genes), including 246 upregulated genes and 11 downregulated genes, were identified. In addition, there were 337 relationships between all identified genes . The expression of PSMG3 has been reported to be upregulated in a variety of cancer tissues21. Further research is still needed to validate our findings.The GO enrichment analysis revealed that the DEGs were mainly involved in pathways related to transcription DNA-dependent, protein transport, small molecule metabolic process, regulation of DNA-dependent transcription, blood coagulation, cellular protein metabolic process, apoptotic process, negative regulation of DNA-dependent transcription, gene expression, and small GTPase mediated signal transduction. These results confirmed that the pathogenesis of synovitis in osteoarthritis is the result of multiple factors and genes that, directly or indirectly, lead to the occurrence and development of synovitis in osteoarthritis through mutual gene and network regulation. In the present study, we were particularly interested in the apoptotic process (Accession GO:0006915). Apoptosis is a common physiological and pathological phenomenon in the process of biological growth and development. It is the process of self-destruction of living tissue cells that is regulated by genes or other factors. Apoptosis is implicated in various pathological functions, including autoimmune diseases, viral infections, occurrence and regression of malignant tumours, and cardiovascular and cerebrovascular diseases. Our present results indicated a specifically high regulation of the apoptotic process in the OA synovial tissue. Based on this result, we suggest that the development of synovitis of OA is related to autoimmunity and tumour-like lesions22. Our study also suggests that the MAPK pathway also plays an important role in osteoarthritis synovitis. We hypothesized that MAPK-related protein kinase inhibitors not only protect the OA cartilage but also play an important role in the control of synovitis. The procedure of apoptosis is extremely complicated; it is a significant mechanism that regulates the normal development of the body and is involved in a series of regulatory factors that play an important role in maintaining cell homeostasis. The occurrence, development, and treatment of tumours are also regulated by many apoptosis regulating proteins24. Our results indicated that the apoptosis signalling pathway was upregulated in the OA synovial tissue, suggesting that apoptosis is implicated in OA synovial lesions and may also promote cartilage cells apoptosis. In addition, we also found a high regulation of cancer signalling pathways in the synovial tissues, which corroborates our notion that OA synovial tissue lesions have tumour-like properties.In the pathway network analysis, the MAPK, apoptosis, and cancer signalling pathways are the three pathways with the highest degree value, showing that these signalling pathways may play the most important role in osteoarthritis synovitis. Among them, the MAPK pathway is the most important signal transduction system that mediates osteoarthritis cartilage injury, which binds to receptors on the cell membrane by mainly utilizing the specificity of affected joints, inflammatory factors (such as IL-1 and TNF-α), growth factors, and activating intracellular MAPKs signal transduction pathways, causing a series of reactions, such as the increased expression of matrix MMPs , chondrocyte apoptosis, cartilage destruction and so on25. Our research suggests that the FOS gene plays a key role in the synovitis of osteoarthritis, and the NRAS gene indirectly acts on FOS, which provides the theoretical basis and ideas for further experimental study.In the gene signal network analysis, we selected the 15 most important genes, which played a key role in OA synovial lesions, for further analysis. It was essential to reveal the molecular biological mechanism in synovial lesions of OA and even in OA alone. These genes were implicated in the MAPK signalling pathway, cancer signalling pathways, Wnt signalling pathway, and cytokine-cytokine receptor interaction, among others. The results were basically the same as those from the pathway network analysis. The biological processes involved were protein transport, small molecule metabolic process, blood coagulation, cellular protein metabolic process, and apoptotic process, among others and were consistent with the GO functional analysis. The gene NRAS is an N-ras oncogene encoding a membrane protein that shuttles between the Golgi apparatus and the plasma membrane. This shuttling is regulated through palmitoylation and depalmitoylation by the ZDHHC9-GOLGA7 complex. The encoded protein, which has an intrinsic GTPase activity, is activated by a guanine nucleotide-exchange factor and inactivated by a GTPase activating protein. Mutations in this gene have been associated with somatic rectal cancer, follicular thyroid cancer, autoimmune lymphoproliferative syndrome, Noonan syndrome, and juvenile myelomonocytic leukaemia. In the present study, we found that NRAS played an important role in synovitis in OA. Thus, further research is warranted to elucidate the exact effect of NRAS in the OA synovium. The FOS gene is an immediate early gene. When it is stimulated, the FOS gene in the nucleus is activated and FOS protein is synthesized. After phosphorylation, FOS protein returns to the nucleus and forms a complex transcription activator protein 1 with the jun protein, encoded by another family of proto-oncogene c-jun, which binds to the target gene TPA response element to activate transcription. Due to its target gene diversity, transcription activator protein 1 is closely related to the important physiological processes of cells, inflammation and other diseases. FOS not only has an important influence on the proliferation and differentiation of chondrocytes but is also strongly associated with cartilage inflammation. FOS acts as an important messenger in the process of cartilage inflammation, which mediates the inflammatory response of the interleukin family (such as interleukin 17 and interleukin 1β), calcium-containing crystals, and mechanical stimulation26. Therefore, DNA microarrays should be mainly positioned to use the high-throughput approach to observe the overall genome changes under specific experimental conditions to determine effective clues from the complex expression profile to develop the qualitative experiments for further study. It should also be noted that compared with single or few gene expression detected by traditional molecular biology techniques, the specific mRNA expression patterns of expression profiles observed in microarrays can reflect and predict the corresponding biological mechanisms more comprehensively. Before the popularization of proteome technology, DNA microarray technology was the most effective method for studying gene expression at the genome level27.The high-throughput and high-sensitivity detection of the gene chip is a double-edged sword. In practice, the gene expression profiles of animal tissues and cells are not homogenous. There are certain variations among different individuals or cell lines, and the expression profile is also highly susceptible to changes in experimental conditions. Small differences in RNA extraction and cDNA reverse transcription and other steps often cause changes in the expression of different genes, which may make it difficult to determine the biological significance of a large number of genes with positive changes in the microarray assay. On the other hand, actual chip probe hybridization often produces a large number of false positives and false negatives. For the accuracy of single gene determination, the high-throughput microarray is not as accurate as a low-throughput Northern blot, real-time PCR and other technologies. It should also be noted that the other limitation of the application of microarrays is that the changes in determined mRNA levels are only intermediate products of gene expression and are not functional proteins, which are far from directly explaining the mechanisms that functional proteins are mainly involved with in a variety of physiological and pathological changes at the cell and tissue level. Moreover, even if the microarray profile shows different genes with significant changes and the causal relationship among them, the exact judgement cannot rely solely on the microarray technology, and it needs further study using corresponding experimental techniquesIn summary, our present study provided a comprehensive bioinformatics analysis of DEGs, biological processes terms, hub genes, and pathways, which might be associated with OA synovial inflammation. The present results could facilitate improving our comprehension of the underlying molecular mechanisms of OA synovial inflammation. Genes such as NRAS, SPHK2, FOS, CXCR4, PLD1, GNAI2, and PLA2G4F, and their related biological process terms and pathways, such as apoptosis, MAPK signalling pathway, and cancer signalling pathways, may represent potential targets for OA treatment and diagnosis. In the present data analysis, the sample size was small, and the samples were selected from one platform. This may result in a high rate of false positive results. Additional experimental and genetic studies with a larger sample size are still guaranteed to confirm our present findings.https://www.gcbi.com.cn) is a platform combining various kinds of sample information, genetic information, research findings, data algorithms and bioinformatics to generate a “gene knowleDEG base”, which encompasses biology, mathematics, informatics, medicine, computer science, graphics and other disciplines. Over 120 million copies of genomic samples, approximately 90,000 copies of tumour samples and over 17 million copies of genetic information are included in the GCBI platform32. In this study, we used GCBI to identify DEGs between OA and HC samples and perform GO enrichment analysis, pathway analysis, pathway network analysis, and gene signal network analysis.GCBI , the key words of “Synovial biopsies of osteoarthritis patients” were used, and the gene chip data sets GSE82107 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE82107), which were submitted by Broeren, et al. on June 1, 2016, were obtained. GSE82107, which contained a total of 17 samples, including 10 synovial tissues of OA and 7 synovial tissues of HC, was based on the platform of GPL570 [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array13. For microarray analysis, synovial samples were obtained by surgery or via fine-needle arthroscopy from 10 OA patients and 7 controls.In the National Centre of Biotechnology Information (NCBI) Gene Expression Omnibus database  for data analysis. First, the 17 samples were divided into 10 OA cases (OA group) and seven control cases (HC group). Second, the differentially expressed genes (DEGs) were identified. Subsequently, we performed GO enrichment analysis, pathway analysis, and gene signal network analysis. Finally, pathway network analysis was performed based on the results of the pathway analysis. The flow diagram of our study design is shown in Fig. Sample gene chip information was input to the Gene-Cloud of Biotechnology Information (GCBI) analysis platform  to screen out the difference between OA samples and HC samples’ expression of genes.33. Pathway refers to metabolic pathways. Pathway analysis of DEGs can be used to understand the significantly altered metabolic pathways under experimental conditions and is particularly important in mechanistic studies34. We performed GO enrichment analysis  and pathway analyses (P < 0.05) of the DEGs using the GCBI platform.GO, the abbreviation of gene ontology, is a tool that unifies biology by collecting structured, defined and controlled vocabulary for a large scale of genes annotation. Through GO enrichment analysis, it is possible to understand the biological functions of differential gene enrichments35. A large number of studies have shown that the expression of genes is affected by each other. This interactive and mutually restrictive relationship constitutes a complex network of gene expression and regulation. The gene signal network deconstructs the KEGG database, which breaks through the limit of acquiring the interactions of between genes in a single pathway. Therefore, a gene signal network can obtain a protein’s upstream or downstream proteins through the entire KEGG pathway database36. We performed pathway network analysis and gene signal network analysis to determine hub pathways and genes using the GCBI platform.Pathway network analysis is based on the upstream and downstream relationship of signal pathways in the KEGG database, and the interaction network diagram of pathway research is constructed. The pathway network analysis can help to determine the pathway that has a regulating effect on the top stream and on the lowest stream at the same time. By comprehending the relationship between pathways, a deeper understanding of signal pathways can be ascertained


Their accumulation caused by genetic lesions or biochemical inhibition causes severe cellular and developmental phenotypes in all organisms. Functional evidence supports their role as meiosis activators or as signaling molecules in mammals or plants. Oxygenated C4-SBIs like 4-carboxysterols act in major biological processes like auxin signaling in plants and immune system development in mammals. It is the purpose of this article to point out important milestones and significant advances in the understanding of the biogenesis and biological activities of C4-SBIs. Several dozens of sterol structures may be detected and identified in given organisms or tissues . It. ItZea meir host ,118. Receir host . Consequeir host . The plaeir host . Intereseir host ,122. Alseir host ,122. The biomass .Nicotiana benthamiana, as already established in the case of the C14-demethylation step , a group of viruses depending on cellular membranes for replication. The authors also implemented a chemical treatment of plants with 6-amino-2-n-pentylthiobenzothiazole (APB), an inhibitor of the fungal SMO . T. TA. thadundancy . The comdundancy . Howeverosterol) ,135,136.masterol . A carefto auxin . The exo mutants . It is c mutants ,138. Alt mutants . In the ransport . Taken t5/Δ4 isomerase (HSD-1) for the conversion of cholesterol to cholest-4-en-3-one en route to dafachronic acid (S-adenosyl-methionine onto the sterol tetracyclic moiety. The products of the methylation reaction like lophenol or its isomeric 4α-methyl-5α-cholest-8(14)-en-3β-ol are sterol biosynthesis end-products rather than C4-SBIs in this particular context  were found in 34 bacterial genomes from several phyla , thus predicting putative or minimal sterol pathway comprising 2,3-oxidosqualene cyclization products and subsequent C14 and C4 demethylations of those  141]. I. I141].  enzymes . These s elegans  and of trmophila ,144 wherThe overall chemical or genetic inhibition studies of C4-demethylation steps of cholesterol , ergosterol  or phytosterol biosynthesis demonstrate that the removal of both methyl groups at C4-position are necessary for proper growth or development. The accumulation of lanosterol results from the inhibition of the sterol-C14-demethylase, that is a P450-dependent mono-oxygenase. The class of ‘azoles’, that includes imidazoles and triazoles, is widely used as therapeutic and agricultural antifungal drugs ,146. ForPenicillium sp. PF1163A -en-4α-carboxylic acid [The classification of canonical and non-canonical C4-SBIs was proposed by the WD Nes  and the Littman  groups . This delic acid .S. cerevisiae SMO [In mammals, non-canonical oxygenated C4-SBIs are produced by the action of SMO on lanosterol before its demethylation at C14 A. Lanostsiae SMO . Non-cansiae SMO . Mice thsiae SMO . Non-Cansiae SMO . The ransiae SMO .Muscari paradoxum [Ixora coccinea [Skeletonema marinoi, sterol sulfates were associated with programmed cell death that occurs as a mechanism regulating phytoplankton blooms [Conjugated forms of C4-SBIs have been over-looked in biology. Many of these compounds belong to the so-called specialized metabolites . Lanosterol glycosides were reported in aradoxum . Cycloarcoccinea . In the n blooms . In mammn blooms ,159. C4-n blooms . The funn blooms . The limA. thaliana, bulk sterols were quantified 100–200 µg·g−1 fresh weight, canonical C4-SBIs 0.1–0.5 µg·g−1 fresh weight, but brassinolide 4 × 10−5 µg·g−1 fresh weight. Finally, the fate of C4-SBIs as metabolic products requires further investigations, regarding the enzymes implied in this process, and the type of formed products, like for instance hydroxysteroids or sulfates as shown in a study of RORγt receptors [Genes and their products responsible for sterol-C4-demethylation in mammals, yeast, plants, and bacteria have been quite well described by several groups over the last years, as shortly reviewed above. There are striking differences between bacteria and other organisms  regarding C4-demethylation mechanisms recruited during evolution. Plants use distinct C4DMC defined by substrate specificity: SMO1-based demethylation complex of 4,4-dimethylsterols and SMO2-based demethylation complex of 4-methylsterols, whereas other organisms demethylate 4,4-dimethylsterols and 4-methylsterols consecutively with a single SMO-based complex (of three enzymes and a tethering protein ERG28). In eukaryotes, genetic or chemical inhibition of the sterol-C4-demethylation may lead to the accumulation of significant amounts of C4-SBIs or transient C4-SBIs, but also of their oxygenated derivatives classified as canonical and non-canonical. The review of biological activities of 4-methylsterols characterized so far in different kingdoms shows clear common features. In yeast, in plants or mammals, the accumulation of C4-SBIs (including oxygenated derivatives) has deleterious effects on growth and development. In yeast and mammals, the role of C4-SBIs in cell division was shown. In plants and mammals, 4-methylsterols and 4-carboxysterols act as signaling molecules interfering with major pathways like auxin in plants and immune system in mammals. The major challenge remains the identification of physical interactions of sterol ligands with their targets. Another critical issue is the analytical scale of those biosynthetic intermediates: just like some oxysterols or brassinosteroids, 4-carboxysterols may be present at very low concentration, e.g., at “hormone-dose” and are therefore not detected in sterol profiles. For example in eceptors .


Staphylococcus aureus. Here, we report the formicapyridines which are encoded by the same gene cluster and are structurally and biosynthetically related to the fasamycins and formicamycins but comprise a rare pyridine moiety. These compounds are trace-level metabolites formed by derailment of the major biosynthetic pathway. Inspired by evolutionary logic we show that rational mutation of a single gene in the biosynthetic gene cluster encoding an antibiotic biosynthesis monooxygenase (ABM) superfamily protein leads to a significant increase both in total formicapyridine production and their enrichment relative to the fasamycins/formicamycins. Our observations broaden the polyketide biosynthetic landscape and identify a non-catalytic role for ABM superfamily proteins in type II polyketide synthase assemblages for maintaining biosynthetic pathway fidelity.The formicamycin biosynthetic gene cluster encodes two groups of type 2 polyketide antibiotics: the formicamycins and their biosynthetic precursors the fasamycins, both of which have activity against methicillin-resistant  Formicapyridines are similar to pentacyclic fasamycin and formicamycin aromatic polyketides but with a pyridine moiety. Here the authors rationally mutate the biosynthetic gene cluster to increase production and identify a non-catalytic role for the ABM superfamily of proteins. In addition, some pathways are not linear, and the final product is accessed via several routes due to the inherent substrate plasticity of the biosynthetic enzymes; well-studied examples include the rapamycin and erythromycin pathways2. Thus, in practice, any biosynthetic pathway will lead to the accumulation of a mixture comprising the final product plus varying concentrations of pathway intermediates and shunt metabolites. The composition of such a mixture will vary further when alternate growth conditions are used3. Sometimes the final product of the pathway cannot be clearly discerned.An idealised linear biosynthetic pathway to a complex natural product can be imagined proceeding through a series of intermediate structures which would exist for some finite time as the pathway product accumulates. These hypothetical intermediates would exist freely in solution or bound to enzymes in the case of assembly line processes, and eventually all flux through the pathway would end and only the final product would exist. In reality no pathways proceed in this manner as the situation is complicated by varying rates of reaction for the different steps, meaning that some intermediates accumulate, while the inherent reactivity of other intermediates, or their ability to act as substrates for housekeeping enzymes not dedicated to the pathway, means that shunt metabolites often arise. Matters are further complicated by the fact that pathways are often convergent, with multiple units made in parallel before assembly into the final product, for example in the biosynthetic pathways to macrolide or aminoglycoside4. We recently reported the formicamycins, aromatic polyketides produced by Streptomyces formicae KY5 isolated from the fungus-growing plant-ant Tetroponera penzigi  operating in conjunction with post-PKS modifications that include O-methylation and halogenation, plus oxidative and reductive modifications. Intrigued by these compounds, which exhibit potent antibacterial activity, we employed targeted metabolomics to identify further congeners that may have been missed during manual analysis of culture extracts. This led us to identify the formicapyridines (1–9), pyridine containing polyketide alkaloids which represent additional products of the formicamycin (for) biosynthetic gene cluster (BGC). Products of type II PKS systems which contain a pyridine moiety are rare7.Such mixtures of compounds are said to comprise a series of biosynthetic congeners, and their identification can provide valuable information about a biosynthetic pathway and may sometimes lead to new biosynthetic understandingigi Fig. 5. In tot8. In contrast, the formicapyridines are minor shunt metabolites that arise due to derailment of the formicamycin biosynthetic pathway. We show here that reprogramming the for BGC, by deleting the forS gene encoding an ABM family protein, leads to significantly increased production of formicapyridines with concomitant reduction of the combined titre of fasamycins and formicamycins. These and other mutational data lead us to the hypothesis that ForS is not a cyclase but forms part of a multienzyme complex where it acts as a chaperone-like protein to aid in maintaining pathway fidelity and performance. The discovery and engineering of formicapyridine biosynthesis raises intriguing questions regarding the evolution of type II PKS biosynthetic pathways and the origins of natural product chemical diversity.Challis and co-workers have shown that the majority of actinomycete derived polyketide alkaloids, including those containing a pyridine moiety, arise from reactive intermediates formed after transamination of aldehydes generated from reductive off-loading of the thioester bound polyketide chain from a type I modular PK9. Instead, as most fasamycin and formicamycin congeners are halogenated , and a strain in which the halogenase gene was deleted (ΔforV)5 5 Fig. . Our earn = 3) ethyl acetate extracts of the wild type (WT), Δfor and ΔforV strains, along with equivalent extracts from uninoculated mannitol soya flour (MS) agar plates, were analysed by liquid chromatography high-resolution mass spectroscopy (LC-HRMS). Using the Profiling Solutions software (Shimadzu Corporation), the WT dataset , leaving twelve previously identified fasamycin and formicamycin congeners, two additional fasamycin/formicamycin congeners that remain uncharacterized due to trace levels, and the known isoflavone 6-chlorogenistein11  with mass spectra suggesting a close structural relationship to the fasamycins/formicamycins; these varied only in the number of methyl groups present. By searching for equivalent ions lacking chlorine atoms we identified three additional congeners (1–3). Compounds 1–6 were not initially observed in the UV chromatograms of the WT, and qualitative examination of the LCMS data suggested titres at least 100-fold lower than for the formicamycins. Furthermore, the m/z data implied the presence of a single nitrogen atom.Replicate  with MS characteristics like those of 4–6  was added, as our previous study showed this leads to the biosynthesis of brominated formicamycin congeners mM was a4–6 Fig. , support1–9 that were used to search the online chemical database REAXYS. This suggested that they represented new structures. To isolate sufficient material for structure determination and antibacterial assays, the growth of S. formicae KY5 was scaled up . After 9 days incubation at 30 °C the combined agar was chopped up, extracted with ethyl acetate, and the solvent removed under reduced pressure. The resulting extract was subjected to repeated rounds of reversed-phase HPLC followed by Sephadex LH-20 size exclusion chromatography leading to small quantities of purified 1–6.HRMS data allowed us to predict molecular formulae for 2 which was isolated in the largest amount (~2 mg). HRMS and 13C NMR data indicated a molecular formula of C28H25NO6 ; observed m/z 472.1753 ([M + H]+); Δ = −0.4 ppm) indicating 17 degrees of unsaturation. The UV spectrum showed absorption maxima at 229, 249, 272 and 392 nm indicating a complex conjugated system different to that of the fasamycins/formicamycins5. Inspection of the 13C NMR spectrum showed 21 sp2 carbons (δC 99.90–167.54 ppm), one carbonyl carbon (δC 191.81 ppm), four methyl carbons , one methoxy carbon (δC 55.80 ppm) and one sp3 quaternary carbon (δC 40.49 ppm). The 1H NMR spectrum revealed the presence of five methyl singlets , two aromatic proton singlets (δH 7.62 and 7.65 ppm), plus four aromatic proton doublets , 6.70 , 6.34  and 6.42 ). Limited 1H–1H COSY correlations meant we were reliant upon HMBC-based atomic connections, and through-space NOESY correlations, which led to two potential substructures consisting of 26 carbon atoms, leaving one carbonyl, one phenol and one quaternary carbon unassigned , the closest structural congener. The data were in excellent agreement with the main differences at C7, C22 and C23, consistent with the adjacent nitrogen atom , and the methoxy at C5 with H4 (e.g. 3 and 6). We also used NOESY and HSQC correlations to distinguish C14 and C16 once one was chlorinated: given the NOESY correlations to the gem-dimethyl groups of C26 and C27 with H16, and the disappearance of a HSQC linkage for C14, we concluded that C14 was chlorinated (e.g. 4–6). Compounds 1–6 exhibit optical activity with small [α]20D values between +8° and +13° indicating probable atropoisomerism about the axis of the C6–C7 bond. We attempted to assign the absolute configuration by combining computational approaches with electronic circular dichroism (ECD) spectra as done for the fasamycins5. However, despite scaling up production to isolate additional 2, meaningful ECD spectra could not be obtained. Full characterization data for 1–6 are presented in Supplementary Note 7–9, we have assigned preliminary structures based on 4–6, with a bromine atom replacing chlorine at C14.We first determined the structure of 1–6 displayed no antibacterial activity against Bacillus subtilis 16812 using overlay assays at 5 µg mL−1 (the average concentration for illustrating fasamycin and formicamycin bioactivity). Similarly, no inhibition was seen at tenfold this concentration (50 µg mL−1), with only small zones of inhibition at 100-fold (500 µg mL−1). To confirm this was not a result of reduced diffusion from the disc, assays were set up to test growth of B. subtillis 168 in liquid cultures containing compounds 1–6, but again they showed no activity.Compounds for and ΔforV mutants verified that the fasamycin/formicamycin BGC is required for formicapyridine production. The Δfor mutant does not produce 1–6 .Interrogation of LC-HRMS data from extracts of the Δ5. This would be followed by a series of cyclization and aromatisation steps, presumably in a sequential manner, with the final cyclization event probably involving formation of the B-ring. However, premature hydrolysis of the acylcarrier protein from putative intermediate 27, prior to the action of a final cyclase, would liberate the enzyme-free, β-ketoacid species 28 that would be highly facile to spontaneous decarboxylation yielding the methylketone 29. The action of an endogenous aminotransferase could then generate either of the species 30 or 31, which could undergo cyclization, dehydration and oxidation to yield 1. Consistent with a shunt pathway, no aminotransferase encoding gene could be identified in the for BGC, and although numerous putative aminotransferase encoding genes were identified in the genome, none could be linked to a role in formicapyridine biosynthesis. Alternatively, direct addition of ammonia to the C23 ketone would generate an imine, tautomerize to the enamine, and the resulting amino group could then attack the C7 ketone. Subsequent dehydration would lead directly to the formicapyridine system with no requirement of an oxidation reaction.The very low level of formicapyridines made by the WT strain suggests they are shunt metabolites arising from aberrant derailment of fasamycin/formicamycin biosynthesis. On this basis, we suggest a biosynthetic pathway as described in Fig. S. formicae KY5 was cultivated on MS agar plates for two days and then overlaid with a solution of  sodium acetate . This was repeated on the four following days, and after a total of 9 days incubation the agar was extracted with ethyl acetate and the most abundant congener was isolated , and the 13C NMR spectrum was acquired. Due to the small amount of material, and overlapping signals, only eight of the intact acetate units could be unambiguously identified based on their coupling contests. In addition, a singlet was observed for C25 which showed weak enrichment . The function of TcmI has been verified by in vivo mutational analysis19 and biochemical characterisation21. It catalyses cyclization of the final ring during formation of tetracenomycin F1 from tetracenomycin F2, and this reaction is remarkably like that proposed in our hypothesis for the formation of ring-B as the final step of fasamycin backbone assembly  comprising the conserved barrel domain of the cupin superfamily (cl21464)24. Finally, the gene products ForS and ForU are both single domain proteins (pfam 03992) belonging to the ABM superfamily (cl10022). A notable member of this family is ActVA-orf6, which functions as a monooxygenase during biosynthesis of the polyketide antibiotic actinorhodin; the structure of this enzyme has been solved and it is topologically related to the PKS cyclase TcmI and homologues25. Notably, ABM family domains are found in several PKS cyclases including the C-terminal domain of BenH from the benastatin BGC27, which also comprises an N-terminal TcmN like cyclase domain (pfam 03364); and WhiE Protein 1 and other members of the SchA/CurD-like family of PKS enzymes commonly associated with BGCs for the biosynthesis of spore pigments in Streptomyces spp30. SchA/CurD- and WhiE Protein 1-like enzymes are comprised of an N-terminal ABM domain and a C-terminal PKS cyclase domain .The gene product ForD shows significant sequence similarity to aromatase/cyclases such as the N-terminal domain pfam 03364) of the archetypical tetracenomycin polyketide cyclase TcmN, which belongs to the Bet v1-like superfamily (cl10022) of the a31 to make in-frame deletions in each of these five putative cyclase genes . Three independent mutants generated from each gene deletion experiment, along with the WT strain, were grown on MS agar and cultured at 30 °C for 9 days. To assess differences in secondary metabolite production the ethyl acetate extracts from each culture were subjected to HPLC(UV) and LCMS analysis  or the constitutive ermE* promoter (ΔforR/forR), although the titres did not reach that of the WT strain in all cases. For the ΔforU mutant complementation with forU restored production of the non-halogenated congener 10 only, indicating a polar effect on the downstream halogenase forV gene. Subsequent complementation with a forUV cassette under the ermE* promoter in which the two genes were transcriptionally fused led to the production of halogenated fasamycin and formicamycin congeners.To interrogate their roles, we used Cas9-mediated genome editingforS mutant when compared with the WT  and solvent extraction, followed by isolation (3.4 mg) and structural elucidation identified this compound (13) as the C24-carboxyl analogue of fasamycin C (which we have named fasamycin F). This is the first congener identified with the C24-carboxyl group intact.In contrast, production of formicapyridines was increased ∼25-fold in the Δ WT Fig. . These efor BGC is responsible for the production of three structurally differentiated pentacyclic scaffolds. Inspired by evolutionary considerations we introduced a gene deletion into the BGC, which significantly altered the relative levels of these metabolites: the titre of formicapyridines was significantly increased (∼25-fold) in contrast to the fasamycin/formicamycins, which were decreased to approximately one-third of the WT titre. These results raise a series of questions about the cyclization events associated with the fasamycin/formicamycin biosynthetic pathway, and with the maintenance of pathway fidelity.Using targeted metabolomics, we identified a family of pyridine containing polyketide natural products that we have named the formicapyridines. Remarkably, these compounds are derived from the fasamycin/formicamycin biosynthetic machinery meaning that the forS led to the isolation of shunt metabolite 13 with a C24-carboxyl group that was not observed from the WT. However, while ForS is implicated in ring-B cyclization it is not required for this role, nor, apparently, for the biosynthesis of any congeners. Rather, ForS seems to decrease the production of aberrant congeners from the pathway, e.g. formicapyridines, while increasing overall productivity. The most parsimonious interpretation of these data is that another of the for BGC gene products is the actual catalyst for ring-B formation, and that ForS acts as a chaperone, which modulates or stabilises the assembly, or arrangement, of a multienzyme complex to optimise production of the fasamycins, and therefore ultimately the formicamycins. This has the consequence of minimising the production of shunt metabolites (formicapyridines). Thus, while deletion of forS leads to the phenotype desired from our thought experiment, the mechanism by which this occurs is not what was anticipated. Interpretation of the for BGC bioinformatic analysis suggests the most likely candidate for a ring-B  cyclase is ForL due to its close relationship with TcmI, which catalyses a similar final cyclization step during tetracenomycin biosynthesis21. The apparent lack of any intermediates or shunt metabolites being accumulated by the remaining mutants is somewhat surprising and suggests an absolute requirement for the formation of a PKS-cyclase complex before biosynthesis can be initiated.Our data suggest that formation of ring-B of the fasamycin scaffold is the final biosynthetic step, followed by thioester hydrolysis to liberate the ACP and subsequent decarboxylation to remove the carboxyl group attached to C24 Fig. . Consist32. Pradimicins are pentangular polyketides similar to the benastatins27, and the BGC for their production contains three PKS cyclases equivalent to ForD (PdmD), ForL (PdmK) and ForR (PdmL)33. It also contains two ABM domain proteins  which were assigned monooxygenase roles. Through heterologous expression of PKS gene cassettes it was deduced that for the biosynthetic pathway to function correctly, and yield a pentangular backbone, all three cyclase genes (PdmDKL) plus the ABM domain monooxygenase PdmH must be co-expressed. In this pathway one oxidation reaction to form the quinone structure is required. These results led the authors to propose a model in which the two cyclases PdmKL and the monooxygenase PdmH form a multienzyme complex that engulfs the entire polyketide molecule during its assembly and work synergistically to ensure the correct reaction pathway occurs thereby minimising the production of shunt metabolites32. Similarly, formation of the unusual discoid metabolite resistomycin involves an extremely rare S-shaped folding pattern and requires the coordinated function, likely as a multienzyme complex, of core PKS proteins in addition to three distinct cyclase enzymes23. Moreover, heterologous reconstruction of the resistomycin pathway gave no products when the minimal PKS plus first cyclase were assembled23, a rare observation that is in keeping with our data showing the requirement of all the putative cyclases ForDLRU to produce any pathway derived metabolites, including shunts.These observations are reminiscent of studies regarding the biosynthesis of pradimicinfor BGC polyketide backbones does not require a monooxygenase. Consistent with this, deletion of forS does not abolish polyketide production, but instead affects pathway productivity and fidelity. Thus, based on our mutational data and the observations discussed above, we hypothesise that ABM family enzymes can act as monooxygenases and/or as ancillary proteins to tune, in some way, the PKS enzyme complex function, and therefore the biosynthetic pathway, during aromatic polyketide biosynthesis. Genes encoding these proteins occur commonly in PKS BGCs, and multiple paralogues are often present even when monooxygenase reactions are not required. It is noteworthy that several PKS cyclases occur as fusion proteins with an ABM domain, which can be located at either terminus. We speculate that these may represent examples of mature pathways where the chaperone-like function of the ABM family protein has become essential, leading to selective pressure for the encoding gene to become transcriptionally fused with other cyclase encoding genes.Biosynthesis of the 13C2] sodium acetate was purchased from CORTECNET. All solvents were of HPLC grade or equivalent. NMR spectra were recorded on a Bruker Avance III 400 MHz NMR spectrometer equipped with 5 mm BBFO Plus probe. 13C NMR spectra for 2 and isotopically enriched 2 after feeding  sodium acetate were recorded on a Bruker Avance III 500 MHz NMR spectrometer equipped with a DUL cryoprobe at 30 °C.Unless stated otherwise all chemicals were supplied by Sigma-Aldrich or Fisher Scientific.  sodium acetate (1 mL of a 60 mM solution) after 24 h, 48 h, 72 h, 96 h and 120 h. After a further 72 h the agar was extracted and purified using the methods described above to yield a sample of 2 (0.9 mg). This material was analyzed by LCMS and inverse gated 13C NMR . However, due to the weak and overlapping signals, only the following coupling constants (JCC) of the intact acetate units were recorded: C24–C1, 44.61 Hz; C2–C3, 68.58 Hz; C4–C5, 68.16 Hz; C20–C21, 56.18 Hz. In addition, C14, C16, C18 and C22 have coupling constants of 66.47, 67.61, 42.08 and 60.43 Hz, respectively. Spectroscopic data is presented in Fig. S. formicae ΔforS was cultivated on 4 L (~120 plates) of MS agar at 30 °C for nine days. The agar was extracted and purified using the methods described above to yield 13 (3.4 mg). This material was analysed by LCMS and 1D and 2D NMR . Spectroscopic data is presented in Supplementary Figs. S. formicae WT or mutant strains (n = 3) were grown on MS agar at 30 °C for nine days. A rectangle of agar (2 cm3) was excised from each petri dish, sliced into small pieces and shaken with ethyl acetate (1 mL) for 20 min. The ethyl acetate was transferred to a clean tube and the solvent removed under reduced pressure. The resulting extract was dissolved in methanol (200 µL) and analysed by LCMS but using the following modified UPLC method: Phenomenex Gemini C18 column ; mobile phase A: water +0.1% formic acid; mobile phase B: methanol. Elution gradient: 0–2 min, 50% B; 2–14 min, 50–100% B; 14–18 min, 100% B; 18–18.1 min, 100–50% B; 18.1–20 min, 50% B; flow rate 1 mL min−1; injection volume 10 μL.10 , formicamycin C 16 , formicapyridine D 4  and fasamycin F 13 . The content of 10 and 16 was determined by UV absorption at 285 nm. The content of 4 and 13 was determined by MS analysis of the base peak chromatogram (positive mode). Each standard solution was measured three times.Calibration curves (Supplementary Fig. Further information on research design is available in the Supplementary InformationPeer Review FileReporting SummaryDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Source Data


Obesity is defined as an excess amount of body fat and represents a significant health problem worldwide. High prevalence of vitamin D (VD) deficiency in obese subjects is a well-documented finding, most probably due to volumetric dilution into the greater volumes of fat, serum, liver, and muscle, even though other mechanisms could not completely be excluded, as they may contribute concurrently. Low VD could not yet be excluded as a cause of obesity, due to its still incompletely explored effects through VD receptors found in adipose tissue (AT). VD deficiency in obese people does not seem to have consequences for bone tissue, but may affect other organs, even though studies have shown inconsistent results and VD supplementation has not yet been clearly shown to benefit the dysmetabolic state. Hence, more studies are needed to determine the actual role of VD deficiency in development of those disorders. Thus, targeting lifestyle through healthy diet and exercise should be the first treatment option that will affect both obesity-related dysmetabolic state and vitamin D deficiency, killing two birds with one stone. However, VD supplementation remains a treatment option in individuals with residual VD deficiency after weight loss. Its receptors have been found all over the human body indicating multiple functions. Since body VD is mainly result of endogenous synthesis it is nowadays often considered as a hormone more than vitamin in the narrow sense. The active form of VD, 1,25-dihydroxyvitamin D 2D), not only stimulates calcium absorption, osteoclastic bone resorption, and osteoblast function and decreases PTH (parathyroid hormone) secretion but also has extraskeletal functions such as decreasing collagen type 1 production, enhancing muscular function, and stimulating cell differentiation, insulin secretion, and the immune system [ ng mL−1 , has bee ng mL−1 . Hence, Obesity is defined as an excess amount of body fat and represents a significant health problem world-wide. The association between VD deficiency and obesity as well as with obesity-related diseases has been confirmed by numerous studies, but the presence of a causal relationship is still unclear. There are many possible explanations regarding the inverse relationship between increased adiposity, particularly abdominal obesity, and low plasma VD concentrations, as discussed below, but so far none of these hypotheses can completely elucidate this relation. Therefore, it is possible that several mechanisms have an impact on the interaction between VD, obesity, and related diseases. Hence, more studies are needed to investigate each one, not only to investigate the causal relationship, but also to identify and establish VD supplementation treatment that could possibly positively affect adiposity and related disorders. However, so far, studies have shown inconsistent results regarding the clinical implication of VD supplementation, which raises the following main question. Is VD deficiency only a coincidental finding in obese subjects or could it actually have an important role in the development and progression of obesity and chronic illnesses?A low plasma level of VD usually leads to impaired calcium absorption in the bowel and a lower plasma calcium level, which consequently leads to enhanced bone turnover and impaired bone mineral density (BMD). However, obese subjects have a greater BMD then lean people, as well as greater cortical thickness and cortical tissue mineral density. Hence, obesity has a positive impact on peak bone mass acquisition . The lacLow VD is also associated with poor health behaviors. Recently, its deficiency has been associated with a large number of disorders such as metabolic syndrome, cancers, and autoimmune, psychiatric, and neurodegenerative diseases, but the causative role of VD deficiency in many of these conditions remains unclear . Since VNowadays, volumetric dilution of VD is the most probable mechanism of the inverse relationship between vitamin D serum levels and BMI. Even though obese and lean subjects have similar amounts of VD, in overweight people, VD is distributed into a larger volume, making serum concentrations lower. Namely, 25(OH)D is distributed dominantly into the serum, muscle, fat, and liver—compartments that are increased in obesity . The expOn the other hand, VD sequestration in adipose tissue (AT) hypothesis was introduced by Wortsman et al.  who demoAnother possible mechanism for lower 25(OH)D is impaired hepatic 25-hydroxylation. Targher et al.  reportedp < 0.0001) decrease in the expression of the cytochrome P450 2J2 gene, which codes for the enzyme 25-hydroxylase, and a 49% (p < 0.05) decrease in the expression of cytochrome P450 27B1, which codes for the enzyme 1a-hydroxylase in the subcutaneous AT of the obese group compared to lean subjects [Furthermore, one study was performed in order to elucidate whether low serum levels of VD in obese people could be due altered VD metabolism in AT. They showed that there is a difference in gene expression in VD metabolizing enzymes between normal weight and obese people, which suggests that AT could be involved in the metabolism of VD and does not only passively store fat-soluble nutrients. In fact, they found a 71%  radiation from sun exposure is the main source of VD (80–90%) , the lowAdverse dietary habits are common in obesity, which could lead to a lower intake of VD. Since dietary sources are minor contributor to total VD intake in humans, this explanation is less probable . MoreoveLately, there have been some studies and experimental data that support the hypothesis that VD could be involved in the pathogenesis of obesity, rather than just being a consequence. Some experimental data suggest that an increased level of parathyroid hormone, due to VD deficiency, promotes lipogenesis by greater calcium inflow in adipocytes ,22. AnotSince a low VD could contribute, or at least is related to dysmetabolic condition and related diseases, as discussed previously, the main question remains: Could VD supplementation therapy have long-term health benefits for visceral adiposity-related disease patients?Numerous studies have demonstrated conflicting results regarding the management of adiposity-related diseases with VD supplementation. Zittermann et al.  demonstrStudies suggest that an increase in plasma VD is not associated with a significant increase in blood pressure ,32,33. VEpidemiological, experimental, and clinical studies also suggest a possible key role of chronic low-grade inflammation in the development of metabolic syndrome features, such as type 2 diabetes  and cardInsulin resistance is a commonly seen condition in obese people and predisposes individuals to the development of type 2 diabetes. Inconsistent findings have been reported regarding the effect of VD supplementation on insulin resistance. Several cross-sectional studies have shown that VD deficiency is associated with hyperglycemia ,41, hypeWamberg et al.  also expIn The high prevalence of VD deficiency in obese subjects is a well-documented finding that is most probably due to volumetric dilution into the greater volumes of fat, serum, liver, and muscle present in obese people. However, other mechanisms cannot completely be excluded, as they may contribute concurrently. A low VD concentration cannot yet be excluded as a cause of obesity due to the incompletely explored effects of VD receptors found in AT. VD deficiency in obese people does not seem to have consequences for bone tissue, but may affect other organs, even though studies have shown inconsistent results and VD supplementation has not yet been clearly shown to benefit the dysmetabolic state. Hence, more studies are needed to determine the actual role of VD deficiency in the development of these disorders and the effect of VD supplementation. Thus, treatment with VD still remains controversial. Nevertheless, when it is required, due to volumetric dilution in obese patients, higher doses of vitamin D are needed to achieve the same serum concentration compared to lean subjects . Once ac


Head and neck squamous cell carcinoma (HNSCC) remains a morbid disease with poor prognosis and treatment that typically leaves patients with permanent damage to critical functions such as eating and talking. Currently only three targeted therapies are FDA approved for use in HNSCC, two of which are recently approved immunotherapies. In this work, we identify biological pathways involved with this disease that could potentially be targeted by current FDA approved cancer drugs and thereby expand the pool of potential therapies for use in HNSCC treatment. We analyzed 508 HNSCC patients with sequencing information from the Genomic Data Commons (GDC) database and assessed which biological pathways were significantly enriched for somatic mutations or copy number alterations. We then further classified pathways as either “light” or “dark” to the current reach of FDA-approved cancer drugs using the Cancer Targetome, a compendium of drug-target information. Light pathways are statistically enriched with somatic mutations  and contain one or more targets of current FDA-approved cancer drugs, while dark pathways are enriched with somatic mutations  but not currently targeted by FDA-approved cancer drugs. Our analyses indicated that approximately 35–38% of disease-specific pathways are in scope for repurposing of current cancer drugs. We further assess light and dark pathways for subgroups of patient tumor samples according to HPV status. The framework of light and dark pathways for HNSCC-enriched biological pathways allows us to better prioritize targeted therapies for further research in HNSCC based on the HNSCC genetic landscape and FDA-approved cancer drug information. We also highlight the importance in the identification of sub-pathways where targeting and cross targeting of other pathways may be most beneficial to predict positive or negative synergy with potential clinical significance. This framework is ideal for precision drug panel development, as well as identification of highly aberrant, untargeted candidates for future drug development. There are over a half million cases of head and neck cancer diagnosed every year worldwide and almost 90% of these are squamous cell carcinomas (HNSCC). The 5-year survival rate remains around 50% for patients . Most HNStandard treatment for early HNSCC includes surgery or radiation with the addition of chemotherapy for later stages of disease ,6. HNSCCPathway-based approaches offer several advantages for understanding genetic alterations in disease cohorts . MappingThe Genomic Data Commons (GDC) contains National Cancer Institute (NCI)-generated data from the most comprehensive cancer genomic datasets including The Cancer Genome Atlas (TCGA), Therapeutically Applicable Research to Generate Effective Therapies (TARGET), and the Cancer Genome Characterization Initiative (CGCI). These datasets contain genomic and clinical data across 33 cancer types and subtypes with the goal of providing the cancer research community a tool for discovering better care for cancer patients at the molecular level . The genFor this analysis, we used the most updated version of the GDC clinical HNSCC data, which contains a total of 528 patients. Of these, 508 patients have accompanying somatic mutation data and 296 have accompanying copy number data. We annotated 57 (11%) patients as HPV positive and 118 (23%) as HPV negative. Within this dataset, 333 (66%) patients did not have conclusive HPV results. The mean patient age was 61 years, 73% of patients were male, 85% were white, and 75% of patients smoked or used tobacco.Most primary tumor tissues (63%) were in the oral cavity, 22% from the larynx, and 15% from the oropharynx. Over half (54%) of the tumors were in stage IV, and 43% of the tumors were in stages I-III, while 3% did not have tumor stage annotation. At the time of study, 57% were living and 43% were deceased with a median follow-up of 541 days. The majority of patients (66%) had radiation treatment with a mean radiation dose of 3,945 centigray (cGy) and most of these were in combination with other therapy types: 29% of patients had both radiation and surgery, 26% had surgery only, 25% had chemotherapy, radiation, and surgery, 6% had chemotherapy and radiation, 3% had radiation only, and 7% did not have therapy annotation. Additionally, 19 patients were categorized as ‘other’ and had a combination of the standard therapeutics along with immunotherapy or targeted molecular therapy denoting a small percentage of patients that were treated with precision medicine methods.For the 507 patients with somatic mutation data, we identified a total of 15,085 unique gene symbols in the mutation data. The number of mutations per patient ranged from 1 to 1,999 with a median of 82 and a mean of 117.35. There were a total of 67,302 mutations and most of them were missense mutations (84%). According to the GDC annotation, 85% of these mutations were moderate and about 15% were high impact. The top 20 mutated genes, ranked by the number of mutations per gene symbol, include TP53, TTN, FAT1, MUC16, CSMD3, SYNE1, CDKN2A, LRP1B, NOTCH1, KMT2D, PIK3CA, PCLO, DNAH5, USH2A, FLG, NSD1, RYR2, AHNAK, CASP8, and PKHD1L1 .For the 296 patients with copy number alteration data, we identified a total of 21, 872 unique altered genes. The number of copy number alterations per patient ranged from 1 to 2,812 with a median of 308.5 and a mean of 444. The top 20 copy number altered genes, ranked by number of patients harboring a copy number alteration in the gene, include: HAS_MIR-548K, MIR528K, PPFIA1, ANO1, FADD, CTTN, CDKN2A, FGF3, FGF4, FGF19, CCND1, ORAOV1, CDKN2B, C9ORF53, MYEOV, SHANK2, CDKN2B-AS1, MIR2664, ZMAT3, and ABCC5 .By mapping HNSCC-specific mutational and copy number aberrations to biological pathways, we aimed to better understand the pathway context and identify druggable space. We queried the Reactome biological pathways  and calcWe then assessed whether these pathways contained drug targets according to Cancer Targetome information . We desiIf we consider light and dark pathways as a fraction of the total number of HNSCC mutation enriched pathways, approximately 34% are light while 66% are dark. For the HNSCC copy number enriched pathways, approximately 38% are light while 62% are dark. We found that a comparable percentage of mutation and copy number enriched pathways are potentially light to current FDA-approved drugs. On the one hand, for a disease like HNSCC which needs more therapy options, the finding that approximately 35–38% of disease-specific pathways may be potentially in scope of cancer drugs presents an enormous targeting opportunity. The HNSCC mutation enriched light pathways range in size from 1 to 618 genes, with 86–100% of their gene members mutated throughout the cohort. The percentage of gene members targeted within mutation enriched light pathways ranges from <1% to 100%, with between 1 and 87 targets of FDA-approved cancer drugs. The HNSCC copy number enriched light pathways range in size from 1 to 1164, with 90–100% of their gene members have copy number alterations throughout the cohort. The percentage of gene members targeted within copy number enriched light pathways ranges from 3% to 100%, with between 1 and 59 targets of FDA-approved cancer drugs.On the other hand, the 62–65% of HNSCC-enriched pathways that are dark to cancer drugs present an opportunity for developing new chemical compounds or repurposing from non-cancer domains. These HNSCC mutation enriched dark pathways range in size from 1 to 111 genes and range in coverage of mutated genes from 93–100%. Thus dark pathways are comparable to light pathways with regards to extent of enrichment for HNSCC patient mutations. The HNSCC copy number enriched dark pathways range in size from 1 to 29 and all have a 100% coverage of copy-number altered genes across the cohort.Assessment of the overlap of enriched pathways between the two data types revealed that 43 pathways are enriched in both mutations and copy number alterations as well as light to FDA-approved cancer drugs . SimilarBy further prioritizing both the light and dark pathways, we can better focus efforts on drug targeting for HNSCC that takes into account the diverse genetic landscape of this disease. Prioritization of light and dark pathways for further analysis can consider many factors- here we focus on prioritization based on mutational load on pathways and the extent to which the pathway is affected in the overall cohort. Pathway pathogenicity, or percent of pathway mutated, provides a measure of the extent to which a pathway is affected across many samples. Out of our 111 mutation enriched light pathways, 82 had 100% of their gene members mutated across the HNSCC cohort. We then ranked according to proportion of the HNSCC cohort with one or more gene mutations in the pathway. In https://reactome.org/content/detail/R-HSA-373753)  . For thi-373753) .Nephrin (NPHS1) is mostly expressed in kidney podocytes and is an important component of the adherens junction between these kidney cells . HoweverPrevious studies have found the PI3K pathway to be the most highly mutated oncogenic signaling pathway in HNSCC: 12.6% of HNSCC tumors were found to have a mutation in the PI3KCA gene itself, with 30.5% of tumors carrying a mutation in the pathway and 6.6% carrying multiple mutations in the PI3KCA pathway . FurtherSimilarly, when we rank mutation enriched dark pathways according to pathway pathogenicity, 186 had 100% of their gene members mutated across the HNSCC cohort. We also rank dark pathways according to proportion of HNSCC cohort with one or more gene mutations in the pathway . We noteWe can also prioritize copy number enriched light and dark pathways in a similar manner. The top 15 copy number light pathways are shown in Out of our 197 dark pathways, all 197 had 100% of their gene members exhibit a copy number in 1 or more patients. For dark copy number enriched pathways, we also see a similar proportion of the cohort represented as found for the dark mutation enriched pathways. The TRAIL signaling pathway, which is the top ranked copy number enriched dark pathway , has preA substantial subset of HNSCC cancers are associated with human papillomaviruses (HPVs) and have been found to be biologically and clinically distinct from non-HPV associated HNSCC cancers ,32. GiveFor the HPV positive cohort, the proportion of light pathways that are found to be mutated ranges from 30% to 100%, and the proportion of the cohort affects for these light pathways ranges from 1% to 87.7%. For dark pathways, the proportion of pathways found to be mutated ranges from 42% to 100%, and the proportion of the cohort affected for these dark pathways ranges from 1% to 31.5%.For the HPV negative cohort, the proportion of light pathways that are found to be mutated ranges from 44% to 100%, and the proportion of the cohort affected ranges from <1% to 99%. For the dark pathways, the proportion of pathways found to be mutated ranges from 68% to 100%, and the proportion of the cohort affected ranges from <1% to 50%.The pathways in the Reactome database for homo sapiens have a hierarchical structure where the highest level refers to major biological processes such as Cell Cycle, Developmental Biology, Metabolism, Neuronal System, Signal Transduction, etc. Each of these pathways contain “nested” subpathways within it. Here, we denote parent pathways as the top of the hierarchical structure and child pathways as any subsequent pathways that are contained within the parent pathway.It is important that we consider this nested architecture in our analysis, as it is possible that dark pathways can actually be nested within higher level light pathways. An example of this is the “GABA A receptor activation” pathway, which is a dark pathway nested within 3 pathways. The parent pathway is “GABA receptor activation”, which itself was not a significantly enriched pathway. This pathway is however nested within another parent pathway, “Neurotransmitter Receptor Binding and Downstream Events”, which is a light pathway. This is further nested within the “Transmission across Chemical Synapses”, which is a light pathway contained in “Neuronal System”.Further development in targeted therapy options for HNSCC treatment is a high-need area given that current standard of care often leaves patients with loss of critical functions. Here we present our computational approach to explore the potential drug-targetable space in HNSCC based on the known HNSCC genetic landscape. This work fits into the larger context of precision medicine by aiding translational efforts such as the design of cancer-specific drug panels and nominating targets and pathways for further bench testing. While previous approaches have investigated HNSCC from the perspective of multiple data types ,19 and/oWe have identified biological pathways relevant to this disease using both somatic mutation and copy number alteration data and stratified pathways according to whether or not they are currently in scope of FDA-approved drugs. Light pathways offer a strategy for targeting HNSCC-relevant pathways given the set of currently FDA-approved drugs and thus could potentially bring treatment options to patients through an accelerated route. Our workflow uniquely leverages the Cancer Targetome for drug-target interactions, which allows us to consider all possible targets with which a given FDA-approved drug may interact, according to supporting evidence. This differs from other analyses of targetable pathways in cancer, which often focus on the presumed, or so-called primary targets of drugs or on the class of targets with which a drug interacts ,33. AddiAdditionally, our workflow identifies dark pathways, which also offer valuable information for targeting HNSCC pathways, as they indicate pathways likely to have disease importance that are currently not in scope of cancer drugs. Thus, dark pathways present opportunities for further HNSCC research and drug development that will be of interest to the precision therapy community. It is of note that the Fanconi Anemia pathway was among the top 15 copy number-enriched dark pathways (found in nearly 30% of the cohort) given that head and neck cancer risk is high in patients with Fanconi Anemia a rare condition caused by inherited FANC gene family mutations –36.Additionally, we have also stratified this analysis by HPV tumor status and highlighted how these subgroups have differentially affected light and dark pathways. While previous work has established that HPV-positive and HPV-negative subsets of HNSCC cancers are biologically distinct ,37, and With regards to prioritization of targets, our approach offers both light and dark pathways as a means to broaden potential to target tumor vulnerabilities , or to din silico evaluation of GDC genomics data and functional analysis of HNSCC cell response to inhibitor panels. Our approach considers mutational load, copy number alteration load of pathways (significantly enriched pathways) and potential druggability of pathways (light pathways to cancer drugs). Future directions for this work could include the application to expression data to find pathways enriched for over or under-expressed genes as potentially of interest for targeting. We note that by considering drug-target interactions for all FDA-approved cancer drugs, we potentially open up the space to consider repurposing drugs from other cancer areas to treat HNSCC. This work could also be expanded to consider non-cancer drugs for potential repurposing into the cancer domain to treat HNSCC. For instance, recent work by Hedberg, et al. found that the use of nonsteroidal anti-inflammatory drugs was associated with improved outcomes in PIK3CA-mutated head and neck cancer .   We note that one or more of the authors are employed by a commercial company: 'Roche Sequencing Solutions' and 'Regeneron Pharmaceuticals, Inc'.Please provide an amended Funding Statement declaring this commercial affiliation, as well as a statement regarding the Role of Funders in your study. 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Is the manuscript presented in an intelligible fashion and written in standard English?PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: YesReviewer #2: Yes**********5. Review Comments to the AuthorPlease use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: Here, the authors present a timely analysis correlating sequencing data of head and neck cancer subtypes mutational loads with its associated "drugome". The authors submit robust analyses, which well articulate their workflow, results, and significance of their work. I anticipate these data are of high value to otolayrngologists, and translational researchers developing drugs for head and neck cancers. Such work warrants a commentary piece in this reviewer's opinion.Reviewer #2: The authors use a systems biology approach with publicly available data to discern "novel" drug targets in both HPV negative and HPV positive HNSCC based on genetics and drug binding data. Overall, this manuscript has compiled a fairly superficial analysis of public data, which, at first appearance, has already been completed in a similar fashion by other groups, and which is limited to just somatic mutations. In this case, the team applies a unique statistical approach to highlight potential pathways as light or dark, and chose to highlight the PI3K pathway as evidence that the approach was successful .Major comments:1) Demonstrate that this approach has novelty and can make an impact. The authors need to perform some functional validation, or use data from publicly available drug testing databases, in HNSCC models, to show that this approach adds to existing approaches already used in the literature.2) Enhance the discussion to compare and contrast this approach to similar previous approaches, reference existing literature on the topic.3) Why did the authors chose to ignore copy number and/or expression data when designing this approach? Other approaches have successfully implemented additional data for lead target optimization.4) The writing needs to be improved. The authors only have 20 references and simply gloss over important references and details related to this work, for example, in this section "Targeting the PI3K pathway with PI3K inhibitors in enriched tumors was effective in vitro and in vivo". No references.Minor comments:1) Figure quality could be improved substantially, especially the mutation rate figure.**********what does this mean?). If published, this will include your full peer review and any attached files.6. PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool,  16 Sep 2019Please find our response to reviewers uploaded as a separate file, along with our revised manuscript.AttachmentChoonoo_etal_ResponseToReviewers_091619.pdfSubmitted filename: Click here for additional data file. 26 Sep 2019Illuminating Biological Pathways for Drug Targeting in Head and Neck Squamous Cell CarcinomaPONE-D-19-19753R1Dear Dr. Blucher,We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.Within one week, you will receive an e-mail containing information on the amendments required prior to publication. 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Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact With kind regards,Andrew Birkeland, M.D.Academic EditorPLOS ONEAdditional Editor Comments :The authors have strengthened their manuscript with the addition of integrated copy number analysis. They do provide a new approach to analysis of existing genomic datasets that can provide value to the head and neck cancer research community. Overall, the manuscript is well-written.Reviewers' comments:Reviewer's Responses to QuestionsComments to the Author1. 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Is the manuscript presented in an intelligible fashion and written in standard English?PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: YesReviewer #2: Yes**********6. Review Comments to the AuthorPlease use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: Acceptable for publication in its current form, no further revisions requested. Very interesting work.Reviewer #2: The authors have addressed my primary concerns . The manuscript has been improved and adds an additional account of head and neck cancer genetics from publicly available databases to the literature.**********what does this mean?). 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The aim of the study was to identify key long noncoding RNAs (lncRNA) and related subpathways in the periodontal ligament tissue following orthodontic force.  We adopt a novelty subpathway strategy to identify lncRNAs competitively regulated functions and the key competitive lncRNAs in periodontal ligament disorders after undergoing orthodontic force. To begin with, patients with orthodontics in our hospital were enrolled in our research. The relationship of lncRNA-mRNA was established through shared predicted miRNA by using the hypergeometric test, Jaccard coefficient standardization, and the Pearson coefficient to determine the valid interaction relationship. After embedding screened lncRNA interactions to pathways, the significant subpathways were recognized by lenient distance and Wallenius approximation methods to calculate the false discovery rate value of each subpathway. DNAJC3-AS1, WDFY3-AS2, LINC00482, and DLEU2 were screened.  The lncRNA-mRNA intersections including 263 lncRNAs, 1,599 mRNAs, and 3,762 interacting pairs were obtained. The enriched mRNAs were further enriched into various candidate pathways such as the PI3K-Akt signaling pathway. Several subpathways were screened, including the PI3K-Akt signaling pathway, 04510_1 focal adhesion, and p53 signaling pathway, respectively. The network of pathway-lncRNA-mRNA was constructed. Several key lncRNAs including DNAJC3-AS1, WDFY3-AS2, LINC00482, and DLEU2 as aberrantly expressed lncRNAs involved in orthodontic force might play crucial roles in periodontal ligament disease pathogenesis. Orthodontic tooth movement was considered a response to remodeling processes of the alveolar bone, which are initiated by the application of orthodontic force in the periodontium . Bone reVAMP3 [CCND2 [Long noncoding RNAs (lncRNAs) have longer than 200 nucleotides regulating gene expression in epigenetics and transcription at the level of posttranscriptional repression of their target genes in a sequence-specific manner . In addiVAMP3  and CCND3 [CCND2 . HoweverAccording to the competitive endogenous RNA (ceRNA) hypothesis, lncRNAs competitively bind to microRNA sites and regulated mRNAs expression levels , 11. ThrA total of 16 cases of adult patients that designed for the extraction of four first molars were selected randomly , including 10 females and 6 males, aged 20–26 years old.All the patients who participated in the experiment have healthy, noncarious human premolar teeth and informed consent from the hospital. The maxillary first premolar in each patient was stressed by 6 ounces of orthodontic traction interact torsion force value, then taking on the force on the maxillary first premolar; four weeks later, we gained the periodontal ligament tissue from the teeth. This study was approved by the Ethics Committee (the School of Stomatology Shandong University).For the control group, the teeth were extracted individually at the same time similar to the treatment group and periodontal ligament tissue was extracted. mRNA were hybridized to Affymetrix Human U133 plus 2.0 gene chip at Shanghai Biotechnology Corporation.MiRNA-mRNA interactions and lncRNA-miRNA intersections were primarily collected from starBase v2.0, TarBase, mortar base, mir2Disease, and miRecords v4.0. Based on the shared miRNAs, lncRNA-miRNA-mRNA interactions were constructed, from which the candidate lncRNA-mRNA competitively regulated interaction will be selected. Briefly, candidate lncRNA-mRNA interactions were determined by the hypergeometric test of shared miRNAs with false discovery rate (FDR) <0.05 and Jaccard coefficient ranked at the top 20%. Hypergeometric functions were computed based on the following formula:Z transformation, P value < 0.01 was considered to be significant lncRNA-mRNA intersections under specific conditions in our study.Based on the matched lncRNA and mRNA expression profiles, the Pearson correlation coefficient was used to evaluate the lncRNA-mRNA intersections. After Fisher's The lncRNA-regulated subpathway comprised two steps: mapping interested mRNAs and lncRNAs into linked pathways, and identifying lncRNA-regulated subpathways .P < 0.01. In order to reconstruct the lncRNA competitively regulated signal pathways (LRSP), lncRNAs were embedded in the different pathways.mRNAs of lncRNA-mRNA interactions enrichment analysis was carried out based on the KEGG. Different pathways were identified by the Fisher test with the threshold of adjusted We reconstructed subways graphs by embedding lncRNAs to different pathways. lncRNA and miRNA participating in the competing regulation were deemed important nodes, which could assist us with precisely locating subpathways.k (k = 1); then, they will be merged into one node. Next, the number of nodes of the molecule sets in pathways were calculated, whose nodes count was no smaller than m (m = 8) were considered as candidate LRSP subpathways.Important nodes were linked to LRSP and lenient distance similarity and network topology feature were used to identify the LRSP subways . The detP value ≤ 0.05). DNAJC3-AS1  was downregulated while DLEU2  was the most upregulated lncRNA , THAP7-AS1-C19orf70 (P=0.0099), IQCH-AS1-PPFIBP2 (P=0.0151), PITPNA-AS1-SOSTDC1 (P=0.0296), and EPHA1-AS1-TRAPPC9 (P=0.0238)  .P value < 0.01, a total of 8 different pathways were identified (−4), PI3K-Akt signaling pathway (FDR = 4.05 × 10−4), and focal adhesion (FDR = 1.06 × 10−3).KEGG is a database resource that integrates genomic, chemical, and systemic functional information. In the present study, we firstly mapped mRNAs of the candidate lncRNA-mRNA interactions to KEGG, with the Fisher test entified . The enrThe screened lncRNAs were embedded into candidate pathways according to the results of lncGenePairs. Then, 76 lncRNAs of the candidate lncRNA-mRNA interactions were embedded to the 8 different pathways, and 66 pairs of matched lncRNAs and genes were obtained . LRSP inWe reconstructed condition-specific LRSP by embedding lncRNAs to different pathways based on matched lncRNA-mRNA expression profiles and shared miRNA. LRSP subpathways including interesting lncRNA were located to pathways according to the “lenient distance” similarity method. After calculating the significance by Wallenius approximation, LRSP subpathways were ranked .LINC00482 in focal adhesion pathway was closely associated with MET and ITGA3. Besides, insulin-like growth factor-1 (IGF1) involved in the PI3K-Akt signaling pathway, focal adhesion, and p53 signaling pathway. Furthermore, IGF1 in the PI3K-Akt signaling pathway was associated with DNAJC3-AS1 and FAM201A.After identification, several subpathways were screened, including the PI3K-Akt signaling pathway, focal adhesion, and pathways in cancer . In the DNAJC3-AS1, WDFY3-AS2, LINC00482, and DLEU2. In addition, the subpathways regulated by the lncRNAs and functions of these lncRNAs were predicted, which allowed us to identify lncRNAs of interest that may underline orthodontic forces pathogenesis for further studies.Increasing evidence has shown that lncRNAs were important factors for regulating gene expression . HoweverDNAJC3-AS1 has performed dysregulation in human periodontal ligament [DNAJC3-AS1 was important for recovery of periodontal tissue by regulating IGF1.Insulin-like growth factor-1 (IGF1) in the PI3K-Akt signaling pathway, p53 signaling pathway, and focal adhesion was regulated by DNAJC3 divergent transcript (DNAJC3-AS1). Previous research found that IGF1 was one of the most potent natural activators of the Akt signaling pathway, by stimulating cell growth and proliferation and potently inhibiting programmed cell death . Sant'Anligament ; this isPTEN and CCND2 are protein-coding genes. PTEN encoded a phosphatidylinositol-3,4,5-trisphosphate 3-phosphatase [CCND2 belongs to the highly conserved cyclin family, and they were mainly present in cytosol and nucleus. In a previous study, CCND2 was confirmed to be related to the regeneration of periodontal tissue [CCND2 is a key component for facilitating the G1-to-S-phase transition and subsequently increased cell proliferation. Besides, additional research demonstrated that PTEN might take part in the imbalance between cell proliferation and death [WDFY3-AS2 was enriched into the focal adhesion signaling pathways. Consistent with the study of Molina et al. [FAK activation in stretched human periodontal ligament fibroblasts. Thus, WDFY3-AS2 served as a potential key lncRNA for the recovery of periodontal ligament cells after the application of orthodontic force by regulating PTEN and CCND2.WDFY3 antisense RNA 2 (WDFY3-AS2) was found to regulate two mRNAs via focal adhesion, including phosphatase and tensin homolog (PTEN) and cyclin D2 (CCND2). Both sphatase , the genl tissue . As a mend death , 21. In a et al. , mechaniITGA3, SNF, MET, and SERPINB5, respectively. Integrin, alpha 3 (ITGA3) belonged to the family of integrins and were mainly present in cytosol and cytomembrane. ITGA3 encodes the integrin alpha 3 chains. Alpha chain 3 undergoes posttranslational cleavage in the extracellular domain to yield disulfide-linked light and heavy chains that join with beta 1 to form an integrin that interacts with many extracellular matrix proteins [ITGA3. As we all know that SNF presents in mammalian as SWI/SNF complexes utilizing either Brahma (Brm) or Brahma-related gene 1 (Brg1) catalytic subunits to remodel nucleosomes in an ATP-dependent manner [MET regulates physiological processes including transferase activity, transferring phosphorus-containing groups and protein tyrosine kinase activity. In adults, MET participates in wound healing as well as organ regeneration and tissue remodeling in order to promote differentiation and proliferation of hematopoietic cells [LINC00482 may be involved in the mechanism following periodontal ligament cell by regulation of the immune response and inflammation process.Long intergenic nonprotein coding RNA 482 (LINC00482) was screened as lncRNA in the present study and resulted in the regulation of various genes, such as proteins . Saminatproteins  found tht manner . MET prot manner  is a proic cells . Mammaryic cells . Serpinsic cells . ConsequITGA4 belongs to the integrin alpha chain family of proteins, like ITGA3. Interestingly, this gene encodes an alpha 4 chain associated with either beta 1 chain [YWHAE, UBA52, SNF, and so on. Based on this information, we inferred that DLEU2 was an important lncRNA for the recovery of periodontal ligament cells after the application of orthodontic force by regulating by regulating ITGA4 and YWHAE.Moreover, it is not difficult to see that deleted in lymphocytic leukemia 2 (DLEU2) directly regulated ITGA4 and YWHAE in the PI3K-akt signaling pathway . Yu et al.  found th 1 chain  or beta  1 chain . Notably 1 chain  demonstrDNAJC3-AS1, WDFY3-AS2, LINC00482, and DLEU2 in the subpathways of PI3K-Akt signaling pathway, focal adhesion, and prostate cancer might play crucial roles in orthodontic forces pathogenesis.In conclusion, the strategy of subpathways is feasible to predict marker pathways for periodontal ligament disorders. By integrating the expression profiles of lncRNA and mRNA, several screened lncRNAs including