This paper illustrates how the penetration of electromagnetic waves in lossy media strongly depends on the waveform and not only on the media involved. In particular, the so-called inhomogeneous plane waves are compared against homogeneous plane waves illustrating how the first ones can generate deep penetration effects. Moreover, the paper provides examples showing how such waves may be practically generated. The approach taken here is analytical and it concentrates on the deep penetration conditions obtained by means of incident inhomogeneous plane waves incoming from a lossless medium and impinging on a lossy medium. Both conditions and constraints that the waveforms need to possess to achieve deep penetration are analysed. Some results are finally validated through numerical computations. The theory presented here is of interest in view of a practical implementation of the deep penetration effect. The achievement of electromagnetic deep penetration is of extreme importance in many applications, e.g., detection of buried or immersed objects, information transmission in lossy media, material analysis and microscopy, and interaction with biological tissues. A variety of techniques are commonly employed to improve the penetration, depending on the field of application, which, in turn, is strongly dependent on both the frequencies and the media involved. Typical examples employed in the literature span from antenna optimisation techniques ,2 to theAn alternative approach may consist of improving penetration by designing structures able to generate inhomogeneous plane waves with specific properties that will be discussed here. This approach could lead to an increase in penetration without reducing frequency, and, consequently, resolution ,6. BasedThe problem analyzed in this paper is illustrated in j is the imaginary unit, In All plane waves considered are of the form lossless . Analogoormalism .In the lossy medium 2, defining .14a) of , that, wThe amplitude .14b) in  (The rig.14b) in ), which,The penetration depth is commonly defined as the distance, inside a lossy medium, at which the electric field, or the magnetic field, amplitude reduces of a factor x-axis.It is well known that, if e fields   being the one reported in . In this (6) and  will be The first medium is supposed lossless, therefore if we consider the incident inhomogeneous wave suitable for obtaining deep penetration  can be rewritten exploiting the orthogonality of the phase and attenuation vectors in the first medium. Since the angle formed by Hence, by using (9) and (12), it is:If we look for the critical angle at which the transmitted attenuation vector =90∘ in 1, obtaini=90∘ in 1, obtaini2=90∘ in , obtainiLet us consider the absolute value under square root: hence, by using , i.e., uSubstituting (17) in (15) and (16), we found that the solution It is now important to understand when the two solutions (6) and  are releLet us impose the phase type solution, Furthermore, defining the quantity Equation (18) can be written as:Using the two conditions of Equations (6) and , (20) caImportant physical constraints for the media involved in the deep penetration phenomenon can be derived by the previous analysis. Looking at the expression (19), we see that the sign of iphysics   and (13), the following system of Equations is obtained:Removing Now, Simplifying the expression above, the following is obtained:With reference to sion see , in ordeConsidering or which  is satisThis could state e.g., that, in any case, a lossy medium will attenuate the exponential increase of the field intensity of an improper leaky wave to a certain degree. In case the deep penetration condition Numerical results, produced by a code in Matlab implementing the analytical expressions of This article presented theoretical results concerning the electromagnetic penetration in lossy media by means of inhomogeneous plane waves. The physical properties of the wave responsible for deep penetration have been carefully investigated and the important result that only leaky waves of the improper type can be used allows for the possible design of suitable leaky-wave radiators.A complete set of conditions for obtaining deep penetration has been analytically derived in terms of the physical properties of the involved media, thus permitting for defining the characteristics of the impinging inhomogeneous wave that optimize this promising phenomenon in different practical scenarios. A parametric analysis of the electromagnetic propagation in the lossy medium around the minimal condition that allows for deep penetration showed the limit of the phenomenon as well as the possibility to obtain even deeper effects, i.e., The objective of the described research is to build a solid theoretical support to practical applications. Results achieved in this paper will therefore be relevant to design practical radiating structures that can be used to reproduce the deep penetration scenario demonstrated here. An attempt to achieve the deep penetration condition (An exhaustive comparison in terms of penetration between leaky waves produced by finite sources and homogeneous waves is beyond the scope of this paper: anyway, a direct consequence of the results found here is that, when the deep penetration effect is obtained, a finite leaky wave guarantees penetration in the near field larger than the one obtained through homogeneous waves if the peak of the field amplitude at the interface with the lossy medium for the two waveforms coincides. This result has important implications in practical applications: employing an opportune leaky-wave antenna design, a requested amplitude of the electric field may be obtained in the lossy medium avoiding the boosting of the field at the interface, and reducing consequently the risks of overheating or burning of the surface layers that are present when more traditional homogeneous waves are employed. In other cases that may be of interest, e.g., when the peaks of the field amplitude for the two waveforms coincide in the absence of the lossy medium, but the homogeneous wave presents an amplitude of the field at the separation surface larger than the one of the leaky wave, the analysis of various factors, such as the shape of the beams, and the media considered, may be necessary for determining which waveform produces the larger penetration: in these cases, further investigations involving realistic antenna structures will have to be performed.The current article presented an analytical study of the deep-penetration effect achievable by inhomogeneous plane waves. The impact of different materials on the deep-penetration condition were studied, and suitable finite waveforms, said leaky waves, were also indicated as a way to reach deep penetration in near field. Some of the limits and potentialities of those waveforms were also indicated. Future studies will focus on numerical simulations to determine the extent of this effect reachable by employing realistic and finite structures.


S-acylation of the beet severe curly top virus C4 protein is essential for its membrane association, interaction with CLV1, and symptom determination during geminivirus infection. in planta, and that this post-translational lipid modification is necessary for its membrane localization and functions, especially its regulation of shoot development of host plants. Furthermore, the S-acylated form of C4 interacts with CLAVATA 1 (CLV1), an important receptor kinase in meristem maintenance, and consequentially affects the expression of WUSCHEL, a major target of CLV1. The abnormal development of siliques in Arabidopsis thaliana infected with BSCTV is also dependent on the S-acylation of C4, implying a potential role of CLAVATA signaling in this process. Collectively, our results show that S-acylation is essential for BSCTV C4 function, including the regulation of the CLAVATA pathway, during geminivirus infection.Geminiviruses, such as beet severe curly top virus (BSCTV), are a group of DNA viruses that cause severe plant diseases and agricultural losses. The C4 protein is a major symptom determinant in several geminiviruses; however, its regulatory mechanism and molecular function in plant cells remain unclear. Here, we show that BSCTV C4 is S-acylated  Geminiviruses are a family of plant viruses with circular single-stranded DNA genomes that cause plant diseases worldwide. Geminivirus-encoded proteins regulate host cellular processes, including DNA replication, gene silencing, and hormone signaling, to complete their infection cycle ; for insC4 genes from several geminiviruses, including beet curly top virus (BCTV) and beet severe curly top virus (BSCTV), results in ectopic cell division in Arabidopsis thaliana and Nicotiana benthamiana (RKP for cell cycle regulation (ATHB7/12 for infected cell development (The conserved C4 (or L4) proteins in monopartite geminiviruses and AC4 (or AL4) proteins in bipartite geminiviruses have important functions . The disthamiana , supportN. benthamiana, causing severe symptoms  were surface-sterilized for 2 min in 75% ethanol followed by 5 min in 1% NaClO solution, rinsed five times with sterile water and plated on Murashige and Skoog (MS) medium with 1.5% sucrose and 0.8% agar. They were then stratified at 4 °C in the dark for 2 d and subsequently grown under long-day conditions (16/8 h of light/dark) at 22 °C.Seeds of C4 was cloned into a pBluscript-based vector under the 35S promoter, fused with yellow fluorescent protein (YFP), 3× FLAG, and 6× histidines  of WT-YFP-FLAG3His6 or C4C8S-YFP-FLAG3His6 were transformed into protoplasts as described by g for 10 min, and the pellet was washed with 70% acetone, re-suspended in 300 µl of binding buffer  and mixed with 40 µl of pre-washed Thiopropyl Sepharose 6B (Sigma). Then, 40 µl of either 2 M NH2OH (pH 7.5) or 2 M NaCl (control) was added into this mixture. The mixtures were rotated at room temperature for 2 h and 20 µl of each supernatant was saved as the total input. Resins were washed five times with binding buffer. Elution was performed using 60 µl of binding buffer containing 50 mM DTT at room temperature for 20 min. Supernatants were removed and mixed with protein sample buffer, incubated at 95 °C for 5 min, and then used for SDS-PAGE and immunological detection.Acyl-resin-assisted capture (Acyl-RAC) assays were performed following the protocol previously described by C4-YFP-FLAG3His6 were transiently expressed in protoplasts. At 48 h after transformation, the protoplasts were collected for cell fraction as previously described  by the floral-dip method  was cloned into pGADT7. Yeast two-hybrid (Y2H) assays were performed using the Matchmaker GAL4-based Two-Hybrid System 3 (Clontech) according to the manufacturer’s instructions. Interactions were tested by stringent selection (SD/–Leu/–Trp/–His) supplied with 1 mM 3-amino-1,2,4-triazole (Sigma). Yeast cells with pGBKT7-p53 and pGADT7-T (Clontech) were used as positive controls; the yeast cells with pGBKT7-Lam and pGADT7-T (Clontech) were used as negative controls.The CDS of C4 was cloned into the pSPYCE-35 vector, while CLV1 was cloned into the pSPYNE-35S vector  antibody at 4 °C overnight, and rotated with the protein A resin for another 3 h. Then the resins were centrifuged and washed three times with washing buffer . Proteins were eluted with SDS sample buffer and analysed by immunoblotting using anti-GFP  or anti-MYC  antibodies. For Co-IP between CLV1-GFP and C4-MYC, the reagents and protocol were similar except that the GFP-Trap resin (Chromotek) was used instead.For expression of CLV1-MYC in protoplasts, the full length CDS of WUS and CLV3, the WUSpro::GUS-GFP or CLV3pro::GUS-–GFP construct  staining. The GUS activity analysis was performed according to the method described by For detection of the expression of JXB online), suggesting a potential conserved mechanism among these virus species. An acyl-RAC assay  . An acylBecause myristoylation at the N terminus of proteins may regulate their S-acylation, the potential myristoylation site (G4) on BSCTV C4 was also mutated from a glycine to an alanine residue, which disrupted both the S-acylation and membrane association of C4 . The N tC4 were expressed in Arabidopsis to elucidate the effect of S-acylation on C4 function in the host plants. Transgenic plants constitutively expressing BSCTV C4 could not be obtained because of their ectopic cell division  was used to inoculate a leaves , B. Compd shoots ; howeverd shoots . These rd shoots , supportOur data showed that S-acylation was important for the effect of C4 on shoot development; therefore, we next attempted to uncover the potential mechanism regulating this process. We hypothesized that the target of C4 may be associated with the plasma membrane and may play a role in regulating shoot development. A previous study showed that the intracellular catalytic domain of a leucine-rich repeat receptor-like kinase (At3g24240) interacts with BCTV C4 in a Y2H assay . A Y2H ain vivo, but that the depletion of S-acylation disrupted this association  to modulate cell differentiation in the shoot and floral meristems (WUS, a GUS gene under the control of the WUS promoter was crossed into pER8-C4WT and pER8-C4C8S plants. WUS was expressed in the shoot-meristem region of the seedlings in the absence of C4 expression (C4 expression and showed that the expression of WUS was inhibited in the meristem but enhanced in the cotyledons, suggesting the abnormal regulation of this pathway by the viral protein. By contrast, the expression of WUS was not affected by the C4C8S mutant protein, supporting the role of S-acylation in this process.CLV1 is a receptor-like kinase that perceives the secreted peptide CLV3 on the plasma membrane, then negatively regulates the expression of the homeodomain transcription factor pression . The patCLV3 expression and establish a feedback loop to balance the number of stem cells produced with the size of the meristem (CLV3 in the presence of C4. The expression of CLV3 was detectable but weaker in the meristems in the presence of wild-type C4, but it also abnormally occurred in the cotyledons (WUS expression by C4 was not a result of a misorganization of the shoot meristem; therefore, C4 may interact with CLV1 on the plasma membrane following its S-acylation, causing it to interfere with the CLAVATA pathway.The WUS protein migrates into adjacent cells to activate tyledons . This reThe mutation of components involved in the Arabidopsis CLAVATA pathway often causes phenotypic changes in the siliques , providiC4 is expressed in infected plant cells, the protein may be recognized and S-acylated by the host cell; however, this modification contributes to the efficiency of the viral infection, implying a functional interaction between pathogen and host during evolution.Our current study revealed a novel mechanism for the role of S-acylation in the interaction between geminiviruses and plants. Certain viral proteins are regulated by S-acylation in mammalian host cells; for instance, S-acylation is essential for the association of the influenza virus haemagglutinin protein with the membrane microdomains of the animal cell . When C4C8S proteins were distributed in the cytosol, but a small amount of mutated proteins were retained at the membrane (It has been reported that myristoylation is also involved in the localization of geminivirus C4 proteins to the host membrane . The mutmembrane , possiblmembrane . Becausemembrane  and manyOur results showed that the S-acylation of C4 did not have an effect on virus replication , but mayWUS expression (WUS expression in the meristem regions but stimulated it in the cotyledons (WUS expression in the presence of C4. BSCTV induced cell division in the shoot tips of the plants, including the siliques, which is a similar phenotype to those of plants with mutations in components of the CLAVATA pathway; therefore, the CLAVATA signaling pathway may contribute to these symptoms. Previous studies indicated that BCTV C4 interacts with AtSKs and BIN2 (The identification of the interaction between C4 and CLV1 provided a potential mechanism to explain the effects of C4 on shoot development. Although the data from Y2H assays indicated that C4 interacted with the intracellular domain of CLV1, the Co-IP results using full-length proteins suggested that membrane localization of C4 mediated by S-acylation was critical for this interaction in plant cells . Becausepression . Our dattyledons , suggestAlthough the protein sequences of geminivirus C4 proteins vary, cysteine residues are found in many members , suggestJXB online.Supplementary data are available at Table S1. Primers used in this study.Fig. S1. Sequence alignment of C4 proteins from different geminiviruses.C28S mutant.Fig. S2. Localization and S-acylation analysis of the C4G4A mutant.Fig. S3. Localization and S-acylation analysis of the C4Fig. S4. Localization and S-acylation analysis of the C4D2 mutants.C4 in transgenic plants.Fig. S5. The expression levels of C8A mutant in N. benthamiana leaves.Fig. S6. Functional analysis of the C4Fig. S7. The effect of C4 S-acylation on BSCTV accumulation in local inoculated leaves and newly emerged shoots.Fig. S8. The effect of C4 S-acylation on symptoms at 2 weeks and 3 weeks after BSCTV inoculation.Supplementary Tables and FiguresClick here for additional data file.


Escherichia coli, Corynebacterium glutamicum and yeast have been established for their production. As a result, the yield of GA and EG produced from sugars has been significantly improved. Here, we describe the recent advancement in metabolic engineering efforts focusing on metabolic pathways and engineering strategies used for GA and EG production.Glycolic acid (GA) and ethylene glycol (EG) are versatile two-carbon organic chemicals used in multiple daily applications. GA and EG are currently produced by chemical synthesis, but their biotechnological production from renewable resources has received a substantial interest. Several different metabolic pathways by using genetically modified microorganisms, such as  ECU0401 . EG is predominantly produced either by the hydration of ethylene oxide using either thermal or catalytic reaction or from ethylene oxide by carbonation and subsequent hydrolyzation with a base catalyst. Direct EG production from renewable resources such as plant-derived materials through chemical catalysis has been reported, and small amount of EG in the market is produced by dehydration of biobased ethanol .Ethylene glycol (EG) is a two-carbon dihydroxy alcohol Fig. a. The moE. coli, C. glutamicum, Saccharomyces cerevisiae or Kluyveromyces lactis as hosts  is the most studied of the metabolic pathways for GA production Fig. . The glyis Table .E. coli, these included adaptive evolution and optimisation of transcription levels of genes encoding e.g. glyoxylate reductase and isocitrate lyase. Further, improvement in GA yield was achieved by deletion of side pathways such as lactate and acetate production and glycolate oxidases and aldehyde dehydrogenase converting GA to glyoxylate or glycolaldehyde, respectively  and malate synthase. In order to enhance the flux from isocitrate to GA, isocitrate lyase and glyoxylate reductase encoding genes have been overexpressed Fig. . These mely Fig. b  that is then aldolytically cleaved to yield glycolaldehyde and dihydroxyacetone phosphate (DHAP) Fig. . In thisE. coli, the human or rat D-xylulose-1-kinase and human D-xylulose-1-phosphate aldolase were selected from couple of enzyme candidates tested for D-xylulose phosphorylation and X1P cleavage  was obtained  due to several reasons. The conversion of the D-xylose to D-xylulose by D-xylose isomerase was inefficient, and also, the activities of the other pathway enzymes were low in yeast. Moreover, the pathway created a redox imbalance by producing excess NAD+ and NADP+, and there was a potential ATP deficiency due to phosphorylation of D-xylulose by ketohexokinase early on the pathway. In addition, metabolites from the X1P pathway were leaking to pentose phosphate pathway and D-xylulose-1-phosphate was likely consumed by unknown endogenous enzyme activities.The pathway was also expressed in E. coli for production of GA or EG from D-arabinose or D-xylose. The enzymes of the pathway FucI, FucK and FucA not only cleave D-arabinose into glycolaldehyde and DHAP but also L-fucose into lactaldehyde and DHAP. In the synthetic GA or EG pathways, the FucO and AldA enzymes catalyse the reduction and oxidation of glycolaldehyde into EG and GA, respectively . However, the authors noticed that the deletion of the copies of XYL3 gene was enough to cause EG production from D-xylose during microaerobic, high cell-density cultivation even in the absence of the R1P pathway. The authors therefore investigated the endogenous enzyme activities which were responsible for EG production and found out that the most likely candidates for converting D-xylulose to glycolaldehyde and DHAP were phosphofructokinase Pfk1/2 and fructose-1,6-bisphosphate aldolase Fba1, and therefore, the endogenous pathway has D-xylulose-1-phosphate as an intermediate rather than D-ribulose-1-phosphate. EG was formed from glycolaldehyde via endogenous alcohol dehydrogenase activity. Interestingly, a yeast strain with no D-xylulose kinase activity that overexpressed only phosphofructokinase PFK1/2 produced the highest amount of EG (about 2.8 g/L from 50 g/L D-xylose in bioreactor). This amount was further increased to over 4 g/L by fed-batch fermentation with D-glucose feed, which also increased the viability of the producer strain. The discovery of GA as a by-product of the EG-producing strains also indicates that this pathway could be modified for GA production in yeast. The authors speculate that for the endogenous pathway to work, the strain has to have sufficient D-xylose isomerase activity but no D-xylulose kinase activity , but carbon was lost to GA and deletion of aldA-encoding aldehyde dehydrogenase converting glycolaldehyde to GA led to accumulation of D-xylonate. In a more recent study, the toxic effect of D-xylonate accumulation was alleviated by controlling xdh expression through a weak promoter and EG production from glycolaldehyde was improved by expressing a more efficient aldehyde reductase, yjgB. This resulted in EG titer of 1.52 g/L with a yield up to 98% of the theoretical yield  for EG production, in silico genome-scale metabolic simulation was used to optimise the fluxes through the native D-xylose catabolic and the Dahms pathways. Interestingly, the highest EG productivity and titer were obtained by increasing the biomass formation. This was done by reduction of the yqhD expression and by increasing the flux through the native D-xylose catabolic pathway through downregulation of the Dahms pathway. Drawback of this strategy was that increased biomass production competed for the carbon source with EG production and thus decreased the EG yield  from E. coli in yeast S. cerevisiae enabled GA production from D-xylose up to 150 mg/L  and D-xylonate dehydratase (XylD) from E. coli with the additional metabolic modifications including overexpression of the biosynthetic pathway of L-serine and deletion of enzyme activities consuming the intermediates of the pathway. As a result, 4.1 g/L of EG at a cumulative yield of 0.14 g/g D-glucose was produced  in batch bioreactor cultivation.Serine can be converted to glycolaldehyde either (i) via deamination of serine to hydroxypyruvate by aminotransaminase or amino acid dehydrogenase and decarboxylation of hydroxypyruvate to glycoaldehyde by α-ketoacid decarboxylase or (ii) via decarboxylation of serine to ethanolamine by serine decarboxylase and oxidation of ethanolamine to glycoaldehyde by monoamineoxidase  intermediates. The pathways are simple and energetically favourable; 1 ATP and 1 NADEG Table . The draEG Table . EG can E. coli, GA and EG yield over 90% of theoretical that have been achieved by the Dahms pathway but titers remained relatively low (> 12 g/L)  Table , 2018. I2, CO and H2 (Islam et al. E. coli are, however, already close to be competitive with its chemical synthesis. It would be highly interesting to test whether the same strain engineering strategy would also enable GA production via the Dahms pathway with the similar titer and rate. In general, metabolic engineering of E. coli for GA or EG production has been more successful than engineering yeast species. More efforts have not only been put in engineering of E. coli but also the enzymes of the GA and EG pathways have turned out be more difficult to express in yeast. Improved pathway enzyme activities, balanced enzyme expression and balance between the growth and the metabolic fluxes towards product seem to be of key importance for further improving GA and EG in metabolically engineered microbes. The use of genetic circuits that sense the metabolic needs of both the growth and the product formation would perhaps be an option for future research efforts.Due to the environmental issues, there is an increasing demand for production of platform chemicals like GA and EG from renewable resources by sustainable biotechnological processes. GA and EG are chemicals with a relatively low price, and therefore, the development of efficient production processes with a low cost is highly important for their commercialisation. Consequently, it is important to design potential metabolic routes that enable utilisation of preferably several carbon sources at the same time from various cheap and renewable feedstocks. These include for example industrial and agricultural waste streams like lignocellulosic sugars or starch from non-edible plants. However, recently pathways for EG production were designed even for gaseous substrates CO


Disease classification based on gene information has been of significance as the foundation for achieving precision medicine. Previous works focus on classifying diseases according to the gene expression data of patient samples, and constructing disease network based on the overlap of disease genes, as many genes have been confirmed to be associated with diseases. In this work, the effects of diseases on human biological functions are assessed from the perspective of gene network modules and pathways, and the distances between diseases are defined to carry out the classification models. In total, 1728 diseases are divided into 12 and 14 categories by the intensity and scope of effects on pathways, respectively. Each category is a mix of several types of diseases identified based on congenital and acquired factors as well as diseased tissues and organs. The disease classification models on the basis of gene network are parallel with traditional pathology classification based on anatomic and clinical manifestations, and enable us to look at diseases in the viewpoint of commonalities in etiology and pathology. Our models provide a foundation for exploring combination therapy of diseases, which in turn may inform strategies for future gene-targeted therapy. Many research projects are aimed at improving the ability to diagnose, treat and prevent diseases through a better understanding of the genetic basis of diseases based on biomolecular data. For example, the Cancer Genome Atlas (TCGA) [With the advancement and deepening of disease research, more and more genes have been confirmed to be associated with one or more specific diseases. Hence, a number of institutions and organizations that collect and manage such data have emerged. The National Center for Biotechnology Information   is part p/kegg/)  is an efim.org/) ,8 is a cs (TCGA)  is a pros (TCGA)  has beens (TCGA)  is an onet al. draw on dynamical systems theory to decipher how multiple individual functions are coordinated in health and disease, as intractable diseases such as cancer are associated with multiple individual function breakdowns, which conspire to create unhealthy phenotype combinations [et al. [Availability of genomic relationship data gives rise to a large research area of network medicine illuminating the multifactorial nature of diseases . Networkinations . Wang et [et al.  develop et al. [et al. [et al. [Many network-based approaches are proposed in order to explore the associations between human genetic diseases and the relations between their effector genes –25. Goh et al.  conduct et al. . Rzhetsk [et al.  construc [et al. . Lee et  [et al.  construc [et al. .et al. take a comprehensive perspective on oncogenic processes based on Pan-Cancer Atlas analyses, giving prominence to the complex impact of genome alterations on the signalling and multi-omic profiles of human cancers as well as their influence on tumour microenvironment [et al. [et al. [Disease classification is the foundation for achieving precision medicine, and methods developed for disease classification are promising for revolutionizing personalized medicine and increasing our understanding of disease etiology . With thironment . Hoadley [et al.  show tha [et al.  make an In this work, disease classification models based on gene network are proposed, as we consider not only individual genes, but also gene network modules and pathways. The largest connected component (LCC) of the human gene network is divided into 10 modules by using the fast unfolding algorithm. We integrate disease genes, topological modules and biological pathways to assess the influence of diseases on the human body, and perform different classifications of diseases for different interests. In total, 1728 diseases collected from KEGG are divided into 12 categories by the intensity of effects on pathways, and are identified as 14 categories by the scope of effects on pathways. The number of disease categories that contain cancers is the smallest among 15 types of diseases, which suggests the similarity that cancer diseases are complex and have a great impact on pathways. Each category is a mix of several types of diseases identified based on congenital and acquired factors as well as diseased tissues and organs, which implies that the human gene network gives a perspective of disease classifications, and guides future gene-targeted therapy and combination therapy of diseases.2.2.1.https://ftp.ncbi.nih.gov/gene/DATA/GENE_INFO/Mammalia/Homo_sapiens.gene_info.gz). In this file, 16 types of information of 60 674 human genes are listed, including GeneID, Symbol, Synonyms, type of gene, etc. Also, gene–gene interaction information  is acquired from NCBI (https://ftp.ncbi.nih.gov/gene/GeneRIF/interactions.gz). Each record is verified by one or more literatures (unique Pubmed ID) curated from publications.We obtain a human gene information file  from NCBI (KEGG PATHWAY  is a colhttps://www.kegg.jp/kegg-bin/get_htext?htext=br08402_gene.keg).Diseases are viewed as perturbed states of the biological system in KEGG DISEASE ,30. Each2.2.A is the adjacency matrix of the network, m is the total number of edges, and i. The indices i and j run over the N nodes of the graph.The module reflects the local characteristics of individual behaviours in the network and their interrelationships. The modules in the research network play a vital role in understanding the structure and function of the entire network, and can help us analyse and predict the interaction between the elements of the entire network. A key step was taken when Girvan and Newman popularized graph-partitioning problems by introducing the concept of modularity . In its The fast unfolding algorithm is a multistep method based on a local optimization of modularity in the neighbourhood of each node and implemented as follows :Algorit2.3.i  in 10 modules. As each module has a different number of genes, we apply the adjusted cosine similarity to measure the correlations of pathway genes distribution in network modulesi, j) measures the correlation of gene distribution deviation between pathway i and j.To assess the overlap of function pathways and topological modules, we get the proportion vector 2.4.In the biological literature, there is a tacit assumption that these three concepts are interrelated: cellular components that form a topological module have closely related functions, thus they also correspond to a functional module, and a disease is a result of the breakdown of some particular functional modules ,33. It iNd = 1728, the number of modules is represented by Nm = 10, and the number of pathways is represented by Np = 317. Each disease k  is represented by disease genes group Ml  is represented by genes group Pt  is represented by genes group Ml| = nl is the size of Ml, and dij is the length of the shortest path between gi and gj in the LCC. δ = 1 has the value 1 for all gi in Ml and the value 0 for all gi not in Ml. AE describes the summation of the closeness centrality of disease genes within the module Ml. The more disease genes within module Ml, the more significant Ml plays a role in developing the disease.The number of diseases is represented by Ml∩Pt| is the size of intersection of Ml and Pt, and |Ml∪Pt| is the size of union of Ml and Pt. JSC describes similarity between finite gene groups Ml and Pt. This measure is selected mainly because it provides an intuitive way to characterize the set similarity.Then we define the relevance of modules and functional pathways by jaccard similarity coefficient as follows:Dk is defined as follows:Dk, Pt) is the impact score on pathway Pt, and IS(Dk) is the impact score vector on pathways by disease Dk.The impact score on function pathways by disease N(Dk) as the impact score vector of disease Dk, whereTo classify diseases by the intensity of effects on pathways, we use the normalized vector ISB(Dk) as the impact score vector of disease Dk, whereB put emphasis on the pathways which are affected beyond average. The distance between two diseases is defined by the Manhattan distance to describe the proportion of different pathways affected by two diseases, as follows:To classify diseases by the scope of effects on pathways, we use the binary vector IS2.5.s×s, where Distance is the average distance between diseases in i, j) is the average distance between diseases in Given a partition Pm, r ≤ m ≤ Nd − 1 where the corresponding Using Ward.D2 method , we get 3.Different from previous works such as classifying diseases according to the gene expression data of patient samples , or unde3.1.γ = 1.696 (a).We obtain human gene information from NCBI in which 16 types of information of 60 674 human genes are listed, including GeneID, symbol, synonyms, type of gene, etc. Also, gene–gene interaction information is acquired from NCBI (see details in Methods). Each record is verified by one or more literatures (unique Pubmed ID) curated from publications. See in  = 1.696 a.Table 3.2.c). The number of pathways in which one gene involved has a power-law distribution (figure 2b), and γ = 1.966. It suggests that minority genes involve in a large number of pathways to participate in cellular activities. In the 85 metabolism pathways, majority genes do not interact with non-human genes. However, in another 232 human pathways, most genes interact with non-human genes .We select 317 human pathways which include gene information from KEGG (see details in Methods). For each of the six types of pathways, one subject to significant influence by diseases is selected as example to illustrate the subnetwork consisting of pathway genes . In totafigure 2As expected, human pathway genes that interact with both human and non-human genes (component IV) are more crucial. The degree of human genes which interact with both human and non-human genes is much larger than that of human genes which only interact with human genes. Specially, component IV occupies many hubs , for theFor example, TRIM25 is the maximum-degree gene, and its protein is a bona fide RNA binding protein associated with many proteins involved in RNA metabolism and that interacted with numerous coding and non-coding transcripts . TRIM25 In total, 1728 diseases (with known disease genes) of 15 primary classifications are screened out in KEGG DISEASE (see details in Methods). See in 3.3.et al. have reviewed three modularity concepts: topological modules, functional modules and disease modules [Modularity proposed by Newman is one measure of the structure of networks . The mod modules .et al. in 2008 is recognized as one of the fastest and accurate non-overlapping community discovery algorithms, especially for networks of unprecedented sizes [In this part, the LCC is considered. The fast unfolding algorithm proposed by Blonde ed sizes . The LCCi  in 10 modules, and apply the adjusted cosine similarity (see details in Methods) to measure the correlations of pathway genes distribution in network modules. To assess the overlap of function pathways and topological modules, we get the proportion vector 3.4.The tacit assumption in network medicine is that the topological, functional and disease modules overlap, so that functional modules correspond to topological modules and a disease can be viewed as the breakdown of some particular functional modules ,33.A topological module represents a locally dense neighbourhood in a network, such that nodes have a higher tendency to link to nodes within the same local neighbourhood than to nodes outside it. A functional module represents the aggregation of nodes of similar or related function in the same network neighbourhood, where function captures the role of a gene in defining detectable phenotypes. Finally, a disease module represents a group of network components that together contribute to a cellular function and disruption of which results in a particular disease phenotype .In this work, 10 topological modules are identified by network clustering algorithms; 317 pathways represent the function modules. For each of 1728 diseases, the collection of disease genes is regarded as a disease module. We perform different classification models of diseases for different interests. In the past, classification of diseases was mainly based on congenital and acquired factors as well as diseased tissues and organs, which did not help people to realize the influence on integrated pathway functions. Here, we consider disease genes, topological modules and functional pathways to assess the influence of diseases on pathways of human body (see details in Methods).3.4.1.N(Dk) corresponding to 317 pathways as the intensity of effects on pathways, and used Euclidean distance to measure the distance between two diseases. Hierarchical clustering of 1728 diseases by the intensity of effects on pathways yields 12 categories (see P changes and get the minimum when 1716 merges are conducted (a). The distance matrix between 1728 diseases arranged in the clustering order corresponding to the dendrogram is illustrated in figure 6c. Each of 12 categories is a fairly heterogeneous mix of several types of diseases, which suggests that diseases of different pathological classifications may have similar intensity of effects on pathways. Cancers mainly are grouped in CATG 1 as well as CATG 2, CATG 3, CATG 4. Most cancers in CATG 2 and CATG 4 are of haematopoietic and lymphoid tissues, and of soft tissues and bone. Only three cancers  are grouped in CATG 3. In CATG 1, many other diseases, such as fanconi anaemia and type II diabetes mellitus, are grouped together with many cancers, showing that these diseases affect human pathways similarly and seriously as cancers. Fanconi anemia (FA) is a genetic disorder that is characterized by bone marrow failure, developmental abnormalities and predisposition to cancer. Monoallelic inactivation of some FA genes, such as FA complementation group D1 , leads to adult-onset cancer predisposition but does not cause FA, and somatic mutations in FA genes occur in cancers in the general population. Studies of FA have revealed opportunities to develop rational therapeutics for this genetic disease and for malignancies that acquire somatic mutations within the FA pathway [et al. [et al. [et al. [For each of 1728 diseases, we calculate and normalize a score vector ISonducted a. The di pathway ,47. Type pathway . Coughli [et al.  suggest  [et al.  show tha [et al.  conduct Diseases in CATG 1, CATG 3, CATG 4 mainly affect such functions of regulating cell proliferation, survival, growth, migration, differentiation, adhesion by affecting ras signalling pathway, rap1 signalling pathway, MAPK signalling pathway, Jak-STAT signalling pathway and PI3K-Akt signalling pathway. Diseases in CATG 6, most of which are metabolic diseases, mainly affect oxidative phosphorylation pathway, neuroactive ligand–receptor interaction pathway, thermogenesis pathway, Alzheimer’s disease pathway, Parkinson’s disease pathway, Huntington’s disease pathway. However, Alzheimer’s disease and Huntington’s disease are in CATG 4 and Parkinson’s disease is in CATG 3. Compared with CATG 6, diseases in CATG 8 also affect many genetic information processing pathways and environmental information processing pathways. Diseases in CATG 2 are associated with virus infection and carcinogenesis, as they mainly affect cell cycle pathway, cellular senescence pathway, transcriptional misregulation in cancer pathway, viral carcinogenesis pathway, HTLV-I infection pathway, Epstein–Barr virus infection pathway, human papillomavirus infection pathway. Diseases in CATG 7 mainly affect metabolism pathways and genetic information processing pathways, such as glycolysis/gluconeogenesis pathway, purine metabolism pathway, protein processing in endoplasmic reticulum pathway, ubiquitin-mediated proteolysis pathway. Moreover, endocytosis pathway is affected so much. Diseases in CATG 10 mainly affect RNA transport pathway, mRNA surveillance pathway, Hippo signalling pathway, endocytosis pathway, oocyte meiosis pathway, tight junction pathway, adrenergic signalling in cardiomyocytes pathway, dopaminergic synapse pathway, human papillomavirus infection pathway. Diseases in CATG 9 mainly affect genetic information processing pathways including spliceosome pathway, ribosome pathway and RNA transport pathway. Diseases in CATG 5 mainly affect complement and coagulation cascades pathway. Diseases in CATG 11 mainly affect ras signalling pathway, tight junction pathway, microRNAs in cancer pathway. Diseases in CATG 12 mainly affect ubiquitin-mediated proteolysis pathway.3.4.2.B(Dk) corresponding to 317 pathways as the scope of effects on pathways, and use Manhattan distance to measure the distance between two diseases. Hierarchical clustering of 1728 diseases by the scope of effects on pathways yields 14 categories (see P changes and get the minimum when 1714 merges are conducted (b). The distance matrix between 1728 diseases arranged in the clustering order corresponding to the dendrogram is illustrated in figure 6d.For each of 1728 diseases, we use a binary vector ISonducted b. The diDiseases in CATG I mainly affect nucleotide metabolism pathways, signal transduction pathways, cellular community pathways, cell motility pathways, development pathways, cancers pathways, viral infectious diseases pathways. Diseases in CATG II mainly affect nucleotide metabolism pathways, replication and repair pathways, cancers: specific types pathways, viral infectious diseases pathways. Diseases in CATG III mainly affect signal transduction pathways, signalling molecules and interaction pathways, cellular community pathways, cell motility pathways, immune system pathways, development pathways, cancers pathways. Diseases in CATG IV mainly affect nucleotide metabolism pathways, development pathways, viral infectious diseases pathways. Diseases in CATG V mainly affect signalling molecules and interaction pathways, cellular community pathways, cell motility pathways, circulatory system pathways, development pathways, viral infectious diseases pathways. Diseases in CATG VI mainly affect xenobiotics biodegradation and metabolism pathways, signalling molecules and interaction pathways, cell motility pathways. Diseases in CATG VII mainly affect membrane transport pathways, signalling molecules and interaction pathways. Diseases in CATG VIII mainly affect carbohydrate metabolism pathways, nucleotide metabolism pathways, metabolism of terpenoids and polyketides pathways, xenobiotics biodegradation and metabolism pathways, cell motility pathways, ageing pathways. Diseases in CATG IX mainly affect translation pathways. Diseases in CATG X mainly affect nucleotide metabolism pathways, transport and catabolism pathways, cell motility pathways. Diseases in CATG XI mainly affect membrane transport pathways, cellular community pathways, cell motility pathways, development pathways, ageing pathways, cancers pathways. Diseases in CATG XII mainly affect nucleotide metabolism pathways, cellular community pathways, cell motility pathways, circulatory system pathways, development pathways, ageing pathways, environmental adaptation pathways, cancers pathways. Diseases in CATG XIII mainly affect translation pathways, cell motility pathways, circulatory system pathways. Diseases in CATG XIV mainly affect nucleotide metabolism pathways, xenobiotics biodegradation and metabolism pathways, membrane transport pathways, cell motility pathways.3.4.3.a).In this paper, we integrate disease genes, topological modules and functional pathways to assess the influence of diseases on pathways of human body. When it comes to associations between the two classifications, the first is that both of them are based on the impact score, which is the basic metric in this paper (more details can be found in §2.4). The second is the criteria for classifications  is closely connected with these cancer diseases. Giant cell tumour of the bone is a relatively uncommon tumour of the bone. Malignancy in giant cell tumour is uncommon; however, if malignant degeneration does occur, it is likely to metastasize to the lungs [For example, in the middle of figure 7he lungs . So it ia, many allergies and autoimmune diseases including asthma (#85), systemic lupus erythematosus (#86), Hashimoto thyroiditis (#87), Graves' disease (#88), dermatitis herpetiformis (#99), systemic sclerosis , Sjogren syndrome , are grouped together with dilated cardiomyopathy (#389 in cardiovascular diseases), celiac disease (#637 in digestive system diseases), etc. One of the reasons is that HLA class II alpha chain paralogues  are the common disease genes which play a central role in the immune system by presenting peptides derived from extracellular proteins.In the top of figure 7a, congenital muscular dystrophies , muscular dystrophy–dystroglycanopathy type A , muscular dystrophy–dystroglycanopathy type B , muscular dystrophy–dystroglycanopathy type C (#719), Fukuyama congenital muscular dystrophy (#720), congenital muscular dystrophy type 1C , congenital muscular dystrophy type 1D  are closely connected with each other. The fact that one disease may be classified as more than one category in KEGG DISEASE database motivates us to find a method to understand the relationships between different categories of disease from the genetic level.In the right of figure 7N(Dk) to measure the difference in intensity between the pathways. The score of the most affected pathway is 1, and the score of the least affected pathway is 0. To classify diseases by the scope of effects on pathways, we use the binary vector ISB(Dk) to mark pathways with a score exceeding the average. The second is the distance of diseases. In the first classification, the distance of diseases Di and Dj is defined as the Euclidean distance of ISN(Di) and ISN(Dj). In the second classification, the distance of diseases Di and Dj is defined as the Manhattan distance of ISB(Di) and ISB(Dj). Note that for two binary vectors, the following equation holds: distancemanhattan × dimensionvector = (distanceeuclidean)2. The third is that there exist some disease pairs that are grouped differently in the two classifications .When it comes to differences between the two classifications, the first is that they emphasize the impact of disease on pathways from different perspectives. To classify diseases by the intensity of effects on pathways, we use the normalized vector ISications b,c.b).If the diseases are only grouped together in the first classification, it means that the impact on function by these diseases is concentrated in certain pathways but each disease uniquely affects several other pathways. The pair of type II diabetes mellitus (#536) and basal cell carcinoma (#23) is an example. Besides, hypertrophic cardiomyopathy (#387) and dilated cardiomyopathy (#389), retinitis pigmentosa (#284) and leber congenital amaurosis (#306), deafness, autosomal dominant (#346) and deafness, autosomal recessive (#347) are other disease pairs that are only grouped together in the first classification b.c and figure 7c).If the diseases are only grouped together in the second classification, it means that these diseases may affect similar pathways, but with different intensities. For example, laryngeal cancer (#6), fallopian tube cancer (#44), chronic myeloid leukaemia (#77), myelodysplastic syndrome (#506) are four of nine diseases that are grouped together in CATG 2 and CATG I . The number of genes in the LCC increases; however, the number of modules does not change much . This fact may be due to properties of scale-free networks that most interactions newly discovered connect a hub gene and an isolated gene. Although the human gene network will inevitably undergo local changes with the discovery of new genes and gene–gene interactions, the overall organization and layout of the network will not be changed significantly, because it is unchanged that hub nodes play a major role in the modules. As gene–gene interaction relations are constantly explored, gene interaction networks are evolving. To describe how much a gene has been studied, an approach called gene saturation which is based on a logistic model for each gene has been proposed recently . This apDisease classification is a progression towards precision medicine with the need for precise patient characterization, currently based on clinical phenotypes but in future augmented by laboratory-based tests . As illuPrior to clinical implementation, major challenges must be addressed from a clinician’s perspective, including understanding how network approach based genomic science is generated and linked to patient-oriented science, which is the framework for evaluating genomic studies, as an evidence base for providing effective precision medicine to patients in the future . The diset al. [A comprehensive description of the associations between pathways and diseases requires identification of not only multiple pathways associated with a specific disease but also pathways associated with multiple diseases. In 9125 tumour samples, Sanchezvega et al.  point ouet al. . Anotheret al. . These ret al. ,69. In a


Fatty liver (FL) disease is the most common type of chronic liver disease. We hypothesized that liver’s response to the process where large droplets of triglyceride fat accumulate in liver cells is reflected also in gene pathway expression in blood. Peripheral blood genome wide gene expression analysis and ultrasonic imaging of liver were performed for 1,650 participants  of the Young Finns Study. Gene set enrichment analysis (GSEA) was performed for the expression data. Fourteen gene sets were upregulated  in subjects with FL. These pathways related to extracellular matrix (ECM) turnover, immune response regulation, prothrombotic state and neural tissues. After adjustment for known risk factors and biomarkers of FL, we found i) integrin A4B1 signaling, ii) leukocyte transendothelial migration, iii) CD40/CD40L and iv) netrin-1 signaling pathways to be upregulated in individuals with FL . From these all but not ii) remained significantly upregulated when analyzing only subjects without history of heavy alcohol use. In conclusion, FL was associated with blood gene sets of ECM turnover, inflammatory response, immune system activation and prothrombotic state. These may form a systemic link between FL and the development of cardiovascular diseases. Currently NAFLD is the most common cause of chronic liver disease3, affecting up to one third of US population4 and 70–90% of the obese and diabetics5. The main characteristic of FLD irrespective of the cause is the accumulation of triglyceride lipid droplets (>5% of liver weight) in liver cells6. The increased level of intrahepatic fatty acids may lead into cell damage and inflammation and provide a source of oxidative stress promoting fatty liver progression from steatosis to steatohepatitis and subsequently to cirrhosis or ultimately to hepatocellular carcinoma6.The fatty liver disease (FLD) is a common liver disorder in western industrialized countries and an emerging problem in the Asia-Pacific region. FLD is often associated excessive alcohol consumption  or obesity with or without insulin resistance i.e., non-alcoholic fatty liver disease (NAFLD)7. For example, clinically patients who eventually develop progressive liver cirrhosis or liver failure cannot be differentiated from those who do not. Immune system activation and inflammation are key players in the pathogenesis of FLD11. NAFLD is considered as an early mediator of systemic disease12. Innate immune system is deeply involved in pathophysiological events of fatty liver by following mechanisms: TLR-4 dependent signaling activates the Kupffer cells, complement pathway activation, balancing the cytokine network towards pro-inflammatory mediators, alternation in natural killer (NK) and NK T cell number and activity, and activation of the adaptive immune system leading to severe liver disease8. The involvement of adaptive immunity in the evolution of fatty liver and its complications is under research. Present evidence suggests that adaptive immunity contributes not only to the maintenance of fatty liver but also to the progression and comorbidities of it13. Immuno-inflammatory mechanisms are present in several comorbidities related with FLD, including obesity, type 2 diabetes, chronic kidney disease, metabolic syndrome, and cardiovascular diseases12. However, the actual pathophysiological mechanisms connecting these states are not well known.The primary causes leading to hepatocellular lipid accumulation are not yet well understood, but they are thought to include alterations in the hepatic lipid uptake, synthesis, degradation and secretion. Also, its metabolic, systemic and clinical consequences are still incompletely understoodThe purpose of the present study was to reveal alterations in gene pathways related to FLD in a population based study cohort. We hypothesized that accumulated fatty acids and large droplets of triglycerides in liver cells trigger an inflammatory response in liver, which is visible via differential immuno-inflammatory gene pathway expression in blood. Understanding the pathogenic mechanisms of FLD and its metabolic and systemic consequences will give us new insight to the disease process and comorbidities of FLD.14. In the present study, we used the data from the follow-up in 2011, when 2,063 subjects participated in blood sampling and in clinical examinations. Participants in the follow-up studies have been found to be more often women and older than those who dropped out, but no significant differences in risk factors have been found14. The present study has been approved by the Ethics Committee of the Hospital District of Southwest Finland on September 21st, 2010. The study protocol of each study phase corresponded to the proposal by the World Health Organization. All subjects gave written informed consent and the study was conducted in accordance with the Helsinki declaration.The Cardiovascular Risk in Young Finns Study (YFS) is a Finnish longitudinal population study on the evolution of cardiovascular risk factors from childhood to adulthood. These subjects for the baseline study in 1980 were randomly selected from Finnish national registry from six different age groups between 3 to 18 years and five different study districts15.Waist and hip circumference, height and weight were measured and body mass index (BMI) calculated as kg/m². Blood pressure was measured as the average of three measurements taken at 2-minute intervals in a sitting position from the right arm brachial artery by random zero sphygmomanometer. The metabolic syndrome was defined according to the harmonized definition16. The participants were classified as smokers if they smoked daily. Subjects were classified as diabetics based on questionnaires, blood fasting glucose and glycated hemoglobin measurements and national medication registry records. Complete information on history of HIV and hepatitis C were obtained from the Finnish National Hospital Discharge Register. None of the participants had a diagnosis of hepatitis C or HIV, which could potentially have contributed to the diagnosis of fatty liver.Data regarding daily alcohol consumption, physical activity, and smoking were obtained by questionnaires in 2011. Alcohol consumption data were acquired by standardized questionnaires and calculated in standard doses (12 g pure ethanol) per day by dividing the total number of doses consumed per week  by 7. Physical activity index was calculated (range 5–15)17 and Sequoia 512 ultrasound mainframes  with 4.0 MHz adult abdominal transducers. Evaluation of hepatic steatosis was performed according to liver-to-kidney contrast, parenchymal brightness, deep beam attenuation, and bright vessel walls18. According to these criteria the presence of hepatic steatosis was assessed visually from images by a highly trained ultrasonographer. The participants were classified into fatty liver (FL) and normal liver (NL) groups.Ultrasound imaging of the liver was performed for 2,040 study participants using a validated protocolVenous blood samples were drawn from the right antecubital vein after an overnight fast and anticoagulated with EDTA. For liver enzyme quantification serum was separated, aliquoted, and stored at −70 °C until analysis. Serum alanine aminotransferase (ALT), aspartate aminotransferase (AST), gamma-glutamyltransferase (GT) and triglyceride concentrations were measured by enzymatic methods . Apolipoprotein B (ApoB) and C-reactive protein (CRP) were determined immunoturbidimetrically . The serum triglyceride concentration was assayed using the enzymatic glycerol kinase–glycerol phosphate oxidase method . All the above-mentioned assays were performed on an automatic analyzer .Whole blood (2.5 ml) was collected into PaXgene Blood RNA Tubes . The tubes were inverted 8–10 times then stored at room temperature for at least 2 hours. The tubes were frozen (−80 °C) and thawed overnight before RNA isolation  with a PAXgene Blood microRNA Kit (Qiagen) including the DNase Set using the QiaCube (Qiagen). The concentrations and purity of the RNA samples were evaluated spectrophotometrically . The RNA isolation process was validated by analyzing the integrity of several RNAs (n = 26) with the RNA 6000 Nano Chip Kit (Agilent).2,049 individuals gave blood samples to RNA isolation in the 30-year follow-up in 2011–2012. 63 samples were discarded during the RNA isolation protocol, leading to 1,987 samples including 1 technical replicate taking part in the genome wide expression analysis. 322 samples had too low concentration after amplification step. Finally, 1,667 samples were run with the expression BeadChip including 3 technical replicates (2 from the mRNA amplification step).The expression levels were analyzed with an Illumina HumanHT-12 version 4 Expression BeadChip (Illumina Inc.). In brief, 300–500 ng of RNA was reverse transcribed into cDNA and biotin-UTP labelled using the Illumina TotalPrep RNA Amplification Kit (Ambion), and 1,500 ng of cDNA was then hybridized to the Illumina HumanHT-12 v4 Expression BeadChip. The BeadChips were scanned with the Illumina iScan system.http://www.r-project.org/) using the Bioconductor (http://www.bioconductor.org/) packages. The HT-12 v4 BeadChip contains 47,231 expression and 770 control probes. The transcripts detected  in less than 5% of samples were excluded from the analysis. After this filtering 19,637 genes were used for analysis. We disregarded 4 samples with less than 6,000 significantly detected expression probes . The expression data was processed using nonparametric background correction, followed by quantile normalization with control and expression probes, and log2 transformation using the neqc function in the limma package. Based on RPS4Y1–2 and XIST mRNA levels on the Y and X chromosomes, respectively, we excluded 9 samples due to mismatch with the recorded sex. After quality control, expression data were available for 1,654 samples including 4 technical replicates, which were used to examine batch effects and excluded subsequently. 1,650 non-related samples were used for further analysis.Raw Illumina summary probe-level data was exported from Beadstudio and analyzed in R  group (n = 223). Demographics of the study participants were analyzed with R (version 3.2.2). These include all the parameters shown to be connected with fatty liver in a previous study of YFS participants including age, sex, BMI, alcohol consumption, smoking, systolic and diastolic blood pressure, waist to hip ratio, ApoB, triglycerides, insulin, liver enzymes  and physical activity index20. Data are presented as mean (SD). P values < 0.05 were considered significant. FL, NAFL and AFL groups were separately compared to control group using Mann-Whitney test for continuous parameters and χ2 test for classified parameters.Subjects were classified into two groups: FL (n = 316) and NL  by a highly trained ultrasonographer and FL cases were considered as alcoholic fatty liver (AFL) (n = 55) if the daily alcohol consumption exceeded 20 g for women and 30 g for men21 were used in gene set enrichment analysis (GSEA). Only pathways with 15 to 500 genes were included, resulting in using altogether 870 pathways in the analysis. The alternative probes representing the same gene in the beadchip were collapsed to single genes by GSEA software, resulting in 14403 genes in the analysis.The Canonical Pathways (CP) from the curated gene sets collection (C2) of the molecular signature database (MSigDB v5.0) database23 was used to analyze the association of gene pathways with the phenotype. A false discovery rate (FDR) q-value < 0.25 can be considered as a significant according to the criteria recommended by Subramanian et al.22. However, we used a more stringent FDR q-value < 0.05 to select the statistically significant pathways. GSEA performs 1000 permutations when calculating the FDR values. Therefore, in the result tables we present p < 0.001 instead of 0 when the calculated FDR value appears as 0 in the GSEA results. R language (version 3.2.2) was used for adjusting the gene expression data before the analysis with GSEA software. The expression levels were adjusted for sex and age (Model 1) and with those independent confounding factors previously associated with FL in our study sample (fully adjusted Model 2)20: adjusting the gene expression for age, sex, BMI, alcohol consumption, ApoB, triglycerides, insulin, systolic blood pressure, ALT, and physical activity index. In addition, to reduce the effect of technical factors24 we adjusted the expression levels for the first 20 principal components as described elsewhere25. Finally, inverse normal transformation was applied to the probes’ residual variance before analyzing them with the GSEA algorithm.GSEA softwareSimilar analysis setup using the MSigDB canonical pathways were performed for all measurements: FL vs. NL, NAFL vs. NL and AFL vs. NL. In addition, GSEA was performed for NAFL vs. AFL to reveal the differences in gene expression pathways between these states.The datasets analysed during the current study are not publicly available due to the regulations of Finnish law and the ethical permissions that prohibits uploading the YFS data to public platforms.20 was that daily smoking did not vary from the NL in the FL or NAFL groups in our study. However, smoking differed significantly (p = 0.022) between the subjects with AFL and NL. In addition, in the NAFL subgroup alcohol consumption was similar to the NL group. The subjects in the present study are relatively young and include only 11 individuals with cardiovascular diseases including heart failure, coronary artery disease, coronary artery bypass grafting, coronary angioplasty, cerebrovascular disease and stroke. However, the number of subjects with cardiovascular diseases is insufficient to provide significant pathway enrichment results for this group.General demographics of the study population is presented in Table ) in Model 1 . These gene sets can be grouped into four groups describing the origin of the pathways: (1) pathways related to the extracellular matrix (ECM) turnover, (2) pathways regulating immuno-inflammatory response, (3) pathways contributing to thrombosis and (4) pathways related to neural tissues.Fourteen gene sets were upregulated (FDR < 0.05) in subjects with FL Table  in ModelPathways related to the extracellular matrix (ECM) turnover include two different integrin pathways (integrin A4B1 and integrin cell surface interactions), ECM receptor interaction and mCalpain pathway. All these gene sets share several integrin genes that mediate cell adhesion to ECM.CD40/CD40L signaling pathway related to immune response was strongly enriched. Also, leukocyte transendothelial migration gene set associated to increased inflammation procedures in individuals with FL. Releated to these inflammation mechanisms, a pathway containing genes involved in signaling to extracellular signal-regulated kinases (ERKs) was also upregulated in FL group. Another upregulated pathway was HIVNEF, which promotes inflammation by helping CD4 helper T cells to prevent apoptosis.Three upregulated pathways increase the probability for thrombosis in FL. They include integrin alphaIIB beta3 signaling, platelet aggregation plug formation and urokinase plasminogen activator (UPA) – UPA receptor (UPAR) pathway.26. Another upregulated gene set was Agrin in postsynaptic differentiation (AGR) pathway, which is related to neuromuscular junctions and organization of cytoskeleton in skeletal muscle. Alpha-synuclein signaling pathway, is previously identified from presynaptic neurons in human brain.Interestingly, some of the upregulated pathways we identified in FL individuals have originally been identified from neural tissues. Netrin-1 signaling pathway, was initially linked to axon development and was recently also associated with atherosclerosis, controlling the trafficking of monocytes between blood and plaque(Table ). One of them was related with ECM turnover , two gene sets were related with immune response regulation  and one gene set associated with neuroimmune guidance cue 1 (netrin-1) signaling.With fully adjusted Model 2, four gene sets remained significant  in FL group Table . One of We also present the results of FL vs. NL comparisons for Model 1 in Supplementary Table ).All the 14 gene sets upregulated in FL group with Model 1 were also significantly upregulated in NAFL group in comparison to controls , using Model 1 adjustments. With Model 2 adjusting, we identified three upregulated gene sets: integrin A4B1 signaling, CD40/CD40L signaling pathway and netrin-1 signaling Table .However, the corresponding results for AFL in Model 1 analysis indicated five of the defined gene sets were significantly enriched for upregulation. They consist of integrin cell surface interactions, HIVNEF pathway and all three thrombosis related pathways . For Model 2 analysis with AFL, none of the defined 14 gene sets was significantly upregulated.When we compared the results of fully adjusted Model 2 with the Model 1, only a few gene sets remained statistically significantly upregulated for FL or NAFL. For example, all pathways related to thrombosis could be explained by the confounding factors. Three pathways were upregulated also in Model 2 analysis of FL and NAFL: integrin A4B1 signaling, CD40/CD40L signaling pathway and netrin-1 signaling pathway.This large study with 1,650 participants is to our knowledge the first wide-scale peripheral blood based GSEA analysis of subjects with FL. We hypothesized that hepatocellular accumulation of fatty acids and triglycerides trigger an inflammatory response in liver, which is visible via differential immuno-inflammatory gene pathway expression in blood. Furthermore, we investigated the effect of alcohol consumption history and fully adjusted model with previously known independent confounding factors to the observed gene pathways. After adjusting for the known confounding factors, we identified four activated pathways associated with FL, including integrin A4B1 signaling, leukocyte transendothelial migration (TEM), CD40/CD40L and netrin-1 signaling pathways.27. Integrin A4B1  signaling pathway is thought to be activated by chemotactic agents from injured cells28. Together integrin A4B1 (VLA-4) and leukocyte TEM pathways regulate the leukocyte migration from blood to organs through endothelium30. Netrin-1 signaling pathway increase leukocyte sensitivity to anti-inflammatory netrin-1, which is shown to decrease the accumulation of neutrophils but is downregulated in injured hepatic tissue31. As a consequence of these mechanisms, leukocytes increasingly transmigrate from blood and retain in injured liver tissue. Accumulated lymphocytes are known to induce fatty liver in different ways, such as by activating pro-inflammatory cytokines8. Furthermore, activated inflammatory mechanisms increase accumulation of lipids into liver, which then induce or exacerbate the progression of fatty liver. In mouse model of NASH, inflammatory cells also regulate hepatic lipase and lipoprotein lipase: CD8+ T cells, NKT cells and cytokines secreted by them induce liver damage, ultimately advancing towards hepatocellular carcinoma32. Our present findings would propose background mechanisms behind these previously known pathological changes in FL.These pathways are related to the following biological leukocyte functions: (a) their adherence to vessel wall, (b) transmigration, and/or (c) retaining in tissues. CD40/CD40L signaling pathway is suggested to increase adherence of CD40 positive cells, such as platelets and monocytes, to the vessel wallet al. found that circulating levels of soluble CD40 ligand (sCD40L) was significantly higher in 113 NAFLD patients when compared to 102 control subjects33. This result is in line with our present findings. Previous results by Ercin et al. controversially indicated that sCD40L levels did not differ between 50 NAFLD subjects and 30 healthy controls34. However, that evaluation was performed with smaller number of subjects than the previous and the present study. CD40L deficiency has been investigated in rodent models as well. Interestingly, Poggi et al. found that both the genetic CD40L deficiency and neutralizing anti-CD40L antibody therapy attenuated the development of FL and other metabolic disorders in mice35. Based on these previous studies and the present results we would suggest that C40/CD40L signaling pathway plays an important role in metabolic disorders such as FL. More specifically, CD40/CD40L signaling is suggested to increase adherence of CD40 positive cells, such as platelets and monocytes, to the vessel wall modulating atherothrombosis27.In the present study, upregulated CD40/CD40L signaling pathway was associated with FL, especially NAFL. Sookoian 28. A4B1 has also been shown to bind osteopontin, while facilitating hepatic neutrophil infiltration and liver injury in the rat alcoholic steatohepatitis model36. Another significantly enriched pathway after adjusting for confounding factors was leukocyte transendothelial migration (TEM). That pathway is close to the VLA-4 pathway since they both regulate the migration of leukocytes from blood to tissue through endothelium30. These gene sets correspond to the similar biological functionality but the TEM pathway reflects more biological functions by 75 genes (leukocyte – endothelium cell interactions) while integrin A4B1 signaling targets only 25 genes . While Banerjee et al. associated leukocyte TEM functions with rat alcoholic steatohepatitis model36, we observed that the TEM pathways were activated in NAFL group. However, these pathways may reflect common pathogenic inflammatory mechanisms behind both AFLD and NAFLD9, and it is possible that the number of subjects with AFL (n = 55) is insufficient to reveal this change in the present study. To determine if there exists difference in TEM pathways between NAFL and AFL in humans, further research is recommended with more subjects in the AFL group. To conclude, in the present study fatty liver is reflected to blood gene pathways in such way where specific factors (adherence and transmigration) driving leukocytes to tissues are activated.Integrin A4B1 signaling pathway was significantly enriched in FL and NAFL groups. As previously indicated, integrin A4B1 (VLA-4) is normally expressed on leukocyte plasma membranes. Activation of this antigen requires chemotactic agents from the site of injury i.e., endothelium or other cells. Endothelium expresses vascular cell adhesion molecule-1 (VCAM-1) which binds to the VLA-4 and enables the transmission of leukocytes from blood to organs37. Interestingly, in the present study netrin-1 gene was not differentially expressed in our groups. Corresponding results have earlier been observed in another study, where expression of netrin-1 associated with atherosclerotic plaques, but did not differ in blood of patients with coronary artery disease compared with healthy controls26. Activated netrin-1 signaling pathway could indicate increased sensitivity of leukocytes and macrophages to the inflammatory mediators such as netrin-1. A recent study with mice suggests netrin-1 as an anti-inflammatory factor in liver ischemia/reperfusion injury31, whereas our results extend that also into blood of patients with fatty liver. Netrin-1 is suggested to retain macrophages in atherosclerotic plaques26, and by the present results, we would suggest the corresponding effect in patients with FL.Neuroimmune guidance cue netrin-1 signaling pathway was upregulated in FL. Initially, netrins were considered as cell and axon guidance cue proteins in embryogenesis, and later identified outside the nervous system influencing tissue morphogenesiset al., where increased adipocyte size associated with elevated liver enzymes38. In addition, Kälsch et al. observed that in population based Heinz Nixdorf RECALL Study increasing liver enzymes (transaminases) even within normal ranges were significantly associated with metabolic changes, i.e., HbA1c, BMI and Type 2 diabetes39. These observations support the present results of metabolic disturbances associated with FL. Interestingly, fatty liver status shared several pathways also associated with atherosclerosis or atherothrombosis  i.e., another lipid related condition where lipids are accumulated to artery wall. Furthermore, recent research results would also suggest to link integrin A4B1 to atherosclerosis, where macrophages activate integrin A4B1 by expression of kindlin-340 in atherosclerotic plaques41. These shared pathways may form a common link between these two disease states where fatty liver is shown to increase the risk of cardiovascular diseases5. This suggestion is also supported by Baars et al., who found that elevated liver parameters are associated with stenosis diameter in acute myocardial infarction42.To conclude, these pathways strongly suggest activation of immunological and inflammatory processes in blood of patients with fatty liver. A blood cascade including three different steps was identified with association of fatty liver. Firstly, adherence and transmigration of leukocytes from blood to tissues such as liver and vessel wall is activated. Secondly, interaction of monocytes and platelets is promoted which increases prothrombotic state. Thirdly, netrin-1 signaling pathway induces anti-inflammatory mechanisms by retaining leukocytes into tissues. However, in the present study setting we cannot determine the causality, whether the FL participates in production of these inflammatory factors into blood or do the dysregulated gene pathways refer to systemic disturbances from which FL is one result. Therefore, we are unable to evaluate whether the activation of inflammation system in liver or blood is more important for FL. At least, our results may reflect common metabolic and inflammatory disturbances behind atherosclerosis and FL. Corresponding metabolic results have been identified in a study by Wree A strength of the present study is that the subjects are based on the well-powered prospective randomly selected population based cohort, followed up for over 30 years with long well-known history of alcohol use and disease history. This study had large cohort  and this adds the confidence to the present results. In addition, we used strict FDR limit in GSEA results and heavily adjusted our analysis with known fatty liver risk factors. The original suggestion was FDR < 0.25 but we used FDR < 0.05 as a limit. The strict FDR limit combined to the large number of subjects and adjustment with known risk factors makes the results of this study reliable.Limitations of the study include population homogeneity: all subjects are Finnish, ethnically homogenous Caucasian people. Because of that these results can be only generalizable in Caucasians. We unfortunately had no possibility for validation of the results in other comparable cohort. Therefore, we recommend the replication of our results and corresponding analysis for further research. In addition, majority of individuals with FL in our population are men. Even though sex has been included in statistical analyses as covariate and sex stratified analyses were performed, possible features specific for FL in women could have been missed due to low number of women with FL. The results cannot therefore be directly generalized to older populations with equal amounts of women and men with FL. The number of subjects in AFL group limited (n = 55), which is probably insufficient to reveal the alterations in the expression of gene pathways in this subgroup. Subjects with FL had more metabolic disorders, which may influence to the present results.Furthermore, there is a risk of complications when the liver biopsy is taken and it is therefore unacceptable  to take liver biopsies from healthy asymptomatic individuals on population based studies. Analyzing blood samples is much safer and thus makes it possible to have much larger cohorts. Thus, we decided to analyze gene expression from blood tissue, which restricts the present observations to extrahepatic changes.The key concept of analyzing gene sets instead of separate genes is considered as a strength of this study. The blood gene pathways reflect real metabolic and systemic functions. We were able to reveal much broader biological mechanisms related to the FL using metabolic pathways instead of single genes.In conclusion, FL was associated with blood gene sets of inflammatory response, immune system activation and prothrombotic state. These activated blood pathways may serve as a systemic link between FL and the development of cardiovascular diseases.Supplementary information


Despite the significant achievements in chemotherapy, cancer remains one of the leading causes of death. Target therapy revolutionized this field, but efficiencies of target drugs show dramatic variation among individual patients. Personalization of target therapies remains, therefore, a challenge in oncology. Here, we proposed molecular pathway-based algorithm for scoring of target drugs using high throughput mutation data to personalize their clinical efficacies. This algorithm was validated on 3,800 exome mutation profiles from The Cancer Genome Atlas (TCGA) project for 128 target drugs. The output values termed Mutational Drug Scores (MDS) showed positive correlation with the published drug efficiencies in clinical trials. We also used MDS approach to simulate all known protein coding genes as the putative drug targets. The model used was built on the basis of 18,273 mutation profiles from COSMIC database for eight cancer types. We found that the MDS algorithm-predicted hits frequently coincide with those already used as targets of the existing cancer drugs, but several novel candidates can be considered promising for further developments. Our results evidence that the MDS is applicable to ranking of anticancer drugs and can be applied for the identification of novel molecular targets. Target drugs have one or a few specific molecular targets in a cell  that is indicative of tumor response to these drugs  may be used as an aggregated biomarker   project  to obtain mutation profile for each tumorWe used the full COSMIC dataset to increase the statistical significance and to investigate the effectiveness of potential target drugs for a maximum range of cancer localizations. However, we excluded the samples related to cell cultures or tumor xenograft to standardize the analysis. We excluded records having the following marks in the “Sample source” field: organoid culture, short-term culture, cell-line, xenograft. Thus, the final dataset included 6,027,881 mutations records in 18,273 in tumor samples of 35 primary localizations. The COSMIC data were processed with script written in R (version 3.4.3) to return mutation rates for all genesWe extracted clinical trials data from the web sites of NIH The gene contents data about 3,125 human molecular pathways used to calculate mutation drug scores were extracted from Reactome  methods proposed here deals with quantization of mutation enrichment for the molecular pathways having molecular targets of a drug under investigation. Overall, they are based on the rationale that the greater is the mutation level of the respective pathways, the higher will be the expected drug efficiency. The mutation enrichment of a molecular pathway called pathway instability (PI) is calculated based on the relative mutation rates (MR) of its member genes. Under mutations, we meant here the changes in protein coding sequence understood as such in the Catalog of Somatic Mutations in Cancer (COSMIC) v.76 database  and International Cancer Genome Consortium (ICGC).The principle of Mutation rate (MR) is calculated according to the formula:MRn, g is MR of a gene n in a group of samples g; N mut is the total number of mutations for gene n in a group of samples g; N samples (g) is the number of samples in a group g. The MR values strongly positively correlated with the lengths of gene coding DNA sequence . In order to remove bias linked with the CDS length, we took for further consideration a normalized value termed Normalized Mutation Rate (nMR) expressed by the formula:where nMRn is the nMR of a gene n; MRn is the MR of a gene n; Length CDS(n) is the length of CDS of gene n in nucleotides. Indeed, normalization of this metric enabled to terminate any CDS-linked bias (data not shown).where n is included in pathway p, we introduced a Boolean flag pathway-gene indicator PGn, p expressed by the formula:To determine if gene Pathway Instability (PI) score is then calculated as follows:The PIp is pathway instability score for a pathway p; nMRn is the normalized mutation rate of a gene n, PGn, p is pathway-gene indicator for gene n and pathway p. Pathway instability score characterizes the mutation enrichment of a pathway . To formalize if gene n is molecular target of drug d, we introduced another Boolean flag drug target index, DTId, n:where DTI database for this study, we used the data about molecular specificities of 128 target drugs extracted from the databases DrugBank :To link d is drug name; n is gene name; p is pathway name; MDSd is MDS for drug d; DTId, n is drug target index for drug d and gene n; PIp is Pathway Instability of pathway p; PGn, p is pathway-gene indicator for gene n and pathway p.where 1) was modified to generate several alternative methods of drug scoring.The above basic formula  where normalization is performed for MDS on the respective number of genes for each PI member:kp is number of genes in pathway p.-Single count-normalized. Impact of each gene participating in pathways targeted by drug d is counted only once:GIId, n – Boolean flag gene involvement index,- Number of pathways-normalized. MDS for drug d is normalized on the number of its targeted molecular pathways.md – number of pathways targeted by drug d.- Number of pathways-normalized. MDS_N is additionally normalized on the number of pathways targeted by drug d (md).- Number of target genes-normalized. MDS_bd is additionally normalized on the number of target genes for drug d, (bd).- Number of target genes-normalized MDS_N. MDS_N, normalized on the number of target genes for drug d, (bd).- Number of target genes-normalized MDS_gene. MDS_gene, normalized on the number of target genes for drug d, (bd).- Target genes dependent only. MDS2 is calculated considering only mutation frequencies of target genes.- Single count-normalized, target genes dependent only. MDS2_gene is calculated, considering each target gene for drug d only one time.- MDS according to formulae (1–10) for 128 anticancer target drugs, for eight cancer types . It reflects the percentage share of known molecular targets among predicted molecular targets.To do this, we introduced an auxiliary value termed TC was 2.17%. However, there was an clear-cut incremental TC growth trend when the potential molecular targets were sorted in the ascending order of MDS value  list of potential molecular targets, MDS are clearly enriched by the existing clinically approved drugs compared to those with low MDS scores  are the targets of approved non-oncological drugs , thus illustrating MDS method potential in drug repurposing.Our results provide principal evidence that the mutation drug scoring is applicable to ranking of anticancer drugs. On the other hand, our data suggest that these drug scoring algorithms can be applied for the identification of novel molecular targets for the prospective anticancer drugs. Although many genes with high MDS approach exemplified by bigger proportion of real cancer targets among the genes with higher MDS values. In this application, we assessed integral MDS for all cancer types. However, in further applications the same approach can be used for any specific tumor type or subtype to identify targets that may seem most promising for this particular disease. This could be valuable, for example, for drugs repurposing among the different tumor types and for more effectively identifying the patient cohorts in clinical trialsThis study can be regarded as proof-of concept trial of The present mutation drug scoring approach scores the molecular pathway instability caused by accumulation of mutations and ranks drugs according to a simple rationale—the higher is mutation burden of a pathway, the greater may be the efficiency of a drug targeting this pathway. We hope these findings will be interesting to those working in the fields of oncology, drug discovery, systems biomedicine, high throughput mutation data analysis, personalized medicine and molecular diagnostics.https://cancer.sanger.ac.uk/cosmic/download).The datasets analyzed for this study can be found in the COSMIC repository (COSMICv76; CosmicGenomeScreensMutantExport. tsv.gz, MZ developed algorithms, did mutation drug scoring analyses, planned the research and wrote the manuscript. PB planned the research, extracted and filtered cancer mutation data. AG planned the research and developed algorithms. MS completed the molecular pathway database. DK completed and processed Clinical Status database. AE organized the information about molecular specificities of anticancer target drugs. NB developed algorithms, did statistical analyses, and planned the research. AB completed Clinical Status database, developed algorithms, planned the research, and wrote the manuscript.MS, NB, AG were employed by company Omicsway Corp. MZ, AB were employed by company Oncobox Ltd. The remaining 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.


Background: The carboxylesterase Notum has been shown to act as a key negative regulator of the Wnt signalling pathway by mediating the depalmitoleoylation of Wnt proteins. LP-922056 (1) is an orally active inhibitor of Notum. We are investigating the role of Notum in modulating Wnt signalling in the central nervous system and wished to establish if 1 would serve as a peripherally restricted control. An accessible and improved synthetic route would allow 1 to become more readily available as a chemical tool to explore the fundamental biology of Notum and build target validation to underpin new drug discovery programs.Results: An improved, scalable synthesis of 1 is reported. Key modifications include: (1) the introduction of the C7-cyclopropyl group was most effectively achieved with a Suzuki–Miyaura cross-coupling reaction with MIDA-boronate 11 (5 → 6), and (2) C6 chlorination was performed with 1-chloro-1,2-benziodoxol-3-one (12) (6 → 7) as a mild and selective electrophilic chlorination agent. This 7-step route from 16 has been reliably performed on large scale to produce multigram quantities of 1 in good efficiency and high purity. Pharmacokinetic studies in mouse showed CNS penetration of 1 is very low with a brain/plasma concentration ratio of just 0.01. A small library of amides 17 were prepared from acid 1 to explore if 1 could be modified to deliver a CNS penetrant tool by capping off the acid as an amide. Although significant Notum inhibition activity could be achieved, none of these amides demonstrated the required combination of metabolic stability along with cell permeability without evidence of P-gp mediated efflux.Conclusion: Mouse pharmacokinetic studies demonstrate that 1 is unsuitable for use in models of disease where brain penetration is an essential requirement of the compound but would be an ideal peripherally restricted control. These data will contribute to the understanding of drug levels of 1 to overlay with appropriate in vivo efficacy endpoints, i.e., the PK-PD relationship. The identification of a suitable analogue of 1 (or 17) which combines Notum inhibition with CNS penetration would be a valuable chemical probe for investigating the role of Notum in disease models. The Wnt signalling pathway has been shown to regulate crucial aspects of cell fate determination, organogenesis, cell migration and polarity . Importa1, 1 has shown that Notum is a potential drug target for stimulating bone formation and treating osteoporosis pyrimidine (3), which is readily available from commercial suppliers, and generally follows published procedures pyrimidine (3) was either purchased or prepared from thienopyrimidin-4(3H)-one (2) by C4 chlorination with oxalyl chloride/DMF following the method of Mitchell et al. [3 with NaOMe displaced the C4–Cl to give 4 in a good yield as described by Atheral et al. [d]pyrimidine 4 is now suitably functionalised for the introduction of the C7-cyclopropyl group, the C6-chlorine atom and elaboration of the thioacetic acid moiety at C4.4-Chlorothieno and/or 11 failed to drive the reaction to completion. We found the most efficient way to complete the reaction conversion was to isolate the crude product (mostly 6) and subject this material to a repeat reaction; this procedure gave 6 in a good yield 95% and simplified purification.Electrophilic bromination at C7 with nversion . Palladi6 in hand, attention was turned to the C6 chlorination step. Unfortunately, despite this reaction being reported in the literature, there were no experimental details for this specific transformation as only a ‘general procedure’ was described pyrimidines and we wished to see if we could extend the scope to include sulphur containing heterocycles such as thienopyrimidines . It was also important to explore if 12 would efficiently chlorinate 6 at the less activated C6 position in the presence of the C7 cyclopropyl group.A large number of electrophilic chlorinating reagents for the direct chlorination of aromatic rings have been reported . Recentl12) –19. The 6 with 12 (1.5 equiv) in DMF at 50 °C for 16–24 h gave the desired chloro product 7 in 77–94% isolated yield. Analysis of the crude reaction mixture showed only trace amounts of cyclopropyl ring opened products such as 14/15 as detectible by LC–MS. Hence, 12 proved to be a far superior reagent, when compared to NCS, for the C6 chlorination of thienopyrimidine 6 .Treatment of 1 followed established procedures although it proved expedient to carry material through several of these later steps without the need for extensive purification beyond a simple work-up procedure (7 → 8 → 9). Activation of C4 was accomplished by a two-step procedure of acid hydrolysis of the C4-OMe of 7 to give thienopyrimidin-4(3H)-one 8, followed by chlorination with POCl3 to give 9. Finally, nucleophilic displacement of the C4–Cl of 9 by methyl thioglycolate (13) gave ester 10 which was hydrolysed with NaOH to afford 1. This route has been reliably performed on large scale to produce multigram quantities of 1 in good efficiency  and high purity (>99%).Completion of our synthesis of 5 by an alternative route. The low-yielding C7 bromination of 4 with NBS to give 5 as described above (d]pyrimidine (16) which is readily available from commercial suppliers. Treatment of 16 with NaOMe displaced the C4–Cl to give 5 in good yield on 10 g scale (16 is somewhat more expensive than 2 or 3 per unit cost (by ca. 5-fold), this updated route shortens the sequence to just 7 steps from 16 and improves the overall yield to 40–50 %.A shorter synthesis was then developed by accessing bromide ed above , step c  g scale . Even th1 in mouse liver microsomes (MLM) showed excellent metabolic stability (Cli 1.0 μL/min/mg protein) which predicts for low clearance in vivo. Binding to mouse plasma proteins (mPPB) was very high with percent unbound drug (fu) of just 0.1%; this mPPB value can be used to calculate free drug concentrations from measured drug levels in plasma taken during in vivo experiments. The high mPPB is entirely consistent with the physicochemical properties of 1 as a lipophilic acid .Assessment of 1 was generated in vivo in mouse to evaluate brain penetration (Cmax and AUC) are consistent with published preclinical PK data [Pharmacokinetic (PK) data for etration  . At this level of exposure, a significant proportion of the compound detected in brain samples is likely to have arisen from residual blood in the brain tissue.CNS penetration of 1 could be modified to deliver a CNS penetrant tool by capping off the acid as an amide. A small library of amides 17 were prepared from acid 1 by activation with HBTU and then subsequent reaction with the amine 18  consistent with CNS drug-like space [With a peripherally restricted control in hand, we elected to explore if amine 18 . These ake space .50 < 100 nM), none of these specific amides demonstrated the required combination of sufficient MLM stability along with cell permeability as measured by transit performance across a MDCK-MDR1 monolayer without evidence of P-gp mediated efflux. This collection of data for amides 17a–r is shared as ‘open data’ to assist others in evaluating these results with the objective of solving this challenge (see Although significant Notum inhibition activity could be achieved (ICenge see . Our own1 is reported. Key modifications include: (1) the introduction of the C7-cyclopropyl group was most effectively achieved with a Suzuki–Miyaura cross-coupling reaction with MIDA-boronate 11 (5 → 6); and (2) C6 chlorination was performed with 1-chloro-1,2-benziodoxol-3-one (12) (6 → 7) as a mild selective electrophilic chlorination agent. This 7-step route from 16 has been reliably performed on large scale to produce multigram quantities of 1 in good efficiency.An improved, scalable synthesis of Notum inhibitor 1 is very low with brain/plasma concentration ratio of just 0.01 based on AUC(0→∞). Hence, 1 is unsuitable for use in models of disease where brain penetration is an essential requirement of the compound but would be an ideal peripherally restricted control. These data will contribute to the understanding of drug levels of 1 to overlay with appropriate in vivo efficacy endpoints, i.e., the PK-PD relationship. The full PK data set is presented and shared as ‘open data’. This complete data set  will also assist with the creation of improved predictive pharmacokinetic models.Pharmacokinetic studies in mouse showed CNS penetration of 17 were prepared from acid 1 to explore if 1 could be modified to deliver a CNS penetrant tool by capping off the acid as an amide. Although significant Notum inhibition activity could be achieved, none of these amides demonstrated the required combination of metabolic stability along with cell permeability without evidence of P-gp mediated efflux. The identification of a suitable analogue of 1 (or 17) which combines Notum inhibition with CNS penetration would be a valuable chemical probe for investigating the role of Notum in disease models. This collection of data for amides 17a–r is shared as ‘open data’ to assist others in evaluating these results with the objective of solving this challenge.A small library of amides 3–10 and 1; (2) mouse PK data which includes: study design summary; plasma concentrations; brain concentrations; (3) Notum IC50 (nM), MLM Cli (μL/min/mg protein) and MDCK-MDR1 AB/BA Papp (× 10−6 cm/s) data for 17a–r.(1) experimental procedures and characterisation data for To view the NMR spectra, use the file within the pdata folder of File 1Experimental section, mouse pharmacokinetics and profiles of amides.File 2Profiles of amides.File 3Raw NMR data files for compound LP-922056.