This research aimed to determine biological pathways and protein-protein interaction (PPI) networks for 305-d milk yield (MY), 305-d fat yield (FY), and age at first calving (AFC) in the Thai multibreed dairy population.Genotypic information contained 75,776 imputed and actual single nucleotide polymorphisms (SNP) from 2,661 animals. Single-step genomic best linear unbiased predictions were utilized to estimate SNP genetic variances for MY, FY, and AFC. Fixed effects included herd-year-season, breed regression and heterosis regression effects. Random effects were animal additive genetic and residual. Individual SNP explaining at least 0.001% of the genetic variance for each trait were used to identify nearby genes in the National Center for Biotechnology Information database. Pathway enrichment analysis was performed. The PPI of genes were identified and visualized of the PPI network.Identified genes were involved in 16 enriched pathways related to MY, FY, and AFC. Most genes had two or more connections with other genes in the PPI network. Genes associated with MY, FY, and AFC based on the biological pathways and PPI were primarily involved in cellular processes. The percent of the genetic variance explained by genes in enriched pathways (303) was 2.63% for MY, 2.59% for FY, and 2.49% for AFC. Genes in the PPI network (265) explained 2.28% of the genetic variance for MY, 2.26% for FY, and 2.12% for AFC.These sets of SNP associated with genes in the set enriched pathways and the PPI network could be used as genomic selection targets in the Thai multibreed dairy population. This study should be continued both in this and other populations subject to a variety of environmental conditions because predicted SNP values will likely differ across populations subject to different environmental conditions and changes over time. Bos indicus  and Bos taurus  breeds [Bos taurus breeds in temperate regions [The Thai multibreed dairy population is primarily composed of crossbred animals with over 75% Holstein 91%) and the remainder comes from various % and the regions ,3. Use o regions  found thThe GWAS for milk production and reproductive traits in Holstein in temperate regions identified regions associated with MY, FY, and AFC in all autosomes –8. SimilThis research utilized 8,361 first-lactation cows from 810 farms located in the Northern, Northeastern, Central, Western, and Southern regions of Thailand. These cows were the progeny of 1,210 sires and 6,992 dams. Eighty-eight percent of animals in the database were Holstein (H) crossbreds (75% H and above); the remaining 25% belonged to other breeds (O) including Jersey, Brown Swiss, Red Danish, Sahiwal, Red Sindhi, Brahman, and Thai Native.Pennisetum purpureum), Guinea grass (Panicum maximum), Ruzi grass (Brachiaria ruziziensis), and Para grass (Brachiaria mutica). Cows were fed approximately 30 to 40 kg/d of roughage and 5 to 10 kg/d of concentrate, or equivalently, 1 kg of concentrate per 2 kg/milk produced. The concentrate (14% to 22% of crude protein and 63% to 83% of nitrogen-free extract) was provided twice per day during milking (morning: 4:30 to 7:00 am and afternoon: 2:30 to 4:30 pm). Agricultural byproducts , hay, and (or) silage were used as supplements as green roughage decreased in winter and summer [Cows were housed in open barns where they had access to roughage, concentrate and a mineral supplement. Green roughage consisted of freshly cut grasses (cut and carry) including Napier grass  of the Thai multibreed dairy population. Genomic DNA was extracted from blood using a MasterPure DNA Purification kit for blood version II  and from semen using a GenElute Mammalian Genomic DNA Miniprep Kit . The DNA quality was assessed with a NanoDrop 2000 spectrophotometer . DNA samples from all animals  were ensured to contain sufficient DNA for genotyping (absorbance ratio of approximately 1.8 at 260/280 nm and DNA concentration higher than 15 ng/μL). DNA samples were genotyped with GeneSeek Genomic Profiler (GGP) 9K, 20K, 26K, and 80K chips .Animals genotyped with GGP9K, GGP20K, and GGP26K were imputed to GGP80K using program FImpute version 2.2 ,11. The a where H was the genomic-polygenic relationship matrix, Va was variance-covariance matrix among additive genetic effects for these traits, and ⨂ was the Kronecker product. Residual variance-covariance matrix was equal to I⨂Ve where I was identity matrix and Ve was variance-covariance matrix among residual effects. The H equal to 11 was the submatrix of additive relationships between non-genotyped animals, A12 was the submatrix of additive relationships between genotyped animals, 22 was the matrix of genomic relationships among genotyped animals [22 = ZZ′/2 ∑ pj (1 – pj), where pj was the frequency of the second allele in locus j and Z was the incidence matrix of SNP effects whose elements were defined as zij = (0 – 2pj) if the genotype for locus j was homozygous 11, zij = (1 – 2pj) if the genotype for locus j was heterozygous 12 or 21 and zij = (2 – 2pj) if the genotype for locus j was homozygous 22. Matrix G22 was scaled using the default parameters of the BLUPF90 Family of Programs [22 to be equal to the mean of the diagonal elements of A22 and the mean of the off-diagonal elements of G22 to be equal to the mean of the off-diagonal elements of A22.A GWAS for MY, FY, and AFC was performed using ssGBLUP . AnimalsPrograms , i.e., tVariance and covariance components for MY, FY, and AFC were estimated using restricted maximum likelihood procedures and computed via program AIREMLF90  using anBos taurus 3.1 assembly of the bovine genome at the National Center for Biotechnology Information (NCBI) using R package Map2NCBI [Individual SNP that explained at least 0.001% of the genetic variance for MY, FY, and AFC were selected to determine potential genes associated with these traits. The position of these SNP markers in base pairs was used to locate genes or nearby genes in the UMD Map2NCBI . Only SNBos taurus at the Kyoto encyclopedia of genes and genomes database using the ClueGo plugin of Cytoscape [Genes associated with MY, FY, and AFC were used to identify biological pathways in ytoscape . The staThe name of genes for MY, FY, and AFC was used to identify PPI from neighborhood, co-occurrence, gene fusions, co-expression, experiments, databases, and text mining using program STRING . The STRThe percentage of genetic variance explained by each SNP are shown in Numbers of SNP per gene ranged from 1 to 37 for MY, 1 to 25 for FY, and 1 to 29 for AFC . SeventyNumbers of genes and total genetic variance per chromo some for MY, FY, and AFC identified by SNP genotypes inside or within 2,500 bp of genes in the NCBI database are shown in Enriched pathways were classified into four categories: cellular processes, nervous system, digestive system, and environment adaptation are shown in Cellular process pathways related to cell proliferation, differentiation, migration, survival and apoptosis are essential for physiological changes in the ovarian follicle and mammary gland . Therefo2+). Specifically, Ca2+ acts as an activator in the mitogen-activated protein kinase (MAPK) signaling pathway in ovarian follicle and mammary gland cells [http://www.genome.jp/dbgetbin/www_bget?pathway:bta04015), Ras proteins from the Ras signaling pathway (http://www.genome.jp/dbget-bin/www_bget?pathway:bta04014), and Wnt proteins from the Wnt signaling pathway (http://www.genome.jp/dbget-bin/www_bget?pathway:bta04310).Ovarian follicle and mammary gland development are influenced by the calcium-signaling pathway, which in turn is regulated by growth factors through changes in the concentration of free calcium ions , a key intermediate in milk fat synthesis during lactation [http://www.genome.jp/dbget-bin/www_bget?pathway:bta04022). The ceramide and sphingosine-1-phosphate from the sphingolipid signaling pathway acts an as a regulator of cell responses to stress (http://www.genome.jp/dbget-bin/www_bget?pathway:bta04071). The oxytocin hormone , produced by the hypothalamus, stimulates the contraction of mammary gland myoepithelial cells, causing milk to be ejected into the ducts, and cisterns during milking (http://www.genome.jp/dbget-bin/www_bget?pathway:bta04921).Phospholipase D from the phospholipase D signaling path way is an essential enzyme for the production of phosphatidic acid . The circadian clock is influenced by the length of photoperiod [The circadian entrainment pathway was the only significant pathway in the environmental adaptation category. This pathway contained 30 genes that explained 0.38% of the genetic variance for MY, 0.36% for FY, and 0.37% for AFC , and thetoperiod . Dairy cThe PPI network for MY, FY, and AFC contained 265 nodes  connected via 1,158 edges . ApproxiPRKCB) gene had the largest number of significantly enriched pathways for MY, FY, and AFC (14) and accounted for 0.012% of the genetic variance for MY, 0.009% for FY and 0.016% for AFC . Further, PRKCB is also involved in the circadian entrainment pathway. This pathway contributes to the adaptation of organisms to their environment [PRKCB on body temperature regulation during climate stress was reported in Angus and Simmental cattle [PRKCB was 0.226 for MY, 0.005 for FY, and −0.002 for AFC , PLCB4, adenylate cyclase 2 (ADCY2), ADCY8, calcium/calmodulin dependent protein kinase II beta (CAMK2B), CAMK2D, mitogen-activated protein kinase 11 (MAPK11), MAPK14, epidermal growth factor receptor (EGFR), growth factor receptor bound protein 2 (GRB2), Fyn proto-oncogene, Src family tyrosine kinase (FYN), and integrin subunit beta 5 (ITGB5) genes were involved in 12 significantly enriched pathways and accounted for 0.126% of the genetic variance for MY, 0.109% for FY, and 0.197% for AFC . ADCY2 and ADCY8 code for adenylyl cyclase type 2 and 8 that act as catalysts for the formation of cAMP which is involved in many cellular processes . CAMK2B and CAMK2D code for calcium/calmodulin-dependent protein kinases that function as mediators of calcium signaling in cells . MAPK11 and MAPK14 code for p38 mitogen-activated protein kinases 11 to 14 that function as mediators of the cellular response to external signals [EGFR codes for epidermal growth factor receptor that act as a receptor for the growth factor (http://www.genecards.org/cgi-bin/carddisp.pl?gene=EGFR&keywords=EGFR). GRB2 codes for a growth factor receptor-bound protein that functions as a signal transducer (http://www.genecards.org/cgi-bin/carddisp.pl?gene=GRB2&keywords=GRB2). FYN codes for protein-tyrosine kinase that acts as an activator of molecular signals (http://www.genecards.org/cgi-bin/carddisp.pl?gene=FYN&keywords=FYN). ITGB5 codes for integrin beta type 5 that functions as a receptor for fibronectin (http://www.genecards.org/cgi-bin/carddisp.pl?gene=ITGB5&keywords=ITGB5), which regulates cell proliferation and differentiation during the development of ovarian follicles and mammary gland cells.The phospholipase C beta 1 , G protein subunit gamma transducin 1 (GNGT1), and G protein subunit alpha O1 (GNAO1) genes were involved in 6 significantly enriched pathways and accounted for 0.040% of the genetic variance for MY, 0.027% for FY, and 0.020% for AFC  and will also likely differ over time as population characteristics and environmental conditions change.


Escherichia coli and yeasts provides a promising alternative owing to available genetic tools in pathway engineering and genome editing, and a comprehensive understanding of their metabolisms. This review summarizes recent progresses in engineering of industrial model strains, E. coli and yeasts, for terpenoids production. With advances of synthetic biology and systems biology, both strains are expected to present the great potential as a platform of terpenoid synthesis.Terpenoids, also called isoprenoids, are a large and highly diverse family of natural products with important medical and industrial properties. However, a limited production of terpenoids from natural resources constrains their use of either bulk commodity products or high valuable products. Microbial production of terpenoids from  Artemisia annua (Qinghao) just yields artemisinin of <0.8% by dry biomass weight (Saccharomyces cerevisiae) before civilization, and antibiotics  at an industrial scale since the Second World War  natural products with diverse biological functions including cell integrity, hormones, electron transport, and photosynthetic machinery. The extracted products from some herbs or animal liver are realized as flavors, fragrances, colorants, commodity chemicals, vitamins, and pharmaceuticals from ancient time. Nevertheless, their low yields from natural sources limit mass production of terpenoid for industrial application. For instance, s weight , which sorld War . Nowaday5 isoprene units which are successively assembled by biogenic isoprene rule. Precursors of the C5 isoprene units are isopentenyl diphosphate (IPP) and dimethylallyl diphosphate (DMAPP), which are synthesized by either mevalonate (MVA) pathway in eukaryotes, archaea and a few bacteria or 2-C-methyl-D-erythritol 4-phosphate (MEP) pathway in prokaryotes and plant plastids . Both pathways require energies (ATP) and reductive powers [NAD(P)H] to proceed multi-enzymatic reactions to produce IPP and DMAPP. Given no consideration of energy balance, MEP pathway consumes 1 molecule of glucose per IPP with a carbon molar yield of 0.83, while the MVA pathway consumes 1.5 molecules of glucose per IPP with the lower yield of 0.56   . In thiss. 1, 2) .10), farnesyl diphosphate , geranylgeranyl diphosphate , and even longer chain isoprenyl diphosphates , which determine the primary diversity of terpenoids in the chain lengths. Thus, terpenoids are classified to hemiterpenoids (C5), monoterpenoids (C10), sesquiterpenoids (C15), diterpenoids (C20), triterpenoids (C30), tetraterpenoids (C40), and long chain isoprenyl products .Next, prenyltransferases catalyze chain elongation of isoprenyl diphosphates including geranyl diphosphate  a large metabolic potential supporting efficient synthesis of products of interest with robust and fast cell growth, (ii) a well-understood metabolism and well-developed genetic tools , and (iii) a great capacity to grow on cheap carbon sources. Terpenoids synthesis relies on MEP or MVA pathway for the generation of building units, IPP and DMAPP. Therefore, two representative microbial hosts E. coli are limited to small amounts . The native MEP pathway could be less efficient in IPP and DMAPP supply for terpenoid production. E. coli has been engineered to improve IPP and DMAPP synthesis by augmenting bottleneck enzymes of MEP pathway or introducing heterologous MVA pathway. S. cerevisiae accumulates a large amount of ergosterol, which presents a potential of its MVA pathway for terpenoid production. S. cerevisiae has redox systems that allow cytochrome P450 to modify terpenoids skeleton, whereas E. coli has none. S. cerevisiae is superior to E. coli in synthesis of value-added terpenoids with complicated structures. Yarrowia lipolytica is another good yeast for production of terpenoids based on MVA pathway because of its abundant acetyl-CoA, the initial substrate of MVA pathway . Owing to a versatility of E. coli and S. cerevisiae, many strategies of metabolic engineering have been developed and examined to increase terpenoid production in the both model hosts. The engineering strategies can be classified to “static” and “dynamic” according to a regulatory means of synthetic pathway. Both approaches have been applied to terpenoid synthetic pathways for an enhancement of IPP/DMAPP flux, a minimization of byproducts formation, a toxicity reduction of pathway intermediates, etc., Static engineering employs variation of vectors, promoters, ribosome binding sites (RBSs), and genes of interest, which are assembled in a plethora of biological systems, to optimize a synthesis of target products . Brewer’s yeast has been engineered by combinatory regulation of pathway genes to biosynthesize aromatic monoterpenes that impart hoppy flavor to beer , which are incorporated as a metabolic controller for dynamic flux control  condensing two FPPs to a squalene could be a critical regulation point to divert FPP flux to synthesis of terpenoids of interests. A dynamic control of Erg9 expression using ergosterol-responsive promoter could alter FPP flux to amorphadiene synthesis (40) at a titer of 722.5 mg/L . MAGE was applied to optimizing MEP pathway in E. coli for lycopene production . β-Carotene synthesis pathway was integrated into E. coli genome by CRISPR/Cas9-based method. Both MEP pathway and central metabolic pathways were optimized in the engineered strain, and the final strain was cultured in fed-batch, yielding 2.0 g/L of β-carotene  and yield  after 48 h of shaking flask culture . There are successful examples of terpenoid production driven by systems biology to debottleneck synthesis pathway . A key issue of synthetic microbial consortia engineering is to balance each microbial population for a successful collaboration to attain the goal (E. coli and P450 modification in S. cerevisiae (E. coli was engineered to use xylose, but produce a wasteful acetate, while auxotrophic S. cerevisiae assimilated solely acetate for cell growth in presence of xylose as a sole carbon source. The mutualistic consortium could maintain a population balance of the co-cultured strains and doubled the production in comparison with a co-culture using glucose. Deeper understanding of autotrophy, interspecies cross-feed, and QS machinery would provide more exquisite approaches for production of complicated terpenoids using synthetic microbial consortia.Complicated structures of terpenoids are often a hurdle of the production using a single microbe. As eraction , it requeraction . With grthe goal . In orderevisiae . E. coliE. coli, and yeasts have proved as the most attractive microbial platform, which can be conveniently developed to generate either bulk or value-added terpenoids owing to versatile enabling technologies for the microbes. A great potential of E. coli and yeasts as platform strains has been demonstrated with many successes of synthetic pathway rewiring, genome editing, and microbial consortia building for improvement of production. With deeper understanding of their metabolism, more promising approaches are expected to boost production of terpenoids in E. coli and yeasts.Currently, many enabling technologies of synthetic biology allow us to tailor microbes for terpenoids production. Among the microbial species, CW and S-WK developed the ideas and drafted the manuscript. ML, J-BP, and S-HJ collected the literatures and drew the figures. GW, YW, and S-WK professionally edited the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


This study is aimed at exploring the potential metabolite/gene biomarkers, as well as the differences between the molecular mechanisms, of osteoarthritis (OA) and rheumatoid arthritis (RA).  Transcriptome dataset GSE100786 was downloaded to explore the differentially expressed genes (DEGs) between OA samples and RA samples. Meanwhile, metabolomic dataset MTBLS564 was downloaded and preprocessed to obtain metabolites. Then, the principal component analysis (PCA) and linear models were used to reveal DEG-metabolite relations. Finally, metabolic pathway enrichment analysis was performed to investigate the differences between the molecular mechanisms of OA and RA.  A total of 976 DEGs and 171 metabolites were explored between OA samples and RA samples. The PCA and linear module analysis investigated 186 DEG-metabolite interactions including Glycogenin 1- (GYG1-) asparagine_54, hedgehog acyltransferase- (HHAT-) glucose_70, and TNF receptor-associated factor 3- (TRAF3-) acetoacetate_35. Finally, the KEGG pathway analysis showed that these metabolites were mainly enriched in pathways like gap junction, phagosome, NF-kappa B, and IL-17 pathway.  Genes such as HHAT, GYG1, and TRAF3, as well as metabolites including glucose, asparagine, and acetoacetate, might be implicated in the pathogenesis of OA and RA. Metabolites like ethanol and tyrosine might participate differentially in OA and RA progression via the gap junction pathway and phagosome pathway, respectively. TRAF3-acetoacetate interaction may be involved in regulating inflammation in OA and RA by the NF-kappa B and IL-17 pathway. Osteoarthritis (OA) and rheumatoid arthritis (RA) are all inflammatory joint diseases . GloballMetabolites have various functions including signaling, defense, and interactions with other organisms . NumerouIn the present study, a metabonomic-transcriptome integration analysis was performed. To investigate the genes and metabolites that differentially expressed between OA and RA, the differentially expressed genes (DEGs) and metabolites were revealed from microarray data and metabolite expression profile data, respectively. Then, principal component analysis (PCA) and the linear model were used to explore the DEG-metabolite interactions. Finally, based on these interactions, pathway analysis was performed on DEG-associated metabolites to reveal the pathways that participate in the process of OA and RA. This study was expected to investigate the role of key metabolites and genes as well as their interactions in the pathogenesis of OA and RA, and further understand the complex biological regulatory mechanisms of metabolites in these two diseases.The transcriptome dataset GSE100786 was downloaded from the Gene Expression Omnibus (GEO) database. The platform was the GPL570 Affymetrix Human Genome U133 Plus 2.0 Array. A total of 8 OA bone marrow (BM) monocyte samples and 8 RA BM monocyte samples were included in GSE100786.https://www.ebi.ac.uk/metabolights/index). The platform was Bruker (using NMR spectroscopy technology). A total of 10 OA synovial fluid (SF) samples and 14 RA SF samples were included in MTBLS564.The metabolite profiling MTBLS564 was downloaded from the Ensembl-European Bioinformatics Institute (EMBL-EBI) MetaboLights database   method iP value between OA samples and RA samples in transcriptome data was calculated by the Linear Models for Microarray Data  package [P < 0.05 was selected as the threshold for the identification of DEGs. Then, based on Euclidean distance, the bidirectional hierarchical clustering for DEMs was performed by pheatmap software (version: 1.0.8) [The  package  in R sof: 1.0.8) . The resIn the current study, the average value of each gene in the transcriptome data was calculated and ranked from high to low (deleting the last 10% of the genes). Meanwhile, the proportion of the deletion value in the expression value of each metabolite was counted, and the metabolites with more than 80% deletion value were deleted. Then, the principal component analysis (PCA) was performed on the data in two groups.P values of DEG-metabolite relevance between OA samples and RA samples were obtained using the IntLim  linear model algorithm [m” and “g” in the formula represent metabolite abundance and gene expression level, respectively; “p ” in the formula represents phenotype (OA samples vs. RA samples); “(g : p)” in the formula represents the association between gene expression and phenotype; and “ε” in the formula represents normal distribution. Then, the difference of correlation coefficients between the two groups (|rOA − rRA|) > 1 and P value < 0.001 were selected as the cut-off values for DEG-metabolite interaction investigation.Based on the results of PCA analysis on gene expression data and metabonomic data, the lgorithm . The comP value of DEG-metabolite interactions, the KEGG pathway enrichment analysis was used to investigate pathways enriched by the DEGs associated with metabolites. P value (the significance threshold of the hypergeometric test) < 0.05 was chosen as the cut-off criterion for the present enrichment analysis.The clusterProfiler software (version: 3.2.11)  is an onAfter preprocessing, a total of 171 metabolites from metabonomic data were enrolled for further investigation. Meanwhile, a total of 20,192 genes were obtained from 54,675 probes in the current transcriptome data. Among these 20,192 genes, a total of 416 upregulated genes (such as hedgehog acyltransferase (HHAT)) and 669 downregulated genes (such as Glycogenin 1 (GYG1), Unc-51 Like Kinase 3 (ULK3), and breakpoint cluster region protein (BCR)) were revealed between OA samples and RA samples in transcriptome data. The heat map for all these DEGs is shown in P values of the DEG-metabolite relevance between OA samples and RA samples is shown in P < 0.001, such as TRAF3-acetoacetate_35 , gap junction , phagosome , NF-kappa B signaling pathway , IL-17 signaling pathway , and inflammatory mediator regulation of TRP channels   .Escherichia coli infection, gap junction, phagosome, NF-kappa B signaling pathway, and IL-17 signaling pathway.Although metabolites are proven to be associated with the pathological process of OA and RA, the difficulty in interpreting metabonomic data impedes the understanding of the differences of the molecular mechanisms between these two diseases. The current metabonomic-transcriptome integration analysis revealed a total of 976 DEGs and 171 metabolites between OA samples and RA samples. The PCA and linear module analysis explored 186 DEG-metabolite interactions including GYG1-asparagine_54, HHAT-glucose_70, and TRAF3-acetoacetate_35. Finally, the KEGG pathway analysis showed that these metabolites were mainly enriched in pathways like pathogenic HHAT is an enzyme in the endoplasmic reticulum that palmitoylates hedgehog proteins . HHATs pA previous study shows that gap junctions have an important function in the control or coordination of bone cell activity . The intκB signaling pathway [κB signaling pathway [Studies had reported the roles of NF-kappa B and IL-17 on regulating inflammation in RA –52. Shui pathway . In this pathway , 56. Rep pathway . Liu et  pathway . TherebyIn conclusion, genes such as HHAT, GYG1, and TRAF3, as well as metabolites including glucose, asparagine, and acetoacetate might be implicated in the pathogenesis of OA and RA. Metabolites like ethanol and tyrosine may participate differentially in OA and RA progression via the gap junction pathway and the phagosome pathway, respectively. TRAF3-acetoacetate interaction may be involved in regulating inflammation in OA and RA by the NF-kappa B and IL-17 pathway.


Inference of cancer-causing genes and their biological functions are crucial but challenging due to the heterogeneity of somatic mutations. The heterogeneity of somatic mutations reveals that only a handful of oncogenes mutate frequently and a number of cancer-causing genes mutate rarely.We develop a Cytoscape app, named ZDOG, for visualization of the extent to which mutated genes may affect cancer pathways using the dominating tree model. The dominator tree model allows us to examine conveniently the positional importance of a gene in cancer signalling pathways. This tool facilitates the identification of mutated “master” regulators even with low mutation frequency in deregulated signalling pathways.We have presented a model for facilitating the examination of the extent to which mutation in a gene may affect downstream components in a signalling pathway through its positional information. The model is implemented in a user-friendly Cytoscape app which will be freely available upon publication.https://github.com/rudi2013/ZDOG). It is also available in the Cytoscape app store (http://apps.cytoscape.org/apps/ZDOG) and users can easily install it using the Cytoscape App Manager.Together with a user manual, the ZDOG app is freely available at GitHub ( Cancer is a disease caused by genomic mutations. A mutation in a proto-oncogene changes the gene into an oncogenic state in which the gene promotes uncontrolled cell proliferation, loses its function, or promotes the migration of tumour cells to form new tumours in other parts of the body . With thThe accumulation of cancer genomic data demonstrates mutational heterogeneity between different cancers and between different genomes of the same cancer. This reveals that only a handful of oncogenes mutate frequently, whereas many cancer-causing genes mutate rarely . Better D with a distinguished start node, called the root, in which every node is connected to the root by a path if arc orientation is ignored. We call a node u a dominator of another node v if every directed path from the root to v contains u. In the signalling pathway context, this implies that any signal flowing to the protein component v from the entry point must be relayed through the protein u. The dominator relation is a transitive binary relation on the vertices of D. It can be represented by a tree, called the dominator tree of D, in which there is an edge from x to y if x is the least dominator of y for every pair of nodes x and y of D [Consider a digraph d y of D . The domd y of D .Fig. 1IThe ZDOG has two key functions Fig. . First, Second, the user can further zoom in on key mutated genes by viewing them in the dominator tree model of the signalling pathway Fig. b. Here, Re-examining mutated genes in the dominator tree of the signalling pathway allows the user to identify conveniently dominating mutated genes and to find out how they may affect the activation of downstream components that control important biological processes - cell proliferation, growth, apoptosis, etc. To this end, the user first selects a stimulus or a receptor protein as the entry point of the signal in the pathway displayed and then clicks the “calculate dominator tree” button. The gene mutations are redisplayed in the dominator tree of the pathway Fig. b.http://www.cytoscape.org. ZDOG requires the yFiles Tree Layout procedure and the KEGGscape tool for support. After these programs are installed, ZDOG can be easily installed via the App Manager in Cytoscape. Simple search for ZDOG in the App Manager and click Install. Alternatively, the ZDOG-1.0.jar file can be downloaded from https://github.com/rudi2013/ZDOG and installed using the App Manager – install from local file. Presently, only pathways in KEGG format are supported. Detailed installation instructions can be found in the user manual /AKT signalling pathway Fig. : Fig. S13) and to AKT (drawn in the middle of the tree) has full control on the downstream proteins that function in the glycolysis/gluconeogenesis process, the cell cycle process, the apoptosis process, the NF-kappa B signalling pathway and the p53 signalling pathway.The key signalling subpath from PIK3CA to Phosphatidylinositol -trisphosphate  at the CSF1 receptor (CSF1R), suggesting that HRAS interacts with PIK3CA, but it cannot fully control PIK3CA and thus its downstream components. A similar observation can also be made for the signalling subpath RAF1 to MAP2K1 and to MAPK1 that interacts with the MAPK signalling pathway.3. This suggests that AKT is upstream of RPS6KB1, but cannot fully control it.In the dominator tree, the ribosomal protein S6 kinase beta-1 (RPS6KB1) is found in a path branching off at PIPWith the colony stimulating factor 1 (CSF1) gene being chosen, the dominator tree of the PI3K/AKT signalling pathway is given in Fig. AKT, subunits of PI3K and mTor through mutation and amplification, as well as loss of function of tumour suppressors such as PTEN. Because of the RPS6KB1 to IRS1 feedback interaction, over-activation of PI3K may occur through PTEN loss of function through this feedback besides through either upstream activation or the presence of activating mutations in PI3K itself. Therefore, therapy with either dual PI3K/mTOR inhibition or IGF-IR inhibition may provide an anti-proliferative advantage over specific AKT inhibitors [K-Ras mutations were less sensitive to a dual PI3K/mTOR inhibitor NVP-BEZ235 than the rest of the tested cell lines. This corresponds well with the fact that neither PI3K nor mTor is a dominator of the Ras component in the signalling pathway  . The PI3hibitors . This ishibitors  showed tOverlaying mutations on the dominator tree of the PI3K/AKT signalling pathway clearly indicates that every downstream biological process was affected by some mutations occurring in breast cancer. In contrast, the MAPK signalling pathway was not affected much by mutation in breast cancer /Akt signalling pathway. Fig. S2. The KEGG MAKP signaling pathway and its dominator tree with TNF as the signaling entry pointAdditional file 2: Table S1. Separation of 6,581,004 COSMIC variations into 47 organs/tissuesAdditional file 3: Table S2. Datasets in the TCGA that are made available to ZDOGAdditional file 4. Supplemental FileAdditional file 5. ZDOG user manual


Pathway enrichment extensively used in the analysis of Omics data for gaining biological insights into the functional roles of pre-defined subsets of genes, proteins and metabolites. A large number of methods have been proposed in the literature for this task. The vast majority of these methods use as input expression levels of the biomolecules under study together with their membership in pathways of interest. The latest generation of pathway enrichment methods also leverages information on the topology of the underlying pathways, which as evidence from their evaluation reveals, lead to improved sensitivity and specificity. Nevertheless, a systematic empirical comparison of such methods is still lacking, making selection of the most suitable method for a specific experimental setting challenging. This comparative study of nine network-based methods for pathway enrichment analysis aims to provide a systematic evaluation of their performance based on three real data sets with different number of features (genes/metabolites) and number of samples.The findings highlight both methodological and empirical differences across the nine methods. In particular, certain methods assess pathway enrichment due to differences both across expression levels and in the strength of the interconnectedness of the members of the pathway, while others only leverage differential expression levels. In the more challenging setting involving a metabolomics data set, the results show that methods that utilize both pieces of information  exhibit superior statistical power in detecting pathway enrichment.The analysis reveals that a number of methods perform equally well when testing large size pathways, which is the case with genomic data. On the other hand, NetGSA that takes into consideration both differential expression of the biomolecules in the pathway, as well as changes in the topology exhibits a superior performance when testing small size pathways, which is usually the case for metabolomics data. Pathway enrichment has become a standard tool in the analytic pipeline for Omics data, since it reduces the complexity and provides a systems view of the biological question under investigation –5. DozenDespite the plethora of available methods, there has been a scarcity of systematic comparisons of their performance in controlled settings based on synthetic or real data sets. In  and 24]24], seveself-contained and (II) competitive null hypotheses [Pathway enrichment methods aim to compare the ‘activity’ of pathways of interest across two or more biological conditions or groups of specimens . At the technical level, an important feature is the nature of the statistical null hypothesis being tested. Most methods can be categorized to those testing (I) potheses . A compepotheses , the autin silico experiments that offer advantages over simulation experiments conducted on purely synthetic data [In the current comparative study, we examine nine popular topology-based pathway analysis methods that investigate different null hypotheses. Methods considered in this review have good user interface in R, and include Pathway-Express , SPIA 99, NetGSAtic data  or evalutic data , 24. On To systematically evaluate the type I error and power of the nine pathway enrichment methods, we consider a variety of settings using three different data sets, two from cancer genomics and one based on metabolomics.q<K out of a total K pathways were first randomly selected as ‘dysregulated’. Next, a pre-specified proportion of genes/metabolites within each dysregulated pathway was chosen to be altered. This proportion, referred to as detection call (DC), was set to be 20% for the metabolomic study and 10% for the cancer genomic studies. Due to the smaller pathway sizes and limited number of known interactions within each pathway, affected metabolites were selected randomly from each dysregulated pathway. Since genetic pathways are typically larger in size and have well documented interactions in the graphite package, we considered three mechanisms to select affected genes within each dysregulated pathway following the practice in [We analyzed primary metabolic pathways mapped from KEGG in the metabolomic study, and KEGG pathways that describe signaling and biochemical functions and their interactions in the cancer genomic studies  that are densely connected internally and loosely connected in between. For a pre-specified pathway with a given topology, we find the communities within the pathway using a community detection algorithm, e.g., using the function cluster_edge_betweenness from the igraphR package [ package . We thenNeighborhoodUnder the neighborhood dysregulation design, members within a certain shortest path distance of a randomly chosen gene are used to define the affected genes. The distance parameter is optimized such that a 10% DC is reached after looping over all members within the pathway.p genes/metabolites and n samples . This corresponds to the model π is a random permutation of the sample index 1,…,n.where We considered both settings with and without sample permutation. The main difference between models  and 2)  2) is whThe above data generating mechanism relaxes the distributional assumptions and allows us to evaluate different methods with respect to a ground truth. This is in contrast to most existing reviews in the literature based on either publicly available data where the ground truth is unknown , 24, or The complete set of test designs is summarized in Additional file We present the results of our comparative study by assessing the performance of the nine methods in terms of their type I errors, followed by their statistical powers. A type I error, also called a false positive, occurs when a true null hypothesis is rejected, whereas the power quantifies the probability of a test correctly rejecting the null when the alternative hypothesis is true. Of course, power comparison is only meaningful if the tests all have valid type I error control. Due to the large size of the two gene expression data sets, the type I errors and powers were calculated as the proportion of null hypotheses rejected among 200 simulation replications, but they were evaluated over 1000 replications in the metabolomic data example.p-value thresholding. To this end, we used univariate two-sample t-test assuming unequal variances to calculate the p-value for each gene/metabolite. These p-values were corrected for multiple comparisons using the Benjamini & Hochberg procedure for controlling false discovery rate (FDR) [Several methods require te (FDR) . The FDRp-values primarily for signaling pathways, which is expected given their testing procedures. Second, topologyGSA only works for pathway whose topology is a DAG and whose size is smaller than the minimum number of samples in the two conditions/groups. As a result, our comparisons in the two cancer genomic applications only include selected pathways that were analyzed by all methods. Lastly, the network in the metabolomics data example is not directed acyclic and the pathways are smaller biochemical pathways that often have incomplete coverage and high degrees of overlap. Thus we excluded topologyGSA, Pathway-Express, SPIA and PRS in the metabolomics data example.In our analysis, multiple method-specific difficulties made it impossible to compare all available pathways. First, in the two gene expression data examples, SPIA and Pathway-Express returned In power comparisons, we summarize the relative performance of all nine methods based on the ranking of each pathway’s empirical power. Specifically, for each pathway, we rank the empirical powers of all methods at each level of mean change from low (indicating higher power) to high (indicating lower power). Methods that produce ‘NA’ (e.g. ORA methods when there are too few DE genes) are ranked the highest. For each pathway, the geometric mean of all rankings across different mean changes is taken as the final measure of relative performance. Spreadsheets with disaggregated empirical powers for each pathway, across all the experimental settings, are provided in .There are 11 KEGG pathways  whose topologies satisfy the input requirement needed for topologyGSA and Pathway-Express. We thus focused on type I error and power comparisons on this subset of pathways under the betweenness dysregulation design. Details on the pathways and more comparisons are available in Additional file p-value cutoff) can not assess the pathway significance. These methods have thus been excluded from type I error comparison. On the other hand, PE.noCut does not require p-value thresholding and is therefore included in Fig. The scatter plot in Fig. Figure Similar figures for other dysregulation designs and other pathways, including those for the prostate cancer study, are available in Additional file The findings in the metabolomics data example differ qualitatively from those in genomic examples. This difference can be attributed to the relatively small number of edges in the metabolic network and small pathway sizes, which are due to the incomplete coverage of metabolomic assays. Note because SPIA, Pathway-Express and PRS were proposed specifically for genetic pathways, and the metabolic network is not directed acyclic, we only compared NetGSA, DEGraph, CAMERA, CePa and PathNet in this example. In addition, CePa requires the presence of DE metabolites, which only appear when the mean change is greater than 0.5. The mean signal in this example was thus allowed to vary from 0.1 to 1.0. To run all 5 methods, we filtered out 33 of the 65 KEGG pathways whose topologies are too sparse—fewer than two edges—and focused on type I error and power comparisons on the remaining 32 pathways.Figure Figure The methods considered in this study exhibit differences in terms of the network information that they incorporate. For example, CePa, PathNet, Pathway-Express, PRS and SPIA only account for the pathway topology. CAMERA does not directly take into account the pathway topology, but estimates the correlations among the biomolecules from data. In contrast, NetGSA, DEGraph and topologyGSA assume the underlying networks come from a known class of graphs, whose parameters are inferred from data. NetGSA can also incorporate existing network information from user-provided sources for improved power. The downside with the more flexible version of NetGSA that accounts for differences in networks is that the type I errors may be slightly inflated, if too few samples are available for the estimation of the network parameters, corresponding to network edges. This was observed in the metabolomics data example. This issue can be addressed by aggregating data from multiple related studies for more accurate network estimation.Taking a broader view point, NetGSA is capable of assessing pathway enrichment due to changes both in the mean levels of the biomolecules, as well as their connectivity; it can thus be more suitable for studies involving comparisons across different disease states, where a possible strong dysregulation of the interactome can occur. It can also seamlessly accommodate more than two conditions , as wellImportantly, existing methods such as CAMERA, CePa, DEGraph, PathNet, Pathway-Express, PRS, SPIA and topologyGSA often analyze one pathway at a time while ignoring the fact that pathways overlap with each other. This practice is common because it is conceptually and computationally convenient, yet it may lead to undesirable consequences as the interactions between genes/metabolites that show up in multiple pathways may be different when analyzing these pathways separately. Separate analysis is particularly problematic for metabolomic studies where the pathways are considerably smaller in size and exhibit a high degree of overlap  and topologyGSA. In comparison, ORA-type methods  require the presence of DE genes and perform well only in specific settings. Our experience with Pathway-Express is that PE.noCut  seems to dominate PE.Cut . Additionally, due to inaccurate coverage and special features of topology information on smaller metabolic pathways, only DEGraph shows robust performance . Our second comparison is based on a TCGA gene expression data on prostate cancer from . The datThe third and final data set comes from a metabolomics study on non-obese diabetic mice, where the metabolic profiles of 41 non-diabetic and 30 diabetic animals of 100 named metabolites were collected, with the goal of identifying metabolic signatures of Type I diabetes progression .G defined as The Pathway-Express method  for analPNDE in the first term on the right hand side of  for each gene is defined as Here,  side of  evaluateΔE(gi) is the signed normalized expression change of biomolecule gi. The denominator Nds(gj) represents the number of downstream biomolecules of gj and the sum is only over biomolecules gj directly upstream of gi. The interactions between gi and gj are encoded in the coefficients βij, e.g. + 1 for activation, − 1 for inhibition. Therefore, the second term on the right hand side of , and (ii) the mean absolute fold changes among all DE genes in the data, which is IF(G) is shown to have a Γ distribution, such that for any realization of IF(G)=f the significance of a pathway can be calculated as In Eq.  side of  can be tPathway-Express is implemented in the R package ROntoTools. The latest version of this package (2.10.0) also permits a cutoff-free version which eliminates the need to select DE genes . In thisPNDE with a second evidence, PPB, that quantifies the amount of perturbation in each pathway. To calculate the second type of evidence, the total net perturbation accumulation for a given pathway G is defined as PF(gi) is defined in  just by chance.Signaling pathway impact analysis  tfined in . A bootsG is calculated as The overall significance of pathway I−B for Bij=βij/Nds(gj) cannot be analyzed using Pathway-Express and SPIA. In addition, both Pathway-Express and SPIA require the presence of DE genes to define the impact of pathways. Hence pathways that do not have any DE genes will not be analyzed. This phenomenon was observed in [It is worth noting that Eq. erved in , 36 and The NetGSA method , 11 emplA of the network, NetGSA defines the propagated effect of genes on each other through the influence matrixΛ, defined as ΛΛ′=(I−A)−1 with I denoting the identity matrix. It then decomposes the measurements in the ith sample, Yi)( and noise εi)(=Λγi) [Σ1=Σ2, or based on the Behrens-Fisher problem [Σ1≠Σ2. The method is designed specifically for gene expression data.where (MANOVA)  if Σ1=Σ2 problem  if Σ1≠Σ2topologyGSA has several limitations. First, it requires the pathway topology to be organized as a DAG, which may not be possible for, e.g., metabolomics studies. In addition, topologyGSA relies on the likelihood ratio statistic for testing equal covariances, which restricts its use to relatively small pathways. For large pathways with more members than the sample size—which are frequently observed in studies involving gene expression data—topologyGSA can become computationally very inefficient. Lastly, differential network and differential expression are tested separately in topologyGSA, which may also limit the power of the test when the two populations differ in both means and variances.p-value) resulted in inflated type I errors in our numerical analyses, we used the p-value for testing equality of means as representing significance for pathway enrichment.In our data examples, we implemented both test on differential network using pathway.var.test and test on differential expression using pathway.mean.test from the R package topologyGSA. Since naive combination of the two tests  in pathway G, CAMERA tests whether their mean expression p be the total number of biomolecules, both inside and outside the pathway, and spool is the pooled residual standard deviation and Correlation Adjusted MEan RAnk gene set test CePa is a centrality-based pathway enrichment analysis method , 48. CePm nodes, the CePa score is defined as For a given pathway with di=1 if the ith node is differentially expressed and di=0 otherwise, and wi corresponds to the ith node’s weight defined based on various centrality measures. A small offset of 0.01 is added to each wi to ensure positive weights. CePa allows equal weights and weights defined from centrality measures such as node degree, betweenness [where weenness  and the p-values obtained using different weights as representing significance for pathway enrichment. The score definition in  are not expressed, 1 if they are expressed but not differential, or the maximum fold-change value if one or more genes in node i are differential. If vi>1, node i is then assigned a weight wi, which is the number of downstream DE nodes . The score for a pathway with m nodes is defined as The Pathway Regulation Score  ensi=wivi if node value vi>1 and 0 otherwise. The pathway score PRS is then normalized with respect to pathway size by multiplying the proportion of DE genes over the total number of expressed genes.where node score p-value of each pathway is determined as the proportion of normalized permuted scores greater than or equal to the normalized raw scores.PRS assesses the significance of each pathway using gene permutation. The raw and permuted scores, calculated respectively from the original data and permuted data, are first standardized with respect to the permuted scores in order to derive the empirical null distribution. The pooled pathway, defined as the union of all pathways. The interactions among genes in the pooled pathway are represented by an adjacency matrix A, which is a binary matrix with 1 and 0 indicating the presence and absence of an interaction. Given the network A, PathNet calculates the biomolecule-level significance gi, the indirect evidence gi from its neighbors. Specifically, PathNet defines the indirect evidence score for gi as i is greater than expected by chance. Similar to SPIA, PathNet accounts for biomolecule interactions only through the topology information available in the database, e.g., KEGG.PathNet  combinesTable The first distinction is in the type of null hypothesis. CAMERA and PathNet test a competitive null hypothesis of whether the genes in a given pathway are at most as differentially expressed as those outside the pathway. Pathway-Express, SPIA, CePa and PRS test the competitive null by comparing the pathway of interest to a random pathway (while holding the sample labels fixed). In contrast, NetGSA, topologyGSA and DEGraph consider the self-contained null hypothesis by testing a pathway against itself. Although the competitive null hypothesis can have an appealing interpretation, assessing the significance of the competitive null is challenging, as it corresponds to a gene sampling framework which treats genes as independent (see discussion in ).p-values. With the exception of CAMERA, all methods based on testing the competitive null need to determine DE genes based on a pre-specified threshold of corrected p-values. While the thresholding may seem intuitive, the resulting enrichment results may be sensitive to the p-value cutoff. The emphasis on p-value thresholding also implies that these methods will not work in settings where there are too few DE genes, i.e. when gene-wise expression change is too small, as illustrated in the “Another major difference among these methods is whether the method takes as input expression data or thresholded gene In evaluating these methods, we also make the distinction between pathway topology and pathway membership, where the former refers to both pathway membership and the interactions amongst pathway members.Importantly, the pathway topology information required by different methods can be quite different, which may affect the user experience. CAMERA only uses pathway membership and requires the least effort. The R package graphite provides functionality to retrieve the list of KEGG pathways, and the resulting topology information can be readily passed to Pathway-Express, SPIA, topologyGSA, DEGraph and PRS (as implemented in ToPASeq).In comparison, NetGSA, CePa and PathNet require additional steps of processing before graphite pathways can be analyzed. However, this additional step also implies flexibility in the sense that the user can specify desired network information. For example, NetGSA requires a weighted network, where the weights reflect the interactions between genes/metabolites. This can be either available network connectivity information from a database, or estimated from data based on partial correlations complemented with connectivity information from a database. In the gene expression data examples, we used gene-gene interactions available in BioGrid 3.5.170  compiledFinally, although NetGSA allows condition-specific networks, we implemented NetGSA assuming equal networks to ensure fair comparisons with topologyGSA and DEGraph.All methods tested have well-maintained R packages available on CRAN or Bioconductor. Input genes are named by Entrez IDs in all methods with the exception of topologyGSA and CePa, which use instead gene symbols. Pathway topology information was obtained from the KEGG database , extractAdditional file 1 Supplementary materials. Supplementary Materials include details on test design, additional simulation results on the two gene expression data and analysis of a synthetic data set as well as information on data and code availability. (pdf)


Non-alcoholic fatty liver disease (NAFLD) represents a wide spectrum of disease, ranging from simple fatty liver through steatosis with inflammation and necrosis to cirrhosis. One of the most challenging problems in biomedical research and within the chemical industry is to understand the underlying mechanisms of complex disease, and complex adverse outcome pathways (AOPs). Based on a set of 28 steatotic chemicals with gene expression data measured on primary hepatocytes at three times  and three doses , we identified genes and pathways, defined as molecular initiating events (MIEs) and key events (KEs) of steatosis using a combination of a time series and pathway analyses. Among the genes deregulated by these compounds, the study highlighted OSBPL9, ALDH7A1, MYADM, SLC51B, PRDX6, GPAT3, TMEM135, DLGDA5, BCO2, APO10LA, TSPAN6, NEURL1B, and DUSP1. Furthermore, pathway analysis indicated deregulation of pathways related to lipid accumulation, such as fat digestion and absorption, linoleic and linolenic acid metabolism, calcium signaling pathway, fatty acid metabolism, peroxisome, retinol metabolism, and steroid metabolic pathways in a time dependent manner. Such transcription profile analysis can help in the understanding of the steatosis evolution over time generated by chemical exposure. NAFLD rxidation .1.Some toxicogenomics studies have been reported on drug-induced steatosis (DIS) or steatohepatitis (DISH) , howeverIn this study, we decided to analyze transcriptomic data on a set of 28 drugs tested in primary human hepatocytes and suspected to cause steatosis. In order to obtain an overall understanding of the disease, steatosis-producing chemicals were compiled and analyzed together. An interesting feature is that compounds have been studied at different times and doses, so we were able to perform a time-series analysis at the gene level but also at the pathway level. The distinction between time points and concentrations can explain how the different KEs affect one another, which will help in explaining complex hepatotoxicity. The results of our analysis support previously reported finding and provide new hypotheses that could be investigated further.in vivo such as necrosis, cellular infiltration, fibrosis and granuloma (Supplementary Table S1). The TG-GATEs database contains data from PHH exposed to those compounds and collected using Affymetrix HG U133 Plus 2.0 gene expression microarrays. Two replicates were tested at three dose levels  and at three time points . For each experiment, corresponding untreated controls are also tested. The 35 chemicals used in the study are summarized in Table 1. The specific dose and times can be found in Supplementary Table S2.For the current analysis, 28 compounds were selected according to their ability to induce steatosis in primary human hepatocytes (PHH) and the availability of gene expression data in the TG-GATEs  database . Furthert-test comparison between two groups) was performed  methodology in the Bioconductor R package for background-adjusted, normalized, and log-transformed perfect matched values of individual probes from the Affymetrix Human Genome U133 Plus 2.0 array . 54,675 erformed . (4) Subp-value <0.05 were considered). In a final step, the R-squared of the regression model was used as cut-off value in order to reduce the amount of false positive findings (genes). R-squared was set to 0.6 to allow flexibility to the regression model, since we are working with all the compounds associated with steatosis, as suggested by MasigPro. Overall, MasigPro provides information on genes that change over time and in respect to the control. Such analysis can be visualized, plotting DEG of every single gene for each compound studied according to time and dose.To characterize the deregulation of genes related to steatosis over time, after drug administration, a time-series analysis was performed on the 28 compounds using the package MasigPro in R . This anIn addition to the DEG and the time-series analysis, a pathway analysis was performed based on our gene expression analysis for the 28 compounds. Compared with the individual gene/molecule-based approach, pathway analysis is more sensitive, consistent and informative on the outcomes studied . In our Figure 5 was performed on the compounds at 24 h. The determination of the number of clusters was done by the elbow method. A range of k values from 1 to 10, k value being the number of chemical belonging to a cluster, were considered in our analysis. For each k value the sum of squared errors (SERs) was calculated and the selection of the number of cluster was based on a compromise between the number of clusters and low SER. K = 4 was selected as it showed a close to maximum separation of the samples and low SER.Previous studies have shown that the use of gene expression clustering can group samples in clusters that may lead to a good prediction of the gene-outcome relationship . TherefoFirstly, we analyzed DEGs under all different conditions versus control for the 28 compounds with a global normalization of the 255 experiments . 742 genes are highly deregulated in at least one condition, i.e., one compound at a specific time and concentration .Figure 1). Mapping the genes deregulated by the set of 28 compounds on the NAFLD pathway led to the observation that some genes are up regulated, in red , by a large set of compounds , whereas another set of genes is more often down regulated, in green  by others compounds . There are several compounds that show opposite effects by up/down-regulating the same genes. This is the case of CYP2E, AMPK and other mitochondrial genes. This supposes that there are different mechanisms of action that can trigger steatosis.For the pathway analysis, we looked specifically at the NAFLD pathway, a general pathway related to fatty liver, and for which steatosis might be related for some genes. Through the pathway enrichment analysis, many genes involved in the NAFLD are deregulated . The enrichment in terms of pathways based on GO terms was used in this study. At the first level of the pathway hierarchy, the deregulated genes are related to several pathways, including cellular process, metabolic process, localization, developmental process and immune system process. The two most significant are the cellular processes and metabolic processes. Within metabolic processes, primary metabolic processes are the most significant pathways targeted by the deregulated genes. In primary metabolic processes, the two most targeted pathways are nucleobase-containing compound metabolic processes and lipid metabolic processes. The latest contains steroid metabolic process, phospholipids metabolic process and FA metabolic process. Looking into the specific pathways, the most represented in the GO analysis are FA β-oxidation and acetyl-CoA metabolic process. So, we can note that many genes deregulated by the set of compounds affect lipids and FAs and play a role in steatosis.Gene Ontology pathway enrichment was also performed with the 742 genes in order to get an impression of the biological processes that were affected (Supplementary Table S3) (Table 2). These genes are mainly involved in metabolic and immune system pathways. Among them, some genes have previously been reported to play a hepatotoxic role such as MYADM  , MasigPras MYADM , SLC51B as MYADM , PRDX6  after exposure to the 28 compounds. We can observe that NEURL1B is regulated in positive direction over time for many compounds. Other examples are presented in supplementary information .An example of the visualization of the time-series analysis is shown in From the gene list, other genes might play a role in steatosis and could be investigated further. For example, ALDH7A1 is highly deregulated in a time/dose-dependent manner. This gene is involved in oxidoreductase mechanisms and in protection of cell against oxidative stress by metabolizing a number of lipid peroxidation-derived aldehydes and could lead to steatosis. For some compounds, i.e., AM, APAP, LS, and MP, the de-regulation of ALDH7A1 is also dependent on the treatment concentration. The deregulation of these proteins will lead to a higher production of lipids, which together with a reduction of beta-oxidation of lipids could promote their accumulation in cells. Finally, a set of compounds deregulated some genes differently, suggesting that they trigger steatosis through another mechanism. This is the case for example for DSF and EE, which showed a weak deregulation of OSBL9 and a higher deregulation of the ALDH7A1.Some genes known to be commonly associated to steatosis in human are not in this top list. This is the case for example of PNPLA3, which does not appear as one of the highest deregulated genes in our study. The genetic variation in PNPLA3 has been previously shown to play a role in the increase of FA accumulation in liver leading to steatosis . So, theOverall, this list of genes provides an insight into the mechanistic pathways already related to steatosis, as well as new hypotheses that can be analyzed further. Interestingly, the expression for many genes vary a little from control as a function of dose and the difference in the pattern of expression between control and treatment is relatively low. This confirmed a previous analysis showing that the doses differences between treatments in rat primary hepatocytes explain less than 0.1% of variation in all cases . One posFigure 4).Due to the broad pharmacological and physicochemical characteristics of the compounds used for this study, we developed a new type of time-series analysis at the pathway level. For this purpose, all the compounds were analyzed to obtain the most significantly deregulated pathways including their corresponding GFE score .Finally, to obtain a more characteristic view on the specific action points of the different compounds, we performed a similar analysis after clustering the compounds through the gene’s signature similarity. Using the Euclidean distance based on the log2FC of the gene expression, all compounds were clustered in four sets . For the compounds of the larger cluster, cluster 1 , at low concentrations of treatment metabolic pathways such as fat digestion and absorption, linoleic and linolenic acid metabolism, calcium signaling pathway and others are up regulated over time. Other pathways, such as FA metabolism, peroxisome, retinol metabolism, and some steroid metabolic pathways are down regulated. At higher concentrations from time 2 to 24 h, the FA metabolism, calcium signaling pathway and steroid hormone biosynthesis increase over time, showing a de-regulation of these pathways promoted by the treatment. The FA degradation pathway is down regulated. It means that the FAs inside the cell are increasing and there are no pathways to deplete them. The oxidative phosphorylation becomes down regulated over time. So, the oxidative conditions in the mitochondria are starting to be reduced at this concentration. Finally, at the highest concentration tested, many signaling pathways known to be steatosis-producing related are targeted. FoxO, MAPK, PPAR signaling pathways, are highly up regulated. Steroid hormone biosynthesis, FA biosynthesis, glycerophospholipid metabolism, glycosphyngolipid biosynthesis and other lipid metabolic pathways are also up regulated. Moreover, SNARE interactions in vesicular transport are also up regulated over time, which could indicate the internalization of the FAs into vesicles, and so accumulation inside the cells. In contrast, pathways, such as oxidative phosphorylation, vitamin digestions and absorption and FA degradation are down regulated over time.After clustering, the pathway time-series analysis was performed on each of the four clusters  at low concentrations the most highly up regulated pathways are the PI3K-Akt signaling pathway, the Mtor signaling pathway and the adipocytokine signaling pathway. Some metabolic pathways like FA degradation, peroxisome and retinol metabolism are down regulated. With the increasing concentration, steroid biosynthesis starts to be up regulated. At the highest concentration, glycolysis/gluconeogenesis, FA degradation, TNFα signaling pathway, tyrosine metabolism, peroxisome, PPAR signaling pathway and retinol metabolism become down-regulated over the time and FA metabolism, adipocytokine signaling pathway, MAPK signaling pathway, among others, become up-regulated. This could be explained by a lesser effect of these compounds. Therefore higher concentrations are needed in order to deregulate the cell to a steatotic pattern.For cluster 2  , at low concentrations, oxidative phosphorylation is highly down regulated, together with retinol metabolism and some lipid metabolism such as ether lipid metabolism and glycolysis/gluconeogenesis. On the other hand, FA elongation, TNFα signaling pathway, PI3K-Akt signaling pathway and sphingolipid signaling pathway and sphingolipid metabolism are highly up regulated. At higher concentrations, retinol metabolism continues to be down-regulated, glycolysis/gluconeogenesis, FA elongation are down-regulated and protein processing in the ER, steroid biosynthesis, SNARE interactions in vesicular transport among others are up regulated. These deregulations could affect the export of FAs to the exterior of the cell and their accumulation within organelles.In the case of cluster 3   at low, middle and high doses, FA degradation, PPAR signaling pathway and retinol metabolism, all of them involved in the elimination of FAs are up regulated. This compound produces a strong effect on the metabolism of FAs and lipids and therefore the cells react with increasing the pathway activities associated with FA degradation.For the last cluster, containing only VPA , our study confirms the deregulation of these biomarkers and highlighted new genes that produce steatosis. With the development of a time-series analysis combined with pathway analysis, it is possible to follow the evolution of the pathways over time and how they are connected to the different stages of steatosis.Interestingly, the integration of a large and diverse set of compounds in the analysis pinpoints their specificity in leading to steatosis. However, our results show that the time seems to have a higher impact in the DEGs and pathways analysis than the concentration. The early dedifferentiation of PHH in 2D cultures might explain this observation. It is also possible that the global normalization reduces the specific signal of some genes. Additionally, for more than half of the compounds studied, only two times points have been tested experimentally, which might influence the results.in vivo data beyond the 24 h time point are available in TG-GATEs and could be analyzed similarly in order to evaluate the overlap between in vitro and in vivo data. Finally, it has been reported that 3D cell cultures could be a more suitable system to mimic human organs that 2D cultures , 36 , 57 (AhR activation leading to hepatic steatosis), 58 (NR1I3 (CAR) suppression leading to hepatic steatosis), 60  activation leading to hepatic steatosis), 61 (NFE2L2/FXR activation leading to hepatic steatosis). Besides, the capture of coenzyme A by VPA was added to the mechanistic pathway . We can see that all the cell compartments can be involved in steatosis, many of which undertake functions within the mitochondria and the nucleus. More specifically, perturbation in endoplasmic reticulum and vesicles through the genes MUL1, TMEM135, OSBPL9, SCD1, SREBP-1C, GPAT3 can lead to steatosis.To summarize, most of the genes that are associated with a steatosis AOP, described in AOP wiki, have been found in our study. The integration of the previously published information on steatosis with the newly found genes and pathways from our analysis can enrich the knowledge of developed AOPs on steatosis. The  pathway , as well pathway . In thisOther studies have reported computational approaches to leverage large-scale toxicogenomic information, biological pathways and high throughput data for the identification of toxicity pathways. For example, Overall, our findings illustrate how an integrative computational chemical system biology approach can be used to study steatosis and obtain new metabolic pathways that are deregulated during the process of liver injury by chemical exposure. Obviously, these findings need to be further validated with additional experimental studies. These associations are potentially not causative but more reflect biomarkers along the pathway to develop steatosis. In many case, changes in gene expression are a response to a stressor and it is only when these adaptive changes are overwhelmed that the adverse effect occurs.AA-O performed the experiments. AA-O and OT analyzed the results and wrote the paper. FB and SB contributed in the writing of the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Axon loss is a shared feature of nervous systems being challenged in neurological disease, by chemotherapy or mechanical force. Axons take up the vast majority of the neuronal volume, thus numerous axonal intrinsic and glial extrinsic support mechanisms have evolved to promote lifelong axonal survival. Impaired support leads to axon degeneration, yet underlying intrinsic signalling cascades actively promoting the disassembly of axons remain poorly understood in any context, making the development to attenuate axon degeneration challenging. Wallerian degeneration serves as a simple model to study how axons undergo injury-induced axon degeneration (axon death). Severed axons actively execute their own destruction through an evolutionarily conserved axon death signalling cascade. This pathway is also activated in the absence of injury in diseased and challenged nervous systems. Gaining insights into mechanisms underlying axon death signalling could therefore help to define targets to block axon loss. Herein, we summarize features of axon death at the molecular and subcellular level. Recently identified and characterized mediators of axon death signalling are comprehensively discussed in detail, and commonalities and differences across species highlighted. We conclude with a summary of engaged axon death signalling in humans and animal models of neurological conditions. Thus, gaining mechanistic insights into axon death signalling broadens our understanding beyond a simple injury model. It harbours the potential to define targets for therapeutic intervention in a broad range of human axonopathies. These axons can be extremely long, ranging from millimetres to centimetres to metres depending on the host, the type of neuron and the target cell ,2. In otin vivo . Su. SuWldS)(Nmnat1) . This fuS harbours an evolutionary conserved function. While it was first found to delay axon death in mice . C. Ca) ..dnmnat gnmnat2 and by WldS [sarm1 also attenuates axon death, suggesting that its function is conserved in fish [2+, Nmnat2 and Sarm1 remains to be determined (c).In fish, axon death is attenuated by the over-expression of  by WldS ,45. More in fish . Howevertermined c.Above, we have summarized conserved axon death mediators, and we also highlighted differences observed across species. Despite these subtle differences, it is tempting to extract a core axon death mechanism which could be evolutionarily conserved. This will be discussed below.5.3.+. Under normal, healthy conditions, the ratio between NMN and NAD+ is highly in favour of NAD+ (NMN ≪ NAD+), which is supported by the observation of lower axonal concentrations of NMN and higher concentrations of NAD+ [a). Injury leads to below-threshold levels of the labile axonal survival factor Nmnat2, which in turn leads to a temporal rise of NMN and lower levels of NAD+, thus reducing the ratio (NMN < NAD+). This ratio could already be sufficient to activate axon death. Importantly, below-threshold levels of Nmnat2 also discontinue Nmnat2-mediated inhibition of Sarm1, which discharges the NADase activity of Sarm1: the resulting Sarm1-mediated NAD+ consumption tips the ratio towards NMN (NMN ≫ NAD+). This ratio could therefore dictate whether axons should survive, or degenerate. Axon survival is favoured by NMN ≪ NAD+: preventing the rise of NMN, or the consumption of NAD+, both potently attenuate axon death. Vice versa, conditions altering this ratio will induce axon death: the forced depletion of NAD+ by a constitutively active NADase activity of Sarm1/dSarm is sufficient to trigger axon (and cell body) death. If this were true, high NMN levels—significantly higher than NAD+—should also be capable of triggering axon death in vivo. This remains to be tested, but also harbours technical difficulties: Nmnat proteins efficiently synthesize NAD+ from NMN, thus a rise in NMN automatically results in a rise of NAD+ [+ synthesis) are sufficient to activate Sarm1 in cultured neurons, thereby depleting NAD+, which results in non-apoptotic cell death. Whether this is also true in severed axons remains to be determined [One speculative possibility is a crucial ratio between NMN and NAD of NAD+ ,74,88 , and to childhood-onset polyneuropathy and erythromelalgia ,110. TheS and its capacity to slow down injury-induced axon degeneration offered the first opportunity to attenuate axonopathies which occur in the absence of injury. Over recent years, distinct outcomes were observed in a broad range of disease models, including extended lifespan, age-dependent effects, improved performance, or a lack of amelioration [S also led to efforts developing drugs to stabilize Nmnat proteins [s or any other form of engineered Nmnat protein seems promising in certain models of neurological conditions, therapeutic potential may be limited due to a gain-of-function protein: its efficacy relies on dosage, which could be challenging during long-term therapeutic treatments [S harbours age-dependent effects [Wldioration . The proproteins . Howevereatments . Moreove effects , and whi effects . Therefo+ ratio could offer an attractive alternative. It could be achieved by pharmacological targeting of specific metabolic pathways, or by simple oral supplement of NAD+ or its precursors. Preventing either NAD+ consumption or a temporal rise of NMN could serve as attractive strategies [+ metabolism also bears great risks [The manipulation of the NMN/NADrategies ,114. Howat risks .S, Sarm1 mutant mice offer a range of outcomes in mouse models of neurological conditions:The discovery of Sarm1/dSarm has offered a novel and unique therapeutic opportunity. In order to block axon death signalling, Sarm1 protein or its activity has to be decreased, rather than increased such as with Nmnat2. Like Wld−/−Sarm1 mice harbour reduced neurological deficits and a better functional outcome in a model of TBI [−/−Sarm1 mice also contain significantly reduced axonal lesions in a model of TAI with impact acceleration of heads [in vivo gene therapy approach using adeno-associated virus to deliver a dominant-negative version of Sarm1 had similar effects in an injury-induced axon degeneration model to that in Sarm1−/− mice [When axons are challenged by mechanical forces, l of TBI . Sarm1−/of heads . In addi−/− mice . These fSarm1 prevent the distal degeneration of myelinated axons and electrophysiological abnormalities [In two models of CIPN (e.g. vincristine or paclitaxel), and in a model of metabolic-induced peripheral neuropathy, mice lacking malities ,120. Themalities ,122—coulSarm1 does not suppress motor neuron degeneration in a mouse model of ALS [Sarm1 is dispensable in other ALS mouse models.However, while Sarm1 harbours the potential to ameliorate certain axonopathies, it is important to note that the lack of l of ALS . It rema−/−Sarm1 mutants have beneficial effects in certain models of neurological conditions. This raises hope that Sarm1 and other axon death mediators could serve as druggable targets to halt axon loss. Axon death signalling serves therefore as an attractive pathway to develop therapies against, with the ultimate goal to prevent and treat axon loss in a broad range of neurological diseases.So far, 7.The discovery and characterization of axon death signalling, which is activated by injury, not only provided exciting insights into the underlying mechanism mediating axonal self-destruction after injury. It also revealed that the axon death pathway is highjacked in other challenging conditions for the nervous system where axonopathies occur in the absence of injury. Therefore, it is crucial to fill missing mechanistic gaps among already identified axon death mediators, and to identify other key mediators required for axon death signalling. This will help to expand our understanding of a signalling pathway that ultimately leads to the death of the axon. Overall, there is certainly a lot left to do, and axon death research will therefore continue to be a lively field in the future.


Protective clothing manufacturers routinely test their products for resistance to liquid and viral penetration. Several of the test methods specified by the American Society for Testing and Materials (ASTM) and the International Organization for Standardization (ISO) for penetration testing produce binary results (i.e. pass or fail), deliver imprecise pressure regulation, and do not record the location at which penetration events occur. Instead, our approach measures a continuous variable (time of penetration) during a slow and continuous increase of hydrostatic pressure and retains the location of penetration events. Using a fluorescent dye to enhance visual detection, we evaluate temporal and spatial patterns of penetration events. We then compare the time of liquid penetration with the time of penetration of two bacteriophages (Phi-X174 and MS2). For the fabric tested, the mean viral penetration occurred 0.29 minutes earlier than liquid penetration when solved by logistic regression. The breakthrough time of MS2 was not different from the Phi-X174 bacteriophage. The time of liquid penetration was a latent indicator of the time of viral penetration. Healthcare personnel (HCP) have an increased risk of exposure to disease from close contact with infected patients . During In 1945, the American Association of Textile Chemists and Colorists (AATCC) devised a test that measures the resistance of fabrics to the penetration of water by impact . For thiIn 1991, the Occupational Safety and Health Administration (OSHA) published its first bloodborne pathogen standard . The staTo classify protective apparel and evaluate performance, the Association for the Advancement of Medical Instrumentation (AAMI) incorporated ASTM F1670 and ASTM F1671 with two additional tests (AATCC 42 and AATCC 127) into their PB70 standard . Also, tASTM F1670 and ASTM F1671 provide clear delineation between materials that are protective and those that are not, however, these tests provide a poor means to differentiate fabric performance . ISO 166Another concern is that several tests  do not accurately account for pressure delivery. The vertical orientation of the penetration cell adds gravitation head pressure (98 Pa per centimeter of liquid). Furthermore, the tolerance of the manual pressure regulator used in ASTM F1670 and ASTM F1671 delivers 2,758 Pa (±0.2 psi) of variability.Our approach is to challenge fabric under a slow and continuous increase of hydrostatic pressure and to evaluate temporal and spatial patterns using a fluorescent dye to enhance visual detection. This approach is similar to the AATCC 127 test method . HoweverASTM and ISO test methods  specify a quick visual screening test and a laborious viral penetration test, however, the difference between liquid and viral penetration is not clear. Shimasaki et. al. demonstrated that the amount of virus was proportional to the volume of carrier liquid that penetrated . By showThe purposes of this study are 1) to evaluate a new approach to measure liquid and viral penetration; 2) to provide temporal and spatial analysis of liquid penetration; 3) to compare the breakthrough time of liquid and viral penetration; and 4) to compare the breakthrough time of two different viruses Phi-X174 and MS2. This paper is part of a larger study to improve the scientific understanding of protective clothing test methods , 17.All liquid and viral penetration tests in this study were conducted using a penetration apparatus based on the ASTM F903 standard . Custom The primary mass flow control regulator  was configured to pressure regulation mode. A secondary regulator  was added to provide 4 cc/min nominal airflow through the primary regulator. Pressure was validated with a NIST traceable manometer to be within 1% from 0 to 90 kPa.This paper is part of a multi-dimensional comparison of ten garment models, four test liquids, and factors of pre-wetting, apparatus screen, and elevated temperature and humidity. For clarity, this manuscript details methods and highlights only one garment model: an ANSI/AAMI Level 3, five layer, non-woven, spunbond and melt blown surgical gown . Top layTwenty-six gowns were selected from the same production lot. From each gown, 24 swatches were cut from the continuous chest region with a die cutting machine . Individual specimens were randomly selected among the 624 swatches (26 gowns x 24 swatches per gown). Specimens were conditioned at 23 ± 2°C and 50 ± 5% relative humidity at least 24 hours prior to testing. Two polyethylene closed-cell foam gaskets were placed between each side of the fabric to provide a liquid-proof seal . A total of 155 specimens were evaluated for liquid or viral penetration .Seventy-eight liquid penetration tests were conducted using a nutrient broth challenge solution . The nutThe composition of the nutrient broth was specified by ASTM F1671 , howeverA series of eight penetration cells loaded with eight fabric specimens each were filled with nutrient broth simultaneously from the bottom through a network of flexible polymer tubes. When the liquid reached the middle of the sight glass, the test cell valves were closed. Pressure was not applied for 5 minutes, then followed by a continuous and linear pressure increase of 1.0 kPa/min. Eight cameras positioned 30 cm from each penetration cell recorded the eight specimens simultaneously. Testing concluded after five penetration events were seen on each specimen. After testing, each video recording was analyzed to determine the time and location of those penetration events. The time and location of all five penetration events were recorded for future reference, however, only the first penetration event was evaluated. By using only the first penetration event, the liquid penetration data maintained a one-to-one symmetry with the viral data that cannot differentiate multiple penetration events by virtue of detection methodology .2 and the density of nutrient broth, 1.0 g/mL.Correction for pressure head was necessary to find the true hydrostatic pressure on a vertically oriented penetration test cell. The additional force caused by pressure head was not described in the ASTM or ISO test methods. Pressure due to liquid depth  = p/ρg . By solvψ is pressure head (m)p is fluid pressure (Pa)3)ρ is the density of the fluid (kg/m2)g is acceleration due to gravity  have three possible outcomes: 1) no visible penetration and no virus detected; 2) no visible penetration and virus detected; or 3) visible penetration with virus . A 4When testing begins, we expect no penetration and no virus (outcome 1). Eventually, we expect specimens to show visible and viral penetration (outcome 3). If a test is purposely stopped prior to visible liquid penetration, we expect to see a transition from outcome 1 to outcome 3 and some specimens with no liquid penetration but containing virus (outcome 2).Unlike liquid penetration in which events are seen, viral penetration is not visible. Because viral penetration was not visually observed, it is not possible to know the location. Therefore, there is no way to correct for gravitational head. Viral penetration is detected by extracting virus from the surface of the specimen after the pressure sequence is concluded. Time trials, i.e. stopping the test at predetermined times and evaluating the transition from outcome 1 to outcome 3, were used to determine the time of viral penetration.Without knowing when to expect viral penetration, tests were stopped at 5, 6, 7, 8, 9, 10, 11, and 12 minutes. For the tested specimens, no virus was detected from 5 to 9 minutes (outcome 1). The specimen that was stopped at 10 minutes appeared clean but contained virus (outcome 2). The specimens that were stopped at 11 and 12 minutes had visible liquid penetration (outcome 3). The first estimate was that viral penetration occurred between 9 and 11 minutes. We then conducted 29 additional tests (Exploratory Phase) in which we stopped every 15 seconds from 7 ¼ to 12 minutes and repeated times around 10 minutes when viral penetration would likely occur. Because the stopping times in the Exploratory Phase were not randomly selected, a Validation Phase was comprised of 20 equispaced times from 8.5 to 11.35 minutes for Phi-X174 and MS2 bacteriophages .2 of 0.966. The bivariate correlation between the cumulative distributions of viral failures between the Exploratory Phase and the MS2 Phase was 0.97 leading to an adjusted R2 of 0.962. The bivariate correlation between the cumulative distributions of viral failures between the Phi-X174 and the MS2 Phase was 0.98 leading to an adjusted R2 of 0.966. The correlation and adjusted R2 suggested that the three phases of data resulted in near singular distributions of cumulative observed failures. Therefore, all three phases were not different from each other and all three phases comprised a unitary dataset of viral penetration (n = 77).The randomly selected stop times  were compared to the biased stop times (Exploratory Phase) through correlation and simple linear regression. The bivariate correlation between the cumulative distributions of viral failures between the Exploratory Phase and the Phi-X174 Phases was 0.98 leading to an adjusted REscherichia coli (ATCC 13706), bacteriophage MS2 (ATCC 15597-B1), and E. coli (ATCC 15597) were obtained from American Type Culture Collection . E. coli (ATCC 13706) and E.coli (ATCC 15597) were grown in nutrient broth according to ASTM F1671 . The next day, a 1:100 dilution of each overnight E.coli culture was prepared in 100 mL of 271 B growth medium (ATCC Medium: 271 Escherichia medium). The use of the 271 B is a departure from the ASTM F1671 standard, which uses nutrient broth only. However, 271 B provided a greater yield and contribution to the challenge solution was negligible (<0.7% volume). Each E.coli culture was grown to a density of 2 to 4 × 108 CFU/mL. This cell density corresponded to a 0.3% to 0.5% absorbance reading at 640 nanometers .The challenge suspension was prepared according to ASTM F1671. Bacteriophage Phi-X174 (ATCC 13706-B1), TM F1671 . Both E.E. coli (ATCC 13706) culture was inoculated with 1.0 mL of Phi-X174 bacteriophage stock (ATCC 13706-B1) having a titer of 3.6 x 1010 PFU/mL, a ratio of 1.8 bacteriophage to bacterial cells. The dilution was incubated at 35–37°C with shaking (225 revolutions/min) overnight then centrifuged for 20 minutes at 10,000 g (g-force) to remove large cell debris. The bacteriophage-containing supernatant was filtered through a 0.22-μm Millipore ExpressPlus Stericup filter. Serial dilution and plating confirmed that the final titer of the bacteriophage Phi-X174 stock was 4.2 × 1010 PFU/mL.The E.coli (ATCC 15597) culture. The dilution was incubated at 35–37°C with shaking (225 rpm) overnight then centrifuged for 20 minutes at 10,000 g to remove large cell debris. The bacteriophage-containing supernatant was filtered through a 0.22-μm Millipore ExpressPlus Stericup filter. Serial dilution and plating was used to calculate the final titer of the bacteriophage MS2 stock (9.3 × 1010 PFU/mL).The lyophilized MS2 bacteriophage (ATCC 15597-B1) was rehydrated with 1.0 mL of 271B broth and used to inoculate the 10 PFU/mL) in 149 mL preprepared nutrient broth and used in the penetration tests immediately. The MS2 challenge suspension (300 mL) was prepared by adding 0.9 mL bacteriophage stock solution (9.3 ×1010 PFU/mL) in 299.1 mL pre-prepared nutrient broth and used in the penetration tests immediately. The bacteriophage nutrient broth used in this study had a surface tension 0.048 N/m (at 20 minutes).Compatibility testing for both Phi-X174 and MS2 was performed as described in ASTM F1671. The ratio of control assay titer over the test material assay titer (PFU/mL) was 1.4 for Phi-X174 and 1.2 for MS2. The Phi-X174 challenge suspension (150 mL) was prepared by adding 1.0 mL bacteriophage stock solution . Compatibility tests were conducted to show that the test apparatus contained no inhibiting substance to the bacteriophage Phi-X174 (n = 3) or bacteriophage MS2 (n = 3). Prior to each viral test, settle plates were prepared in accordance with ASTM F1671 section 13.3 and placed inside the test chamber within the vicinity of the mounted test cells. Pre- and post-challenge suspensions for each swatch run were assayed to verify no loss of Phi-X174 or MS2 bacteriophage challenge titer during the test. Duplicate plates were prepared for each assay, replicate, and negative control.All statistical analyses were performed with R statistical programming software . To asseTo assess the spatial characteristics of liquid penetration the x and y coordinates of each penetration event was bounded on a two-dimensional plane enclosed by a 57.3 mm diameter circle created by the radius of the penetration cell. The data structure containing each x and y coordinate and circular bounding was defined within a ppp object (spatstat package ). Each pTo assess the time difference between liquid and viral penetration we selected a widely used logistic model for regression analysis. Because viral penetration data is non-parametric (i.e. Pass/Fail), parametric statistic regression was not an option for the viral penetration data.The liquid penetration time ranged from 8.15 to 11.18 minutes and the mean penetration time was 10.21 minutes  With preThe times of liquid penetration without pressure head were not normally distributed; α<0.05%, W = 0.905, p-value = 0.001. The time of liquid penetration fit a bimodal distribution with mean penetration times of 10.44 minutes (83.4% failures) and 9.05 minutes (16.6% failures) .To evaluate spatial distribution of the penetration events, we selected Ripley’s K function because the K(r) statistic is able to evaluate clustering over both small and large scales that would not be possible with nearest neighbor statistics. Liquid penetration events were evaluated as a full set (n = 78) and also subdivided into fail modes; premature  and primary . Ripley’s K function found that all three datasets were normally distributed when evaluated for all distances (r) ranging from 0 to 14 mm. However, density plots suggested a stratified distribution with greater density of penetration events on the bottom .Logistic regressions of the Exploratory, Phi-X174, and MS2 Phases are shown in The three instances of non-visible virus penetration, in which virus was detected on “clean looking” specimens did not provide the minimum number of samples needed for logistic regression analysis. However, based upon the infrequency of observing the event , an estimate of the time of viral penetration was 0.29 minutes by calculating 9.7% of the duration of the 3.03 minute long penetration event.Mean penetration times of the Exploratory, Phi-X174, and MS2 Phases were 9.87, 9.76 to 10.11 minutes, respectively. Because regression analysis showed that, the phases were unitary, these differences were attributed to variability and we, therefore, considered the mean penetration of all viral testing (9.95 minutes). Compared to the mean time of liquid penetration, 10.21 minutes , viral pTemporal analysis showed a non-normal distribution of liquid penetration times between 8.15 to 11.18 minutes (n = 78). An ideal material containing equal-sized pores would have a normal distribution, while irregularities, imperfections, or calendaring the fabric may result in additional failure modes. The test apparatus itself may also cause pinching or stresses on the fabric that could result in early failures. The fabric tested had 83.4% (λ = 0.834) uniform structure with 16.6% (λ = 0.166) early failures. The magnitude of each distribution mode (83.4% and 16.6%) indicates that this fabric is mostly uniform but contains one-in-five premature failures.Spatial analysis was used to determine if the premature failures were inherent within the fabric or were caused by stress during the test. If failures were clustered around fabric features  that may indicate that the feature caused the early failures. If failures were clustered around the perimeter of the apparatus that may indicate that the apparatus or pressure had weakened the fabric. The absence of clusters indicate that the cause of early failures are likely inherent within the fabric structure.Spatial patterns were uniformly distributed with a slight stratification in which more penetration events occurred on the bottom of the apparatus. Because gravity added pressure that increased with depth, we expected to see fewer failures at the top .The time of mean viral penetration tests in all phases only slightly preceded liquid penetration. For the fabric tested, liquid penetration may therefore be a latent indicator of viral penetration. Using a very different approach, these results are in agreement with Shimasaki et. al.Pressure control within 1% was necessary to provide accurate penetration times. The ASTM test apparatus has a manual pressure control with a tolerance of 2,758 Pa (±0.2 psi). With 1,000 Pa corresponding to one minute, the original ASTM apparatus would not be able to determine difference between liquid and viral penetration in the fabric tested.Continued testing on this fabric would improve statistical precision. More instances of the infrequent event (n = 3) in which the fabric appears ‘clean’ but virus is detected, may perhaps be necessary before using a liquid penetration test in lieu of a viral test for this fabric. However, the techniques presented provide an efficient approach in which we may add additional viruses or evaluate factors .‘viral compatibility’ between the liquid and viral test for this fabric may allow the manufacturer to substitute an inexpensive quick screening technique for a costly viral test.This paper presented a quantitative approach to evaluate a fabrics’ resistance to liquid and viral penetration. To our knowledge, it is the first paper to compare the time of liquid penetration to viral penetration. Testing estimated that the difference between liquid and viral penetration was 0.29 minutes for this fabric. Further evidence of the The viral tests suggested that two similar viruses (Phi-X174 and MS2) did not have different viral penetration times. The technique presented here would provide an efficient way to evaluate additional viruses or additional fabrics.All liquid and viral penetration tests were conducted on the custom modified apparatus based on ASTM F903 standard. Based upon the pressure head in vertical orientation of the penetration cell, we recommend a horizontal orientation of penetration cells to improve test accuracy. Based upon the tolerance of a manual pressure control, we recommend precision of an electronic pressure control. The temporal and spatial analysis techniques and apparatus developed in this study revealed multiple failure modes in the fabric tested. Information like this may be helpful for fabric manufacturers to improve quality control and to design garments that are more protective.S1 Datasets(XLSX)Click here for additional data file.