Drosophila melanogaster and the core of the pathway is evolutionary conserved in mammals. The mammalian MST/Hippo pathway regulates organ size, cell proliferation and cell death. In addition, it has been shown to play a central role in the regulation of cellular homeostasis and it is commonly deregulated in human tumours. The delineation of the canonical pathway resembles the behaviour of the Hippo pathway in the fly where the activation of the core kinases of the pathway prevents the proliferative signal mediated by the key effector of the pathway YAP. Nevertheless, several lines of evidence support the idea that the mammalian MST/Hippo pathway has acquired new features during evolution, including different regulators and effectors, crosstalk with other essential signalling pathways involved in cellular homeostasis and the ability to actively trigger cell death. Here we describe the current knowledge of the mechanisms that mediate MST/Hippo dependent cell death, especially apoptosis. We include evidence for the existence of complex signalling networks where the core proteins of the pathway play a central role in controlling the balance between survival and cell death. Finally, we discuss the possible involvement of these signalling networks in several human diseases such as cancer, diabetes and neurodegenerative disorders.The MST/Hippo signalling pathway was first described over a decade ago in  Drosophila melanogaster over a decade ago [Drosophila, the core of the canonical mammalian pathway is formed by the homologues of the fly proteins; the serine/threonine kinases MST1/2 (Hippo) and LATS1/2 (Warts), the scaffold Salvador (Sav), the adaptor protein MOB (Mats), the transcriptional co-activators YAP1/2 and TAZ (Yki) and the family of transcription factors TEAD1-4 (Sd) [Drosophila the pathway exists in two activation states. The “on” state where the homologues of the core kinases are active and LATS1/2 can phosphorylate YAP and TAZ and sequester them in the cytoplasm. Phosphorylation of YAP on the Ser127 (TAZ Ser89) residue prevents YAP/TAZ-TEAD-dependent transcriptional activity ultimately preventing proliferation [Drosophila proteins [Drosophila pathway. The terms non-canonical MST/Hippo pathway/network is used for any other regulator and effectors of the pathway described so far that are not considered part of the canonical view.The Hippo (Hpo) pathway was originally described in cade ago . The worcade ago . Since tcade ago ,4 there cade ago ,5,6. Thecade ago ,7,8. Thecade ago ,9. By anproteins ,11,12,13Although the model of a linear, canonical signalling pathway is attractive, it is evident that this pathway is embedded in a complex signalling network. Numerous crosstalks with signalling pathways involved in cell fate regulation and cellular homeostasis such as the Ras/ERK, AKT, WNT, NOTCH, TGFβ, SHH and JNK pathways have been shown ,14,15,16in vivo that YAP mediates a pro-apoptotic signal in hepatocyte unless there is a secondary growth promoting signal [Drosophila and non-canonical functions of the proteins of the pathway in the fruit fly. Finally we discuss the relevance of the pro-apoptotic signals mediated by the pathway in several pathological processes.Apoptosis is the best characterised form of programmed cell death and is essential in the regulation of cellular homeostasis ,27. Althg signal . Cell deg signal ,31. Thisg signal ,33,34,35g signal . In thisD. melanogaster [Drosophila Hippo pathway is extensively reviewed elsewhere [Drosophila. Hpo and Warts are both Serine/Threonine kinases and are activated in response to different extracellular signals that increase Hpo kinase activity. Hpo in turn directly phosphorylates Wts. The interaction and activation of both kinases requires the participation of the scaffold protein Sav and the adaptor protein Mats. Active Wts phosphorylates its downstream effector Yki promoting the binding of this protein with 14-3-3 resulting in the sequestration of Yki to the cytoplasm [CYCE and DIAP (Drosophila inhibitor of apoptosis) [Drosophila eye model, indicated that Hpo, Wts and Sav knockout results in unrestricted growth while overexpression of activated Yki resulted in hyperplasia. This led to the classification of Hpo, Wts and Sav as putative tumour suppressors and Yki as a putative oncogene [The Hippo pathway is essential in cell proliferation control and regulation of apoptosis in nogaster ,37 by innogaster . The coonogaster ,40. Mostnogaster . The Drolsewhere ,42 and Fytoplasm . When thoptosis) . Thus, nDrosophila [Other proteins are now considered to be part of the pathway and each new discovery points to the existence of a complex signalling network rather than a linear pathway in osophila . In partosophila ,37. Thesosophila ,37,43. Mosophila , and recosophila . Conversosophila . Additioosophila , Dachsouosophila . These posophila . Additioosophila  and Wbp-osophila .Head involution defective (Hip) resulting in the activation of apoptosis [et al. showed that Hpo could phosphorylate DIAP in vitro and proposed it regulates DIAP protein levels by direct phosphorylation which induces ubiquitination dependent degradation [et al. also came to similar conclusions [reaper [Drosophila pathway with Dmp53 one of the most important regulators of cell death that remarkably is also a downstream effector of LATS1/2 [The first articles that described the role of the proteins indicated that the involvement of the Hippo proteins in the activation of apoptosis might be more intricate. In particular, active Hpo was shown to promote the transcription of the pro-apoptotic gene poptosis . Intriguradation . Contempclusions . The worclusions . Therefoclusions . Finally [reaper  These wo LATS1/2 .Drosophila Hippo signalling network is still a work in progress. This was clearly illustrated by the results of a proteomics study in Drosophila which led to the identification of 153 proteins and 204 interactors within the Hpo signal transduction network [As indicated above, the canonical Hippo pathway belongs to a signalling network and numerous proteins have been shown to play a role in its regulation. Thus, the mapping of the  network . This woDrosophila can be found in the literature [Importantly, further support for non-canonical Hippo pathway functions in terature . For insterature . In the terature ,58. In aterature ,60.Drosophila are likely to contribute towards our understanding of the mammalian network and shed light how this signal transduction network is involved in different pathologies.The existence of non-canonical functions can also be extended to Yki which may switch between different effectors to regulate specific functions. For example, there is evidence that Yki can also interact with and regulate other transcription factors such as Homothorax (Hth), Protein mothers against DPP (Mad) and Teashirt (Tsh) ,61. ThesDrosophila were key in the fast delineation of the mammalian pathway and will be further explained in this review. Pan’s group, using mouse genetics, demonstrated a mammalian Hippo pathway closely resembling the Drosophila Hippo pathway. Thus, MST1/2 and LATS1/2 would be tumour suppressors that negatively regulate by direct phosphorylation YAP1, which would behave as an oncogene. The role of YAP and MST1/2, LATS1/2 and SAV1 (WW45) in the regulation of cellular homeostasis and organ size in several tissues was supported by the work from several groups published shortly after [Sd, had been already described [In 2007 two papers showed the existence of a signalling pathway that included the human homologues of the Drosophila pathway MST2, LATS1 and YAP ,4. Thesely after ,68,69. Tly after ,68,69. Tescribed , and sooescribed . These wescribed ,75,76,77et al. of the proto-oncogene RAF1, a member of the MAPK pathway, as a direct interactor of MST2 [Drosophila we investigated if LATS1 would interact with YAP1. We identified an association between YAP1 and LATS1 and could show that this interaction is regulated by RASSF1A. It was confirmed that this LATS1-YAP1 association was MST2 dependent, illustrating that YAP1 was a component of the RASSF1A-MST2 pathway [Drosophila our work clearly showed that LATS1 phosphorylation of YAP1 resulted in a decrease of LATS1-YAP1 interaction and promoted translocation of YAP1 to the nucleus. We did not show that LATS1 phosphorylated YAP1 on Ser127. In fact, our kinase assays indicated that different YAP1 residues were phosphorylated by LATS1 upon RASSF1A-MST2 activation which would be similar to observations in Drosophila and other mammalian systems [PUMA [YAP mRNA have better survival rates that those patients that lose expression of this mRNA [Work published by our group, followed by works from two other groups ,79, also of MST2 . This wo of MST2 . Previou of MST2 ,82,83. T of MST2 . Our fol of MST2 , identif of MST2  which in of MST2 ,86. Our  systems . The com systems  and Basu systems  that hadms [PUMA , and indms [PUMA ,24,66,88his mRNA ,89. Shorhis mRNA . This wohis mRNA . This woSince the description of the MST/Hippo pathway, several groups have extended the evidence for the involvement of these proteins in the regulation of apoptosis. As it has happened with other signalling pathways the picture that is emerging shows that the regulation of cell death by the pathway is mediated through a complex signalling networks . We stilRASSF1–10) with some giving rise to multiple isoforms via alternative splicing and distinct promoter usage [RASSF1 and is one of the most commonly deregulated genes in cancer [D. melanogaster dRASSF, which is the only homologue of the C-terminal RASSF family of proteins identified, antagonises the Hippo pathway. This evolutionary divergence has been used for excluding RASSF proteins as canonical regulators of the mammalian pathway or cast doubts about the physiological relevance of the regulation of MST1/2 by the pathway [The RASSF family is formed by 10 genes  by repressing the core kinases of the pathway [RAS mutant tumours [The original work that described that RASSF1 and NORE1 as regulators of MST1 showed that the formation of the RASSF1-MST1 and NORE1-MST1 complexes was regulated by the members of the RAS family ,111,112. fashion . Similarignaling . Interes and p53 ,115. The pathway . Several pathway ,118 and  pathway . This re tumours . Thus, tFurther evidence from different groups supports the idea that this pathway is at the cross-road of proteins involved in cellular homeostasis. In particular, different lines of evidence indicate that this pathway is closely related to two of the best characterised RAS effectors, the MAPK (ERK) pathway and the PI3K/AKT pathway . Since RThe crosstalk of MST/Hippo proteins with the MAPK pathway was evident from our early works since we had already shown that RAF1 is a direct regulator of the pathway ,121. AddThe cross regulation of the MST/Hippo pathway with the RAS effector PI3K/AKT pathway is mediated by different mechanisms . AKT is All the evidence gathered so far indicate that the Hippo pathway forms a signalling network with the MAPK and AKT pathways that has a central role in the regulation of cellular homeostasis.These proteins are the initial kinases that trigger the intrinsic pathway in response to DNA damage. Both kinases seem to mediate the regulation of the RASSF1A-MST2-LATS1 module by ionizing radiation . ThDrosophila known as the NDR pathway. The NDR pathway has also been shown to regulate cell fate in coordination with the proteins of the Hippo pathway but due to space limitations we will not discuss this here, for a review see [MST1/2 and LATS1/2 are part of a kinase cassette conserved through evolution. It is worth noting that the other members of both families MST3/4 and NDR1/2 also form a kinase module conserved from view see ,136. Altview see ,13,137. MST1 and MST2 are viable while the double knock-out is embryonic lethal indicating that both proteins have a high degree of functional redundancy in development [The Ser/Thr kinases MST1 and MST2 are part of the Ste20 kinase family and were cloned in 1995 by Chernoff’s group ,138,139.elopment . Despiteelopment .MST1 and MST2 it was clear that these proteins have pro-apoptotic functions [Since the cloning of unctions . Before unctions . Upon stunctions . Later iunctions . The crounctions ,141. Othunctions . Thus thunctions ,81. Thusunctions . Both RAunctions ,149. Intunctions . The regunctions .In addition to the regulation of caspases, MST1 and MST2 regulate apoptotic pathways by interacting with apoptotic proteins in response to different signals. For example, MST2 has been shown to interact with Apoptosis-inducing factor (AIF), which is involved in caspase-independent programmed cell death. DNA damage was shown to promote the interaction between these proteins enhancing MST2 phosphorylation at T180 in renal cell carcinoma . FurtherNOXA and BIM [BIM and the activation of apoptosis. Importantly, the regulation of the MST-FOXO1 pathway is not only restricted to neuronal cells as it has also been seen that in Jurkat T lymphoma cells FOXO activates apoptosis downstream of MST1 by promoting the expression of the BH3 pro-apoptotic protein NOXA [The other MST1/2 dependent pro-apoptotic signalling pathway that has been extensively studied is the MST1-FOXO pathway . The tra and BIM ,159. Imp and BIM , which s and BIM . Similar and BIM ,163. Imp and BIM . β-AmyloMST1 and MST2 has been shown to prevent autophagy in different species. Hansen’s group showed that both kinases can regulate this cell fate by direct phosphorylation of LC3, a key protein in the formation of autophagosomes [The role of MST1/2 is not limited to the regulation of apoptosis. Loss of agosomes . On the agosomes . UnderstLATS1 and LATS2 are also emerging as important signalling hubs that are related to the classical extrinsic and intrinsic apoptotic pathways both downstream of MST1/2 and through MST/Hippo pathway independent mechanisms. These genes were identified by two independent groups as homologs of Wts [s of Wts ,167. LATs of Wts . The C-ts of Wts  indicatis of Wts . Both prs of Wts . In addis of Wts . Althougs of Wts .As mentioned above, the MST/Hippo pathway pro-apoptotic signal is not only mediated by YAP-p73 but it is also by p53 . How LATFinally, the relation of LATS1/2 with p53 seems to be central in the regulation of apoptosis by these kinases and is closely related to the involvement of both kinases in cell cycle progression ,172. LATThe transcription regulator YAP is the most studied member of the MST/Hippo pathway. This gene was identified as a YES kinase interacting protein by Sudol in 1995 . There aet al. that upon treatment with DNA damage agents, PML and YAP directly interact which results in YAP stabilisation. The YAP-PML complex form a complex with p73 that promotes the transcription of several pro-apoptotic genes including BAX and PML itself [BAX and PUMA, which resulted in an increase of apoptosis. The binding of YAP1 with its different effectors is not well understood, but it is likely regulated by a combination of different post-translational modifications [Drosophila, the regulation of YAP by phosphorylations seems to be much more complex [et al. showed that the phosphorylation status changes upon genotoxic signalling and identified several residues that are phosphorylated by p38 and JNK preventing YAP-TEAD transcription [β-TRCP to YAP, YAP ubiquitination by this protein and ultimately YAP degradation by the proteasome [in vitro and in cells to regulate the interaction between YAP and androgen receptor in prostate cancer cell lines [et al., showed that YAP1 forms a transcriptional complex with β-CAT and the transcription factor TBX, that upon YES-catalysed phosphorylation of YAPY357, binds to the promotors of the anti-apoptotic genes BIRC5 and BCL2L1 [Drosophila model, the phosphorylation status of the YAPS127 is still widely considered to be the crucial determinant of YAP activity. Frequently, the phosphorylation status of this residue is used as a read out for YAP “activation” in cancer, but this ignores the possibility that YAP may mediate its functions without changes in the phosphorylation levels of this residue. In fact, recent work from Pan’s group showing that YAPS112A  knock-in mice are normal despite having increased nuclear YAP, which according to the authors, clearly caution against this assumption [As explained above, several groups showed that YAP activates apoptosis in mammalians systems through the regulation of p73 . It mustL itself . InteresL itself . AdditioL itself . The EGRications . The bes complex ,65. This complex ,64. The  complex . Moreove complex . Importa complex . Thus ph complex . The worcription . YAP difcription . These poteasome . Therefoll lines . The exid BCL2L1 . Surprissumption . Moreovesumption . This cosumption .Further regulation of cell death by YAP may be dependent on non-transcriptional functions. The focus on YAP’s role as a co-transcription factor may be masking alternative functions of this protein in the cytoplasm such as its role as a regulator of β-CAT stability . For insYAP transcripts have been predicted and several splicing isoforms have been shown to be expressed [In order to understand how YAP mediates the activation of cell death, knowing which splicing isoforms are expressed in the different cell types should also be taken into account when studying YAP functions . To datexpressed . Importaxpressed . The phyxpressed ,197. It In summary, the knowledge that we have about YAP biological functions is still very limited and we should not be blinkered by the assumption of canonical YAP signalling. YAP may be a potent oncogene in one context, but this should not interfere with its possible role in many other biological functions that have been proposed so far. These antagonistic functions of YAP probably depend on different stimuli, the cell type, the presence/absence of different partners, the existence of concomitant mutations and p53 status ,178,198.Given the complexity of the signalling networks that is mediated by the MST/Hippo pathway it is not surprising that the proteins of the pathway have been proposed to be involved in the development of several human pathologies. The discovery of its role in cancer has boosted the research of this network but it has also obscured the implication of the core proteins in other diseases such as diabetes, Alzheimer’s disease, ALS or cardiac failure.The role of the proteins in cancer has been extensively reviewed elsewhere ,14,19,23YAP of which, to the best of our knowledge, no activating mutation of the Ser127 has been described in human tumours [RASSF1A is one of the most frequently deregulated genes in solid tumours [RASSF family that regulate the pathway are also tumour suppressors, and are downregulated by DNA methylation [The data included in this review illustrating the important role of the MST/Hippo network in the regulation of apoptosis must be also considered in the context of human tumours. Mutations of the members of the canonical pathway are rare including  tumours . YAP has tumours . This da tumours ,13. Impo tumours . The comhylation . Criticahylation ,16,66. Thylation . Other mhylation ,206. Whihylation . The mechylation . Thus, thylation . In facthylation ,210. Conhylation . In lighNeurodegenerative diseases are a group of diseases characterised by the death of neurons . These dThe role of the pathway in neurodegenerative disease may have a close link to the possible involvement of MST1, LATS1 and other proteins of the pathway in the death of pancreatic β-cells that occurs in the development of diabetes ,213. AccThe death of myocytes after heart failure produce fibrosis that can severely affect heart function and for this reason the identification of methods and drugs that can prevent this cell death and promote myocyte proliferation is the object of intense research ,215. AftIn summary, the MST/Hippo signalling network may be important in the development of several diseases caused by aberrant deregulation of the balance between pro-survival and cell death signals. Even if the knowledge is still limited, the list of diseases described above have a high socio-economical effect. They include two of the most devastating epidemic diseases of the XXI century, diabetes and Alzheimer’s, and the three most common causes of death cancer, cardiovascular disease and neurological disease. The study of the common features of these diseases at the molecular level could help to identify some unexpected common mechanism between these diseases and hopefully will lead to the development of new treatments against some of these pathologies. In addition to these diseases, the wider MST/Hippo network has been proposed to be deregulated and involved in the development of other pathologies and the numbers are rapidly increasing. For example, it may play a role in intestinal disease (reviewed in ). It hasSince the description of the mammalian MST/Hippo pathway, our knowledge of the pathway has increased at an impressive rate. The combination of genetic models, classical cellular and molecular biological studies, the use transcriptomics and proteomics techniques along with clinical evidence, is showing that this pathway is at the cross-road of several signalling networks and regulates a plethora of biological functions that we are only beginning to comprehend. The data gathered in this first decade of the pathway, shows that the MST/Hippo proteins are a crucial part of the key signalling networks that regulate cellular homeostasis. As we unravel this complex network, we should expect surprises that expand the current view of the functions and regulations of this network. Understanding the functions of the core proteins of the pathway will help to complete our understanding of some of the molecular mechanisms regulating cell physiology and how best to tackle several pathologies. To do this we have to get a systemic understanding of this network and consider all the biological functions of the pathway that the data is pointing at, both varied and antagonistic.


Saccharomyces cerevisiae’s ideal bioprocessing traits make yeast a very attractive chemical factory for production of fuels, pharmaceuticals, nutraceuticals as well as a wide range of chemicals. However, future attempts of engineering S. cerevisiae’s metabolism using synthetic biology need to move towards more integrative models that incorporate the high connectivity of metabolic pathways and regulatory processes and the interactions in genetic elements across those pathways and processes. To contribute in this direction, we have developed Metabolic Engineering target Selection and best Strain Identification tool (MESSI), a web server for predicting efficient chassis and regulatory components for yeast bio-based production. The server provides an integrative platform for users to analyse ready-to-use public high-throughput metabolomic data, which are transformed to metabolic pathway activities for identifying the most efficient S. cerevisiae strain for the production of a compound of interest. As input MESSI accepts metabolite KEGG IDs or pathway names. MESSI outputs a ranked list of S. cerevisiae strains based on aggregation algorithms. Furthermore, through a genome-wide association study of the metabolic pathway activities with the strains’ natural variation, MESSI prioritizes genes and small variants as potential regulatory points and promising metabolic engineering targets. Users can choose various parameters in the whole process such as (i) weight and expectation of each metabolic pathway activity in the final ranking of the strains, (ii) Weighted AddScore Fuse or Weighted Borda Fuse aggregation algorithm, (iii) type of variants to be included, (iv) variant sets in different biological levels.Metabolic engineering and synthetic biology are synergistically related fields for manipulating target pathways and designing microorganisms that can act as chemical factories. Database URL:http://sbb.hku.hk/MESSI/ Saccharomyces cerevisiae for the production of a range of products, such as alcohols, acids, proteins and hydrocarbons as well as pharmaceutical and nutraceutical ingredients has been demonstrated numerous times. Its attractiveness as a cell factory is mainly attributed to the fast growth on relatively cheap carbon sources, the robustness and tolerance towards harsh industrial conditions (e.g. high osmotic stress and low pH) and the well-developed genetics , a web server for predicting efficient chassis and regulatory components for yeast bio-based production. MESSI uses publicly available metabolomic data from characterized S. cerevisiae strains for computing metabolic pathway activities and ranks the strains based on user-defined pathways of interest (single or multiple pathways). Furthermore utilizing the natural variation between the S. cerevisiae strains MESSI applies genome-wide association mapping for identifying putative genes and other genetic elements that correlate with the measured phenotype (metabolic pathway activity). MESSI is a user-friendly platform and the output generated is easy to interpret allowing the users to quickly select the most promising plug-and-play S. cerevisiae strain for a specific product. Candidate genes related with the pathway activity, e.g. regulatory role in controlling metabolic fluxes towards that product, are also provided.Following this trend we present MESSI implemented two major tasks. First, metabolic pathway activities were calculated based on large-scale metabolomic measurements and strain rankings based on pathway activities were further produced. Second, pathway activities and genetic variants were used to predict the potential metabolic engineering targets (variants or genes). The computational pipeline is illustrated in Datasets compatible with MESSI are expected to encompass metabolomic data from large-scale genetic studies. Whole genome sequencing data are also included for predicting pathway activity associated variants and identifying metabolic engineering targets.Since large-scale population studies of yeast with both genome and metabolome data available are still limited, we incorporated one major dataset published in 2013 . Based oS. cerevisiae database version EF4.69 (To identify genetic variants (SNPs and InDels), all DNA sequencing reads were mapped to the S288C reference genome using BWA . Variantn EF4.69 . VariantS. cerevisiae were removed (The pathway activity profiling (PAPi) algorithm  was empl removed .P values were calculated with R  of the pairwise comparisons were shown significant correlation, calculated for 47 non-universal pathways with activity scores in the DB01_SC_21 database.To provide users a wider range of potential metabolic engineering targets, MESSI is designed to identify variants and genes in correlated pathways, which associate significantly with the target metabolic pathway in terms of the metabolome-based pathway activities. The universal pathways, including ‘Metabolic pathways’, ‘Biosynthesis of secondary metabolites’, ‘Microbial metabolism in diverse environments’, ‘Carbon metabolism’, ‘2-Oxocarboxylic acid metabolism’, ‘Fatty acid metabolism’, ‘Biosynthesis of amino acids’, ‘Degradation of aromatic compounds’, ‘ABC transporters’ and ‘Aminoacyl-tRNA biosynthesis’, were excluded in pathway activity correlation analysis. The pairwise Pearson correlation coefficients and d with R . P valuePA) generated from PAPi for different pathways are not comparable, they are further linearly normalized to 0–1, as normalized pathway activity scores (PAN), before aggregation. For strain i and pathway j, PANi,j is calculated by the following formula:To generate a unified ranking of strains based on user-defined pathways, pathway activity aggregation is performed. Since the raw pathway activity scores (E) and weight (W), are provided from the user. Expectation (E) is to characterize how active the target pathway should be. Three options are available, including Very Active, Medium and Very Weak. Their corresponding values in the aggregation algorithm are E = 1, 0.5 and 0, respectively. For example, when E is set to 1, the strain with the highest pathway activity score (PAN = 1) will rank first for the selected pathway; while if it is set to 0, the strain with PAN = 0 will rank the first; when it is set to 0.5, the strain with PAN nearest to 0.5 will rank the first, and the strains with strongest or weakest pathway activity will be in the bottom. The transferred pathway activity score (PAT) generated for strain i and pathway j is calculated as follows:For each target pathway, two parameters, pathway activity expectation (W), is to characterize the relative importance of a particular pathway compared with other pathways in aggregation. It can be any non-negative number used as weight during aggregation. When more than one pathways are targeted, aggregation will be carried out for strain ranking. By default, universal pathways defined in the previous section, like ‘Metabolic pathway’, will receive a weight of 0 in aggregation. Users can also revise this default setting to a positive number when universal pathways are taken into consideration.Another important parameter, weight (PAT).Two optional algorithms for aggregation are provided and they are the Weighted AddScore Fuse (WASF) (the default option) and Weighted Borda Fuse (WBF) . In the FS) for strain i will be:For WASF, the final fused score  level, the efficient mixed-model association (EMMA) method was adopted to calculate genetic relatedness to pathway activities . As adjuP values were calculated based on significant LD blocks and proxy clusters , are also provided to users. They serve as alternative candidates for potential metabolic engineering targets. iAS is defined by the following formula:iL stands for the length of the gene i region , and i,jP stands for the P value for significant variant j in gene i.Besides gene-level MESSI was written in R and PHP, running under the Debian 7 Linux environment. Stylesheets and functions of Shiny R package were introduced . There iTwo input formats, KEGG pathway names and KEGG compound IDs, are acceptable in MESSI A. Users Variants and genes associated with input pathways or compounds could be summarized and demonstrated in different biological levels, serving as potential metabolic engineering targets. Optional levels include: (i) level 0, no variant and gene information will be processed; (ii) level 1, variants and genes in metabolic reactions of target compounds (only when input is a compound list); (iii) level 2, variants and genes in target pathways associated with their pathway activity score; (iv) level 3, variants and genes in correlated pathways associated with their activity scores; (v) level 4, variants and genes in whole genome level associated with activity in target pathways. When a higher level is chosen, data from lower levels are automatically processed.Variants (SNPs and InDels) could be filtered optionally according to their mutation effects/positions, including upstream non-coding variants, synonymous coding (including synonymous amino acid coding codon or synonymous stop codon), missense coding, nonsense variants , frame shifts, codon insertions or deletions, variants in intron and variants in splice sites.S. cerevisiae KEGG database; (iii) no pathway activity scores could be calculated due to data incompleteness in the metabolomic profiling of these pathways; (iv) for recognized compounds, pathway activity scores are not available for all associated pathways (similar to (iii)).In this step, ‘Expectation’ of pathway activity, defined as how active the target pathway should be, and ‘Weight’ of pathway activity, defined as the relative importance of the pathway compared with others in aggregation, are seleted for target pathways B. When tE) can be set as very weak, medium or very active. Weight (W) can be any non-negative value. Weight for universal pathways is set to 0 by default. ‘Call Variants’ should be selected if metabolic engineering targets (variants and genes) are supposed to be presented for the target pathways.For recognized pathways, expectation  an eight-column basic information , (ii) a data matrix of strains  and (iii) a matrix of P values, significance level AS, gene region lengths, total variant numbers, significant variant numbers and statistics for diverse effects. This information is sorted by P values thus genes with the highest significance levels from their LD blocks will rank first.Gene-level information includes gene IDs, gene-level P value-based and AS-based rankings are listed, with hyperlinks to the corresponding information in Saccharomyces Genome Database (SGD)  . Strain se (SGD) , for facTo evaluate the reliability of MESSI in the prediction of metabolic engineering targets, we run a number of tasks and confirmed the correlations with experimental studies in the literature. All the studies presented here were performed under the same aeration condition (aerobic) and carbon source (glucose) as the strain characterization in our current pathway activity database.S. cerevisiae is a ‘hot’ topic for years. We created a job named ‘eg_fattyacid’, with the input pathway list including ‘Fatty acid metabolism’, ‘Fatty acid biosynthesis’, ‘Fatty acid degradation’ and ‘Fatty acid elongation’. ‘Fatty acid degradation’ was marked as unused pathway and ‘Fatty acid metabolism’ was marked as a universal pathway. For the ‘Fatty acid biosynthesis’, the pathway ‘Biosynthesis of unsaturated fatty acids’ was found as correlated pathway based on their activity scores. For ‘Fatty acid elongation’, ‘Biosynthesis of unsaturated fatty acids’ and ‘Arginine and proline metabolism’ were captured as significantly correlated. Two lists of top 3 genes  were generated for the two target pathways and the two correlated pathways. In a recently published study . Three target pathways, ‘Nicotinate and nicotinamide metabolism’, ‘Thiamine metabolism’ and ‘Oxidative phosphorylation’, were identified and processed. In the most relevant pathway, ‘Nicotinate and nicotinamide metabolism’, all the five genes from the two top 3 lists (with one gene shared by both rankings) have been experimentally shown to be strongly correlated with the NAD+ concentration [HST1, HST4 (PNC1 (NRK1 (BNA6 (HST1 resulted in up to 71% increase in NAD+ levels (Δnrk1 and Δpnc1 double-mutant strain incapable of incorporating supplemented nicotinamide riboside (NR) into NAD+ in nicotinic-acid-free media  were compared between the top 3 genes from the P value-based ranking, and the rest of the genes for each pathway, respectively. It was observed that the proportions of significantly altered fluxes of the top ranked genes are significantly higher than the rest ones in single gene deletion simulations , illustrating the potential regulatory role of the MESSI output.We carried out l iTO977  by the Rl iTO977 . The gluS. cerevisiae web server, where we incorporated bioinformatics methods—that are being employed in NGS-based human genetics—for prioritizing genetic changes that need to be experimentally tested. The ultimate goal of MESSI is to provide a more solid and comprehensive basis for selecting the most promising host for desired phenotypes and discover which mutations would be expected to contribute most to that phenotype for metabolic engineering efforts. We believe that MESSI offers new opportunities for establishing links between genotype and phenotype in S. cerevisiae strains and can be efficiently used for searching genome-wide spaces for small variants and genes conferring phenotypic characteristics of interest.The ability to perform deep sequencing of industrially relevant microbial species at increasingly affordable costs can help to revolutionize microbial cell factory engineering in a similar way that revolutionized fields like human genetics and epigenetic studies. With this in mind, we developed MESSI, a S. cerevisiae strains for best strain selection and genotype-to-phenotype mapping. Even though the number of yeast strains is comparable with other studies in the literature . Furthermore, based on the better-defined pathways and the in-house strain set, we will work on an algorithm development to compute the optimized pathway expectation (E) and weights (W) automatically from the pathway topology, and then evaluate the method with downstream engineering in our strain set. Last but not least, we will continuously add more strain databases from different cultivation conditions, for instance, diverse carbon sources (ethanol is next in the pipeline) and cultivation conditions , to improve the practical value of MESSI in industrial bioengineering.The first version of MESSI contains 21 terature , 17 we iReactome , SGD (22


Nicotine is the addictive substance in tobacco and it has a broad impact on both the central and peripheral nervous systems. Over the past decades, an increasing number of genes potentially involved in nicotine addiction have been identified by different technical approaches. However, the molecular mechanisms underlying nicotine addiction remain largely unclear. Under such situation, a comprehensive analysis focusing on the overall functional characteristics of these genes, as well as how they interact with each other will provide us valuable information to understand nicotine addiction. In this study, we presented a systematic analysis on nicotine addiction-related genes to identify the major underlying biological themes. Functional analysis revealed that biological processes and biochemical pathways related to neurodevelopment, immune system and metabolism were significantly enriched in the nicotine addiction-related genes. By extracting the nicotine addiction-specific subnetwork, a number of novel genes associated with addiction were identified. Moreover, we constructed a schematic molecular network for nicotine addiction via integrating the pathways and network, providing an intuitional view to understand the development of nicotine addiction. Pathway and network analysis indicated that the biological processes related to nicotine addiction were complex. Results from our work may have important implications for understanding the molecular mechanism underlying nicotine addiction. Cigarette smoking is a worldwide epidemic, and one of the major preventable causes of morbidity and mortality –2. AlthoNicotine, as the primary psychoactive component of tobacco smoke, binds to neuronal nicotinic acetylcholine receptors (nAChRs), a family of ligand-gated ion channels , facilitDuring the past decade, rapid advances in high-throughput technologies have brought unprecedented opportunities for the large-scale analysis of the nicotine addiction-related genes/proteins, leading to a rapid generation of large-scale nicotine addiction-related data. These datasets are often heterogeneous and multi-dimensional, which makes integrating and arranging such datasets to ascertain the key molecular mechanisms and to transform the data into meaningful biological phenomenon a major task and challenge. To meet the demand, pathway and network-based analyses have become an important and powerful approach to elucidate the biological implications underlying complex diseases –16. SuchIn this study, we performed a systematic analysis on genes potentially involved in nicotine addiction by identifying the enriched functional categories and pathways, as well as examining the crosstalk among the significantly enriched pathways. Then, we extracted a nicotine addiction-specific network and constructed a molecular network of nicotine addiction.In this study, the candidate genes for nicotine addiction included 220 genes prioritized via a multi-source-based gene approach . Brieflyftp://ftp.ncbi.nlm.nih.gov/gene/) , including the Entrez gene information database of human (Homo_sapiens.gene_info.gz), the dataset specifying relationship between pairs of NCBI and UniProtKB protein accessions (gene_refseq_uniprotkb_collab.dz), and file containing mappings of Entrez Gene records to Entrez RefSeq Nucleotide sequence records (gene2refseq.gz). Then the human PPI data were mapped to NCBI human protein-coding genes and the unmapped proteins were discarded. After removing self-interactions and redundant interacting pairs, a final human PPI network containing 15,093 nodes and 161,419 edges was obtained.The human protein-protein interaction (PPI) data were downloaded from the Protein Interaction Network Analysis (PINA) platform  , which ihttps://analysis.ingenuity.com) were applied for functional enrichment analysis, including Gene Ontology (GO) term analysis and pathway analysis. WebGestalt is a web-based integrated data mining system to evaluate the significance of GO terms enrichment in the candidate genes. IPA is designed to identify global canonical pathways from a given list of genes. Basically, the genes with their symbol and/or corresponding GenBank Accession Numbers are uploaded into the IPA and compared with the genes included in each canonical pathway. All the pathways with one or more genes overlapping the candidate genes are extracted, with each of them assigned a p value to denote the probability of overlap between the pathway and the input genes via Fisher’s exact test. Then, the corresponding multiple testing correction p-value is calculated with the method of Benjamini and Hockberg, namely PBH-value [To examine the functional features of NAGenes, WebGestalt  and IngeBH-value .We further performed pathway crosstalk analysis to explore the interactions among significantly enriched pathways. To describe the overlap between any given pair of pathways, we introduced two measurements, i.e., the Jaccard Coefficient BH-value less than 0.01 were used. Meanwhile, the pathways containing less than 5 candidate genes were removed because pathways with too few genes may have insufficient biological information.Select a set of pathways for crosstalk analysis. Only the pathways with PCount the number of shared candidate genes between any pair of pathways. Pathway pair with less than 3 overlapped genes was removed.Calculate the overlap of all pathway pairs and rank them. All the pathway pairs were ranked according to their JC and OC values.Visualize the selected pathway crosstalk. To focus on the most important biological themes, we only chose those crosstalks with scores in the top 20% using the software Cytoscape . Of noteL) smaller than that of the nicotine addiction-specific network and the number of random networks with average clustering coefficient (nC) higher than the observed clustering coefficient, we were able to estimate the significance of non-randomness. Finally, we calculated the empirical p-value = nL/1000 and nC/1000, respectively.Steiner minimal tree algorithm uses a greedy search strategy to merge the smaller trees into larger ones until only one tree connecting all input seeds is built . We applThe nicotine addiction-related genes (NAGenes) were involved in diverse biological functions . For exaBH = 1.21×10–36), transmission of nerve impulse (PBH = 1.69×10–36) and neurological system process (PBH = 3.23×10–32) were identified (BH = 1.36×10–26), excitatory extracellular ligand-gated ion channel activity (PBH = 2.62×10–22) and acetylcholine receptor activity (PBH = 5.12×10–22). In the cellular component category, the significantly enriched terms included synaptic membrane (PBH = 5.18×10–27), neuron projection (PBH = 4.51×10–24), axon (PBH = 3.55×10–21), dendritic spine (PBH = 9.87×10–10). Similarly, GO terms related to drug response  and metabolism , were also enriched in NAGenes. These results were consistent with the pathophysiological background of nicotine addiction, which also indicated the candidate genes are relatively reliable for the following up bioinformatics analysis.Functional enrichment analysis revealed a more specific function pattern of these genes. Among the GO terms significantly enriched in the candidate genes , includientified . ConsistBH≤0.01) (th), serotonin receptor signaling (ranked 11th), glutamate receptor signaling (ranked 14th) and GABA receptor signaling(ranked 19th), all of which play important roles in signaling transduction. Moreover, two pathways, synaptic long term potentiation (PBH = 1.07×10–3) and synaptic long term depression (PBH = 8.13×10–3) were enriched in the NAGenes  . The 20  NAGenes . These tTo take a further step beyond identifying lists of significantly enriched pathways and to understand how they interact with each other, we performed a pathway crosstalk analysis among the 97 significantly enriched pathways. The approach was based on the assumption that two pathways were considered to crosstalk if they shared a proportion of NAGenes . There wTo distill insight into the interaction of NAGenes in a local environment, we extracted the specific subnetwork of nicotine addiction (NA-specific network) from the human PPI network using the Steiner minimal tree algorithm. Basically, this approach linked as many as possible members of NAGenes via the minimal number of connections. As shown in Moreover, to test randomness of the NA-specific network, 1000 random subnetworks were generated using the Erdos-Renyi model and their average shortest-path distance and average clustering coefficient were compared with the corresponding values of the NA-specific network. For these random subnetworks, the average shortest-path distance was 3.72, which was significantly larger than that of the NA-specific network . The average clustering coefficient of the random subnetworks was 0.02, which was significantly smaller than that of the NA-specific network . Thus, the NA-specific network extracted from the whole PPI network was a non-random network.Summarizing the results from pathway analysis and network analysis, we were able to obtain a relatively comprehensive view about nicotine addiction . In suchIn the past decades, we have made considerable progress in understanding the molecular mechanisms underlying nicotine addiction, which is largely due to the identification of various neurotransmitter receptors, genes or pathways associated with addiction and the development of animal or cell models. Additionally, with the development of high throughput analysis technology, more and more genes/proteins have been suggested to be linked to nicotine addiction and provide a valuable resource to analyze candidate genes function, biochemical pathways and networks related to nicotine addiction. In this study, we provided a comprehensive analysis of the functional features and interaction network of nicotine addiction-related genes.Function enrichment analysis revealed the specific biological processes involved by NAGenes. Our GO enrichment analysis indicated that these genes participated in neurodevelopment-related process and ion channel or neurotransmitters activity. For example, terms such as acetylcholine receptor activity, dopamine receptor activity and glutamate receptor binding were significantly enriched in NAGenes, indicating the importance of these neurotransmitters in the development of nicotine addiction. Of note, we found the GO terms of cognition and learning or memory were also in the enriched list, consistent with previous findings of the roles of nicotine in the regulation of various physiological processes, including learning and memory –36.Pathway analysis revealed that pathways related to neurodevelopment were enriched in NAGenes, which further verified the existence of close relationship between the pathology of nicotine addiction and the signaling pathways of nervous system. Four pathways that were related to monoamine neurotransmitters were found to be enriched in the NAGenes, consistent with their central roles in the development of nicotine addiction. Stimulation of nicotinic acetylcholine receptors (nAChR) releases a variety of neurotransmitters in the brain, e.g., dopamine, serotonin, glutamate and GABA. Dopamine is critical for the reinforcing effects or rewarding behaviors of nicotine –38, glutBH = 2.00×10–14, ranked 1st and PBH = 3.16×10–13, ranked 3rd, respectively)  –51. For ctively) . And the2+ concentration and directly interact with dopamine and G protein-coupled receptors to regulate their expression Click here for additional data file.S2 Table(DOC)Click here for additional data file.


TOR1AIP1. LAP1 is involved in maintaining the nuclear envelope structure and appears be involved in the positioning of lamins and chromatin. To date, LAP1’s precise function has not been fully elucidated but analysis of its interacting proteins will permit unraveling putative associations to specific cellular pathways and cellular processes. By assessing public databases it was possible to identify the LAP1 interactome, and this was curated. In total, 41 interactions were identified. Several functionally relevant proteins, such as TRF2, TERF2IP, RIF1, ATM, MAD2L1 and MAD2L1BP were identified and these support the putative functions proposed for LAP1. Furthermore, by making use of the Ingenuity Pathways Analysis tool and submitting the LAP1 interactors, the top two canonical pathways were “Telomerase signalling” and “Telomere Extension by Telomerase” and the top functions “Cell Morphology”, “Cellular Assembly and Organization” and “DNA Replication, Recombination, and Repair”. Once again, putative LAP1 functions are reinforced but novel functions are emerging.Lamina-associated polypeptide 1 (LAP1) is a type II transmembrane protein of the inner nuclear membrane encoded by the human gene  The eukaryotic nucleus is a complex organelle enclosed by a highly organized double membrane, the nuclear envelope (NE). The NE separates the nucleus from the cytoplasm and is essentially composed by the inner nuclear membrane (INM), the outer nuclear membrane (ONM), the nuclear pore complexes (NPCs) and nuclear lamina. The INM and ONM are separated by the perinuclear space of 40–50 nm of diameter and are crossed and therefore connected at the NPCs. The perinuclear space is continuous with the lumen of the rough endoplasmic reticulum (RER) and the ONM is continuous with the rough endoplasmic reticulum membrane that comprises the ribosomes. The lamins, which are type V intermediate filament proteins exclusively found in the nucleus and associated with INM proteins, can be found in the nuclear lamina. The NE and NE proteins have received more attention in the last few years and there is increasing evidence that the NE is responsible for integrating many cellular functions, including chromatin organization, signalling pathways, transcription regulation and cytoskeletal organization ,2,3.TOR1AIP1. In rats, three LAP1 isoforms were described and are derived by alternative RNA splicing, these are LAP1A, LAP1B and LAP1C with molecular weights of 75, 68 and 55 KDa, respectively ,7673,76]e scored . All theBiNGO  was alteUpon examining the biological processes of the proteins found in the filtered list of interactions , signifiThis output reinforces the idea that LAP1 might function as a stabilizing element by forming multiple complexes as noted before. In particular, integrating a response to DNA damage through ATM activation and subsequently participating in telomere regulation, through the interaction with RIF1, TERF2IP and TRF2 . FurtherPhysiological and functional analysis of the pathways’ with which LAP1’s interactome integrates is presented in the next section, with the aid of the Ingenuity Pathway Analysis.p-value was below a significance threshold of 0.05.Gene identifications of the previously described interactors were imported into the Ingenuity Pathways Analysis (IPA) tool, once more extrapolating the mouse and rat interactions to human and excluding viral connections. Function classifications, signal pathways and interacting networks were constructed and analysed based on the underlying biological evidence from the IPA’s database. The core analysis performed in IPA retrieved seven distinctive functional sets: canonical pathways, upstream regulators, diseases and disorders, molecular and cellular functions, physiological system development and functions, associated network functions and toxicological lists that are depicted in IPA reinforced the idea that LAP1 might be communicating with the shelterin complex, as is evident in Regarding the rank for associated networks in Additionally, IPA retrieved BRAC1 as the most probable upstream regulator of the LAP1 interactome . This inLastly, IPA’s analysis of molecular and cellular functions attributed to LAP1’s interactome  acknowleIngenuity pathway analysis confirmed the hypothesis that LAP1, besides the functional associations with lamins and nuclear morphology, might be a crucial player in cell cycle progression and DNA damage responses. These conclusions are in accordance with the prior analysis performed with GO and GeneMANIA.In order to develop a comprehensive computational analysis of the LAP1 interactome, experimentally detected protein-protein and protein-nucleic acid interactions were extracted from the following public databases: BioGRID   contain interactions based on indirect evidence, for instance, data mining, genetic interactions, metabolic evidence and co-localization data. In this case, the gene set had a dimension that enabled the manual confirmation of each binary interaction. However, when the protein of interest retrieves a greater number of interactors, individually validating the curated interactions, of the results obtained might not be feasible. In these cases, filtering on the website enables the authors to clean the data prior to export. Alternatively, it is possible to import PSI-MI XML or XGMML into Cytoscape and use the filtering tools available. The output from our approach resulted in the identification of 41 LAP1 interactors .Search GO data tool  used to describe the set of genes that users entered on the previous page, the background frequency, the sample frequency, expected p-value, an indication of over/underrepresentation for each term, and p-value. The statistical analysis included Bonferroni’s correction for multiple testing and a term was reported as enriched if the adjusted p-value is below a significance threshold of 0.05. All the input genes were mapped, however two were unclassified: TOR3A for biological process ontology and HPDL for cellular component. The p-values were corrected for multiple testing using the Bonferroni procedure and transformed by taking the -log10 for easier visualization. The online GO term enrichment service, retrieved from the online platform was stored and analysed as Excel spreadsheet files (Gene Ontology (GO) provides a system of terms to consistently describe and annotate gene products . GO termlable at ) 73] to toSearchet files .Data obtained from the online databases already described were stored and analysed as Excel spreadsheet files. The network of LAP1 interactors was built using Cytoscape  23]. C. C23]. Cphysical interaction data, the network will only show genes linked if the proteins in question share a binary interaction established through physical experiments [The GeneMANIA plugin (version 3.4.0) was installed using the Cytoscape App Manager (Cytopscape plugin can be download at ) 61]. G. G61]. Geriments . The intp-values that were corrected for multiple testing using the Bonferroni correction. Each term is size and colour-coded, so that larger nodes have more genes and darker nodes are more significantly enriched. Intermediate terms that are not significant are present as small white nodes [biological process and cellular location. As GO terms might have more than one parent, they are technically structured as a network called a DAG (BiNGO is a tool used to determine which Gene Ontology (GO) categories are statistically overrepresented in a set of genes and outputs a network that allows interactive visualization of results mapped by the GO hierarchy . The BiNte nodes . GO termed a DAG .® Pathway Analysis  is a co CA, USA ) is depiThe integration of multiple bioinformatic tools allowed for the development of an intricate study of LAP1 interactors. These analyses attribute functions to LAP1 that have not been previously described. For instance, various DNA damage response proteins that incorporate or regulate the shelterin complex were shown to directly interact with LAP1. This function has been previously associated with INM proteins such as LMNA, SUN1, LAP1 and BAF. Additionally, the previously described association of LAP1 with mitosis was confirmed through the interaction with MCC. Therefore, these and other prospective functions of LAP1 convey the significant role of INM proteins in the regulation of multiple cellular processes and disease conditions.


Biological processes are usually distributed over various intracellular compartments. Proteins from diverse cellular compartments are often involved in similar signaling networks. However, the difference in the reaction rates between similar proteins among different compartments is usually quite high. We suggest that the estimation of frequency of intracompartmental as well as intercompartmental protein-protein interactions is an appropriate approach to predict the efficiency of a pathway.Using data from the databases STRING, ANDSystem, IntAct and UniProt, a PPI frequency matrix of intra/inter-compartmental interactions efficiencies was constructed. This matrix included 15 human-specific cellular compartments. An approach for estimating pathway efficiency using the matrix of intra/inter-compartmental PPI frequency, based on analysis of reactions efficiencies distribution was suggested. An investigation of KEGG pathway efficiencies was conducted using the developed method. The clusterization and the ranking of KEGG pathways based on their efficiency were performed. "Amino acid metabolism" and "Genetic information processing" revealed the highest efficiencies among other functional classes of KEGG pathways. "Nervous system" and "Signaling molecules interaction" contained the most inefficient pathways. Statistically significant differences were found between efficiencies of KEGG and randomly-generated pathways. Based on these observations, the validity of this approach was discussed.The estimation of efficiency of signaling networks is a complicated task because of the need for the data on the kinetic reactions. However, the proposed method does not require such data and can be used for preliminary analysis of different protein networks. Estimation of efficiency of signaling networks is one of the most relevant problems in the study of biological systems. Analysis of effectiveness of biological networks is needed to meet the challenges of medicine and biotechnology ,2. In paOne of the difficulties in modeling a signaling pathway is that biological processes in cells are allocated to different intracellular compartments . Thus, tPreviously we developed the CELLmicrocosmos PathwayIntegration (CmPI) to support and visualize the subcellular localization prediction of protein-related data such as protein-interaction network . Here, wThe method for estimating the efficiency of the pathway is based on consideration of PPI frequencies between different intracellular compartments. We assume that if PPI in general occurs more frequently between proteins from particular compartments, the interactions which contain proteins located in these compartments would be more effective within the pathways. Thus, the optimality of a pathway reaction distribution over the intracellular localization may reflect the efficiency of the pathway, with the most optimal distribution being the one where the frequency of observed interactions between proteins localized in intracellular compartments involved in the pathway has a maximum value.To analyze the effectiveness of intra/inter-compartmental interactions, 15 major locations of eukaryotic cells were selected: Cytoplasm, Nucleus, Secreted, Membrane, Chromosome, Endoplasmic reticulum, Golgi apparatus, Endosome, Lysosome, Mitochondrion, Cell junction, Lipid-anchor, Cell projection, Peroxisome and cytoplasmic vesicle. The localizations were selected by following rules. We considered only the highest hierarchy level of localizations presented in UniProt , data onOn the basis of these data we find efficiency of a reaction and a molecular-genetic network by following approach:i and j, Let q of the network Q, The efficiency There were 282 KEGG human pathways analyzed, including totally 50.000 reactions  of KEGG pathways with the one of random pathways, Eff distributions of 282 KEGG pathways and more than 10000 random pathways were calculated. Random pathways were generated by permutation of KEGG pathways in following way: for each KEGG pathway we generated 1000 of random pathways by replacing the proteins in each reaction by randomly chosen ones from the list of all KEGG proteins. If one protein is involved in several reactions of the pathway - we replace it in all these reactions by the same random protein. It was found that the Eff distribution of KEGG pathways Figure  has a stAlso, it was important to check whether there is a correlation between the length of the pathways and their efficiency. The value of Pearson correlation coefficient was equal to R =- 0.1 . The value of R was low, so we cannot make any concrete conclusions. However, it is negative, suggesting a weak reciprocal relationship with the length of the pathways.To identify similar KEGG pathways, hierarchical clustering was performed on the basis of the correlation distance between pairs of pathways Figure . The corSeven major clusters can be identified in the dendogram presented in Figure KEGG functional classes of pathways were ranked by the mean efficiency of included pathways. The highest efficiency is observed within pathways from "Amino acid metabolism", "Genetic information processing" and "Carbohydrate metabolism" classes."Metabolism" class consists of various subclasses, therefore it is located in the middle of the list. The less effective pathways include "Lipid metabolism", "Nervous system" and "Signaling molecules interaction" classes. Despite the fact that the average efficiency varies slightly between some classes Figure , the disEvaluation of the efficiency of the signaling networks currently remains an important issue. The method for preliminary analysis of networks lacking the data on kinetic parameters was suggested to avoid one of the main obstacles on the way to practical application of existing methods for modeling the dynamic of the molecular genetic systems. The matrix of intra/inter-compartmental interactions efficiencies was constructed for 15 specific human cellular localizations based on PPI data and data on protein distribution over cellular compartments. The analysis of the matrix revealed that the frequency of PPI of proteins from the same compartment is higher in comparison to frequency of PPI of proteins from different compartments. A new method for evaluating pathway efficiency was proposed; all KEGG human pathways were estimated by mean efficiency and clustered based on correlation distances between the distributions of pathway reaction efficiencies. The distribution of pathway functional classes over clusters shows that some classes are mainly presented in one cluster.The proposed method can be used for the preliminary analysis of the effectiveness of various signaling networks, including networks, for which there is not enough data for modeling them with more accurate methods.PPI data was extracted from STRING , IntAct The authors declare that they have no competing interests.OVP, OVS and VAI conceived the method. INL and RH carried out expert assessment and interpretation of results. OVP and OVS implemented the method and BS tested the method. EDP performed the statistical analyses. OVP and VAI drafted the manuscript. VAI supervised the whole studies. All authors read, corrected and approved the final manuscriptClick here for fileClick here for file


Kvik is an open-source framework that we developed for explorative analysis of functional genomics data from large epidemiological studies. Creating such studies requires a significant amount of time and resources. It is therefore usual to reuse the data from one study for several research projects. Often each project requires implementing new analysis code, integration with specific knowledge bases, and specific visualizations. Although existing data exploration tools are available for single study data exploration, no tool provides all the required functionality for multistudy data exploration. We have therefore used the Kvik framework to develop Kvik Pathways, an application for exploring gene expression data in the context of biological pathways. We have used Kvik Pathways to explore data from both a cross-sectional study design and a case-control study within the Norwegian Women and Cancer (NOWAC) cohort. Kvik Pathways follows the three-tier architecture in web applications using a powerful back-end for statistical analyses and retrieval of metadata.In this note, we describe how we used the Kvik framework to develop the Kvik Pathways application. Kvik Pathways was used by our team of epidemiologists toexplore gene expression data from healthy women with high and low plasma ratios of essential fatty acids. Visual explorative analysis is essential for understanding biological functions in large-scale omics’ datasets. However, enabling the inclusion of omics’ data in large epidemiological studies requires collecting samples from thousands of people at different biological levels over a long period of time. It is therefore usual to reuse the data for different research questions and projects. Although an existing tool may be useful for one project, no tool provides the required functionality for several different projects.1 contained a prototype system for exploring biological pathways and gene expression data. From this prototype we built the Kvik Framework, which provides developers a simple interface to powerful systems for statistical analyses and meta-databases, and Kvik Pathways: a publicly available data exploration application. From our experience in developing a framework for building data exploration applications, we identified four requirements such applications should satisfy:Interactive The applications should provide interactive exploration of datasets through visualizations and integration with relevant information. To understand the large quantities of heterogeneous data in epidemiological studies, researchers need interactive visualizations that provide different views and presentations of the data. Also, to understand the results it is important to have instant access to existing knowledge from online databases.Familiar They should use familiar visual representations to present information to researchers. For more efficient data exploration it is effective to use representations that researchers are familiar with both from the literature and from other applications.Simple to use Researchers should not need to install software to explore their data through the applications. The applications should protect the researcher from the burden of installing and keeping an application up to date.Lightweight Data presentation and computation should be separated to make it possible for researchers to explore data without having to have the computational power to run the analyses. With the growing rate data is produced at, we cannot expect that researchers have the resources to store and analyze data on their own computers.We have designed and implemented Kvik, a framework that makes it easy to develop new applications to explore different research questions and data. The initial version Kvikvisant.bu.edu) byvanted.ipk-gatersleben.de)3, enRoute bycaleydo.org). However, these tools do not provide the adaptability needed for exploration of multi-study datasets. Many existing tools place the visualization, data analysis and storage on the user’s computer, making it necessary to have a powerful computer. In addition, the tools are often standalone applications that require users to install and update the applications. Kvik Pathways satisfies the above requirements as follows:Interactive Kvik Pathways provides interactive pathway visualizations and information from the popular Kyoto encyclopedia of genes and genomes (KEGG)6 database (available online atkegg.jp).Simple to use Kvik Pathways uses HTML5 and modern JavaScript libraries to provide an interactive application that runs in any modern web browser.Familiar Kvik Pathways uses the familiar pathway representations from KEGG and graphical user interfaces found in modern web applications.Lightweight Kvik Pathways uses a powerful back-end provided by the Kvik framework to perform statistical analyses.There are several tools for exploring biological data in the context of pathways, such as VisANT  or Google Compute Engine (cloud.google.com/compute), we provide a Docker image atregistry.hub.docker.com/u/fjukstad/kvik.Both Kvik and Kvik Pathways are open-sourced atIn this note we describe how we used Kvik to implement Kvik Pathways, a tool for exploring gene expression in the context of biological pathways. In Kvik Pathways researchers can explore gene expression data fromKvik Pathways, a publicly available web application for exploring gene expression data in the context of biological pathways without any additional applications than a web browser.A requirement analysis for interactive exploration tools for epidemiological studies.A detailed description of how we have used Kvik Pathways to explore gene expression data from healthy women with high and low plasma ratios of essential fatty acids.Kvik Pathways allows users to interactively explore a molecular dataset, such as gene expression, through a web application. It provides pathway visualizations and detailed information about genes and pathways from the KEGG databases . Throughbioconductor.org).The Kvik framework provides a flexible statistics back-end where researchers can specify the analyses they want to run to generate data for later visualization. For example, in Kvik Pathways we retrieve fold change for single genes every time a pathway is viewed in the application. These analyses are run ad hoc on the back-end servers and generates output that is displayed in the pathways in the client’s web browser. The data analyses are implemented in a simple R script and can make use of all available libraries in R, such as Bioconductor  used to adjust thejs.cytoscape.org) library to generate the interactive pathway visualizations, and D3 (d3js.org) for Document Object Model (DOM) manipulation such as generating bar charts with HTML <svg> elements. We integrate these with the popular Bootstrap front-end framework (getbootstrap.com) to provide a familiar and aesthetically pleasing user interface.We implemented interactive visualizations using the Cytoscape.js (Kvik Pathways has a three-tiered architecture of independent layers . The brorpy.sourceforge.net) to interface with R. To access data and run analyses the Data Interface exposes a HTTP API to the browser layer  from the front-end layer using RPy2 (rest.kegg.jp). This KGML representation of a pathway is an XML file that contains a list of nodes  and edges (reactions or relations). Kvik parses this file and generates a JSON representation that Kvik Pathway uses to create pathway visualizations. Kvik Pathways Cytoscape.js to create a pathway visualization from the list of nodes and edges and overlay the nodes on the pathway image. To reduce latency when using the KEGG REST API, we cache every response on our servers. We use the average fold change between the groups  in the dataset to color the genes within the pathway maps. To highlightp-values, the pathway visualization shows an additional colored frame around genes. We visualize fold change values for individual samples as a bar chart in a side panel. This bar chart gives researchers a global view of the fold change in the entire dataset.To create pathway visualizations the Kvik back-end retrieves and parses the KEGG Markup Language (KGML) representation and pathway image from KEGG databases through its REST API  that compared gene expression in blood from healthy women with high and low plasma ratios of essential fatty acids. Gene expression differences between groups were assessed usingt-tests . There were 184 differentially expressed genes significant on the 5% level. When exploring this gene list originally, functional information was retrieved from GeneCards and other repositories, and the list was analyzed for overlap with known pathways using MSigDB (available online atbroadinstitute.org/gsea/msigdb). The researchers had to manually maintain overview of single genes, gene networks or pathways, and gather functional information gene by gene while assessing differences in gene expression levels. With this approach, researchers are limited by manual capacity, and the results may be prone to researcher bias. Kvik Pathways eliminates this researcher bias and does not limit the information retrieval to a researcher’s manual capacity.We used Kvik Pathways to repeat the analyses in a previous published project (github.com/fjukstad/kvik/blob/master/dataengine/data-engine.r). Instead of loading the unpublished dataset, we could load the dataset fromInitially, Kvik Pathways was implemented to explore gene expression data from a not yet published dataset. To use Kvik Pathways to explore the data from the analyses inAs an example of practical use of Kvik Pathways, we chose one of the significant pathways from the overlap analysis, the renin-angiotensin pathway .ncbi.nlm.nih.gov/geo), under accession number GSE15289.Data used in the use case is available in the Gene Expression Omnibus , to understand the actual intent of the framework and the Kvik pathways. To my understanding, the current paper adds little value to this previous publication, even with the use case described.The authors could stress on the actual functionality of Kvik, describe more elaborate use cases, and compare it to existing approaches. The advantages of the tool are not clear: those are mentioned in the introduction and the summary, but do not really correspond to the actual implementation state, and are not addressed in the text body.Title:general idea of three-tier data exploration tools and their value forgeneral use in epidemiological studies. However, the paper describes only Kvik pathways developed with their framework, and only a simple mapping of gene expression data onto KEGG pathways.The title refers to theAbstract:The abstract concentrates on the problem of multiple usages of the same data in different studies, and mentions that Kvik somehow solves this problem. There is no at least understandable message in the text describing how exactly Kvik is a solution.Article content: The content was hard to comprehend. I’d like to mention the following major points that might be relevant:The introduction section is redundant with double notice of the features a good framework should satisfy and which features Kvik itself possesses. Probably the authors could concentrate more on what features they intended to incorporate in Kvik, and why those features are important, in the same paragraph, and describe how exactly these features are satisfied further in the implementation.The introduction itself messes up the target audience: in the beginning it mentions that Kvik is to provide a simple framework for developers to apply advanced statistics on the data, but later on mentions only that the ‘easy to use’ feature of Kvik is for the end-users to see the visualization results.As continuation of the previous comment, the paper fails to describe how exactly a user can modify the statistical analysis algorithms in the R scripts (“outside the application” is not sufficient). This seems to be one of the main features of Kvik, and it is mentioned in the introduction and mostly in the summary, however no further explanations or examples are provided in the body.It is not true that having a browser is everything that is needed for using Kvik. It’s true for exploration of Kvik pathways only, but not for applying custom statistical analysis. Additionally, the users need to be familiar with how to use Docker images, and this again poses the question of who is the target audience.http://kvik.cs.uit.no/, and see FC values without knowing what these FC values actually mean. The user does not need to refer to the paper to see which values of which dataset are compared with each other to derive those FC values, and the browser only gives the information that the data is from the NOWAC biobank. Again, the paper fails to fully describe how the R script should be modified to include their own dataset and how these modifications will appear on the browser.It is confusing to load pathways atIt is too slow. With 13 Mbps internet speed, it requires more than 30 seconds to load the list of pathways, and nearly 30 seconds to load a single pathway.In the use case, the comparison of Kvik with the previously published approach is not appropriate. The authors do not clearly distinguish between gene set enrichment and pathway overrepresentation (overlap) analysis. The fact that the researchers should manually lookup gene functions is not solved by Kvik, since the output of Kvik is not the same as the output of those researchers: it simply provides mapping of expression data onto pathways. Moreover, the notion of “restricted to researcher’s manual capacity and the results may be prone to researcher bias” is not addressed by Kvik. Even though the genes appear in the pathway automatically, the researcher still has to scroll through the pathways and subjectively decide what the output means in biological sense. Thus, the output is limited and the bias is still there.“However, these tools do not provide the adaptability needed for exploration of multi-study datasets” in the introduction, which is not clear.  There is no comparison with existing tools, except for the sentenceSummary The summary is a good overview of the authors’ initial intent and idea, which is great. However, the summary does not really correspond to the body of the text.Minor pointsThe browser is unresponsive when in the middle of loading one wants to push the back button.Too little data. The majority of genes in the pathways do not have associated FC values. This makes the visualization results not clear. The authors may either mention this in the paper, or provide a richer dataset.It is not clear what is meant by:required functionality for multistudy data exploration” in the abstract. “from thousands of people at different biological levels” in the introduction.““A requirement analysis for interactive exploration tools” in the introduction. For example, in Kvik Pathways we retrieve fold change for single genes” part in the second paragraph of the Methods section.The authors should make clear what fold change values are used exactly, when they mention them in the text. E.g. in the “When mentioning bar charts in the Implementation section, the authors can provide a figure with a bar chart, or provide a respective link.“the statistical significance level is indicated by the color of the node’s frame”. However, the color code is not clear and it is also not mentioned in the Figure 1 legend, nor there is any information found in the Kvik pathways.In the Use case, it is mentioned thatI have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. All criticized points were considered in the revision. I think the paper has substantially improved. The distinction between `Kvik` and `Kvik Pathways` is made clear and a list of contributions makes it easier to follow the structure. I think you should consider putting the requirement analysis into a dedicated section.I see no reason to not approve the paper in its current state.Typo: Missing whitespace after sentence "Kvik Pathways follows the three-tier architecture in web applications using a powerful back-end for statistical analyses and retrieval of metadata."AbstractMissing verb? "Kvik Pathways Cytoscape.js to create a pathway visualization from the list of nodes and edges and overlay the nodes on the pathway image."ImplementationApart from the paper, I have one comment on your response on my review w.r.t. the differences regarding the NIK paper. You stated thatI have noticed two minor typos:we removed the security since we believe that data should be publicly available, [...]""I completely agree with the notion of open data. In my experience though, epidemiologists are often bound to contracts enforcing confidentiality of the data to protect the privacy of the participants. Our project partners legally must not use a system, which does not allow for secure data handling.This has no consequence for your paper at all, I just think that this is a observation from our collaboration with epidemiologists you might appreciate.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. The manuscript presents Kvik as an open-source system  for explorative analysis of functional genomics data from large epidemiological studies. The authors seem have excellent ideas, but the implementation of the tool is far behind these ideas. I would like to approve the manuscript if the following points can be addressed:The target of the tools. There are in general two type of tools: data provider and tool provider. The two of course can be combined. The prior in general provides knowledge, and the later provides functions to analyze users' own data. Kvik however seems lack the data to be a knowledge provider, and also does not provide enough functionality to be the later. To be the former, I will recommend authors to add more epidemiological data, to be the later, the author need to give clear instruction how user's own data can be analyzed using Kvik. For an example, the idea to connect to the cloud service is excellent, but how can Kvik to achieve this?The implementation of Kvik seems to be improved, especially the performance. When I tried the Kvik, the browser tells me several time that the page is not responding. Yet I know the page is responding, but just take too much time. In addition, from data security point of view, it is not good to use RPCs from the browser layer to data engine directly, it shall be avoid in general in the three-tier architecture.The manuscript need to focus more on the functionality of the tool. The current manuscript has too many technical details. I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Since we have open-sourced the application we believe that Kvik Pathways can provide knowledge in itself, but also be used by other researchers to gain knowledge in their data. We have modified the use case to make it more clear how users can do so themselves. We also refer to the online repository where this information is also available. As we state in the Introduction we provide Docker images for running Kvik Pathways in a cloud service.We agree, the current implementation can be improved. We are working on a second version where we have reduced the latency and that gives more feedback to the user if he has to wait. Regarding using RPCs from the browser layer we agree. We have updated the note with more details on how the system is implemented. The Data Engine provides an HTTP REST API that the browser layer queries. It does not send RPC requests from the browser layer, but from the frontend when it receives a request to the HTTP REST API. Also, the only RPCs that are allowed to run are defined in the R analysis script.We agree. We have reduced the number of implementation details to make the functionality stand out more.We would first like to thank the reviewer Zhenjun Hu for his thorough feedback and comments. et al. pursues in pushing the notion of open science in epidemiology. It describes Kvik, a web-based tool for analyzing genomic pathways. I really like the ideas behind it and value the detailed implementation section as well as the state-of-the-art techniques used. On the other hand I think that the paper should focus more on describing the epidemiological context, associated requirements and target groups to communicate the design choices for Kvik. Go into detail on the application workflow.The work presented by FjukstadGeneral FeedbackI like the approach the authors take with the paper. The tool they describe seems to be well suited for analyzing genomic pathways in the epidemiological context.Introduction section to tell the reader what things can be done with your software, which could not be done before!I miss a clear statement on the papers contribution. Maybe you can put a bullet list in theWhich of these are your target group?In my understanding, epidemiology is a very interdisciplinary in terms of associated experts. You have your clinicians deriving hypotheses from their day to day practice, statisticians deriving statistically sound conclusion as well as biologists and computer scientists associated with such projects. workflow of you system!When you described your target users, describe what they are trying to find out. How does your tool help them doing that? Does it allow them do their work faster? Do they derive insights they could not get before? The latter would be a huge contribution! Please give more details on theKvik: Interactive exploration of genomic data from the NOWAC postgenome biobank". The paper was also not cited in this work. It seems to me that the majority of the content of the presented paper can already be found in the NIK-2014 paper. Please elaborate on the differences and cite the paper.If you can not state clear differences, there is, in my opinion, no point in publishing this paper and I will rate it 'Not Approved'.I miss a clear distinction from the NIK-2014 paper "TitleThe title of the work is appropriate.Abstractreduce the amount of implementation detail. You tell the reader later on which frameworks and libraries you useexplain who are your users.what can be done with your tool now, which could not be done before?Minor comments on the abstract:required functionality is! I think I know what you are trying to get at, in the introduction you describe it better with: "Although an existing tool may be useful for one project, no tool provides the required functionality for several different projects.""Existing data exploration tools do not provide all the required functionality for such multi-study data exploration." This is a dangerous statement, since you do not say anything about what theIntroductionThe abstract motivates the need for new tools, which allow to assess the vast amount of epidemiological data well. In my opinion it can be improved by:The introduction can be improved by clearly stating the contributions .I would like to see some reference or a method on how the five requirements were acquired. These are all things, which are important in almost all applications. Where is the difference of software in the epidemiological context towards other context and how does Kvik adapt to the arising requirements? You answer many of these questions, later on when you repeat all the requirements again, but to me it is not structured well.MethodsThe method section is written well. I would like to know how the users modify the R scripts (beginning second paragraph). Do they do this inside Kvik or do they have to switch into another software for it?Figure 1 caption: What can the user do now after he or she selected the gene? The workflow is not clear to me.Figure 2 was already presented very similarly in the NIK'14 paper.Minor: Closing parenthesis in sentence "In our setup the Data Engine in the back-end layer provides an interface to the NOWAC data repository stored on a secure server on our local Stallo Supercomputer Table 1 provides the interfaces)."Use CaseThe use case section can be strengthened by reducing the amount of implementation details  and focusing more on the involved actors and tasks and contexts associated with the use case. What feedback was given by the user(s)?ReusabilityThe effort of the authors to make the software publicly available is worth a special note. Modern state of the art techniques are combined with powerful back-end systems, which scale well on different application scenarios.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. We would first like to thank the reviewer Paul Klemm for his thorough feedback and comments.Difference between the NIK paper and the Application NoteWhen we wrote the NIK paper, Kvik was only a system for exploring genomic data in the context of pathways. Since then we have realized that exploring genomic data in pathways is not enough, that we need a framework that allows us to build different applications for exploring data from different studies with different designs. With this in mind we have refined the requirement analysis from our initial Kvik system and developed more general requirements that these applications should satisfy.From our initial Kvik implementation we have now decoupled the application (Kvik Pathways) from the framework, allowing fast development of new applications. The Kvik framework provides interfaces to a Data Engine that provides statistical analyses, and interfaces to online databases such as KEGG. Kvik Pathways is the first application that we developed using the Kvik Framework. Using Kvik we have developed several other applications that will be published in the near future.In the NIK paper we described from a computer science point of view the features of Kvik, both looking at the application itself, and the backend features that are now a apart of the Kvik Framework. The NIK paper was written to give a more in detail view of how the system works and performs, while in this application note we want to describe how our epidemiology researchers helped to develop the application and how they used it to reproduce results they found in an already published dataset.Using an already published dataset was important to us since it allows us to provide a publicly available Kvik installation for others to use. We will revisit the second paragraph of the Use case section where we discussed how we used the initial Kvik system to explore different data from a different study design.Publicly available applicationPublicly available Docker containers that researchers can use to set up local installations of Kvik Pathways.kvik.cs.uit.noReproduced the results from an already published dataset to make the system publicly accessible atA more refined requirement analysis that reflects our experiences after publishing the NIK paper. The important changes are:i) emphasis on integration of online knowledge bases (interactive requirement),ii) emphasis on the system being flexible to adapt to data and different statistical analyses,iii) we removed the security since we believe that data should be publicly available, andiv) put emphasis on separating computation and visualization (lightweight).We have cited the NIK paper in the application note and improved the text to highlight the differences between the framework and the Kvik Pathways application.To sum up, our new contributions in the application note are as follows:We have included a list of contributions in the Introduction section.We agree that epidemiology is a very interdisciplinary. Kvik Pathways has been developed in a team of epidemiologists, biologists, statisticians and computer scientists. The application note targets such groups of researchers working together to develop systems for gaining biological insights in genomic data. We have re-written parts of the note to clarify how researchers have used the application.Abstract : We agree and have reduced the amount of implementation detail and made it more specific what our users have done with the application.Introduction: We have revisited the requirements and specified how these are different from regular applications. We believe that it is best to separate the requirements and how we solved them in two different lists.Methods: As of today users modify R scripts outside Kvik. We have made it clear in the methods section. We will expand Figure 1 caption to clearify the workflow. Regarding figure 2. We chose to include it since it highlights the important three-tiered architecture with applications that use the Kvik framework. We have modified the figure to highlight connection between the application and the framework.Use case: As mentioned we will expand this section with a more detailed workflow. Since it is an app note targeted towards users we will reduce implementation details and refer to the source code.