Ephedra sinica as a representative hot medicine and Scutellariae Radix as a representative cold medicine as an example. We explore and explain the theory of treating the same disease with different TCM treatments. Using transcriptomics and network pharmacology methods, GO, KEGG enrichment, and PPI network construction were carried out, demonstrating that Ephedra sinica plays a therapeutic role through the NF-κB and apoptosis signaling pathways targeting PLAU, CD40LG, BLC2L1, CASP7, and CXCL8. The targets of Scutellariae Radix through the IL-17 signaling pathway are MMP9, CXCL8, and MAPK14. Molecular docking technology was also used to verify the results. In short, our results provide evidence for the theory of treating the same disease with different treatments, and we also discuss future directions for traditional Chinese medicine.Pneumonia is a serious global health problem and the leading cause of mortality in children. Antibiotics are the main treatment for bacterial pneumonia, but there are serious drug resistance problems. Traditional Chinese medicine (TCM) has been used to treat diseases for thousands of years and has a unique theory. This article takes the treatment of pneumonia with  Pneumonia is a serious global health problem  and the Traditional Chinese medicine (TCM) is different from modern medicine and has a unique system of preventing, diagnosing, and treating diseases based on the philosophy of naive materialism . The corSince the launch of the Human Genome Project in the 1990s, large-scale research on genes and their expression products has emerged, such as genomics, transcriptomics, and proteomics, and related technologies have developed rapidly. Among them, transcriptomics can systematically detect the cell status and comprehensively reflect the real-time expression status and degree of degradation, which is extremely important for the research of human diseases. There have been many omics studies of TCM before. For example, Wu et al. studied the application of metabolomics in the treatment of coronary heart disease with TCM . Zhao etEphedra sinica (ES), a small shrub in the genus Ephedra, which has the function of dissipating wind-cold, freeing the lungs, and relieving asthma and is considered warm in Chinese medicine, as a representative of warm drugs. Scutellariae Radix (SR), the dry root of the Labiatae plant Scutellaria baicalensis Georgi, which has the functions of clearing heat and dampness, purging fire, and detoxifying and is considered to be cold, was chosen as a representative of cold drugs. Both drugs are believed to have therapeutic effects on pneumonia. Briefly, the aim of this study was to investigate the specific mechanisms of ES and SR, two representative Chinese medicines for the treatment of pneumonia, using network pharmacology and transcriptomics combined with animal experiments. To clarify the four types of qi in Chinese medicine and guide clinical treatment, a research schematic is shown in In TCM theory, both cold and hot medicines can be used to treat the same disease, which is called “treating the same disease with different treatments” , which mhttp://tcmspw.com/tcmsp.php) constructed by previous researchers, and their physical and chemical properties, such as molecular weight (MW), the number of donor atoms for H-bonds (nHDon), the number of acceptor atoms for H-bonds (nHAcc), and the Moriguchi octanol–water partition coeff. (log P), were analyzed [http://www.uniprot.org/) for correction.The molecular information of ES and SR was obtained from the TCMSP database  and 20 healthy controls. These data were used only because children are the main population affected by community-acquired pneumonia. Then, these data were analyzed using GEO2R online tools [http://string.embl.de/) database, the minimum required interaction score is set to medium confidence (0.400), and the organism was Homo sapiens. GO enrichment can be divided into 3 main parts including biological process (BP), molecular function (MF), and cellular component (CC). Generally speaking, BP is the main direction of all gene functions in GO [p value cutoff = 0.05 and q value cutoff = 0.05 [The ES and SR targets and the abovementioned differential genes are intersected. Then, the protein-protein interaction (PPI) network and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. The PPI network was analyzed using the Search Tool for the Retrieval of Interacting Genes , and the production batch numbers were 19046614 and 19086524, respectively. These two medicines are marketed prescription drugs approved by the China Food and Drug Administration and are administered as granules.Streptococcus pneumoniae strain D39 serotype 2 (NCTC 7466) was kindly provided by Professor Jian Huang from Zunyi Medical University  and cultured in Todd-Hewitt broth with 1% yeast extract (THY media) at 37°C with 5% CO2.Streptococcus pneumoniae strain D39 was cultured until the OD600 nm reached 0.4 (midlogarithmic phase), collected by centrifugation (1000 rpm for 10 min), and washed three times with PBS. Each mouse was nasally infected with 1.5 × 108 colony-forming units (CFUs) of bacteria, except for those in the healthy control group. The mice were subcutaneously injected with ES (40 mg/kg), SR (40 mg/kg), or DMSO every 8 h, and the lungs from sacrificed mice (n = 8) were used for histopathological analysis by hematoxylin-eosin staining under light microscopy at 48 h after infection. Furthermore, other mice were used to collect bronchoalveolar lavage (BAL) fluid, and it was centrifuged. The levels of TNF-α and interleukin (IL)-6 were determined in the supernatants using mouse ELISA kits (Biolegend) according to the manufacturer's recommendations.BALB/c mice  were purchased from the Liaoning Changsheng Biotechnology Co., Ltd.  and maintained in accordance with the NIH Guide for the Care and Use of Laboratory Animals. All mouse experiments were approved by the ethics committee of the Changchun University of Chinese Medicine. http://www.rcsb.org/). Then, Mgltools 1.5.6 was used to process the protein via hydrogenation, calculate the charge, merge the nonpolar hydrogen, and save it as a pdbqt file. According to the protein's ligand, we defined the active site, set the grid box coordinates, defined the box size as 40 × 40 × 40 grid points, and set the distance of each small grid point as 0.1 nm. AutodockVina 1.1.2 was used for the docking of molecules and proteins. An affinity of less than 0 indicated that the receptor and ligand could spontaneously bind.Data for the PLAU (PDB ID: 1C5Z), CD40LG (PDB ID: 3LKJ), BCL2L1 (PDB ID: 3QKD), CASP7 (PDB ID: 1SHJ), CXCL8 (PDB ID: 1QE6), MMP9 (PDB ID: 1GKD), CXCL8 (PDB ID: 1QE6), and MAPK14 (PDB ID: 1A9U) proteins were downloaded from the PDB database (n ≥ 3) and analyzed by GraphPad Prism 6.0 (GraphPad Software) using the Student's t-test; ∗∗p < 0.01.Data were expressed by the mean ± SD , druglikeness (DL), molecular weight (MW), the number of donor atoms for H-bonds (nHDon), the number of acceptor atoms for H-bonds (nHAcc), and the Moriguchi octanol–water partition coeff. (log P). The OB and DL represent the pharmacological properties of the drug. As shown in Furthermore, we screened the molecular sets of the two drugs by OB ≥ 30% and DL ≥ 0.18. ES contains a total of 25 molecules, and SR contains a total of 37 molecules, as shown in The differentially expressed genes of 20 normal children and 152 children with CAP are in the supplementary file . In FiguTo further clarify the molecules involved in the interaction between these two drugs and pneumonia, we constructed a “drug-molecule-target-disease” network, as shown in Coincidentally, in the SR network, we also found that eight molecules played a major role in the alleviation of pneumonia. They are wogonin, acacetin, baicalein, 5,7,4′-trihydroxy-8-methoxyflavone, neobaicalein, norwogonin, stigmasterol, and moslosooflavone. Baicalein is the main compound of SR. It inhibits apoptosis and autophagy  and protEphedra and 8 Scutellaria targets in pneumonia. To further explain the biological functions corresponding to the targets, GO-BP and KEGG functions were used to enrich them. The results are shown in κB and apoptosis signaling pathways, while SR acts mainly through the IL-17 signaling pathway.In addition to the drug components, the above research also provided information on 19 κB is considered to be a central regulator of the inflammatory process and an important participant in innate and adaptive immune responses [NF-esponses . It alsoesponses . Apoptosesponses . The dysesponses . Therefoesponses .β, and TNF-α [As a characteristic cytokine secreted by TH17 cells, IL-17 plays an important role in autoimmune diseases and immune-inflammatory damage diseases. There are 6 cytokines in the IL-17 family: IL-17A, IL-17B, IL-17C, IL-17D, IL-17E (IL-25), and IL-17F. Among them, IL-17A has been the most studied and is usually directly referred to as IL-17 ; it can nd TNF-α . In partnd TNF-α . We aimeκB-related, apoptosis-related, and IL-17-related proteins in the figure for further verification.As the executors of cellular activities and functions, proteins do not exist independently. The interactions between proteins play an important role in each stage of life and maintain the steady state of the internal environment. To analyze the interaction between ES and SR target proteins, a PPI network of target proteins was constructed, as shown in κB pathway and the apoptosis pathway, and the 8 active ingredients of SR may play a role in treating pneumonia by targeting the IL-17 signaling pathway. However, these data were not sufficient to verify our results. We used the main target proteins of the NF-κB pathway, apoptosis pathway, and IL-17 signaling pathway in the PPI network, including PLAU, CD40LG, BLC2L1, CASP7, MMP9, CXCL8, and MAPK14, for verification. As shown in Above, we proved that the 8 active ingredients of ES may play a role in treating pneumonia by targeting the NF-Pneumonia is a serious global health problem. Antibiotics, vaccines, bacteriophages, and antimicrobial peptides are all considered to be treatments for bacterial pneumonia, but they are not perfect . ChineseIn this study, we first put forward the theory of treating the same disease in different ways in traditional Chinese medicine. To prove this theory and investigate the compound structures of ES and SR in more detail, we used a database constructed by previous researchers  and obtaκB and apoptosis and ES can play a therapeutic role through the IL-17 signaling pathway.In animal experiments, the alleviation of the effects of ES and SR, two traditional Chinese medicines, on pneumonia was proven. Then, we used OB ≥ 30% and DL ≥ 0.18 to thoroughly investigate the specific molecular components of ES and SR. Through the GEO database, information on differentially expressed genes associated with pneumonia was found. Based on the intersections of molecular target genes and differentially expressed genes associated with pneumonia, we believe that ES and SR have 8 effective components that can act on pneumonia. After GO-BP and KEGG enrichment of related genes, we found that ES can treat pneumonia through NF-κB signaling pathway-related proteins PLAU, CD40LG, BLC2L1, and CXCL8. In addition, it can regulate the BLC2L1  and CASP7 proteins related to the apoptosis pathway. SR can regulate the MMP9, CXCL8, and MAPK14 proteins related to the IL-17 signaling pathway. In summary, we reached the following conclusions:ES and SR can both treat pneumonia in vivo.The active ingredients of ES for pneumonia are ephedrine, psi-ephedrine, herbacetin, kaempferol, quercetin, luteolin, stigmasterol, and naringenin.The active ingredients of SR for pneumonia are wogonin, acacetin, baicalein, 5,7,4′-trihydroxy-8-methoxyflavone, neobaicalein, norwogonin, stigmasterol, and moslosooflavone.κB and apoptosis signaling pathways, and SR plays a therapeutic role through the IL-17 signaling pathway.ES plays a therapeutic role through the NF-κB and apoptosis pathways through the PLAU, CD40LG, BLC2L1, CASP7, and CXCL8 proteins. The eight main components of SR can treat pneumonia by targeting the IL-17 signaling pathway though the CXCL8, MAPK14, and MMP9 proteins, as shown in The eight main molecules of ES exerted a marked effect in treating pneumonia by targeting the NF-Most importantly, ES, as a representative warm medicine, and SR, as a representative cold medicine, can both treat pneumonia.Furthermore, we used molecular docking to verify the results and found that ES can regulate the NF-From another perspective, our ancestors found that in food preparation, labor, and the struggle with nature, they could use animal skins, bark bags, hot stones, or sandy soil as part of the heat for heating. Heating could eliminate some pain and gradually lead to the development of hot ironing and moxibustion. When using stone tools to work, it was found that after puncturing one part of the body, the pain in other parts of the body could be relieved, thereby creating a method of treating pain with vermiculite and bone needles. The consumption of certain animals and plants has the effect of reducing or eliminating disease, which is the origin of Chinese medicine. As humans evolved, they began to purposefully search for drugs and methods to prevent and treat diseases and developed primitive Chinese medicine. Later, with further development, traditional Chinese medicine was continuously improved by the Chinese people in terms of theory and methods. As shown in In short, in this study, we take ES and SR as examples and use a combination of network pharmacology and transcriptomics and experimental results to prove the TCM theory of treating the same disease with different treatments. We think it is very important to explore the theory and continue the practice of TCM.


Omic technologies allow us to generate extensive data, including transcriptomic, proteomic, phosphoproteomic and metabolomic. These data can be used to study signal transduction, gene regulation and metabolism. In this review, we summarise resources and methods to analysis these types of data. We focus on methods developed to recover functional insights using footprints. Footprints are signatures defined by the effect of molecules or processes of interest. They integrate information from multiple measurements whose abundances are under the influence of a common regulator. For example, transcripts controlled by a transcription factor or peptides phosphorylated by a kinase. Footprints can also be generalised across multiple types of omic data. Thus, we also present methods to integrate multiple types of omic data and features (such as the ones derived from footprints) together. We highlight some examples of studies that leverage such approaches to discover new biological mechanisms.  •Functional information on signalling pathways, metabolism and gene regulation can be found across multiple types of omic data.•One way to extract such information is to consider these data as the footprint of the activity of enzymes and pathways.•Information on enzyme/pathway activities and omic data can be integrated together to contextualise multi-scale networks.•Such an approach can lead to the discovery of regulatory events spanning across multiple biological processes. In a cell, numerous molecules are constantly interacting and reacting to adapt to the environment and preserve homoeostasis. These molecules can be separated in distinct classes, mostly DNA, RNA of various natures , proteins and metabolites. They can be subjected to various chemical modifications such as methylation, phosphorylation, ubiquitinilation or glycosylation. Each of these modifications can affect the physical properties of these molecules and, consequently, their functions. In particular, modifications of proteins are often organised in cascades. These cascades are interlinked, forming a complex network that controls most cellular functions. Over the past decades, subparts of this network have been characterised and defined according to the types of reactions and molecules interacting together, notably signalling pathways, regulatory networks and metabolic networks. Roughly, signalling and regulatory networks represent subnetworks composed mainly of kinases, phosphatases and transcription factors (TFs) connecting proteic sensors (such as membrane receptors) to gene expression. Kinases are responsible for the phosphorylation of proteins, whereas TFs, which are also interconnected, regulate the abundance of RNA transcripts. Metabolic networks are mainly composed of small molecules (metabolites) that are transformed into one another through reactions catalysed by metabolic enzymes  a and b.Today, it is possible to measure the abundance of thousands of RNA transcripts, protein peptides  and metabolites. Such data sets, along with the systematic characterisation of other biomolecules , are referred to as omic data sets. All these abundances can be considered as the molecular signature of a biological sample in a specific condition, for example, cells treated with an enzymatic inhibitor. This concept can also be scaled down at the level of specific enzymes, such as TFs or kinases; the abundances of the target transcripts of a TF can be viewed as the footprint of the TF activity. The same concept applies to the target phosphopeptides of a kinase. A footprint can also be derived for a pathway or process and inform us on their activity. In a classic ‘mapping’ strategy, the activity of a pathway is inferred from measurements of its own components and the activity of enzymes is estimated from measurements of their corresponding transcripts/proteins. In contrast, footprint-based strategies estimate activities from molecular readouts considered to be downstream of the pathway/enzyme  c.In this review, we will cover recent methods to analyse and extract relevant functional and mechanistic information using molecular signatures applied to omic data. We will also present strategies to integrate together multiple types of omic data. We will focus mainly on molecular measurements directly related to signalling pathways and metabolic reaction networks that can be obtained from transcriptomic, (phospho)proteomic and metabolomic data. We leave other omic data out of the scope of the review, in particular, (epi)genomic. Accordingly, we will describe features derived from this data, in particular, using footprints. First, we present different types of online knowledge databases that can be used to extract functional insights from omic data sets. Then, we summarise mapping and footprint methods, as well as network-based approaches. Finally, we will discuss how these methods can be used to integrate together different types of omic data sets.A powerful strategy for the analysis of any omic data set is to integrate it with the current knowledge of the underlying biology. This knowledge is available in multiple resources Databases that capture relationships between enzymes and their substrates are useful to extract relevant information about enzymes from transcriptomic and phosphoproteomic data. These relationships are either predicted with computational methods or experimentally validated.TF targets are available in databases such as TRANSFAC Similar databases exist for kinases. PhosphositePlus Finally, information on metabolic enzymes and their targeted metabolites exists in resources such as KEGG Some multilevel interaction databases  already exist. STITCH Gene sets are groups of genes that share a common characteristic . These are available in annotation resources described in Originally, gene set/pathway enrichment analysis was mainly used to assess whether a specific gene annotation is significantly enriched with extremely deregulated genes. However, this method is very flexible and can be adapted for many different uses. For example, associations between drugs and their expression signature  process. This makes it possible to reconstruct networks of interactions based on correlations between multiple measurements and features. For example, correlations between metabolite and metabolic enzyme transcript abundance were estimated in Ref. Indeed, correlation based approaches can also be used downstream of footprint analysis to connect activity scores with other measurements. For example, kinase activities estimated from phosphoproteomics were correlated with metabolites to find kinases that regulate the activity of metabolic enzymes through posttranslational modifications https://www.biorxiv.org/content/10.1101/541888v1Most network resources (such as the ones presented in In the future, it is likely that such approaches will be generalised to directly integrate multiple type of omic measurements at the same time, combining both measurements and/or output of footprint analysis. In fact, there are already a few examples of recent methods to contextualise networks with multiple types of omic data. The prize-collecting Steiner forest algorithm has been used to find optimal subnetworks in a prior combination of PPI and reaction network based on metabolic and protein abundance measurements Escherichia coli has been discovered by integrating genomic, transcriptomic, ribosomal profiling, proteomic and metabolomic data To conclude, we believe that integrating multiple types of omic data together using biological knowledge and appropriate computational models will allow us to better understand cellular mechanisms in many contexts . Novel tAurelien Dugourd wrote the manuscript under the supervision of Julio Saez-Rodriguez.Nothing declared.


Hepatocellular carcinoma (HCC) is one of the diseases associated with human microbiome. The human microbiome is known to affect human disease through the metabolites. The aim of this study was to identify the pathways associated with HCC by integrating microbiome and metabolomic data via a novel pathway-based integrative method named HisCoM-MnM representing Hierarchical structural Component Model for pathway analysis of Microbiome and Metabolome. Application of HisCoM-MnM to datasets from HCC and liver cirrhosis (LC) patients successfully identified HCC-related pathways related to cancer metabolic reprogramming along with the significant metabolome and metagenome that make up those pathways.Aberrations of the human microbiome are associated with diverse liver diseases, including hepatocellular carcinoma (HCC). Even if we can associate specific microbes with particular diseases, it is difficult to know mechanistically how the microbe contributes to the pathophysiology. Here, we sought to reveal the functional potential of the HCC-associated microbiome with the human metabolome which is known to play a role in connecting host phenotype to microbiome function. To utilize both microbiome and metabolomic data sets, we propose an innovative, pathway-based analysis, Hierarchical structural Component Model for pathway analysis of Microbiome and Metabolome (HisCoM-MnM), for integrating microbiome and metabolomic data. In particular, we used pathway information to integrate these two omics data sets, thus providing insight into biological interactions between different biological layers, with regard to the host’s phenotype. The application of HisCoM-MnM to data sets from 103 and 97 patients with HCC and liver cirrhosis (LC), respectively, showed that this approach could identify HCC-related pathways related to cancer metabolic reprogramming, in addition to the significant metabolome and metagenome that make up those pathways. Recent advances in throughput and improvement in the accuracy of metagenomic sequencing have enabled active research regarding specific microbiomes associated with diseases. The human microbiome has been well implicated in the progression of various diseases. Such diseases include infectious diseases ,2, metabHowever, many challenges remain. Most studies have analyzed microbiome and metabolomic data independently, or focused only on analyzing statistical associations between these two data types, underscoring a need for methods of integration. In this study, we present such a new integration method, Hierarchical structural Component Model for pathway analysis of Microbiome and Metabolome (HisCoM-MnM). This model extends our previously developed Pathway-based approach using HierArchical structure of collapsed RAre variant Of High-throughput sequencing data (PHARAOH) method . PHARAOHIn the current work, we used the main framework of PHARAOH to construct a hierarchical component model for integrating microbiome and metabolomic data, using pathway information. Based on HisCoM-MnM, we aimed to identify disease-related pathways, and elucidate the underlying “crosstalk” between human microbiome and metabolome, by discovering pathways associated with pathogenic metagenome and metabolome specific to distinct diseases, using a single model to consider potential correlations, between pathways, and between microbiome and metabolomic data, using a ridge-type penalty. HisCoM-MnM allows us to understand the relationships between different biological “layers,” based on pathway information related to specific phenotypes.Here, we applied our HisCoM-MnM model to analyze microbiome and metabolomic data from HCC and liver cirrhosis (LC) patients ,24. HCC The baseline characteristics of subjects in the HCC and LC groups are shown in p-values smaller than 0.01, after Bonferroni adjustment. Among these 39 pathways, most were related to metabolism (24 pathways), with other pathways related to organismal systems (5 pathways), human diseases (4 pathways), environmental information processing (3 pathways), cellular processes (2 pathways), and genetic information processing (1 pathway), according to classifications by the KEGG pathway database , and the involvement of serine in this pathway was also significant, with a nominal p-value of 0.041. In PHGDH (K00058), which encodes a metabolic enzyme involved in serine synthesis, was identified to be significant in HCC, with a nominal p-value of 0.029.The central carbon metabolism cancer pathway contained a total of 25 KEGG orthologs, and metabolites, in HisCoM-MnM. Among these 25, only serine was significant, with a nominal p-value of 0.0025) cancer-related pathway. We also found glycerophospholipid metabolism (map00564) as a significant result, with a p-value of 0.0012, consistent with previous findings [In addition, we identified other amino acid and lipid-metabolic pathways related to HCC, as well as the choline metabolism in cancer (map05231) pathway . Cholinefindings .p-values of the 111 pathways, as calculated by each method. The correlation between HisCoM-MnM and metagenomic data was higher than that between HisCoM-MnM and metabolomic data, showing that HisCoM-MnM and metagenomic data tend to have more similar pathway results than HisCoM-MnM and metabolomic data. Next, we confirmed whether the multiomics approach, HisCoM-MnM, could find significant results, as well as interpretive aspects, when compared to results from single omics data analysis. First, we applied HisCoM to the same HCC and LC metabolomic and metagenomic data used by HisCoM-MnM. Hospital information, age, gender, and AFP were also used as covariates, and the 111 pathways analyzed by HisCoM-MnM were also analyzed by single omics analysis. This study was designed to identify HCC-related pathways, and KEGG orthologs and metabolites that discriminate HCC from LC. To utilize both microbiome and metabolomic data, we proposed a novel pathway analysis method, HisCoM-MnM, for integrating microbiome and metabolomic data sets. HisCoM-MnM has several advantages over traditional approaches, in that it simultaneously performs not only pathway analysis, but also analyzes components of pathways, such as metabolic genes and metabolites. This method also considers correlations between pathways and between its components. To consider correlations, HisCoM-MnM applies a ridge-type penalization approach. As a result, HisCoM-MnM identified pathways well linked to HCC, suggesting that the development of HCC is more related to amino acid and lipid metabolic reprogramming, compared to LC.One of the significant pathways was central carbon metabolism in cancer . It is wPHGDH, KEGG component K00058). 3-Phospho-hydroxypyruvate is subsequently metabolized into serine, via phosphoserine aminotransferase 1 , and phosphoserine phosphatase  [SHMT, K00600) next catalyze the conversion of serine into glycine. Moreover, HCC cells are known to support liver tumorigenesis by increasing serine and glycine synthesis by upregulating PHGDH, and other metabolic genes [PHGDH as significant results.We also identified the pathway named glycine, serine and threonine metabolism. It is well known that the serine/glycine biosynthetic pathway plays a crucial role in cancer cell proliferation. Serine and glycine are amino acids that are incorporated into proteins, and also serve as precursor molecules and substrates for cell growth and proliferation . This sh K01079)  and seriic genes . This isIn addition, the dysregulation of amino acids , fatty acids, and bile acids, in HCC versus LC, was reported in previous studies ,37,38,39Another significant cancer-related pathway was choline metabolism in cancer. Choline is an essential component of cell membranes, and abnormal choline metabolism is a metabolic feature of oncogenesis and tumor progression . In partCompared to single omics approaches, HisCoM-MnM has the advantages of identifying significant pathways and aspects of interpretation. By a multiomics approach, HisCoM-MnM identified major HCC-related pathways not found by single omics approaches. For example, HisCoM-MnM detected significant pathways, such as metabolic reprogramming of fatty acids and amino acids. Furthermore, there were pathways known to relate to the suppression of cancer. One of the significant pathway was selenocompound metabolism. The potential for the anticancer effects of the micronutrient element selenium was demonstrated previously . AnotherIn addition, HisCoM-MnM is a highly utilized method. First, HisCoM-MnM is flexible to the methods used to obtain the pathway-matching information. In this study, we used 16S rRNA-based microbiome data, and PICRUSt, to predict putative functions of the microbiome. Because PICRUSt provides KEGG-based information, we obtained metabolic gene information, as KEGG orthologs matched to KEGG pathways. To integrate metabolomic and microbiome data, through pathway information, we annotated metabolites to KEGG compound IDs, for pathway matching. Although we used the KEGG database for pathway matching, other pathway database and other types of data, such as shotgun sequencing data or untargeted metabolomic data, can also be used, because HisCoM-MnM only requires matching information between pathways, and components of pathways. Second, while HisCoM-MnM was developed for analyzing hepatocellular carcinoma and liver cirrhosis data, it can be applicable to any complex diseases associated with the microbiome.There are some limitations to the present study. First, this study mainly focused on hepatitis B virus (HBV)-related HCC patients, with no patients with etiologies such as HCV and non-viral. Therefore, further research is required to confirm the generalizability of our results to other HCC etiologies. Second, the information in the microbiome and metabolomic data has not been fully utilized, due to limitations of the annotation. As described before, we used the KEGG database for pathway matching. We annotated metabolites to KEGG compound IDs, generating KEGG orthology tables from 16S rRNA-based microbiome data, using PICRUSt. Then, we matched KEGG IDs with KEGG pathways. In this step, out of 188 metabolites, in targeted metabolomic data sets, only 74 could be annotated to KEGG compound IDs, and matched to distinct pathways. KEGG orthology also excluded those that were not matched to pathways, leaving only 3394 out of 6241 KEGG orthologs. Despite this limitation, HisCoM-MnM successfully discovered HCC-related pathways.We performed integrated analysis of microbiome and metabolomic data originating from our previous studies ,24. We cFor microbiome data, circulating cell-free DNA was extracted for 16S rRNA gene analysis from serum extracellular vesicles (EVs) . First, For microbiome data, normalization of the OTU table was performed by dividing each OTU by the known or predicted 16S copy number abundance. Next, we generated a functional table, using PICRUSt , to pred® p180 kit , with liquid chromatography and tandem mass spectrometry, was used to quantify 188 metabolites [For metabolomic data, targeted metabolomic analysis using the AbsoluteIDQabolites . We filtFor metabolomic data, first, we mapped each metabolite to the KEGG COMPOUND ID database, using MetaboAnalyst , and cheFor the pathway analysis, we merged metagenomic and metabolomic data sets using KEGG pathway information . KEGG orAfter merging two different types of data sets, pathway analysis could be performed using HisCoM-MnM. The structure of the proposed model is given in Let Let In p-values were calculated using permutation tests, by generating 100,000 permuted phenotypes.To estimate the model parameters, we used the alternating least squares (ALS) algorithm, which alternates two steps until convergence. In the first step, the pathway coefficient estimates are updated, in the sense of least squares, for fixing the weight coefficient estimates. In the second step, the weight coefficient estimates are updated in the sense of least squares for fixing pathway coefficient estimates. To control the potential correlation between metabolites and KEGG orthologs, and between pathways, we used a ridge-type penalty. Then, we sought to maximize the following penalized log-likelihood function:In conclusion, we have developed an innovative method, HisCoM-MnM, applied it to 16S rRNA-based microbiome data, and targeted metabolomic data from HCC and LC patients to identify HCC-related pathways, and KEGG orthologs and metabolites. We found that HisCoM-MnM can discover HCC-related pathways that are consistent with previous findings. Further studies with other etiologies of HCC are warranted, to validate the generalizability of our identification of specific pathology-related pathways.


Aβ aggregates, especially the low-molecular weight oligomers, are the primary toxic agents in AD pathogenesis. Therefore, there is increasing interest in understanding their formation and behaviour. In this paper, we use our previously established results on heterotypic interactions between Aβ and fatty acids (FAs) to investigate off-pathway aggregation under the control of FA concentrations to develop a mathematical framework that captures the mechanism. Our framework to define and simulate the competing on- and off-pathways of Aβ aggregation is based on the principles of game theory. Together with detailed simulations and biophysical experiments, our models describe the dynamics involved in the mechanisms of Aβ aggregation in the presence of FAs to adopt multiple pathways. Specifically, our reduced-order computations indicate that the emergence of off- or on-pathway aggregates are tightly controlled by a narrow set of rate constants, and one could alter such parameters to populate a particular oligomeric species. These models agree with the detailed simulations and experimental data on using FA as a heterotypic partner to modulate the temporal parameters. Predicting spatio-temporal landscape along competing pathways for a given heterotypic partner such as lipids is a first step towards simulating scenarios in which the generation of specific ‘conformer strains’ of Aβ could be predicted. This approach could be significant in deciphering the mechanisms of amyloid aggregation and strain generation, which are ubiquitously observed in many neurodegenerative diseases.Aggregation of amyloid- Generated by the proteolytic processing of amyloid precursor protein (APP), Aβ peptides (Aβ40 or Aβ42) spontaneously aggregate to form insoluble fibrils that deposit as senile plaques in the AD brain. During aggregation, the low-molecular weight oligomers formed are known to be the primary toxic species responsible for synaptic dysfunction and neuronal loss [Aggregation of the protein amyloid-nal loss –6. An innal loss ,8. Thereβ aggregation follows a nucleation-dependent, sigmoidal growth kinetics involving a key rate-limiting event of nucleus or nuclei formation [β to environmental factors and many interacting partners due to its intrinsic disorder and amphipathic nature [β adopting multiple pathways depending on the aggregation conditions. An important ramification of this is that the oligomers may not be obligate intermediates of fibril formation but those with distinct conformations can be formed along alternative aggregation pathways (off-pathways) [Aormation –13. Sincc nature –18 play athways) –23. Thisβ affect its aggregation dynamics. Being generated from the membrane-spanning domain of the APP, Aβ displays synchronous and perpetual interaction with membrane lipids [β aggregation in a concentration-dependent manner to generate aggregates by an alternative, off-pathway from the canonical fibril formation, on-pathway [Therefore, it is imperative to gain an understanding of how physiological interacting partners of Ae lipids –30. Intee lipids ,32. Prev-pathway ,20,33–37-pathway ,34,38.β and lipids posit the question of what spatio-temporal parameters govern the modulatory dynamics, and whether one could simulate the temporal emergence and disappearance of aggregates as a function of heterotypic Aβ-lipid interactions. In this work, we have approached to answer these questions using a competition-based  approach to determine the dynamics in the temporal evolution of Aβ aggregates along the pathways influenced by fatty acid surfactants (L). Our rationale for such an approach is that the stochasticity and the often exclusive pathways of Aβ aggregation present ‘win or loss’ scenarios with respect to pathway adoption, tightly governed by the concentration and phase transitions of L. The mathematical analysis of this problem was taken up in two layers, one feeding into the other. The first is a six species, coarse-grained, reduced-order model (ROM), while the second is a more detailed model called ensemble kinetic simulation (EKS), which captures the temporal kinetics of reactions at the atomistic scale (considered as point particles). The ROM approach lends itself to a detailed analysis in a manner that cannot be performed in high-resolution models as we have shown before [The modulation of aggregation by heterotypic interactions between An before ,39. Phen2.2.1.β that interact with the fatty acid surfactant, L. Even with just six species, there are infinitely many rate regimes, most of which would be physically inconsequential. Thus, only physically meaningful rate regimes suggested from experiments and our previous studies [The model presented here consists of a reduced order, comprising only six species of A studies ,39 were β monomers react with the pseudo-micellar fatty acid surfactants, L to modulate the formation of on- or off-pathway aggregates. The system of chemical reactions in our model consists of the following:A schematic of such a model is presented below see also . In thisA1, nA and mA represent on-pathway Aβ monomers (A1) and oligomers ; whereas the prime species, L. The rate constants i = 1–6) are indicated in the reaction schematic above where the ‘+’ represents a forward rate and ‘−’, a backward rate. These reactions were formulated based on experimental evidence demonstrated earlier [n = 4 and m = 20 unless otherwise specified, which denotes the order of oligomer [n, m values in the computations were kept low to minimize computational time. This is also because only significant qualitative features in the system were being sought by ROM, and a more fine-grained approach by EKS modelling provides atomistic temporal analyses using the output from ROM. However, it must be noted that the key results of the study were examined for different values of n and m to ensure qualitative similarities and with no loss of generality as shown previously [The non-prime species,  earlier . In the oligomer . The n, eviously .A0 as the characteristic concentration of monomers and α3 and α4 have a factor L which is responsible for off-pathway aggregation. These two parameters serve as the bridge variables between on- and off-pathway species. Using the law of mass action kinetics, the dimensionless system of differential equations was formulated as follows:The reaction scheme was used to develop the corresponding kinetic model comprising a system of six nonlinear differential equations. This system was then put into non-dimensional form. Using Base Model and Model 2 were analysed, which are distinguished by the choice of fixed parameter values; i.e. the rate constant ratios in the pure on- and off-pathways. In the Base Model, all forward rates  and all backward rates  were set to 1 and 0.001 based on previous mathematical models and experimental data [Base Model, a forward rate is defined as one that converts a smaller oligomer into a larger aggregate, and backward being the reverse process. It must be noted that since ROM is a bulk averaged model, precise one-to-one mapping of its rate constants to that of the detailed EKS model is neither practical nor meaningful. In Model 2, all forward and backward rates were set to unity. Ode 45 solver (Matlab) was used for our numerical computations.As stated earlier, primarily two models referred to as the tal data ,40. In tB1, nB, mB) are greater, less or equal to –(2.6) from a game-theoretic point of view. Such an approach warrants finding the conditions under which the triplet . The following is the reaction scheme considered :In the EKS model, a set of reactions was considered to represent the on-pathway, off-pathway and their switching, and the flux for each reaction was computed. The system of differential equations of each species present in the reaction system were identified and solved using the I. Reactions of on-pathway: (considering A12as F):II. Reactions of off-pathway model:III. On-to-off switching reaction:I.On-pathway reactions flux:II. Off-pathway reactions flux:III. Switching flux:iA denotes an on-pathway i-mer, i-mer, L denotes pseudo-micellar surfactants, F denotes post-nucleation on-pathway aggregates , signal is the total thioflavin-T (ThT) fluorescence intensity which is expressed as the sum of the on-pathway ThT signal  and the off-pathway ThT signal  . Note that in the EKS models, we consider the most general case where there can be switching between any on- or off-pathway oligomer of size A1 to A11. Similarly, smaller off-pathway oligomers from Here, and in Appendix C, reported ; F for tier work ,47.2.3.β42 procured from both Peptide 2.0 and Dr Chaterjee's laboratory at the University of Mississippi was used in this study. ThT, sodium dodecyl sulfate (SDS) and lauric acid (C 12:0) was purchased from Sigma–Aldrich . Monoclonal Ab9 or Ab5 antibodies were obtained from the University of Florida Center for Translational Research in Neurodegenerative Diseases.Synthetic, wild-type A2.3.1.Preparation of Aβ42 monomers: Aβ42 peptide (1–1.5 mg) that was kept desiccated at −20°C was dissolved in 50 mM NaOH and was allowed to stand at room temperature for up to 45 min. The dissolved peptide was then fractionated on a Superdex-75 HR 10/30 size exclusion chromatography (SEC) column (GE Life Sciences) on a BIORAD FPLC system that was pre-equilibrated with 20 mM Tris at pH 8.00, to separate any preformed aggregates as previously reported [−1 and stored at 4°C and were used within 24 h to avoid reaggregation. The concentration of the monomeric fractions was calculated using a Cary 50 UV–Vis spectrophotometer (Varian Inc.). The molar extinction coefficient of 1450 cm−1 M−1 at 276 nm was used (www.expasy.org).reported . FractioOn- and off-pathway aggregation reactions: On-pathway aggregation was initiated with 40 µM monomeric Aβ42 in 20 mM Tris–HCl, 50 mM NaCl at pH 8.0 incubated under quiescent conditions at 37°C with 0.01% NaN3. Off-pathway reactions were initiated using 25 µM monomeric Aβ42 in the same buffer incubated with 50 mM NaCl and 5 mM sodium laurate (C12 FA) in 20 mM Tris, pH 8.00, as reported previously [eviously ,34.2.3.2.β reactions incubated in buffer alone, a 50 µM ThT solution in the same buffer was added and fluorescence emission (λ = 482 nm) was collected using microplate reader (BioTek Synergy Microplate Reader) at 37°C using an excitation at 452 nm. A 5 mM sodium laurate (C12 fatty acid) sample pre-equilibrated with 50 µM ThT was added to the reactions at 3, 8 and 24 h to initiate switching of pathways. The data were collected at 10 min time intervals. For off-to-on-pathway switching reactions, the 150 µl, 50 µM Aβ reactions pre-incubated in the presence of 5 mM sodium laurate were diluted 5- or 10-fold at 5 and 10 h using buffered 50 µM Aβ monomers and 50 µM ThT such that only the fatty acid concentration is dropped below its CMC. Appropriate blank reactions were monitored simultaneously and were corrected before data processing.For on-to-off-pathway switching reactions, to 150 µl, 50 µM A2.3.3.β. Anti-mouse horseradish peroxide was added to the blot and the blot was developed with ECL reagent (Thermo Fisher Scientific) and imaged with a Bio-Rad Gel Doc system.Aliquots of the reactions were mixed with sample buffer comprising 1% SDS (1× Laemmli sample buffer) and loaded on a precast 4–12% Bio-Rad gel. For calibration, pre-stained molecular weight markers (Invitrogen Inc.) were used. The gels were then electroblotted on 0.45 µm nitrocellulose membrane (GE Life Sciences). The blots were then heated in the microwave for 1 min and were blocked with 5% non-fat dry milk solution with 1% Tween-20 in PBS for 1.5 h. Subsequently, the blots were probed with monoclonal antibody Ab5 or Ab9 (1 : 1000–1 : 2500 dilutions) which bind to residue 1–16 of A3.3.1.3.1.1.t greater than some critical time, which was considered acceptable as equilibrium. The equilibrium values were also confirmed through Matlab's fsolve function. In all ROM computations discussed in this paper, the initial conditions were taken to be B1(0) = 1 with all other species set initially at zero. As seen from figure 2a and b, as time increased, the concentration levels exhibited asymptotic behaviour and each species eventually achieved equilibrium. The time to reach this steady state was sensitive to the choice of rate constants; the Base Model took longer to reach steady state than the Model 2. Also, in all cases analysed the fibril concentrations mB and In order to study the stability of the system, bulk rate constants obtained from experiments were used to determine steady state, or concentration at a given time-point . One is B1 stayed high and stable throughout, but large percentage changes in the concentration of B1 and B1 were accompanied by decline in c). A power-law regression indicates that time to equilibrium (indicated by teq) varies with the ratio of forward to backward rates (r) according to teq ∝ r−0.615.Due to low forward rates, the concentration size of is large c. A powen and m in both models was also investigated. In these cases, increasing n and m increased teq but yielded similar qualitative results, some of which are also discussed in Appendices A and B. It appears that the larger the oligomer size, the higher the power of the nonlinear terms in the governing equations, the greater the potential for over- and under-shoot as the model evolves over time. Thus, it takes longer to achieve equilibrium. However, Model 2 does not show an increase in teq as noted earlier.The impact of varying 3.1.2.α3 and α4 which are referred to as ‘bridge’ or control parameters, and they govern the reaction dynamics between on- and off-pathway. The effect of varying both on species formation was verified while holding all other reaction rates constant . This is probably due to the fact that α3 and α4, so that increasing α3 and α4 eventually impacts The key parameters in our model are constant . When inariables a. This ia and b underscores the importance of the bridge variables. Interestingly, if α4 was left unchanged and α3 was increased, there was limited flow-through from α3 continued to increase. Therefore, this suggests that the bridge reaction n and m species. The ROM modelling, therefore, reveals that bridges between larger oligomers are more significant than the ones across monomers in terms of promoting off-pathway fibril formation. Additional tests were performed to verify conditions for any species to outperform others by appropriate choice of the rate constants. Forcing B1 to outperform, for instance, is just a matter of reducing or shutting off all the forward reactions. For species further down the reaction-network, forward reactions were required to increase to get the desired out-performance. In the case of α3, β2 and α6 by an order 104. Out-performance by mB could also be achieved in a similar manner. Such an exercise can be significant in helping to identify pathogenic aggregates and shows the robustness of the network under standard reaction rates.Figure 33.1.3.d provides a schematic of the four equilibrium pathways that our model can achieve, each sensitive to the choices of parameters α3 and α4. In figure 4a, the first schematic highlighted in red is strictly on-pathway, where the non-prime species ‘win’. The next highlighted in blue is strictly off-pathway, where all off-pathway species wins. The paths indicated in yellow and green are a mixture of on/off-pathway. Figure 4d depicts the network graph corresponding to each of the phases. Computations were conducted by varying α3 and α4 between 0 and 2 in increments of 0.02, resulting in 10 000 discrete points. Figure 4a and b shows a phase diagram for the Base Model and Model 2, respectively.Figure 4Base Model,  = ; is a critical point at which the concentrations of on- and off-pathway species are equal. As α3 and α4 were varied, dominance of one set of species or pathways over another emerged. Notable too is the fact that the boundaries between the different equilibrium states were almost linear: the line α4 = 1 determines the switching between on- and off-pathway dominance of n and m species, and the line α3 = 1 determines the switching between on- and off-pathway dominance of monomers. The table in figure 4c shows the equilibrium states as a function of α3 and α4 in the form of a pay-off-like matrix. The Nash equilibrium lies at the point where on-pathway species concentrations are equal to off-pathway species concentrations.For the Model 2 (b). Here too,  =  was a critical point; however, unlike in the Base Model, it does not strictly define out-performance of B1 over B1 outperforming α3 > 1 and low α4, and B1 for α3 < 1 and high α4. The major difference is that the red and blue regions representing the only on-pathway and only off-pathway, respectively, increased at the expense of green and yellow (mixed pathways).A similar computation was performed for Model 2 b. Here tα3 < 1, by increasing β1 and β5, b. The reverse happened in the lower right for α4 < 1 as we observed greater off-pathway aggregation up to a point. Despite increasing α3 above 2, upon reducing α4, a thin band of dominance of nB and mB over their respective primed off-pathway species was continued to be observed. Once again, this shows that the n and m species than the monomer-bridge. If α4 was reduced, an on-pathway dominance of n and m species even for high α3 was still obtained. Thus, it is difficult to control the off-pathway aggregation of n and m species by tweaking the More importantly, the range of points over which on-pathway wins got bigger when backward rates were on par with forward rates. For 3.2.β42 aggregation has been well established in the Rangachari laboratory [β42 aggregation toward off-fibril formation pathway that was populated by low-molecular weight oligomers. At concentrations well below CMC, the fatty acid adopted an on-fibril formation pathway [L concentrations, kinetic rate differences in aggregation was investigated using ThT dye.The effect of FAs on Aboratory ,44,45. S pathway . To expeβ42 buffered in 20 mM Tris, 50 mM NaCl at pH 8.0. The addition of C12 FA resulted in an increase in ThT fluorescence without any observable lag time . By contrast, Aβ42 in the absence of C12 FA showed a lag phase of approximately 50 h before an increase in ThT fluorescence was observed . This behaviour in the presence of C12 FA has been previously observed to generate 12–24mer oligomers of Aβ along the off-fibril formation pathway [β42 reaction after 0 h (positive control (black square), 3 h (red circle), 8 h (blue triangle) and 24 h (purple inverted triangle). Each of such incubations resulted in an exponential increase in ThT fluorescence suggesting switching of pathways from on to off (figure 5b). Analysis of these samples was also performed using a partially denaturing gel electrophoresis (low SDS and no boiling) and immunoblotting (figure 5b). Injections of C12 FA at 3 and 8 h show the presence of 48–60 kDa band corresponding to 12mer oligomers  as compared with the corresponding controls generated upon adding buffer in place of C12 FA (lanes 3 and 4), which show monomers and some on-pathway aggregates. This suggests that off-pathway oligomers are generated (figure 5c). Similarly, FA injected after 24 h and its corresponding control are shown in lanes 5 and 6, respectively, which shows even after 24 h, C12 FA is able to induce the formation of oligomers to a certain extent, with clearly observable emergence of some on-pathway fibrils. These results parallel those observed by ThT fluorescence (figure 5b).Switching of on- to off-pathway . Prior to fractionation, the samples were centrifuged at 18 000g for 20 min to remove any high molecular weight fibrils, and the supernatant was loaded on to the column. After 24 h, the control in the absence of C12 FA shows a small peak near the void volume at fraction 17 and a monomer peak at fraction 24 . Fractionation of the control reaction at 48 h (after injection of buffer at 24 h) showed a diminished peak at fraction 17 and a reduced monomer peak at fraction 24 . The reduction in the monomer peak correlates to being consumed during aggregation. A similar reduction in the aggregate peak between 24 and 48 h can be explained by the fact that many have formed fibrils that are centrifuged out. On the other hand, fractionation of the sample after 48 h with the injection of 5 mM C12 FA at 24 h, showed a larger peak at fraction 18 and a reduced monomer peak at fraction 25 . This suggests two possibilities: (i) the unreacted monomers adopt off-pathway upon introduction of C12 FA, and/or (ii) the pre-formed aggregates along the on-pathway are re-routed back through the off-pathway, in other words, switching. More detailed analysis on this is discussed later in the article.To further quantify the extent of bridging, the aggregates generated after the 24 h injection of C12 FA (or buffer for the control) were fractionated by SEC, after an additional 24 h incubation d. Prior 24 blue; d. Fracti 24 red; d. The re5 black; d. This se) switching again using the established C12 FA kinetics. Incubation of 5 mM C12 FA shows an exponential increase in ThT fluorescence . To effect switching of off- to on-pathway after certain time periods, the sample was diluted 5- and 10-fold such that the effective concentration of C12 FA drops to 1 and 0.1 mM, which are well below the CMC of the surfactant. It is well established that well below CMC, Aβ aggregation is augmented [figure 5f), appropriately blank subtracted data showed an increase in ThT fluorescence as expected for both dilutions suggesting the switching of off- to on-pathway (figure 5f). Partially denaturing gel electrophoresis and immunoblotting further confirmed the switching. The 5- and 10-fold dilutions resulted in an increase in the molecular weight of the aggregates including the formation of fibrils both at 5 and 24 h, respectively  as compared with the sample in 5 mM C12 FA (lane 1).To assess a similar switching of pathways, we performed the off- to on-pathway , 10 off-pathway rate constants  and two off–on switching rate constants were considered . Additionally, one needs to estimate two constants: p  and pseudo-micelle concentration (concentration of the fatty acid near its CMC denoted by L). Following our published model in [kswi, k_swi, the mapping constant p and the pseudo-micelle concentration L) from this off-on switching dataset by significantly reducing the number of free parameters. A large parameter space from 10−6 to 108 units with multiples of 10, was swept to estimate the value of each of the two switching rate constants. Similarly, the pseudo-micelle concentration was varied from 0.01 to 1 units (with steps of 0.01), and p was varied from 105 to 108 units (with steps of 105). The estimated parameter values corresponding to the best fits are shown in R2 of the off-to-on data is 0.974 and that of on-to-off data is 0.981.As mentioned in the Material and methods section, in the EKS model, four on-pathway rate constants  did not show any lag time as can be seen in a usual unseeded on-pathway aggregation. Our model predicts a large conversion of off-pathway species to on-pathway oligomers which results in a rapid formation of on-pathway fibrils (denoted by F).The switching rate constants were sensitive specifically in the off–on dataset. The experimental data could not be fit in the absence of the switching rate constants and only a handful of switching rate constant combinations allowed an acceptable fit; the switching rate constants corresponding to the best fit to the experimental data are reported in figure 6b), stand-alone on-pathway simulations were executed exclusively up to the switching time-point (24 h) and the current oligomer concentrations were noted. These concentrations were then used to restart the combined on- and off-pathway simulation in addition to the pseudo-micelle concentration . Surprisingly, we found that the on-to-off-pathway dataset could be fit to our model both considering the switching rate constants, and also in the absence of switching rate constants generating comparable R2 values; in other words, the switching rate constants had low sensitivity to the on–off experimental dataset. Probably as the on-pathway reactions are slow, very little on-pathway oligomers are formed at the switching time-point; as a consequence, this made the switching reaction flux slower than the previous case of off–on switching system resulting in overall lower sensitivity of the switching rate constants to the ThT data points from the experiments. While this precludes precise characterization of on–off switching, we do observe an overall decrease in fibril concentration compared with control data showing at least a qualitative impact of the switching reactions that convert the on-pathway oligomers into off-pathway species. Furthermore, we have also compared the phase diagram of the EKS model by plotting the oligomer ratio of on-pathway to off-pathway (as a heatmap) with varying bridge parameters and switching parameters during the saturation phase (75 h) (c). The total oligomer count scaled by their size from each pathway was used to compute this ratio. In this heatmap, brighter colour (yellow) denotes a dominance of on-pathway, whereas darker colour (blue) denotes a dominance of off-pathway. By doing so, four phases similar to those obtained from ROM were observed. For a low bridge and switching parameters, a dominance of on-pathway species was observed, whereas for a high value of bridge and switching parameters a prevalent off-pathway was observed (c); the light yellow and light blue regions depict the mixed pathway zones where both on- and off-pathway oligomers coexist. Note that a one-to-one correspondence between the phase diagrams generated from EKS and ROM models is not possible since the EKS models were built considering a detailed set of reactions, whereas the ROM models correspond to more bulk reactions involving fewer species.For the on–off dataset b, stand-e (75 h) c. The toobserved c; the li4.β-fatty acid model system, we have employed a competition-based framework on simplified ROMs to gain preliminary insights. The results re-confirmed our previous observation that fatty acid concentrations modulate Aβ aggregation pathways [α parameters are sensitive to the pseudo-micellar surfactant concentrations, L, which hold the key in modulating pathways. The models also provide insights into the feasibility of bridging pathways as a function of emerging higher-order aggregates. For example, the reduced order, six-species model predicts four different scenarios or dominant pathways of reactions which are strongly dependent upon the bridge, while also suggesting that α4 is the key to the formation of larger aggregates in off-pathway. Stability arguments also show the larger aggregates in this system to be more stable (see Appendix B). The EKS simulations display a similar outcome; the simulations indicate that the larger the oligomer, the more significant the impact of that bridge upon formation of the respective fibril.The data presented here is a first attempt in deciphering the complex phenomenon of protein aggregation pathways using a competition-based approach based on classical game theory. Aberrant protein aggregation is sensitive to environmental factors that determine the outcome of the aggregates ,49. Usinpathways . Additioα3 and α4 to draw the aggregation dynamics away from toxicity. This is particularly significant for possible intervention strategies, pointing new lines of experimental and theoretical inquiries in the future. Numerical simulations and experiments both clearly support this qualitative result  and increasingly becomes weaker as we move toward higher-order aggregates along either pathway. This is in agreement with the theoretical studies noting the fact that in experiments, high molecular weight species refer to fibrils, while low-molecular weight aggregates then refer to the range of oligomers taken up in EKS and ROM simulations. Perhaps, a significant outcome of this study is the ability of the model to predict the emergence of oligomers by a set of kinetic and thermodynamic parameters, from a ‘win’ or ‘lose’ perspective . Another figures  and 6, b18-fold difference in the forward and backward switching rate constants (Results of ROM indicate that when the ratio of non-bridge forward to backward rates is close to unity, the model achieves equilibrium quickly. However, when forward rates are considerably higher than backward rates, as is to be expected under normal circumstances, the system takes considerably longer to achieve equilibrium due to a cycle of over-shooting of species sizes resulting from a large difference in reaction rates. Similarly, corresponding EKS simulations indicate a 105.β, such as fatty acid as demonstrated here. A significant impact of this work could be realized with the potential for the prediction of the emergence of oligomers, which provides a handle for understanding the conditions at which toxic strains are formed and disappear. The simplified model presented here can be further fine-tuned into more sophisticated models by including more species along pathways, additional pathways and more interacting partners that can modulate the pathway, etc. In sum, the results presented here establish a new paradigm in understanding the complex dynamics of Aβ aggregation and provide impetus towards deciphering amyloid pathogenesis along with making therapeutic and diagnostic advances for such debilitating diseases in the future.The results presented here showcase the applicability of game theory on understanding amyloid aggregation pathways. This is significant because it provides an ability to predict the emergence of aggregates along multiple pathways along a temporal and equilibrium landscape map. Such a map can be further refined to see how it evolves as a function of a given interacting partner of A


IS26 is most prevalent in Gram-negative species and can generate the clusters of antibiotic resistance genes interspersed with directly oriented IS26 seen in multiply resistant pathogens.The IS 26 forms cointegrates using two distinct routes, a copy-in mechanism involving one insertion sequence (IS) and a target and a targeted conservative mechanism involving two ISs in different DNA molecules. In this study, the ability of IS26 and some close relatives, IS1006, IS1008, and a natural hybrid, IS1006/IS1008, which are found predominantly in Acinetobacter spp., to interact was examined. IS1006/1008 consists of 175 bp from IS1006 at the left end, with the remainder from IS1008. These ISs all have the same 14-bp terminal inverted repeats, and the Tnp26, Tnp1006, and Tnp1008 transposases, with pairwise identities of 83.7% to 93.1%, should be able to recognize each other’s ends. In a recA-negative Escherichia coli strain, IS1006, IS1008, and IS1006/1008 each formed cointegrates via the copy-in route and via the targeted conservative route, albeit at frequencies for the targeted reaction at least 10-fold lower than for IS26. However, using mixed pairs, targeted cointegration was detected only when IS1008 was paired with the IS1006/1008 hybrid, which also encodes Tnp1008, and the targeted cointegrates formed all arose from a reaction occurring at the end where the DNA sequences are identical. The reaction also occurred at the end with extended DNA identity using IS26 paired with IS26::catA1, an artificially constructed IS26 derivative that includes the catA1 gene. Thus, both identical transposases and identical DNA sequences at the reacting end were required. These features indicate that the targeted conservative pathway proceeds via a single transposase-catalyzed strand transfer, followed by migration and resolution of the Holliday junction formed.ISIMPORTANCE The IS26 family includes the ISs that are commonly found associated with antibiotic resistance genes in multiply resistant Gram-negative and Gram-positive bacteria. IS26 is most prevalent in Gram-negative species and can generate the clusters of antibiotic resistance genes interspersed with directly oriented IS26 seen in multiply resistant pathogens. This ability relies on the novel dual mechanistic capabilities of IS26 family members. However, the mechanism underlying the recently discovered targeted conservative mode of cointegrate formation mediated by IS26, IS257/IS431, and IS1216, which is unlike any previously studied IS movement mechanism, is not well understood. An important question is what features of the IS and the transposase are key to allowing IS26 family members to undertake targeted conservative reaction. In this study, this question was addressed using mixed-partner crosses involving IS26 and naturally occurring close relatives of IS26 that are found near resistance genes in Acinetobacter baumannii and are widespread in Acinetobacter species. However, they can arise at low frequency when the cointegrate is resolved by host-mediated homologous recombination between the two IS26 copies in the cointegrate , likely indicate an ability to perform the copy-in and targeted conservative cointegration reactions  . By inacf the IS . Now, a f the IS –14. One 26 family identified IS1006, IS1007, and IS1008 as members of the clade of the IS26 family that includes those IS most closely related to IS26 and found that the terminal 14 bp of their TIR are identical to the 14-bp IS26 TIRs. The 14 bp are highly conserved in all members of the IS26 family . The crossover occurs between bases 172 and 175, with the first 175 bases identical to IS1006 and the remaining bases identical to IS1008  of IS of IS26 . In the recA-negative E. coli was examined using a mating-out assay to detect cointegrates formed between the conjugative plasmid R388 (Tpr) and the small, nonconjugative, nonmobilizable pUC19 plasmid containing IS1006 , IS1008 , or IS1006/1008   . The threriments . These vonjugant ) and in 1006-, IS1008-, and IS1006/1008-containing plasmids and R388 in the cointegrate were initially determined using the restriction mapping strategy with BglII and BsiWI described in Materials and Methods. Analysis of the fragments showed that in each cointegrate examined, pRMH1011, pRM1012, or pRMH1013 had incorporated at a different position in the R388 backbone , pRMH1012 (IS1008), or pRMH1013 (IS1006/1008) and R388. The R388 backbone is drawn to scale from GenBank accession no. BR000038 with key resistance genes, genes involved in replication (repA), and genes involved in conjugative transfer (tra) shown as arrows inside the circular backbone. Colored arrows pointing towards the circular backbone indicate the location of the mapped cointegrates as follows: red, IS1006; blue, IS1008; and purple, IS1006/1008. 1, the cointegrate was oriented with the tnp genes in the same orientation as the R388 repA gene; 2, the opposite orientation. Download FIG S1, EPS file, 1.3 MB.Cointegrate formation between pRMH1011  and R388::IS1006 (Tpr), pRMH1012 (IS1008 Apr) and R388::IS1008 (Tpr), or pRMH1013 (IS1006/1008 Apr) and R388::IS1006/1008 in a recA-negative background to ensure that all events detected were catalyzed by the available transposase. Cointegrates were formed between identical pairs of IS at frequencies ranging from 3.1 × 10−5 to 5.1 × 10−5 per transconjugant, averaged from three independent determinations  was made with the IS26 cloned into the same location as IS1006, IS1008, and IS1006/1008. R388::IS26-2 formed targeted cointegrates with pRMH977 (IS26) at an average frequency of 8.0 × 10−4 per transconjugant, consistent with the value obtained for R388::IS26  that amplify across each of the two IS separating the two replicons in targeted cointegrates when the two IS in are the same orientation  containing R388::IS26 (Tpr) and either pRMH1011 (IS1006 Apr), pRMH1012 (IS1008 Apr), or pRMH1013 (IS1006/1008 Apr) were mated with E. coli UB1637 (Smr), and in all three cases, Apr Tpr cointegrates were detected  as the donor, cointegrates formed at average frequencies of between 6.6 × 10−7 and 8.1 × 10−7 per transconjugant  did not generate the products expected if the reaction was targeted . DNA from the nine cointegrates from each IS combination screened as described above was examined using this strategy, and either the IS in pUC19 or the IS in R388 was still in the original position in each of the cointegrates tested , with no differences in the putative H-HTH DNA binding domain, and they have identical 16-bp TIR. However, Apr Tpr cointegrates that formed between R388::IS1006 and pRMH1012 (IS1008) or between R388::IS1008 and pRMH1011 (IS1006) were detected at mean frequencies of 5.9 × 10−7 or 4.0 × 10−7 per transconjugant (averaged from three independent determinations) (1006 or IS1008 performing an untargeted copy-in reaction at a naive site in the second molecule (1006 and IS1008 were unable to act together.Even though ISnations) , suggestmolecule , and aga1008 and IS1006/1008 both encode Tnp1008, and they share 649 bp of nucleotide sequence identity at their right ends (1006/1008) was tested in combination with R388::IS1008, cointegrates formed at an average frequency of 4.2 × 10−5 per transconjugant and the reciprocal experiment, pRMH1012 (IS1008) in combination with R388::IS1006/1008, yielded a similar result (shaded rows in r Tpr cointegrates from each of the three independent experiments per pair) confirmed that in all instances, pRMH1012 or pRMH1013 had been incorporated adjacent to the existing IS in R388::IS1006/1008 or R388::IS1008, with the two IS in the same orientation. Hence, IS1006/1008 and IS1008 are able to act together in the targeted conservative cointegration reaction.ISght ends . When pR1008 and IS1006/1008 share significant length of nucleotide identity (649 bp), and not at the left end, where only the first 16 bp are identical and the 175-bp segment derived from IS1006 differs from the IS1008 sequence at 32 positions. The PCR products generated as described above (r Tpr cointegrates formed between pRMH1013 (IS1006/1008) and R388::IS1008 were sequenced. In all 15 instances, IS1006/1008 had been distributed to the right-hand junction of the cointegrate, demonstrating that the reaction must have occurred between the right ends of IS1008 and IS1006/1008 (r Tpr cointegrates formed via the reaction between pRMH1012 (IS1008) and R388::IS1006/1008, IS1006/1008 had been distributed to the left-hand junction  formed via a single-strand transfer event, formed by the transposase. Hence, we examined whether the reaction had occurred only  at the right end, where ISed above  that spa006/1008 . Conversjunction . The absjunction  and D in26 derivative designated IS26::catA1, which includes the catA1 chloramphenicol resistance gene and an upstream promoter inserted downstream of the tnp26 gene  and R388::IS26 were detected at a mean frequency of 5.5 × 10−4 Apr Cmr Tpr cointegrates/Tpr transconjugant  was tested in combination with R388::IS1006 or pRMH1011 (IS1006) with R388::IS1006/1008, Apr Tpr cointegrates were formed at an averaged frequency of 5.6 × 10−7 or 6.9 × 10−7 per transconjugant , and this is consistent with the repair of mismatches arising from branch migration. When HJ migration is followed by HJ resolution involving the nontransferred strands, this would produce the cointegrate products observed  are sufficient to prevent the reaction from occurring at all. Further work is needed to investigate this aspect. For example, the 16 mutants that each cause a single amino acid differences will need to be constructed and tested to begin to identify some of the key residues involved in multimerization and synapse formation.Branch migration would stall when there are multiple mismatched nucleotides and particularly at clusters of mismatches, and this may be one reason why the closely related IS, such as ISE. coli DH5α (supE44 ΔlacU169 [ϕ80 lacZΔM15] hsdR17 recA1 endA1 gyrA96 thi-1 relA1) was used to propagate plasmids. E. coli UB5201 (pro met recA Nxr) was used as a donor in mating-out experiments, and E. coli UB1637 (lys his trp lac recA Smr) was used as a recipient. Antibiotics (Sigma) were added at the indicated concentrations to either Mueller-Hinton broth or Mueller-Hinton agar, as appropriate: ampicillin (Ap), 100 μg/ml; nalidixic acid (Nx), 25 μg/ml; streptomycin (Sm), 25 μg/ml; and trimethoprim (Tp), 25 μg/ml.Plasmid DNA was isolated by alkaline lysis as described previously . DNA was1006, R388:IS1008, R388::IS1006/1008, and R388::IS26-2 using the primers listed in 1008, where about 500 bp is included on one side). DNA from pD36-4 (CP012956) was used as the template for IS1006 (bases 39531 to 40583 were amplified for cloning), pA297-3 (KU744946) DNA was used as the template for IS1008 (bases 88655 to 90375 were amplified for cloning), and pJ9-3  was used as the template for IS1006/1008. All clones were transformed into chemically competent E. coli DH5α cells by heat shock. The presence of the insert in pUC19 was confirmed by amplification of the insert followed by sequencing using the pUC19 universal primers. Likewise, primers RH2735 and RH2563 were used to amplify and sequence the insert in R388. The synthetic IS26::catA1 was created with flanking sequences identical to those in pRMH977 and cloned into the BamHI site of pUC19 via Gibson assembly to form pRMH1015.The plasmids used in this study are listed in m pD36-4  (GenBank pA297-3  (GenBankr Tpr) or an R388 derivative containing the IS of interest into E. coli UB5201  cells containing nonconjugative pUC19-derived plasmids containing IS26 , IS1006 , IS1008 , or IS1006/1008 . Cointegrate formation was assessed by mating these strains with UB1637  and selecting for Apr Smr Tpr colonies. pRMH977 (IS26) and R388::IS26, as previously tested (r Smr Tpr transconjugants (cointegrates) per Tpr Smr transconjugant (R388 or R388 derivative).Donors for cointegration assays were generated via conjugation of either R388 (Su1006 and IS1008 each contain a single BglII restriction site, though the site is in a different position in each IS. The mapping strategy used to determine the cointegrate boundaries utilized this site combined with two BglII and two BsiWI sites in the R388 backbone. Briefly, to determine the positions of cointegrate junctions in the R388 backbone, plasmid DNA was recovered from independent cointegrates and digested with BglII or BsiWI or both, and the restriction fragments were separated in a 0.7% TAE agarose gel. The BglII/BsiWI double digestion of R388 produces four fragments of 14.5 kb, 10.6 kb, 6.8 kb, and 1.9 kb. If a cointegrate has formed, the presence of the BglII site in the IS at each boundary of the fusion splits one of the expected R388 fragments in two and forms an additional fragment corresponding to the original pUC19-based plasmid.ISTargeted conservative cointegrate formation was detected by PCR across each IS in the predicted cointegrate. Primers RH2563 and RH2735 in R388 , flankin


In genome-wide association studies, pathway-based analysis has been widely performed to enhance interpretation of single-nucleotide polymorphism association results. We proposed a novel method of hierarchical structural component model (HisCoM) for pathway analysis of common variants  which was used to identify pathways associated with traits. HisCoM-PCA is based on principal component analysis (PCA) for dimensional reduction of single nucleotide polymorphisms in each gene, and the HisCoM for pathway analysis. In this study, we developed a HisCoM-PCA software for the hierarchical pathway analysis of common variants. HisCoM-PCA software has several features. Various principle component scores selection criteria in PCA step can be specified by users who want to summarize common variants at each gene-level by different threshold values. In addition, multiple public pathway databases and customized pathway information can be used to perform pathway analysis. We expect that HisCoM-PCA software will be useful for users to perform powerful pathway analysis. Availability: HisCoM-PCA is available on the website (http://statgen.snu.ac.kr/software/HisCom-PCA/index.html).In genome-wide association studies (GWAS), researchers have identified many single-nucleotide polymorphisms (SNPs) associated with the traits of interest (phenotypes) . HoweverRecently, we presented a hierarchical structural component model (HisCoM) for pathway analysis of common variants (HisCoM-PCA) to identify pathways associated with traits . HisCoM-In our previous study, we used only pathway information of the Kyoto Encyclopedia of Genes and Genome pathway database  and adopThe workflow of the HisCoM-PCA software is shown in The HisCoM-PCA software takes four inputs: (1) a SNP dataset in csv file or PLINK format files, (2) a txt file with phenotype and covariate(s), (3) a set file that consists of two columns for gene name and SNP id, and (4) a set file that consists of two columns for pathway name and gene name. Furthermore, the program also accepts the published pathway databases in MsigDB ,8.The HisCoM-PCA program generates the following output files: (1) a ‘[prefix]. gene-pca_summary.csv’ file that contains the number of SNPs and PCs for each gene, (2) a ‘[prefix].gene.ressum.csv’ file that contains pathway name, gene name, number of permutation, weight of gene for the pathway, gene coefficient, and permutation p-value of gene, and (3) a ’[prefix].pathway.ressum.csv’ file that contains pathway name, number of permutation, number of genes in each pathway, pathway coefficient, and permutation p-value of pathway.We introduced our HisCoM-PCA software for pathway analysis of common variants in GWAS. HisCoM-PCA software supports pathway analyses using multiple candidate pathways and customized PC selection criterion. We expect that our HisCoM-PCA software is useful for users to perform various pathway analyses. This section should contain sufficient detail so that all procedures can be repeated, in conjunction with the cited references. The manufacturer and model number should be stated in this section—for example, as Sigma Chemical Co. .