Fructus Gardeniae in Jiangxi province by network pharmacology. To provide a valuable research strategy for the rational use and in-depth research and development of Fructus Gardeniae from Jiangxi.  To investigate the pharmacological mechanism of the iridoid glycosides from Fructus Gardeniae from Jiangxi province have a significant difference compared with other regions (P < 0.05). Based on our previous experimental results, this study selected six characteristic high-content bioactive iridoid glycosides components of Fructus Gardeniae from Jiangxi province as candidate components. TCMSP database was used to obtain the process parameters of absorption, distribution, metabolism, and excretion (ADME) of candidate components. PubChem and SWISS online database were used to predict the related targets. Cytoscape software was used to the construct compound-target-disease (C-T-D) network of the Fructus Gardeniae iridoid glycosides ingredients. Furthermore, the GO biological process analysis and the pathway enrichment analysis were carried out using the CTD online analysis platform; then, an illustrated network that contains the main “chemicals-targets-pathway (C-T-P)” was constructed to analyze main biological pathways for obtaining the deep mechanism of Fructus Gardeniae in Jiangxi.  Previous research results of our group show that the contents of iridoid glycosides in Fructus Gardeniae in Jiangxi province were obtained as candidate components through previous work and network pharmacology screening. 36 corresponding targets were acted, such as BCL2, MAPT, F2, BCL2L1, PRKCD, PRKCB, HIF1A, and PRKCA. These targets could joint in pathways, such as signaling by GPCR, neuroactive ligand-receptor interaction, inflammatory mediator regulation of TRP channels, and ion channel transport. Interestingly, these pathways were highly associated with liver diseases, neurological diseases, hypertension, neoplasms, hyperalgesia, and inflammation. Remarkably, we boldly speculate that the Fructus Gardeniae from Jiangxi province can play a pharmacological role in hepatic encephalopathy through regulating multiple signaling pathways in an integrated manner.  6 iridoid glycosides, namely geniposide, gardenoside, geniposidic acid, genipin 1-gentiobioside, gardoside, and shanzhiside, from  The method based on system pharmacology could help to find the key targets of characteristic high-content chemical constituents of herb from different producing areas, the signaling pathway and disease network of TCM, and provide useful information and data support for giving a further study on traditional Chinese medicine resources in different regions of China. Gardenia jasminoides Ellis (Rubiaceae) is an evergreen shrub. The dried ripe fruits of Gardenia jasminoides have been recorded as Fructus Gardeniae (“zhi zi” in Chinese) in the Chinese Pharmacopoeia (2000–2015 edition) [Fructus Gardeniae has been reported to protect the liver and gallbladder cells from infringement, treat cardiovascular and cerebrovascular diseases, and promote ligament cell proliferation and collagen synthesis effectively [Fructus Gardeniae includes iridoid glycosides [Fructus Gardeniae [a 2000–205 editionedition) . Fructusectively . Researcycosides , crocinsycosides , phenylpycosides , glycoprycosides , and polardeniae –13.Fructus Gardeniae are also multiple. The contents of chemical constituents in Fructus Gardeniae from different habitats are different, and the different chemical constituents can regulate the subjects through multiple targets and pathways, thus producing different pharmacological effects. In our previous studies, Fructus Gardeniae cultivated in 10 different provinces in China has different high-content bioactive components and therefore, their different pharmacological activities. Preexperimental results demonstrated that the contents of geniposide, gardenoside, geniposidic acid, genipin 1-gentiobioside, gardoside, and shanzhiside in Fructus Gardeniae from Jiangxi province were significantly higher than those in other regions (P < 0.05) [Fructus Gardeniae from Jiangxi province were searched by network pharmacology, and the related diseases were predicted, and the chemicals-target-disease network was constructed. This study provides a basis for the in-depth study of the pharmacological mechanism of Fructus Gardeniae from Jiangxi province, China. The schematic illustration of this study is shown in It is well known that traditional Chinese medicine (TCM) are normally collected from multiple geographical regions . Accumul < 0.05) ; therefohttp://lsp.nwu.edu.cn/index.php, TCMSP) is a database of systems pharmacology for drug discovery from herbal medicines [Fructus Gardeniae from Jiangxi province are retrieved. And the process parameters of absorption, distribution, metabolism, and excretion (ADME) of candidate components were obtained by the TCMSP database. Each candidate's drug ability was analyzed according to its oral bioavailability (OB) and drug-likeness (DL) indices recommended by TCMSP. OB refers to the speed and degree of absorbing drugs into the circulatory system, which is a reliable indicator to evaluate the intrinsic quality of drugs objectively. The higher the OB of the compound is, the more likely the compound has the potential of being developed into drugs for clinical application [Traditional Chinese Medicines for Systems Pharmacology Database and Analysis Platform  [http://www.uniprot.org/).The targets prediction for the characteristic high-content bioactive components group of ion.ch/)  by inpution.ch/) , 23, witFructus Gardeniae from Jiangxi province was constructed. In order to elucidate the relationship among candidate compounds, potential targets and related diseases, a “compound-target (C-T)” network, and a “disease-target (D-T)” network were generated for studying. (1) C-T network: compound-target interactions were visualized by the C-T network, in which all the active ingredients were connected to their corresponding targets. (2) T-D network: the target name was inputted to the CTD (http://ctdbase.org/) to predict the disease associated with the genes, diseases with genome frequency ≥1% were chosen and others were eliminated. Then, put the data into excel, plotting the network graph of the C-T network and D-T network with Cytoscape 3.7.0 software [In this study, a “chemicals-target-disease (C-T-D)” network related to software . (3) C-Tsoftware .http://ctdbase.org/, CTD) was adopted to conduct the Gene Ontology (GO) function analysis and the signaling pathway enrichment analysis. During this procedure, the significance level was set to 0.01, and organism was selected as Homo sapiens. The GO defines concepts related to gene function and the interrelationships among the functions of different genes [Fructus Gardeniae from Jiangxi province in terms of the molecular function, the cellular component involved, and the biological process affected [Fructus Gardeniae from Jiangxi province at the pathway level. Then, an illustrated network that contains main “chemicals-targets-pathway (C-T-P)” of Fructus Gardeniae from Jiangxi province was constructed. The illustrated network that contains the main chemicals-targets-signaling pathway (C-T-P) of Fructus Gardeniae from Jiangxi province was established to understand their interaction. The results of the GO function analysis and the signaling pathway enrichment analysis were visualized via OmicShare platform2 .The Comparative Toxicogenomics Database  represent the related diseases to the targets, while the edges represent the interactions between them. The node size varies according to degree, and edge thickness varies according to betweenness. Results of the network topology analysis are as follows: network density (0.168), network heterogeneity (0.527), and shortest paths . The average degree of nodes is 12.27027, and there are 34 nodes larger than the average degree. The average betweenness centrality of nodes is 0.01763, and there are 26 nodes larger than the average betweenness centrality. The key core nodes (target or disease) are screened based on the topological properties of the degree of network nodes. The results showed that the eight targets of BCL2, MAPT, F2, BCL2L1, PRKCD, PRKCB, HIF1A, and PRKCA were in the front of degree ranking (degree > 20), and they were the pivotal nodes in the network, indicating that they might be the core targets of the pharmacological mechanism of candidate components; among the disease nodes, the nervous system diseases, cardiovascular diseases, mental disorders, neoplasms, brain diseases, endocrine system diseases, nutritional and metabolic diseases, digestive system diseases, chemically induced disorders, immune system diseases, metabolic diseases, and liver diseases rank ahead, suggesting that the candidate components may play a pharmacological role in these diseases.As shown in As highlighted in P value of enrichment results was calculated , ranking P values according to the order from small to large. The top 20 enrichment results are displayed, and details are shown in Tables 2The enrichment results showed that there were 445 enrichment processes related to the biological processes, which cover response to the oxygen-containing compound, regulation of biological quality, response to organonitrogen compound, response to nitrogen compound, chemical synaptic transmission, and anterograde transsynaptic signaling; 73 enrichment results in the related items of cell composition, involving neuron projection, synapse part, neuron part, plasma membrane region, and postsynapse; 58 enrichment results are related to molecular function, which includes extracellularly the glycine-gated ion channel activity, the extracellularly glycine-gated chloride channel activity, the neurotransmitter receptor activity, protein phosphatase 2A binding, and chloride transmembrane transporter activity. Each P < 0.01). The top 20 pathway with lower P values and more genes enrichment are listed in Fructus Gardeniae from Jiangxi province was established to understand their interaction  potential targets were enriched and involved 181 signaling pathways, and 84 of these pathways were significantly correlated with the target genes , a materia medica commissioned by the government of the Tang Dynasty, stated: “if medicinal material is not produced from its native environment, the effect will be different.” That is to say, different geographical regions are suitable for growing different medicinal materials, and different medicinal materials also have different adaptability to the ecological environment of different producing areas. China has a vast territory, spanning tropical, subtropical, temperate, subfrigid, and plateau climate zones, and its terrain is complex. The conditions of sunshine, temperature, precipitation, and soil vary greatly in different regions, thus forming a complex and diverse ecosystem. There are also differences in the properties, chemical composition, and pharmacodynamics of traditional Chinese medicines bred in different ecosystems. The research on the correlation between the origin of TCM and the content of its chemical constituents has always been a worldwide scientific research hotspot in the field of traditional Chinese medicine resources. However, there are few experimental studies on pharmacodynamics of TCM from different geographical origins. The reason is that the chemical composition system of TCM is very complex. The contents of chemical constituents in medicinal materials from different habitats are different, and different chemical constituents can regulate the subjects through multiple targets and pathways, thus producing different pharmacological effects. Based on the above reasons, it is very difficult to carry out pharmacological experimental studies on traditional Chinese medicines from different origins because a large number of screening experiments need to be carried out from different geographical origins and different pharmacological effects. In the previous study, we collated the content data of Fructus Gardeniae from Jiangxi province were significantly higher than those in other regions (P < 0.05). Then, these six iridoid glycosides constitute the characteristic high-content chemical constituent group of Fructus Gardeniae from Jiangxi province. Iridoid glycosides have been demonstrated to be the major bioactive ingredients in Fructus Gardeniae [Fructus Gardeniae [Fructus Gardeniae (zhi zi), that is, Fructus Gardeniae from Jiangxi province is considered as “daodi medicinal materials” and is called “Jiang zhi zi” in Chinese. The term “daodi medicinal material” refers to a concept that has been widely recognized in the field of TCM for centuries. It is defined as “medicinal material that is produced and assembled in specific geographical regions with designated natural conditions and ecological environment [Fructus Gardeniae from Jiangxi province as candidate components, using Swiss Target Prediction to obtain 36 potential targets related to candidate components, and imported 18 targets into CTD database to predict target-related diseases. 32 diseases with a genome frequency ≥1% were selected to construct the C-T-D network. The nodes in the network ranked according to degree. The results showed that the eight targets of BCL2, MAPT, F2, BCL2L1, PRKCD, PRKCB, HIF1A, and PRKCA were in the front of degree ranking (degree >20), and among the disease nodes, the nervous system diseases, cardiovascular diseases, mental disorders, neoplasms, brain diseases, endocrine system diseases, nutritional and metabolic diseases, digestive system diseases, chemically induced disorders, immune system diseases, metabolic diseases, and liver diseases rank ahead, suggesting that characteristic high-content bioactive components group  of Fructus Gardeniae from Jiangxi province may play a pharmacological role in these diseases through the potential targets such as BCL2, MAPT, F2, BCL2L1, and PRKCD. That is to say, in the field of traditional Chinese medicine resources, if we want to carry out pharmacodynamic experimental research on Fructus Gardeniae from Jiangxi province, we should start with antinervous diseases, anticardiovascular diseases, antimental disorders, antineoplasms, antibrain diseases, and antiendocrine system diseases; if we want to carry out the related pharmacological mechanism experimental research, we can start with BCL2, MAPT, F2, BCL2L1, PRKCD, PRKCB, HIF1A, PRKCA, and other 25 potential targets.Preexperimental results demonstrated that among the 10 different provinces  in China, the contents of six iridoid glycosides  in ardeniae –30, amonardeniae , and gars P < 0.0. Then, tardeniae . This isironment . These fironment .” Althouironment –36. BaseFructus Gardeniae from Zhejiang province. The results showed that the pathways with higher number of related annotated genes count and lower the P value mainly included signaling by GPCR, neuroactive ligand-receptor interaction, inflammatory mediator regulation of TRP channels, ion channel transport, dopaminergic synapse, amphetamine addiction, disinhibition of SNARE formation, EGFR tyrosine kinase inhibitor resistance, morphine addiction, and HIF-1 signaling pathway. Among these pathways, the GPCR pathway, platelet activation signaling-and-aggregation, and ion channel transport are mainly associated with liver cirrhosis and hypertension; neuroactive ligand-receptor interaction, dopamine receptors, dopaminergic synapse, amphetamine addiction, and morphine addiction are mainly associated with neurological diseases such as autism, schizophrenia, and cocaine-related diseases; EGFR tyrosine kinase inhibitor resistance, gap junction, and the HIF-1 signaling pathway are mainly associated with neoplasms and liver cirrhosis; inflammatory mediator regulation of TRP channels is mainly associated with hyperalgesia, inflammation, and liver cirrhosis; ligand-gated ion channel transport is mainly associated with autistic disorder and brain injuries. From the above analysis, we find an interesting phenomenon that among these diseases related to high-enrichment pathways, neurological diseases  and liver diseases  seem to present a particularly high frequency. These results make us naturally associate with a complex disease, which is “hepatic encephalopathy”. Hepatic encephalopathy (HE) is a known neurologic complication of advanced cirrhosis [Fructus Gardeniae from Jiangxi province can play a pharmacological role in hepatic encephalopathy through regulating multiple signaling pathways in an integrated manner.In this study, the GO enrichment analysis and the pathway enrichment analysis were carried out in order to explore the multidimensional pharmacological mechanism of the characteristic high-content bioactive components group of irrhosis . Betweenirrhosis . HE is airrhosis . On the Fructus Gardeniae from Jiangxi province from the perspective of network pharmacology. In conclusion, our study applied the network pharmacology method to the field of traditional Chinese medicine resources innovatively, and it laid a foundation for the study of multidimensional pharmacological mechanism of Fructus Gardeniae from different geographical regions in China.This study reveals the related targets, key biological pathways, and main disease types of the characteristic high-content chemical constituent group of


RELA, VEGFA, and STAT1 were the important target genes in the gene network associated with the THSWT formula for the treatment of ONFH. Therefore, the present study suggested that the THSWT formula has an action mechanism involving multiple compounds and network targets for the treatment of ONFH.The Tao-Hong-Si-Wu-Tang (THSWT) formula, a classic prescription of traditional Chinese medicine, has long been used for the treatment of osteonecrosis of femoral head (ONFH). However, its mechanisms of action and molecular targets are not comprehensively clear. In the present study, the Traditional Chinese Medicine System Pharmacology (TCMSP) database was employed to retrieve the active compounds of each herb included in the THSWT formula. After identifying the drug targets of active compounds and disease targets of ONFH, intersection analysis was conducted to screen out the shared targets. The protein-protein network of the shared targets was built for further topological analysis. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis were then carried out. A gene pathway network was constructed to screen the core target genes. We identified 61 active compounds, 155 drug targets, and 5443 disease targets. However, intersection analysis only screened out 37 shared targets. Kaempferol, luteolin, and baicalein regulated the greatest number of targets associated with ONFH. The THSWT formula may regulate osteocyte function through specific biological processes, including responses to toxic substances and oxidative stress. The regulated pathways included the relaxin, focal adhesion, nuclear factor- Osteonecrosis of femoral head (ONFH) represents a disruption of the blood supply to the femoral head due to trauma, corticosteroids, alcohol, and other ill-defined etiologies . ONFH maTo date, there are no optimal treatments for ONFH . Total hPersicae Semen), Hong-Hua (Carthami Flos), Dang-Gui (Angelicae Sinensis Radix), Chuang-Xiong (Chuanxiong Rhizoma), Shu-Di-Huang (Rehmanniae Radix Praeparata), and Bai-Shao (Paeoniae Radix Alba), of which the main TCM function is to activate blood circulation. The THSWT formula is frequently administered in patients with ONFH in China. Data from animal testing suggest that the THSWT formula may help ameliorate the progression of steroid-induced avascular necrosis [During the past decade, Chinese experts have developed four versions of clinical guidelines, and traditional Chinese medicine (TCM) is consistently recommended as one of the main nonoperative treatments –15. TCM necrosis . HoweverA general solution related to network pharmacology has been proposed recently, which has become a hot topic to investigate multiple molecular mechanisms of multiple-target compounds affecting biological networks for herbal medicines. Therefore, we employed network pharmacology to probe the pharmacological mechanisms of the THSWT formula against ONFH in this study.Persicae Semen), Hong-Hua (Carthami Flos), Dang-Gui (Angelicae Sinensis Radix), Chuang-Xiong (Chuanxiong Rhizoma), Shu-Di-Huang (Rehmanniae Radix Praeparata), and Bai-Shao (Paeoniae Radix Alba). The chemical compounds of these six herbs were identified using the Chinese Medicine System Pharmacology Database and Analysis Platform  [The THSWT formula consists of six Chinese herbs, including Tao-Ren (msp.php) . TCMSP phttp://www.drugbank.ca) [Persicae Semen, 257 in Carthami Flos, 55 in Angelicae Sinensis Radix, 30 in Rehmanniae Radix Praeparata, 104 in Paeoniae Radix Alba, and 39 in Chuanxiong Rhizoma. A total of 155 drug targets were collected after removing duplications. Protein sequences of these drug targets were normalized to official gene symbols using the UniProt database (https://www.uniprot.org/) [The DrugBank (bank.ca)  was emplot.org/) .https://www.ncbi.nlm.nih.gov/geo/, Series: GSE74089, Samples: GSM1909502, GSM1909503, GSM1909504, GSM1909505, GSM1909506, GSM1909507, GSM1909508, GSM1909509) [P-value <0.05 and |log2 (fold change)| >1 were identified as disease targets of ONFH.The differentially expressed genes associated with ONFH were downloaded from the GEO database (1909509) . These o1909509) . The colhttp://bioinfogp.cnb.csic.es/tools/venny/index.html) was used to draw a Venn diagram of drug targets and disease targets to obtain shared targets of the THSWT formula and ONFH. The shared target genes were then inputted into the String database (http://string-db.org), with species limited to Homo sapiens and a confidence score >0.4, to construct the protein-protein interaction (PPI) network. The PPI network of drug targets and disease targets was visualized using Cytoscape software. Maximum Clique Centrality (MCC) is a network topology algorithm of the Cytohubba plugin, which helps identify core targets in the network. In the present study, the top 10 targets with the highest MCC scores were considered the core targets of the THSWT formula against ONFH.The Venny 2.1 online tool . During these procedures, P.adjust <0.05 suggested statistical significance in the enrichment degree. The top 20 GO and top 20 KEGG results with the lowest P.adjust values were displayed in the form of bubble charts using R-studio software. The genes with significantly regulated pathways were selected for gene pathway network analysis to screen out the key target genes of the THSWT formula in the treatment of ONFH.Gene ontology (GO) biological process (BP) enrichment analysis and Kyoto Encyclopedia of Genes (KEGG) pathway enrichment analysis were conducted using the David 6.8 database , and molecular function (MF) analyses of the 37 target genes revealed 603 GO terms that were significantly enriched, including 540 in BP, 23 in CC, and 40 in MF analyses. The GO terms with the top 20 lowest P.adjust values are shown in κB, Toll-like receptor (TLR), and AGE/RAGE signaling pathways.KEGG pathway analysis revealed 78 pathways that were significantly enriched. The top 20 terms are shown in RELA had the maximum degree (number of connected nodes) and thus was considered the core target. Several other targets also had more significant degrees, such as JUN, VEGFA, and CCND1.Finally, the gene pathway network was constructed based on the significantly enriched pathways and genes that regulated these pathways, as presented in TCM holds a similar view that ischemia of the femoral head is a key pathological change in ONFH. Chinese herbal medications with the TCM function of activating blood (Huo-Xue-Fa) have been consistently recommended by Chinese guidelines as an important nonoperative treatment for ONFH –15. The An updated meta-analysis concluded that marketed drugs fail to prevent the progression of ONFH , but an ABCB1, an important determinant in the elimination of toxic substances, is closely associated with the occurrence of steroid-induced ONFH [GO enrichment analysis suggested that the THSWT formula regulates a variety of BPs and affects various CCs and MFs. Cellular responses to toxic substances and oxidative stress are important BPs involved in the development of ONFH. Corticosteroids and alcohol are key toxic substances that cause ONFH. Previous studies have confirmed that the rs1045642 single-nucleotide polymorphism of ced ONFH . Moreoveced ONFH . Our datκB signaling pathways are enriched pathways with important clinical significance. The relaxin signaling pathway is a potent stimulator of osteoclastogenesis from hematopoietic precursors, which regulate the activity of mature osteoclasts [κB. NF-κB, one of the most important transcriptional signaling molecules, participates in downstream inflammatory pathways and the TLR signaling pathway. The essential role of NF-κB in osteoclastogenesis has been demonstrated genetically. NF-κB can transduce signals by recruiting adaptor molecules. In addition, NF-κB can induce the proliferation of monocytes/macrophages, which finally form osteoclasts [KEGG enrichment analysis suggested that the THSWT formula may regulate various signaling pathways. The relaxin, focal adhesion, and NF-eoclasts . Focal aeoclasts ; thus, ieoclasts . Our dateoclasts . The AGEeoclasts .RELA, VEGFA, and STAT1 were among the core targets of the THSWT formula in the treatment of ONFH. RELA is a member of the NF-κB/Rel family. The transcription factor NF-κB is a critical regulator of immune and inflammatory responses. Mice lacking RelA/p65 in the hematopoietic compartment have been shown to have a deficient osteoclastogenic response to RANKL and are protected from arthritis-induced osteolysis. It has been shown that inhibition of NF-κB is an effective approach to inhibit osteoclast formation and bone resorptive activity [VEGFA) gene is located on chromosome 6p31.3 [VEGFA to the disease status of nontraumatic ONFH. The STAT1 signaling pathway is strongly activated in the pathogenesis and progression of osteoporosis [STAT1-deficient mice [Gene pathway network analysis revealed that activity . The vase 6p31.3 . It encooporosis . Accelerent mice .κB, TLR, and AGE/RAGE signaling pathways. RELA, VEGFA, and STAT1 are the important target genes in the gene network of the THSWT formula for the treatment of ONFH.The mechanisms of action and molecular targets of the THSWT formula for ONFH were explored using a network pharmacology approach in this study. Kaempferol, luteolin, and baicalein regulated the most number of targets associated with ONFH. The THSWT formula may regulate osteocyte function through specific BPs, including responses to toxic substances and oxidative stress. The regulated pathways include the relaxin, focal adhesion, NF-


The development of neural circuits is a complex process that relies on the proper navigation of axons through their environment to their appropriate targets. While axon–environment and axon–target interactions have long been known as essential for circuit formation, communication between axons themselves has only more recently emerged as another crucial mechanism. Trans-axonal signaling governs many axonal behaviors, including fasciculation for proper guidance to targets, defasciculation for pathfinding at important choice points, repulsion along and within tracts for pre-target sorting and target selection, repulsion at the target for precise synaptic connectivity, and potentially selective degeneration for circuit refinement. This review outlines the recent advances in identifying the molecular mechanisms of trans-axonal signaling and discusses the role of axon–axon interactions during the different steps of neural circuit formation. The formation of neural circuits is a complex developmental process that gives rise to intricate and precise networks essential for brain function ,2. DefecDuring development, axons navigate along precise pathways to reach their final target by responding to attractive or repulsive guidance cues present in their environment. This navigation is ensured by motile structures at their leading ends, the growth cones, which possess numerous receptors at their surface allowing them to respond to the various extra-cellular signals they encounter ,12. A paThe first indication for a role of axon–axon communication, or trans-axonal signaling, in circuit wiring came from early experiments in chick, amphibians, and mice, showing that sensory projections in the limb were disturbed following motor neuron ablation ,30,31, oHomotypic and heterotypic fasciculation between axons facilitates and coordinates the formation of tracts en route to a common target or between inter-connected brain regions. Early studies demonstrating the importance of pioneer-follower axon interactions in neural circuit formation have suggested a crucial role for adhesion and fasciculation between axons as they navigate towards their target. For example, in the Drosophila olfactory system, ablation of early-born olfactory receptor neurons, whose axons act as pioneers, prevents the targeting of later-born axons to the antennal lobe structure . SimilarCell adhesion molecules (CAMs) are highly conserved across species and have been found to regulate both homotypic and heterotypic axon–axon interactions in several circuits  14,15,4,415,43. ALCAM mRNA is locally translated in growth cones of retinal axons, and reducing ALCAM local translation in chick retinal axons in culture prevents axons from elongating on ALCAM, but not laminin, coverslips [Alcam knock-out mice [Along with L1 and NCAM, early studies in the chick and goldfish retinotectal systems indicate that the adhesion molecule ALCAM  is also necessary for the proper bundling of retinal axons into fascicles ,50. Addiverslips . Retinalout mice , indicatDscam mutants, causing them to stray from their normal path. In the chick peripheral system, the Synaptic cell adhesion molecules SynCAMs were found to regulate axon–axon contacts between afferent fibers as they enter the dorsal root entry zone (DREZ) of the spinal cord [TAG-1 in motor neurons causes a thickening of the ventral root of the spinal cord and a defasciculation of motor axons in vitro. Other members of the immunoglobulin superfamily of CAMs are also known to regulate axon fasciculation. In the mouse visual system, DSCAM (Down Syndrome Cell Adhesion Molecule) is necessary for the fasciculation of retinal axons along the optic tract . Retinalnal cord . Both ovnal cord . SpecifiC. elegans, fmi-1, the ortholog of vertebrate Celsr and Drosophila flamingo, is needed for adhesion between pioneer and follower axons that form the ventral nerve cord [In addition to the immunoglobulin superfamily of CAMs, members of the cadherin superfamily form another class of CAMs that mediate neural circuit formation through axon–axon interactions. In rve cord . More rerve cord . Pcdh17 The function or abundance of adhesion molecules at the axonal surface is tightly regulated by classical guidance cues AIV–VII. Other classical guidance cues have been shown to mediate homotypic fasciculation, however it remains unknown whether they do so directly or by regulating adhesion molecules AVIII–X. 1R from mouse cortical neurons in vivo causes a defasciculation of their axons [1R in mouse cortical neurons, thereby modulating endocannabinoid signaling and axon fasciculation [Along with classical guidance cues, other signaling molecules regulate axon fasciculation in addition to their other better-known functions. Endocannabinoids, for instance, have been identified as guidance cues regulating axon pathfinding and elongation in mouse and xenopus , and delir axons ,76. A reThe nervous system comprises many axonal tracts formed by different neuron types. Selective heterotypic fasciculation between different axon types coordinates the development of these tracts into neural circuits B. For exAnother example of interactions between reciprocal, afferent, and efferent axons involves thalamocortical and corticothalamic axons that meet in the subpallium to form the internal capsule before projecting to their respective targets . SeveralIn the peripheral nervous system, sensory and motor axons also use heterotypic interactions to extend to their appropriate targets B. Along As important as selective homotypic and heterotypic fasciculation, trans-axonal repulsive interactions mediate the segregation of axons within a tract or the dissociation of axons into distinct tracts forming different circuits. Typically, repulsion between homotypic axons leads to their segregation and sorting en route to or at their target, while heterotypic repulsion generates independent axonal tracts . RepulsiAs they navigate to their final destination, axons often encounter several successive choice points where intermediate targets such as guidepost cells steer them along the proper path. Interestingly, axons themselves also provide directional information to other axons at choice points A. In thesema1a and plexA mutants. Interestingly, forward and reverse signaling are both required for motor axon defasciculation, with Sema1a reverse signaling being modulated by two counteracting Rho1 GTPase regulators [sema1a and plexA mutants, motor axons fail to defasciculate in perlecan mutants, and re-expressing perlecan in neurons rescues axon fasciculation defects. Whether Perlecan is selectively secreted by motor axons at choice points to regulate Sema1a/PlexA signaling and motor axon defasciculation remains to be established.Axon–axon repulsion and selective defasciculation at choice points also allow a subgroup of axons to segregate from the main bundle and navigate to an independent target. Periphery muscle innervation by motor axons, for instance, requires selective axon defasciculation. It has been extensively studied in Drosophila, where motor axons initially exit the central nervous system (CNS) as the intersegmental nerve (ISN) and segmental nerve (SN) before further dividing into five distinct motor branches—the ISN, ISNb, ISNd, SNa, and SNc . Each ofgulators . In partgulators . Like inIntegrin mutants, ISNb axons fail to defasciculate from the ISN bundle in DCas mutants and double Dcas/+;Integrin/+ heterozygotes, suggesting that Integrin signaling through Dcas is needed for proper motor axon defasciculation. Surprisingly, overexpressing DCas in neurons causes a similar hyperfasciculation defect in an Integrin-dependent manner, suggesting that Integrin/DCas signaling regulates both axon–axon and axon–ECM interactions at choice points. How integrin signaling and other pathways co-regulate axon–axon repulsion to direct defasciculation has yet to be determined.Complementary to Semaphorin signaling, the cell adhesion receptors Integrins have also been reported to regulate motor axon defasciculation in Drosophila . MutantsTrans-axonal repulsive signaling between heterotypic tracts allows axons to form distinct bundles and take different trajectories towards their respective targets. In the sensorimotor system, for instance, motor and sensory axon bundles elongate closely to each other in a coordinated manner but remain physically segregated through contact-dependent repulsive interactions as they elongate to the muscle and dermis, respectively ,45. InteSimilar trans-axonal repulsive interactions are observed in the developing corpus callosum, where axons from the medial and lateral regions of the cortex project to the contralateral medial and lateral cortical regions, respectively. As they elongate across the midline, medial and lateral cortical axons are organized into separate bundles, with medial axons passing through the dorsal part of the corpus callosum and lateral axons elongating through its ventral part. EphA3 is expressed on lateral-projecting axons and repels medial-projecting axons upon contact, thereby ensuring the segregation of the two axonal tracts . InteresOutside of the corpus callosum, pre-target topographic axon sorting has been observed in many other systems and shown indeed to be an essential step for map formation. For example, thalamocortical axons originating from distinct thalamic nuclei are already ordered in the subpallium before reaching the cortex . Selecticyfip2 zebrafish mutants [Pre-target axon sorting has also been extensively studied in the visual system. In vertebrates, retinal axons are preordered along the dorso-ventral axis in the optic tract before reaching the tectum, with dorsal and ventral axons elongating along the ventral and dorsal branches of the tract, respectively ,109,110. mutants . While tSema3F transcript, in the outer nerve layer of the dorsal OB suggests an “indirect” trans-axonal signaling model, whereby pioneer dorsomedial axons produce and deposit Sema3F in the dorsal OB, which in turn repels ventrolateral axons and restricts them to the ventral OB [Axon–axon interactions continue to dictate the pathway taken by axons as they reach their target and subsequently arborize. In the mouse olfactory system, for instance, olfactory axons are segregated along the dorso-ventral axis and maintain their relative position from their exit from the OE to their entry of the OB. Axons from the dorsomedial zone of the OE project to the dorsal part of the OB first and are followed by axons from the ventrolateral zone that project to the ventral OB ,118,119.ntral OB . A similntral OB .Trans-axonal signaling also dictates the fine mapping of axons at their final destination. Once axons have elongated and reached their target, they form elaborate axonal arbors within specific territories or termination zones, establishing precise synaptic connections essential for an efficient transfer of information. Several studies have demonstrated that competitive axon–axon interactions facilitate different steps of circuit development, including topographic order and mapping . Repulsifmi is mosaically knocked out in R8 cells, growth cones become irregularly spaced and often overlap, suggesting that Fmi mediates repulsive interactions between R8 cells in a cell-autonomous manner. Interestingly, aggregation assays in vitro and clonal analyses in vivo have recently revealed that Fmi interacts in cis with another transmembrane receptor, Golden goal (Gogo) [fmi and gogo genetically interact to regulate R8 axon targeting in the medulla [dscam2 mutants, L1 axons still target the correct layer of the medulla but are no longer restricted to a single column. Homotypic axon–axon repulsive interactions finally ensure that axonal arbors from neurons sharing the same function do not overlap with each other and are properly spaced. This “tiling” mechanism allows axons to maximize the coverage of an area while minimizing redundancy of targeting . Interesl (Gogo) . Like Fml (Gogo) , and fmi medulla . As both medulla , the for medulla . HomophiIn addition to mediating the correct spacing of axonal arbors in the Drosophila visual system, atypical cadherins have recently emerged as regulators of axonal tiling in vertebrates ,132. AmoTrans-axonal signaling regulates a striking number of developmental processes that are essential for neural circuit wiring. Both homotypic and heterotypic axon–axon interactions not only mediate axon adhesion and bundling for guidance to the proper target, but also defasciculation for pathfinding at important choice points, repulsion within and between tracts for pre-target sorting and target selection, and repulsion at the target for precise synaptic connectivity. Interestingly, one axonal behavior that is notably lacking from this list is selective axon degeneration. Local axon degeneration refines nervous system connectivity in many species, for instance by remodeling axonal projections during metamorphosis in insects, or by pruning mistargeted axons or axonal branches in vertebrates . In the While many studies have highlighted the importance of axon–axon interactions, the signaling mechanisms and cellular dynamics governing trans-axonal communication are only beginning to emerge in vivo. The recent development of innovative genetic, molecular, and imaging techniques will undoubtedly open new avenues of research by enabling the molecular profiling of single neurons, the selective manipulation of specific axons, and the visualization of axon–axon dynamics at high resolution. Characterizing the molecular and cellular mechanisms by which axons communicate with each other remains a key question to address for better understanding how precise and efficient neural circuits are formed and maintained.


Objective: To investigate the anti-angiogenesis mechanisms and key targets of total saponins of Panax japonicus (TSPJ) in the treatment of rheumatoid arthritis (RA).Methods: RStudio3.6.1 software was used to obtain differentially expressed genes (DEGs) by analyzing the differences in gene expression in the synovial tissue of RA and to predict the potential targets of active compounds from TSPJ by the PharmMapper and SwissTargetPrediction databases. We evaluated the overlapping genes by intersectional analysis of DEGs and drug targets. Based on the overlapping genes, we used Cytoscape 3.7.2 software to construct a protein–protein interactions (PPI) network and applied Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis to determine the mechanisms of the treatment. Finally, the correlations with angiogenesis-related genes were explored. Collagen-induced arthritis (CIA) model was established and treated with different doses of TSPJ. The manifestations of CIA were determined by evaluation of arthritis index and histology score. Serum levels of vascular endothelial growth factor (VEGF) and the hypoxia-inducible factor 1 (HIF-1) were tested by ELISA. The mRNA levels of IL-1β and IL-17A were detected by real time-quantitative PCR.Results: Altogether, 2670 DEGs were obtained by differential analysis, and 371 drug targets were predicted for four active components . A total of 52 overlapping genes were included in the PPI network and the KEGG analysis. However, only 41 genes in the PPI network had protein interactions. The results of the KEGG enrichment analysis were all related to angiogenesis, including VEGF and HIF-1 signaling pathways. Seven genes with negative correlations and 16 genes with positive correlations were obtained by correlational analysis of DEGs in the VEGF and HIF-1 signaling pathways. SRC proto-oncogene, nonreceptor tyrosine kinase (SRC), and the signal transducer and activator of transcription 3 (STAT 3) had a higher value of degree and showed a significant correlation in the pathways; they were regarded as key targets. Compared with the model group, TSPJ significantly relieved the symptoms and decreased the expression of VEGFA, HIF-1α, IL-1β, and IL-17A in serum or spleens of CIA mice.Conclusion: In the current study, we found that antiangiogenesis is one of the effective strategies of TSPJ against RA; SRC and STAT 3 may be the key targets of TSPJ acting on the VEGF and HIF-1 signaling pathways, which will provide new insight into the treatment of RA by inhibiting inflammation and angiogenesis. Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease affected by both genetic and environmental factors . ApproxiPanax japonicus (T.Nees) C.A.Mey is a kind of Chinese herbal medicine that belongs to the Panax genus, which is widely grown in the southwest of China. Studies have shown that Panax japonicus (T.Nees) C.A.Mey has analgesic, anti-inflammatory, antioxidant, and joint swelling-relieving properties (Panax japonicus (T.Nees) C.A.Mey. Total saponins of Panax japonicus (TSPJ) are extracted from Panax japonicus (T.Nees) C.A.Mey and are widely used to treat RA. Its main ingredients include Araloside A, Chikusetsusaponin IVa, Ginsenoside Rg2, and Ginsenoside Ro  because it plays an important role in the treatment of difficult and complicated diseases . The stuoperties . Saponinoside Ro  , a public functional genomics database built and maintained by the National Center for Biotechnology Information (NCBI), collects microarray, next-generation sequencing, and other forms of high-throughput functional genomic data. Persistent synovitis and joint destruction are the main characteristics of RA  database (cs of RA . In our https://pubchem.ncbi.nlm.nih.gov/) is an open-access database that contains chemical structures, molecular formulas and patents, and article identifiers of drugs (http://www.lilab-ecust.cn/pharmmapper/) and the SwissTargetPrediction database (http://www.swisstargetprediction.ch/).PubChem . Second, the “Limma” package was adopted to identify the DEGs between the healthy control and the RA group. The results also included fold change (FC) and the http://www.uniprot.org/) provides high-quality annotation for proteins  to retrieve the protein–protein interactions (PPI). Cytoscape3.7.2 software was used to import the data to construct and visualize the PPI network. We used the “Network Analyzer” plugin to analyze the network to obtain network-related parameters.The UniProt database (proteins . The Unihttps://david.ncifcrf.gov/) is commonly used for annotation, visualization, and integrated discovery  database. Second, the overlapping genes between the DEGs and pathways were identified by intersection analysis. Finally, the roles of these overlapping genes in the pathway were determined by correlation analysis.Significant signaling pathways were acquired by KEGG enrichment analysis. To learn more about the synergistic mechanisms of these pathways, we performed correlation analysis and identified negative and positive genes in the related pathways. First, we downloaded all targets in these signaling pathways from the KEGG , including 1,071 upregulated  and 1,599 downregulated  DEGs. Meanwhile, the volcano plot was used to display both the DEGs and the background genes  were obtained by KEGG analysis of the 52 drug and DEGs overlapping targets in the DAVID database. The results of pathway enrichment analysis  of TSPJ and the 371 potential targets of TSPJ were predicted. DEGs in RA synovium were obtained by difference analysis and could be involved in the pathogenesis of RA. Acting on these DEGs may be very helpful for the targeted therapy of RA. The pathways containing these DEGs may also be important targets for the treatment of RA. The results of the intersection analysis showed that 52 of the targets may be related. The protein interactions of the 52 overlapping genes were obtained from the STRING database and network visualized by Cytoscape3.7.2 software. The top 10 genes'  degree values were not less than seven according to the network visualization analysis. A high degree value indicates that these genes play a key role in the regulatory network of RA proteins in responding to TSPJ therapy. Similarly, the important pathways of TSPJ in the treatment of RA were found by the KEGG analysis of overlapping genes. To our surprise, the 10 signaling pathways were all related to angiogenesis, and the top four genes  in the PPI network are all involved in the VEGF and HIF-1 signaling pathways. We supposed that TSPJ might treat RA by inhibiting angiogenesis.The VEGF and HIF-1 signaling pathways are two important pathways of angiogenesis. VEGF-A induces endothelial cell proliferation, migration, and survival by activating VEGFR2 and its downstream signal transduction pathway, thus promoting new angiogenesis . SynoviaTo further verify the prediction results above, we successfully established CIA mice model. The data showed that TSPJ could significantly reduce the arthritis index of CIA mice. This indicated that TSPJ can effectively relieve the symptoms of CIA mice. The results of H&E staining and histology score of knee joint sections showed that TSPJ inhibited articular cartilage destruction, synovial hyperplasia, and inflammatory cell infiltration in a dose-dependent manner. In addition, TSPJ could reduce the expression of HIF-1 α and VEGFA in serum. This is consistent with our prediction that TSPJ may act as an antiangiogenic by regulating the VEGF or the HIF-1 signaling pathway. Results also showed that TSPJ could decrease the expression of IL-1β and IL-17A in spleen . The aboThe current study demonstrated that TSPJ may regulate the VEGF or HIF-1 signaling pathways by inhibiting two targets, STAT 3 and SRC, thus inhibiting inflammation and angiogenesis, which will provide a new strategy for the treatment of RA.All data sets presented in this study are included in the article/ZF and ZM conceived and designed the experiments; XG, JJ, XF, and YL analyzed the data; XH provided information support; ZF and XG drafted the manuscript; and ZF, GJ, and ZM amended the final manuscript.This project was supported by grants from the National Natural Science Foundation of China (No. 81703783) and the Hubei Provincial Natural Science Foundation (No. 2017CFB126).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


By means of a single-sample-based algorithm, deregulation for all metabolic pathways in every sample was determined. Deregulation levels match almost perfectly with the molecular classification, indicating that metabolic anomalies are closely associated with gene-expression signatures. Luminal B tumors are the most deregulated but are also the ones with higher within-subtype variance. We argued that this variation may underlie the fact that Luminal B tumors usually present the worst prognosis, a high rate of recurrence, and the lowest response to treatment in the long term. Finally, we designed a therapeutic scheme to regulate purine metabolism in breast cancer, independently of the molecular subtype. This scheme is founded on a computational tool that provides a set of FDA-approved drugs to target pathway-specific differentially expressed genes. By providing metabolic deregulation patterns at the single-sample level in breast cancer subtypes, we have been able to further characterize tumor behavior. This approach, together with targeted therapy, may open novel avenues for the design of personalized diagnostic, prognostic, and therapeutic strategies.Metabolic deregulation is an emergent hallmark of cancer. Altered patterns of metabolic pathways result in exacerbated synthesis of macromolecules, increased proliferation, and resistance to treatment via alteration of drug processing. In addition, molecular heterogeneity creates a barrier to therapeutic options. In breast cancer, this broad variation in molecular metabolism constitutes, simultaneously, a source of prognostic and therapeutic challenges and a doorway to novel interventions. In this work, we investigated the metabolic deregulation landscapes in breast cancer molecular subtypes. Such landscapes are the regulatory signatures behind subtype-specific metabolic features.  Breast cancer is a complex, heterogeneous disease. Manifestations of this heterogeneity can be observed at the transcriptomic, molecular, or histological level . The orisubtype tumors according to their feature similarity. One currently used classification method in breast cancer, which has been particularly useful for capturing biological functional features, is the so-called molecular subtyping  often derive into anomalous cell functioning, including deregulation of metabolism—an important emergent hallmark of cancer  via abnoThe analysis of these gene deregulation signatures provides a comprehensive (genome-wide) approach to dig into the molecular basis of disease. In the case of tumor metabolism, one may argue that metabolomics and phospho-proteomics would be closer proxies to the actual underlying molecular mechanisms. However, despite important advances in experimental-omic techniques, comprehensive metabolomic mapping and fluxomics are still under-developed for the task of describing cellular metabolic processes comprehensively, although this should change in the upcoming years. Approaches to analyzing metabolic deregulation in cancer based on gene expression have been developed , 16. ThoTo overcome this issue, an appealing way to study deregulation of metabolism is by analyzing metabolism-related gene-expression signatures at a single-sample level. In this work, we used TCGA gene-expression data from 735 tumor samples , 18, clahttps://bit.ly/2lJJrgi).Data were acquired from the Genome Data Commons Data Portal  samples were acquired and pre-processed to obtain n values . Data wen values .The following pipeline was already used and reported in Espinal-Enríquez et al. . BasicalNOISeq R library was used for global quality control  expression densities showed a consistent bi-modal pattern, corresponding to noisy lower-expressed genes and global sample behavior. Filtering out features with low counts (CPM < 10 cut-off) retained 15,281 genes, removing the undesired lower density peak. Finally, ARSyN R library was used for multidimensional noise reduction using default parameters  for genes in a given pathway may give rise to a really high (assigning PDS = 1 to that sample) score, making all other deregulated samples (with large but comparatively low gene-expression changes) close to zero, thus appearing to be minorly deregulated. In other words, several deregulated pathways/samples would be missing. In such cases, the outlier sample was removed from the analysis. Finally, an unsupervised clustering method was used to group samples with similar PDS. A graphical representation of the pipeline is presented here in Genes being commonly over/underexpressed in all breast cancer subtypes would suggest that there should be subtype-independent drugs. In order to assess this idea, we performed data mining on transcriptomic/drug data by using a previously developed (by our group) computational pipeline to find differentially expressed pharmacological targets of FDA-approved drugs  for thosFor validation purposes, we used 2,000 microarray samples from the METABRIC cohort , performAs has been observed previously , 6, gene2FoldChange| > 1 and B − statistic > 5 as significance thresholds, the number of DEGs in all the tumors is 204 overexpressed and 287 underexpressed. The numbers of overexpressed and underexpressed genes for each subtype are very similar. Interestingly, the subset of shared overexpressed genes (n = 10) is substantially smaller than that of the underexpressed genes (n = 79). This difference between the number of shared underexpressed and overexpressed genes may be associated with the fact that some metabolic pathways are silenced or decreased in all subtypes; on the other hand, metabolic pathways with incremental activity are subtype-specific.By using |logTo evaluate whether shared overexpressed genes influence the regulation of metabolism, we associated them with the metabolic processes in which they participate. As can be seen from the structure of the bipartite network, there are central molecules involved in several interrelated metabolic processes, giving rise to the so-called pathway-crosstalk events. This is a result of the utmost importance, since crosstalk phenomena have been associated with anomalous therapeutic responses and pharmacological resistance in breast cancer subtypes .n = 7), all related to amino acid biosynthesis  is the one with the most associated metabolic processes , which regulate the nucleotide proportion , AK5, whThe majority of these genes are involved in the formation/degradation of ATP. Since cell proliferation is a hallmark of cancer, we argue that underexpression of these genes may enable the tumors to avoid ATP/GTP degradation, thus providing energetic fuel to cell proliferation.Once it has been shown that common deregulated genes induce regulation patterns in some metabolic processes, the remaining question is whether variations in the whole gene-expression signature correspond to changes in specific metabolic deregulation.Interestingly, unsupervised hierarchical clustering of PDS coincides almost absolutely with the PAM50 classification. The colored bars in the upper part of the figure correspond to each subtype, and, as can be appreciated, each color of the bar is grouped together. This phenomenon reflects the high specificity of metabolic deregulation for each molecular subtype.The PDS values are different between all subtypes, but more importantly, it is clear to observe that Luminal B is the subtype with the highest PDS. This result was unexpected, since it is usually considered that the most aggressive and with worst prognosis is the Basal subtype . In thisp-values, measures that have not been derived with pathway-level assessment in mind, in contrast with the PDS, which is a specific pathway-level measure.Previous reports have also analyzed the relationship between transcriptional deregulation and metabolic changes in cancer , 16. FroRegarding commonalities, metabolic pathways are found to be differentially regulated in all subtypes in both manuscripts, in spite of the different approaches to pathway scoring. Purine and retinol metabolism are also found to be highly deregulated in both studies, particularly in the Luminal B and Basal subtypes. Interestingly enough, the Luminal B and Basal subtypes are the most deregulated phenotypes in both studies. This is reflected in Figure 6d from Rosario's paper and in Another point in common between both studies is the coincidence of the Citric acid cycle as a unique pathway observed in the Basal subtype, with the TCA cycle found in our Basal samples . InteresRegarding differences, Rosario et al. found different pathway scores for the Basal and Luminal A subtypes. However, as can be seen from Figure 6C of Rosario's paper, the low specificity of the average gene-expression Z-scores results in a non-conclusive depiction, as it is hard to distinguish signal from background noise. This is also reflected in the density plot of the Figure 6C heatmap. Additionally, the hierarchical clustering on top of the heatmap reflects a large degree of heterogeneity, resulting from the broad variance of the average gene-expression profiles. However, a clear phenotypic fingerprint of basal tumors is actually captured in terms of average gene-expression profiles, likely due to a reduced heterogeneity in these tumors.In some cases, only a small subgroup of samples presents high PDSs (scattered red pixels), which in turn reflects the intrinsic heterogeneity of samples, even if they belong to the same subtype. In the following, we will make some remarks regarding the most deregulated metabolic pathways observed in Luminal B tumors.Pyruvate-related metabolic reprogramming has been associated with metastatic potential and treatment resistance in cancer . PyruvatAs we have said, the Luminal B subtype is the one with the highest metabolic deregulation. It is known that, in the long-term, the Luminal B subtype presents higher drug resistance, metastasis, and relapses , 51. ThiTo our knowledge, a profound study regarding metabolism in the Luminal B subtype is still necessary. However, we suggest that the long-term malignancy and poor prognosis of the Luminal B subtype are due, in part, to global metabolic deregulation more than to any single-molecule alteration. Further analyses in this regard are required to assess the metabolic deregulation patterns observed here with higher accuracy.For the more than 2,611 drugs annotated in the DrugBank database and the 660 drugs annotated in PharmGKB, all subtype-specific differentially expressed genes were matched. The top 20 identified potential pharmacological targets obtained by the pipeline performed in Mejía-Pedroza et al.  are repoAs can be observed in It is worth noticing that this computational tool provides all FDA-approved drugs that target a list of molecules, together with the effect that is produced in the target. In the case of underexpressed genes, three of the four targets of activator drugs participate in purine metabolism: NPR1, PDE1C, and PDE2A. This result appears to be relevant in terms of the potential therapeutic options that breast cancer patients may have. There is a common deregulated metabolic pathway (purine metabolism) that can be targeted by specific drugs that have activator and inhibitory actions over underexpressed/overexpressed genes, respectively.We performed a comparison with data from METABRIC , anotherHeterogeneity is a crucial factor that impedes the understanding, diagnosis, and treatment of breast cancer tumors. Manifestations of this heterogeneity can be observed at the genomic, histological, or clinical level. In this work, we have provided another instance of this heterogeneity: metabolic deregulation.Each breast cancer subtype has its own pattern of deregulation in metabolism, with Luminal B having the highest deregulation scores. This subtype presents alterations to metabolic processes such as pyruvate metabolism, tyrosine metabolism, fatty acid degradation, and the pentose phosphate pathway.To our knowledge, this is the first time that a single-sample-based pathway analysis in breast cancer subtypes has been performed to identify differences in metabolic regulation. At the same time, this work has allowed us to design a common therapeutic FDA-approved scheme to regulate purine metabolism, independently of the subtype. With this kind of approach, it is possible to determine global deregulation patterns while, at the same time, finding individual signatures that may represent a further step toward personalized medicine.Genomic Data Commons Data Portal).The datasets generated for this study can be found in the (EH-L and JE-E contributed to the conception and design of the study. ES-C collected, organized the database, preprocessed the data, performed the computational analysis, and performed results visualization. ES-C, JE-E, and EH-L discussed and contextualized the results. JE-E and EH-L wrote the first draft of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Fritillariae Thunbergii Bulbus, a traditional Chinese medicine that is most frequently used to relieve a cough. Nevertheless, its molecular targets and mechanisms of action for cough are still not clear. In the present study, potential targets of peimine for cough were identified using computational target fishing combined with manual database mining. In addition, protein-protein interaction (PPI), gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using, GeneMANIA and Database for Annotation, Visualization and Integrated Discovery (DAVID) databases respectively. Finally, an interaction network of drug-targets-pathways was constructed using Cytoscape. The results identified 23 potential targets of peimine associated with cough, and suggested that MAPK1, AKT1 and PPKCB may be important targets of pemine for the treatment of cough. The functional annotations of protein targets were related to the regulation of immunological and neurological function through specific biological processes and related pathways. A visual representation of the multiple targets and pathways that form a network underlying the systematic actions of peimine was generated. In summary, peimine is predicted to exert its systemic pharmacological effects on cough by targeting a network composed of multiple proteins and pathways.Peimine  is the major bioactive and characterized compound of  Cough is a vital defense mechanism, especially for maintaining airway patency and to the elimination of potentially harmful stimuli from the airway and lung ,2,3. It On account of multifactorial causes, multifaceted cough therapies, pharmacologic and nonpharmacologic, includes avoidance of allergens/environmental triggers/irritants, dietary and lifestyle modifications, behavioral exercises, nasal sprays, airway clearance, respiratory inhales, and drugs treatment ,9. CurreFritillariae Thunbergii Bulbus (FTB), listed in the Pharmacopeia of the People’s Republic of China, has been widely used to suppress cough and resolve phlegm for thousands of years [As a complementary and alternative medicine, of years ,13. The of years . Our preof years . Peimineof years . HoweverTarget identification of chemical bioactive compounds is at the heart of drug discovery and development . The infIn the present study, we investigated the potential targets and mechanisms of actions of peimine for the treatment of cough using computational target fishing B. First,Sorted by the probability values, one hundred potential protein targets of peimine were identified using SwissTargetPrediction. Due to the diversity of gene classification, farnesyltransferase subtypes (FNTA and FNTB) were combined into one result, resulting in addition of one additional target to account for the two subtypes of this enzyme. Therefore, we identified 101 potential targets for peimine . Of the The Drugbank database showed that compound R048-8071 is the original ligand of lanosterol synthase. We copied the R048-8071 structure file to the SwissTargetPrediction server to identify its potential targets, and as expected lanosterol synthase was the top predicted target of R048-8071 . The tarPotential protein targets related to cough were identified from the Drugbank, Comparative Toxicogenomics Database (CTD), and DisGeNET databases using the search terms ‘cough’ and/or ‘productive cough’ and ‘Homo sapiens’. Eighty-six targets were obtained from Drugbank, 310 from CTD database, and 100 targets from DisGeNET database . After mThe GO uses a common controlled vocabulary to annotate and unify the homologous gene and proteins in multiple species, making it possibly and easily to query and retrieve shared genes and proteins, becoming very useful tools in computational biological research . It contp < 0.05), including calcium signaling pathway, cholinergic synapse, neuroactive ligand-receptor interaction, cAMP signaling pathway, and salivary secretion  pathway provides systematic analysis and reveals the higher order biological information of gene functions . These tecretion . Based oHerbal medicine and traditional Chinese medicine (TCM) have been used to fight against many kinds of diseases from the ancient times to today . NeverthMAPKs, AKT and PPKCB may be targets of peimine for cough relief that act as hub proteins in the network . MAP kin2+/CaMKII/JNK pathway [The mechanisms of acute or chronic cough are complex and involve heterogeneous mechanisms, including lower airway inflammation (bronchitis), non-eosinophilic asthma and sensory hyperresponsiveness ,7,8. Res pathway . TherefoBased on the hypothesis ‘common proteins targets by similar molecules’ and measures of a combination of 2D and 3D similarity with known ligand, SwissTargetPrediction show high performance in target fishing for bioactive small molecule ,32,33. WIn conclusion, the putative targets of peimine were identified by computational target fishing and manual database mining. Our results showed that peimine may act on 23 protein targets that are associated with its therapeutic suppression of cough. In addition, GO and pathway enrichment analysis were preformed and a drug-target-pathway network was constructed. These results indicate that peimine may exert pharmacological effects on a systemic network of proteins and pathways to relieve cough. Although further studies are needed to support our findings, this study provides a systemic and visual view of the possible molecular mechanisms and signaling pathways that contribute to the mechanisms of peimine in the treatment of cough.http://www.swisstargetprediction.ch/) can predict the most probable protein targets for bioactive small molecules based on the similarity hypothesis  through reverse virtual screening [Computational approaches have the significant advantages to greatly reduce times and costs required for drug target fishing . SwissTacreening ,33. Usincreening ,33. The https://www.drugbank.ca/), the Comparative Toxicogenomics Database  and the DisGeNET database (https://www.disgenet.org/) using the keywords “cough” or “phlegm ”. All the cough targets identified in the DisGeNET and Drugbank database were retained after manual mining because the number of potential targets was not high (no more than 100 targets from each database). However, because the number of cough targets identified in the CTD database was very large, we chose targets that had an inference score >30.0 (a higher inference score indicates a greater correlation between the active compound and the target) [Protein targets related to cough were obtained through searches on the Drugbank database ( target) .https://www.omicshare.com/tools/). GeneMAINA (http://genemania.org/) were used to predict potential PPI networks from these intersected proteins, and the selection parameter for species was set to “Homo sapiens”.PPls plays a crucial role in the regulation of biological systems, and certain class of PPI are amenable to small-molecule inhibition . Proteinhttps://david.ncifcrf.gov/). Pathways that had significant changes of p < 0.05 were chosen for construction of the drug-target-pathway network.To investigate the meaningful biological functional annotation of these potential targets, GO enrichment analysis was used to extract the key GO terms  on the OmicShare platform, and KEGG pathways based on the Database for Annotation, Visualization and Integrated Discovery (DAVID) (


In the prediction of the synergy of drug combinations, systems pharmacology models expand the scope of experiment screening and overcome the limitations of current computational models posed by their lack of mechanical interpretation and integration of gene essentiality. We therefore investigated the synergy of drug combinations for cancer therapies utilizing records in NCI ALMANAC, and we employed logistic regression to test the statistical significance of gene and pathway features in that interaction. We trained our predictive models using 43 NCI-60 cell lines, 165 KEGG pathways, and 114 drug pairs. Scores of drug-combination synergies showed a stronger correlation with pathway than gene features in overall trend analysis and a significant association with both genes and pathways in genome-wide association analyses. However, we observed little overlap of significant gene expressions and essentialities and no significant evidence that associated target and non-target genes and their pathways. We were able to validate four drug-combination pathways between two drug combinations, Nelarabine-Exemestane and Docetaxel-Vermurafenib, and two signaling pathways, PI3K-AKT and AMPK, in 16 cell lines. In conclusion, pathways significantly outperformed genes in predicting drug-combination synergy, and because they have very different mechanisms, gene expression and essentiality should be considered in combination rather than individually to improve this prediction. High-throughput genomic analyses are changing the landscape of cancer diagnosis ,4,5,6,7.https://clinicaltrials.gov/ reflect an increase in clinical studies of multi-drug cancer therapies for the same period‒from 445 in 2007 to 798 in 2018. We have observed a similar trend in the pre-clinical cancer research community. PubMed data demonstrate an increase in the publication of in vitro or animal studies of multi-drug therapies for cancer from 12,341 in 2001 to 26,323 in 2018.The steady growth of research and development of multi-drug therapies during the last decade highlights the profound understanding of the cancer research and drug development communities of the complexity of cancer biology and disease. The number of FDA-approved multi-drug cancer therapies increased from one combination in 2007 to 15 in 2018, and data reported in A drug combination effect (inhibition) on the cell viability can be either greater or lesser than their additive effect achieved by their individual use, resulting in synergistic or antagonistic interactions . Those dHigh-throughput preclinical approaches that include both drug-screening experiments and computational biology approaches are crucial to identify synergistic drug combinations. An exponentially growing number of combination drugs, heterogeneous disease mechanisms, and cell cultural models challenge the capacity of experimentation to assess drugs , so rapiSources of data in the public domain regarding large-scale screenings of drug combinations include those of the National Cancer Institute (NCI), Merck & Co., Inc., and AstraZeneca in partnership with the Sanger Institute. The NCI ALMANAC (A Large Matrix of Anti-Neoplastic Agent Combinations) database comprises more than 5000 drug pairs that utilize 104 drugs in 60 NCI cancer cell lines , while tComputational approaches to model and predict the synergy of drug combinations include machine-learning methods  or shRNA (short hairpin RNA), provide the same drug synergy prediction as gene expression? Do data regarding the expression or essentiality of drug target genes predict drug synergy? Most current computational biology approaches focus on optimizing prediction performance, but none were designed to answer these questions.To investigate these questions, we applied our proposed systems pharmacology models, and statistical analyses. Firstly, we used drug combination synergy data from NCI ALMANAC, and employed logistic regression to test the statistical significance of gene and pathway features in that interaction. Gene expression, gene essentiality, KEGG pathways, and drug targets were used in the analysis. Then, we compared the pathways and genes, gene expression and essentiality, targets and non-targets in drug combination synergy prediction. At last, a validation using gene expression in 16 cell lines was performed.We employed data accumulated in the NCI ALMANAC database regarding the synergy of drug combinations . The ALMA and the qth concentration of Drug B, defined as:A and B, and A or Drug B alone. The expected growth fraction for the combination is:n is the number of the combinations for drugs A and B under different doses. The ComboScore ranges from −100 to 100.We evaluated the potential synergistic drug combinations utilizing ComboScores, as defined by Holbeck’s group, to grade the level of presence or absence synergy of each drug in a given combination. Holbeck’s group defined the Drug ComboScore  as a modA and B based on a ComboScore with a threshold of 10, above which a drug combination is considered synergistic.We calculated a binary synergy status ™ database  but excluded one cell line, MDA_N, for poor quality control [®® Human Genome U133 (HG-U133) Plus 2.0 platform [We downloaded the baseline transcriptome data for the 60 NCI cancer cell lines from NCI’s CellMiner control . This yiplatform . We emplGene essentiality was assessed by shRNA screening. We downloaded shRNA data from the Dependency Map (DepMap) portal of the Broad Institute , performTraining data included 43 cancer cell lines with both base-line transcriptome and essentiality data as features. Validation samples were 16 NCI cancer cell lines with only transcriptome data.Pathway analysis utilized data regarding transcriptome, essentiality, and pathway features as enumerated by the Kyoto Encyclopedia of Genes and Genomes (KEGG) . We emplWe applied several rules to filter genes for analysis. Genes were excluded that were active (present) in fewer than 20% (8.6) of cell lines, demonstrated a coefficient of variance (CV) below 0.1, or for the essential data, were not essential in all 43 cell lines. After filtering, 3024 gene expressions and 4381 gene essentialities remained, and their scores served as gene featuresIn constructing pathway features, we added drug target information into the calculation, utilizing target interactions reported by Feixiong Cheng and associates ,29 that In gene analysis, we built a logistic regression model using the score of expression or essentiality for each gene as the given feature.In analyzing and selecting features to predict drug synergy in pathway analysis, we trained logistic models with different types of features and performed three groups of analysis. Pathway Analysis 1 utilized the first type of feature only, Pathway Analysis 2, the second type only, and Pathway Analysis 3, all features.p-value among all the drug combinations to represent the overall p-value for the given gene or pathway. In the gene-level modes, we set a genome-wide threshold for Bonferroni correction of multiple comparisons For each gene or pathway, we chose the minimum p-value threshold, FDR is calculated as the ratio of the number of expected to the number of observed significant results.To assess and compare the models, we calculated multiple levels of the false discovery rate (FDR). For a given Training and validation focused on the transcriptome features and their related pathway features in predicting drug-combination synergy. Unfortunately, no additional data were available to validate gene essentiality features and their pathway features. It is very challenging to construct a model to predict the synergy of drug combinations that show synergy in only a very few cell lines, so we examined 114 drug combinations that showed synergy in at least five cell lines.p-value thresholds, 0.01, 0.001, 0.0001, the FDRs of gene expression and essentiality features varied from 0.19 to 0.60, and gene expression features were somewhat better predictors of drug synergy than essentiality features with lower FDRs .Gene analyses demonstrated significant association of 30 expressed genes and 16 essential genes with drug synergy scores under the Bonferroni threshold, but they shared no common gene. For drug-synergy scores under the Bonferroni threshold, Pathway Analysis 3 demonstrated significant association of four pathways using gene expression , two using essentiality , and five using the combined data . However, there was no common pathway among them. Under a genome-wide adjusted significance level, these association analyses revealed significant correlation between drug-synergy scores and several genes and pathways. However, the different genes and pathways between expression and essentiality indicated different mechanisms of gene expression and essentiality at work in the synergistic activity of the drug combinations.p-value threshold of 0.001, drug combinations correlated significantly with expression in 34 genes and essentiality in 20, with only one gene showing overlap in expression and essentiality  and essentiality (4381 genes) datasets. Under an overall ntiality a.p-value threshold of 0.001, Pathway Analysis 3 demonstrated significant correlation of drug combinations and expression in 125 genes and essentiality in 119, with only five genes showing overlap in expression and essentiality , medium (Q2), and third quartile (Q3). The curves of violin plots also show the probability density of the data at different values, smoothed by a kernel density estimator.For each drug combination, we separated gene features into target and non-target features, including expression and essentiality features, and we further differentiated whether target genes were in a pathway, including pathways defined by gene expressions and essentialities. Comparison between the target and non-target groups showed no significant differences between genes a,b or pap-value among all pathways as the overall p-value for the drug combination to allow comparison with the model constructed based on the first group of features. Pathway Analysis 1 included information regarding drug-combination targets and gene expression and essentiality based on cell-line information and did not include pathway information. For each drug combination, a separate model was constructed for gene expression and for essentiality. Pathway Analyses 2 and 3 included pathway information, so for each drug combination and pathway, a separate model was trained for gene expression and for essentiality. For each drug combination, we set the minimum p-value threshold of 0.001.We first compared the drug combination level results. In Pathway Analysis 1, of 114 drug combinations, zero (expression data), one , and one (combined data) FDR =  can be properly predicted under a −3, 7.33 × 10−3 and 3.14 × 10−3) can be properly predicted.In Pathway Analysis 2, 25 (expression data), 15 , and 35 (combined data) FDR =  can be properly predicted. Obviously, pathway features were more informative than the basic cell-line information, and integrated features in Pathway Analysis 3 performed best.In Pathway Analysis 3, 36 (expression data), 26 , and 62 (combined data) FDR = , 119 , and 423 (combined data) FDR = . The integration of information regarding pathways and cell lines in Pathway Analysis 3 was more informative than the pathway information alone in Pathway Analysis 2. Results were similar using p-value thresholds of 0. 01 and 0.0001 , 25 , and 78 (combined data) of drug combination-pathways FDR =  can be properly predicted under a d 0.0001 .p-values of 0.3105 for expression and 0.3074 for essentiality in Pathway Analysis 1; 0.0430 for expression and 0.0453 for essentiality in Pathway Analysis 2; and 0.5578 for expression and 0.5766 for essentiality in Pathway Analysis 3  correlation coefficients of alysis 3 . These vFor validation, we selected the two most significant correlations of drug-combination pathways using gene expression data in Feature Group 3. The signaling pathways were PI3K-Akt for the combination of drugs Nelarabine and Exemestane and AMPK for the combination of Docetaxel and Vemurafenib. PI3K-Akt pathway is an intracellular signal transduction pathway that promotes metabolism, proliferation, cell survival, growth and angiogenesis in response to extracellular signals. This is mediated through serine and/or threonine phosphorylation of a range of downstream substrates. Key proteins involved are phosphatidylinositol 3-kinase (PI3K) and Akt/Protein Kinase B . Nelarabp-values are 0.03505, 0.0656, 0.007196 and 0.01504 for groups a–d in p-values of significance showed significant separation of these groups in both the training and validation datasets, even when only a single feature was considered  (A non-parametric Wilcoxon test was used to compare the numbers of active genes in the pathways to classify the groups with and without synergy. The pathway) . The resIn AstraZeneca’s drug-combination prediction DREAM Challenge , the autThere is no statistically significant evidence between target and non-target genes/pathways in the prediction of drug synergy. The possible reason is that the drug target information is still limited. Even several databases are used to acquire the drug targets, it is still limited and unbalanced. The number of target genes varies among drug types, such as monoclonal antibodies, Alkylating agents, Anthracyclines, and Mitotic inhibitors. Therefore, there are some biases on target-based pathways.Currently, NCI ALMANAC is the largest drug combination synergy data. However, there are only 60 cell lines, and only 16 cell lines in the validation. This small sample size limited the power and robustness of our model. The significant correlations of AMPK signaling pathway and the drug combination Docetaxel and Vemurafenib were further validated in literature. The synergistic effect of this drug combination has been confirmed by in vitro study . The tarThis is the first study comparing gene expression and gene essentiality for drug synergy prediction. Gene expression and essentiality each reflect different functional mechanisms, and the consideration of one or the other has been effective in the prediction of drug synergy. Certainly, their combined consideration would enhance that prediction, and prediction should improve even further as more and more gene essentiality data become publicly available.


Anatomical concepts regarding the conduction system of the heart have been a matter of debate since pioneering work done at the beginning of the 20th century. Robert H. Anderson was actively involved in this field for half a century. We aimed to investigate how his own concepts evolved over time. We have assessed anatomical concepts relating to the cardiac conduction system appearing since the key contributions made in the initial decade of the 20th century, analyzing them from the perspective of Robert H. Anderson, particularly focusing on the anatomical aspects of structures such as accessory atrioventricular pathways, including the so-called Mahaim-type fibers, connections between the atrioventricular node and the atrial myocardium, and so-called “specialized” internodal atrial tracts. To accomplish this task, we have taken as our starting point the initial concepts published in the first decade of the century, along with those subsequently reported up to 1976, and assessing them in the light of our most recently published works. The concepts put forward by Robert Anderson with regard to atrioventricular nodal bypass tracts, atrioventricular nodal inputs, decrementally conducting accessory pathways, and “tracts” for internodal atrial conduction, have remained consistent along the time frame of half a century. Robert H. Anderson was a West Point graduate, born in 1835, who served as a brigadier general in the army of the Confederate States during the American civil war. In spite of pursuing combat and potential victory on the battlefield, he subsequently played an important role with the efforts for reunification after the war. A different Robert H. Anderson, a British national, has dedicated most of his time to the research of the normal and congenitally malformed heart. Based initially at the Royal Brompton Hospital, supported by the British Heart Foundation, and known to his friends and colleagues as Bob, he has enjoyed a long, ongoing, and very productive career, with 1200 manuscripts and collaborative interaction with scientists all over the world. Like his namesake, who became famous at seeking reunification, Bob has worked together with other scientists and physicians to translate concepts of anatomy and embryology into relevant clinical and surgical information. Even his “enforced retirement” in 2007 did not diminish his impetus and drive to study. He is currently working at the Institute for Genetic Medicine, Newcastle University, Newcastle upon Tyne, and at Birmingham Children’s Hospital in the United Kingdom, but also collaborates extensively with colleagues based in the United States of America.It has been an honor and joy for us to work with him in a number of projects, all designed to improve our understanding of cardiac arrhythmias and their anatomic–functional relationships. This current review is dedicated to his work in the field of cardiac arrhythmias, with emphasis on a particular set of sub-structures. In his early formative years, Professor Anderson spent a remarkably productive year at the University of Amsterdam, where he collaborated with pathologists and electrophysiologists, in particular with Giel Janse, Anton Becker, and Hein Wellens, with their studies at that time providing the basis for important subsequent contributions. In this review, we reappraise some of the concepts reported back in the 1970s in the light of current knowledge, mostly accumulated by himself.We assessed anatomical concepts relating to the cardiac conduction system appearing since the key contributions made in the initial decade of the 20th century, analyzing them from the perspective of Robert H. Anderson, who has now been actively involved for half a century in understanding the anatomical aspects of structures such as accessory atrioventricular pathways, including the Mahaim-type fibers, connections between the atrioventricular node and the atrial myocardium, and so-called “specialized” internodal atrial tracts. To accomplish this task, we have taken as our starting point the initial concepts published in the first decade of the century ,2,3,4, aIn addition, we have taken advantage of a series of e-mails exchanged directly with Bob, aiming to unravel details not possible to discern from the published manuscripts themselves. References ,6 are chWe will include in our reappraisal the concepts of atrioventricular nodal bypass tracts, as alleged to exist by James and Brechenmacher ,9,10, thConcerning the technical aspects of examination of the conduction system, Bob has stated that his approach in the 1970s, and even now, was no different from that used by Tawara [As Bob stated in 1976, the concept of nodal bypass tracts was introduced by James  on the bUsing this definition, a morphological by-pass tract, without implying any functional significance, will be represented as any aggregation of atrial cardiomyocytes that make contact with the nodal-bundle axis distal to the point of origin of the penetrating bundle, in other words, inserting into the final common pathway distal to the compact atrioventricular node. Such a tract had allegedly been described by Brechenmacher and his colleagues ,10 in a Bob had been unable to identify any aggregated cardiomyocytes coursing from the posteroinferior part of the atrial septum, as had allegedly been described by James. In Bob’s opinion, if the variations described by James were considered to be significant to nodal function, they should be interpreted as variations in nodal structure, and hence representing intranodal pathways, rather than considering them as by-pass tracts. If such pathways are, indeed, of functional significance, which is yet to be proven, then account must also be taken of the arrangement of the inferior extensions of the compact node. Variations in these pathways self-evidently can influence the route taken to the final common pathway. In this regard, we now know that the connection between the leftward inferior extension of the compact node and the left side of the atrial septum and the mitral vestibular myocardium is particularly noteworthy. As Scherf and Cohen  pointed Now, in 2020, 54 years later, Bob and his colleagues, including ourselves, have reassessed the structure of the atrioventricular node and its atrial connections . We achiIt was in the early 1940s that Ivan Mahaim, a Belgian cardiologist working in Switzerland, described the presence of “fines hautes connexions”, translated as delicate proximal connections, and also called paraspecific pathways, which connected the central part of the atrioventricular node and the penetrating bundle directly to the crest of the ventricular septum ,15. ThesBob, along with Janse and Becker, studied a fetal human heart in which multiple atrioventricular connections, as described by Mahaim, were shown to be present. During electrical stimulation of the atrium, however, atrioventricular conduction occurred only through the normal pathways for atrioventricular conduction . In 1971A few years thereafter, Anderson and his colleagues  suggesteIt was then Becker working with Gmeiner and colleagues, who were the first to document the anatomical existence of a functional nodo-ventricular pathway. This pathway was found in an eleven-year-old boy with a previous history of paroxysmal recurrent tachycardia, who had suffered a cardiac arrest while tobogganing. He was found in ventricular fibrillation, was resuscitated, but developed a persistent vegetative state for three years, and eventually died . At autoClinical correlations: fasciculo-ventricular pathways have not been reported to play an active role in re-entrant circuits, but they may be used as bystander structures. The nodo-ventricular pathway may be part of a re-entry circuit, and have a role as the retrograde limb or as the anterograde limb, giving rise to orthodromic or antidromic tachycardias, like in the case reported by Gmeiner and colleagues .Becker in 1978, working with Dürrer, Hein Wellens, and Bob  sought tThe atriofascicular pathways, are usually the antegrade limb of the circuit of an antidromic tachycardia, while the retrograde limb is the atrioventricular conduction axis, or a second accessory atrioventricular pathway. It may also be a bystander structure when the patient has an additional accessory pathway or dual atrioventricular pathways and atrioventricular node re-entrant tachycardia.The pathways for conduction between the atrioventricular and sinus nodes ,2,3, theHis initial findings, published in 1963, furthermore, had already been challenged by the Dutch school represented by Dürrer, who had been unable to find electrophysiological evidence of such “specialized” tracts . Becker It is noteworthy that the views of Bob Anderson with regard to structures such as the inputs and extensions of the atrioventricular node, atrio-nodal, and atrio-His connections, the so-called “specialized” internodal tracts, and the pathways responsible for Mahaim conduction, have not changed over the period of half a century. On the contrary, Bob remembers that “when proposing the arrangements, we did not really have the evidence to back up the concepts. Much of the evidence needed came from the collaborative work with Damián, who is now able to “capture” the images as seen through the microscope, and make direct measurements ,28,29,30We are now in a position to assess the functional significance of these recent endorsements. With regard to the alleged atrioventricular nodal bypass tracts, it is certainly feasible that the last connection between the atrial cardiomyocytes and the atrioventricular node prior to its insulation as the bundle of His can provide potential pathways for the exit of proposed re-entry circuits for atrioventricular nodal reentrant tachycardia. It is likely that these last connections were previously misinterpreted as “atrio-Hisian connections” ,10. In rThe entities were described initially by Mahaim, Bob, and his colleagues in 1975 and were separated into fasciculo-ventricular and nodo-ventricular connections . In an oThe concept that decrementally conducting accessory pathways might be related to the remnants of the atrioventricular ring, already reported in 1974, was revisited in 2009. The more detailed investigation included the reporting of the retroaortic node . AlthougBob has also commented with regard to what is likely to be the very first histologically recognized atriofascicular pathway, which they had identified in 1978 : “With rIf we conclude by returning to the so-called “specialized” atrial pathways, in Bob’s own words: “This topic was debated at length in 1910, and perfect definitions for insulated tracts were provided by Aschoff and Mönckeberg. Those definitions retain their validity. It is a simple fact that there are no insulated pathways extending between the sinus and atrioventricular nodes. The preferential conduction is well explained on the basis of the manner of aggregation of the individual cardiomyocytes. The claims for “specialization” on the basis of immunocytochemistry have never provided evidence of conduction ahead of that adjacent to the alleged “pathways”. With regard to Bachmann’s bundle, if you read his original account you will find that he explains that is the alignment of the cardiomyocytes in the anterior interatrial wall that is responsible for the preferential conduction! He was well ahead of his time!!”Thus, we are able to conclude that the concepts put forward by Bob with regard to atrioventricular nodal bypass tracts, atrioventricular nodal inputs, decrementally conducting accessory pathways, and “tracts” for internodal atrial conduction, have remained consistent along the time frame of half a century. This is very unusual, given that the addition of new technologic tools, combined with critical reappraisal by other investigators, very often allows for a new perspective on a myriad of issues. There can be no question that his concepts have passed the test of time.