Schistosoma mansoni and suggested that its TNF receptor was responsible for this response. The lack of an endogenous TNF ligand reinforced the idea of the use of an exogenous ligand, but also opens the possibility that the receptor actually binds a non-canonical ligand, as observed for NGFRs.The TNF signaling pathway is involved in the regulation of many cellular processes (such as apoptosis and cell proliferation). Previous reports indicated the effect of human TNF-α on metabolism, physiology, gene expression and protein phosphorylation of the human parasite To obtain a more comprehensive view, we analyzed platyhelminth genomes deposited in the Wormbase ParaSite database to investigate the presence of TNF receptors and their respective ligands. Using different bioinformatics approaches, such as HMMer and BLAST search tools we identified and characterized the sequence of TNF receptors and ligand homologs. We also used bioinformatics resources for the identification of conserved protein domains and Bayesian inference for phylogenetic analysis.Our analyses indicate the presence of 31 TNF receptors in 30 platyhelminth species. All platyhelminths display a single TNF receptor, and all are structurally remarkably similar to NGFR. It suggests no events of duplication and diversification occurred in this phylum, with the exception of a single species-specific duplication. Interestingly, we also identified TNF ligand homologs in five species of free-living platyhelminths.These results suggest that the TNF receptor from platyhelminths may be able to bind canonical TNF ligands, thus strengthening the idea that these receptors are able to bind human TNF-α. This also raises the hypothesis that an endogenous ligand was substituted by the host ligand in parasitic platyhelminths. Moreover, our analysis indicates that death domains (DD) may be present in the intracellular region of most platyhelminth TNF receptors, thus pointing to a previously unreported apoptotic action of such receptors in platyhelminths. Our data highlight the idea that host-parasite crosstalk using the TNF pathway may be widespread in parasitic platyhelminths to mediate apoptotic responses. This opens up a new hypothesis to uncover what might be an important component to understand platyhelminth infections. Infections caused by platyhelminths are spread worldwide; three out of 17 neglected tropical diseases have platyhelminths as the etiological agent  and together affect millions of people and cause thousands of deaths annually . The hosSchistosoma mansoni. Pioneer studies of infection with S. mansoni in severe combined immunodeficient (SCID) mice showed a diminished egg-laying that was only restored with exposure to recombinant TNF-α in adult worms [in vitro action of TNF-α in adult worm metabolism leading to a decrease of egg-laying, methionine and tyrosine uptake in females [−/− mice indicated no influence on egg-laying in vivo, but suggested that TNF deficiency results in attrition of schistosomes in the portal system [in vitro action of human TNF-α in S. mansoni was also reported to result in changes in the gene expression profile of adult worms [S. mansoni metabolism, the nature and extent of such influence remain poorly understood.TNF-α is an early pro-inflammatory cytokine produced by several cell types in mammals . The litlt worms . Controv females –10. More females . Indeed,l system . Direct lt worms  and in tlt worms . TherefoSmTNFR [S. mansoni . Phylogenetic analysis of TNFR from S. mansoni suggested a basal position in relation to TNFR1, TNFR2 and CD40 families in vertebrates, which are able to bind bona fide trimeric TNF ligands. However, the bootstrap support for such a classification was only moderate and the structure of the S. mansoni TNFR was also similar to nerve growth factor (NGF) receptors [bona fide TNF ligands. The absence of described endogenous bona fide TNF ligand in platyhelminths and the fact that several platyhelminths are free-living  raise questions about whether these receptors would function by binding non-canonical ligands, as observed by nerve growth factor receptors (NGFRs).With the advances of next-generation sequencing (NGS) techniques several genomes and transcriptomes of parasites have been sequenced and analyzed –19. As aSmTNFR , is parteceptors , which aSmTNFR homologs and their eventual ligands in platyhelminths, using the available sequences in the public databases.All these findings reveal how the molecular crosstalk between parasite and host is important and can modulate several biological processes in the parasite. To investigate if the TNF-α signaling may have a molecular role in other organisms, we aimed to identify and characterize SmTNFR: BLAST (v.2.10.0) [SmTNFR sequence as a query to perform a BLASTp (v.2.10.0) at public sequence databases, GenBank (https://www.ncbi.nlm.nih.gov/genbank/) and Gene DB (https://www.genedb.org/). As result, we were able to identify homologs in 7 organisms: the platyhelminths Echinococcus granulosus (gene ID EgrG 000990500); E. multilocularis (gene ID EmuJ 000990500); Hymenolepis microstoma (HmN 000322000); Taenia solium (gene ID TsM 000678000); Clonorchis sinensis (gene ID GAA49741.1); Opisthorchis viverrini (gene ID XM 009173815.1) and the nematode Trichinella spiralis (gene ID XP 003371690.1). No homologous sequences were found among protozoan sequence data.Two complementary approaches were used to identify the possible homologous genes of .2.10.0)  and HMME.2.10.0)  as descrSmTNFR to construct a probabilistic model using the HMMER tool (v.3.3) [https://parasite.wormbase.org/index.html). The BLASTp (v.2.10.0)   followin2.10.0) ( tool washttps://parasite.wormbase.org/index.html). Extensive searches in other deposited platyhelminth genomes did not reveal any other homologous genes. However, searches in RNA-seq experiments from the sequence read archive  in other platyhelminth species allowed the detection of reads from organisms that did not have their genomes sequenced, displaying high similarity to the TNF homologs.To investigate the TNF homologs (ligands) we performed a search using HMMER  in the predicted proteins from 39 platyhelminth genomes available in the Wormbase ParaSite database (S. mansoni (> 450 residues). Eleven sequences were derived from gene models that were probably incomplete due to the small size of the coded protein. Facing this scenario, some gene predictions were manually corrected by the alignment of ESTs, available at GenBank (https://www.ncbi.nlm.nih.gov/genbank/) and reads of RNA-seq available at SRA (https://www.ncbi.nlm.nih.gov/sra). After a manual correction/verification Spaln 2 [SmTNFR homologs.It was possible to note that in almost all species only one receptor was detected and that in 10 sequences, their sizes were similar to the one observed for  Spaln 2  was usedhttp://www.compbio.dundee.ac.uk/jpred/) was used to identify secondary structures (α-helices) in the primary sequences of TNFR homologs. Sequences of amino acid with a score ≥ 5 were considered as potential α helix regions. Potential DDs were considered when more than three α helix regions were identified.A search on the protein domain database . As a result, 31 TNFR were identified in 30 species of platyhelminths: Echinococcus canadensis; E. granulosus; E. multilocularis (2 isoforms); Hydatigera taeniaeformis; Hymenolepis diminuta; H. microstoma; H. nana; Mesocestoides corti; Schistocephalus solidus; Spirometra erinaceieuropaei ; Taenia asiatica; T. multiceps; T. saginata; T. solium; Gyrodactylus salaris; Macrostomum lignano; Clonorchis sinensis; Echinostoma caproni; Fasciola hepatica; Opisthorchis felineus (2 isoforms); O. viverrini; Schistosoma bovis; S. curassoni; S. haematobium; S. japonicum; S. mansoni ; S. margrebowiei; S. mattheei; S. rodhaini; and Trichobilharzia regenti. It is worth noting that the presence of a TNFR in the free-living platyhelminth, M. lignano, indicates the presence of this receptor in an organism that does not interact with vertebrate hosts.The increased number of platyhelminth genomes available in the public databases offered us a chance to improve the searching for homologs of TNF receptors and their ligands. We initially used the sequence of P-value< 0.01) to the number of TNFR domains that were identified  were correlated ; 4 TNFR domains 83%), transmembrane domain (93%) and the intracellular region (96%) Fig. . The cor%, transmE. multilocularis (2 isoforms); S. mansoni (3 isoforms); and O. felineus (2 isoforms). Duplication of the TNFR gene was only observed in S. erinaceieuropaei. It should be borne in mind that the gene duplication may be an artefact due to the preliminary state of the genome assembly of this organism  and E. caproni have incomplete gene predictions that are at the extremity of genomic scaffolds; because of this, it was not possible to improve or complete these gene predictions.To understand the conservation and phylogenetic context of these TNFRs, we aligned 29 platyhelminth sequences that have 4 TNFR domains . The modular architecture of TNFR domains is based on the three-dimensional assembly and the number of disulfide bridges in each module according to Bodmer et al. . The modPhylogenetic analysis of Platyhelminth TNFR homologs was performed by the method of Bayesian inference using the protein sequences from TNFR domains Fig. .Fig. 2PhExtending the analysis to TNFR superfamily members TNFR1, TNFR2 and NGFR (TNFR16) of several species, it was possible to show that all platyhelminth homologs form a branch separated from vertebrate TNFR1 and TNFR2 . This contrasts with SmTNFR in which no DD was previously detected [It is interesting to note that seven platyhelminth TNFRs display a conserved DD  conserved DD ; (ii) strong evidence of DD (recognized with loose confidence parameters on CDD); (iii) weak evidence of DD ; and (iv) no evidence of DD (not recognized by CDD and with three or less α-helices in the intracellular region). The classification of DD is summarized in Additional file S. mansoni genome failed to find any canonical TNF ligand. Therefore, it has been argued that S. mansoni TNFR either binds a ligand from the human host or to a non-canonical ligand. The first hypothesis is supported by the fact that in vitro incubation with human TNF-α induces changes in the parasite gene expression [An important question about the TNFR in platyhelminths regards the presence of an endogenous canonical ligand. Previous searches of the pression . Howeverpression  we notedhttps://parasite.wormbase.org/index.html).To revisit this question and obtain more definitive answers, we performed a comprehensive search for TNF ligands using HMMER program using the TNF HMM  on the predicted proteins from platyhelminth genomes available on the Wormbase Parasite website (−10) were found in the genome of M. lignano, a free-living platyhelminth. Extensive searches using these proteins as BLASTp queries against other deposited platyhelminth genomes did not produce significant hits. However, TBLASTn searches of these proteins against other platyhelminth RNA-seq sequences from SRA (https://www.ncbi.nlm.nih.gov/sra) allowed the detection and reconstruction of 16 TNF ligands in four species of the Class Rhabditophora: M. lignano ; Macrostomum tuba (4 sequences); Microdalyellia ruebushi (2 sequences); and Microdalyellia fusca (1 sequence) .Additional file 6: Table S3. BLASTp results of alignments between human TNFR and platyhelminth TNFR homologs.Additional file 7: Figure S3. Alignment of TNFR domains of platyhelminth homologs (see detailed description in the figure).Additional file 8: Figure S4. Phylogenetic analysis of conserved TNFR domains of platyhelminths and TNFR homologs of other species . See detailed description in the figure.Additional file 9: Data S2. JPred analysis documentation of platyhelminth TNFR sequences (see details in the file).Additional file 10: Data S3. All manually corrected FASTA sequences of platyhelminth TNF homologs.Additional file 11: Table S4. Detailed information of platyhelminth TNF homologs.Additional file 12: Figure S5. Matrices of BLAST results obtained by the alignment of human TNF ligands and platyhelminths TNF ligands (see detailed description in the figure).Additional file 13: Table S5. BLASTp results of alignments between human TNF and platyhelminth TNF homologs.Additional file 14: Figure S6. Alignment of TNF domain of platyhelminth homologs (see detailed description in the figure).


ADAMTS5 is upregulated in human IgA nephropathy lesions.ADAMTS5 is related to inflammatory infiltrates in affected kidneys.ADAMTS5 digests kidney matrix proteins and cleaves complement C3 and fibronectin. + cells were seen in the tubulointerstitium and glomeruli. The enzyme was expressed by CD64+ cells and its expression was increased by IL-1 and LPS. Analysis of myeloid cell transcriptomics revealed that ADAMTS5 is enriched in human classical monocytes. ADAMTS5+ cells were present in areas of matrix remodelling and associated with ECM proteins lumican, versican, and collagen-4. Liquid chromatography–tandem mass spectrometry proteomics of kidney explants digested with ADAMTS5, identified multiple kidney proteins affected by ADAMTS5 and revealed specific proteolysis of complement C3 and fibronectin associated with IgA on immune complexes. ADAMTS5 processing of immune complex proteins reduced binding to cultured mesangial cells. ADAMTS5 is associated with interstitial inflammatory cells in IgAN and other kidney lesions and fragments relevant extracellular proteins. The proteolytic enzyme might be a new translational target relevant to inflammation and scarring in kidney disease.In IgA nephropathy (IgAN), IgA immune complexes are deposited in the mesangium and drive inflammation and extracellular matrix (ECM) remodelling. The functional links between IgA deposition, inflammation, and matrix remodelling are not well characterized. We recently performed urine liquid chromatography–tandem mass spectrometry proteomics and identified multiple ECM glycoproteins whose expression and function in IgAN is unclear. None of the urine glycoproteins was regulated in IgAN transcriptomics, indicating that tissue remodelling rather than increased expression might contribute to their presence in urine. To investigate this, we examined the IgAN expression profile of metalloproteinases, enzymes involved in the remodelling of ECM proteins, and noted that the proteoglycanase ADAMTS5 was upregulated in IgAN kidneys. ADAMTS5 accumulated in areas of inflammation, and ADAMTS5 In IgA nephropathy (IgAN), accumulation of IgA immune complexes in the glomerular mesangium causes localized inflammation, remodelling of the kidney extracellular matrix (ECM), and scarring . AlthougWhile studying the expression of matrix glycoproteins that we recently identified in IgAN urine by proteomics , we notit test in Prism (v7). ADAMTS5 staining was also correlated against patient estimated glomerular filtration rate (eGFR) and proteinuria values using nonparametric Spearman correlation (Prism v7) and was also compared in patients separated according to their MEST-C (T) score as described above. For more than two groups statistical evaluation was performed using ANOVA followed by pairwise comparisons using Fisher least significant difference test.For tissue staining, we used archived kidney biopsies with a proven diagnosis of primary IgAN from 20 patients  admitted at the John Walls Renal Unit, Leicester General Hospital. IgAN biopsies are routinely scored using the Oxford classification of IgAN (MEST-C score)  and rat kidney tissue extracts as described in relevant sections. Cells and kidney tissue were lysed in 0.2% SDS containing broad proteinase inhibitor mixture (Sigma-Aldrich) and EDTA, protein concentration was estimated (A280 nm) and samples were mixed with standard Laemli sample buffer containing 50 mM DTT. Twenty micrograms of protein per sample were loaded on 4–12% NuPAGE mini-gels (Thermo Fisher Scientific), proteins were transferred on nitrocellulose membranes, blocked in 5% BSA for 1 h, and incubated with anti-human Ag primary Abs against what follows: ADAMTS5, VCAN (DPEAAE), biglycan, fibronectin (FN1), and complement C3  from Abcam. We also used LUM (goat), osteoglycin, and decorin (rabbit) from Santa Cruz Biotechnology. Collagen-4 and myeloperoxidase (both goat) were from Bio-Techne. Complement C3C/C3D Abs (rabbit) from Dako. All Abs were diluted in 5% BSA at 2 μg/ml and membranes were incubated for 16 h at 4°C. Membranes were washed four times in PBS-Tween and incubated with HRP-conjugated swine anti-rabbit, rabbit anti-mouse, or rabbit anti-goat Abs  for 1 h at room temperature. Membranes were washed again with PBS-Tween and exposed to ECL-prime developing reagent (Amersham). Finally, signals were visualized in a Bio-Rad Imaging System. β-actin served as a loading control in immunoblots probed with anti β-actin Abs raised in mouse (Abcam).IgA was isolated from human IgAN serum using jacalin agarose under sterile and LPS-free conditions. Jacalin is the standard total IgA purification methodology and has been used for more than 30 y . One milThe Wieslab Complement System Screen (COMPL-300-RUO) was used to measure classical, alternative, and lectin complement activity present in IgA purified using jacalin from human serum. Jacalin-purified IgA from several IgAN patients was first incubated with or without ADAMTS5 . Following ADAMTS5 treatment, samples were diluted 1/10 in specific complement pathway diluents, and the assay was performed using kit instructions and supplied reagents and standards in duplicate. One hundred microliters of samples, blanks, controls were incubated for 70 min at 37°C. After incubation, wells were washed three times and then incubated with conjugate solutions for 30 min at 20°C. After incubation, the wells were washed and substrate solution was added and left for 30 min at 20°C to develop. The reaction was stopped using 5 mM EDTA, and the plate was read at 405 nm. Results were quantified as per kit instructions; subtract the absorbance of the blank from samples/controls, calculate the mean OD405 nm values and then the percent complement activity as follows: (Sample – NC)/(PC – NC) × 100. There were separate positive controls for the three different pathways. Whereas the classical and lectin pathways returned low levels of activity in comparison with positive controls, alternative pathway activity was undetectable.Primary human mesangial cells from healthy kidneys and immortalized human (HK2 type) were obtained from American Type Culture Collection. Mesangial cells were used between passages 8 and 10, whereas PTECs were at passage 16–18. Both cell types were cultured and maintained in high-glucose DMEM and 10% ultra-low Ig FCS (Thermo Fisher Scientific) in 24-well plates . Before stimulation, cells were serum-starved for 6 h in serum-free DMEM (Sigma-Aldrich). Cells were stimulated in 250 μl of serum-free DMEM for different periods and with different factors as indicated. Following stimulation periods, the serum-free culture medium was collected, cells were washed twice with PBS and lysed with 0.2% SDS (with proteinase inhibitor mixture) for immunoblotting.Human leukocytes were isolated from the blood of healthy donors using two 5 min rounds of erythrocyte lysis  and five washes and spins with RPMI 1640 supplemented with 2% ultra-low Ig FCS (Thermo Fisher Scientific). Five hundred thousand leukocytes were plated per well of a 48-well plate. Cells were stimulated with 100 ng/ml rIL-1B, IL-10, IFNG (PeproTech), 1 μg/ml LPS (Sigma-Aldrich), or 200 μg/ml IgAN serum jacalin-purified total IgA for 24 h. Following stimulation, leukocytes were spun and washed two times with PBS. Leukocyte pellets were finally lysed with 0.2% SDS (with proteinase inhibitor mixture) and were used for immunoblotting as indicated. The leukocyte culture medium was used for ADAMTS5 ELISA (Abbexa) at 1/100 dilution using kit instructions and supplied reagents and standards in duplicate.2, and 0.05% Tween-20 (pH 7.5) as per the vendor’s instructions. For enzymatic digestion of jacalin-purified IgA and associated proteins, purified IgA was mixed with 2 μg/ml ADAMTS5 in digestion buffer for either 36 or 18 h (as indicated) at 37°C. For treatment of primary cells with ADAMTS5 in culture, cells were incubated with 2 μg/ml ADAMTS5, which was diluted in sterile PBS rather than digestion buffer. Importantly, the catabolic activity of ADAMTS5 in cell culture might not be optimal as the enzyme operates at maximum efficiency in the presence of Tween-20 (see ADAMTS5 digestion buffer below), which was not included in culture because the detergent might affect the cells. In addition, in cell culture conditions the enzyme is susceptible to cellular metabolism and likely interference by natural metalloproteinase inhibitors (TIMPs) that might be produced by cells in culture.For ADAMTS5 in vitro tissue digestion, kidneys from euthanized healthy adult rats, were snap-frozen to stop cellular metabolic activity, diced into smaller tissue explants and then washed four times in PBS to remove blood contamination. Forty milligrams of diced kidney explants were incubated either in 400 μl of plain enzyme digestion buffer (control) or digestion buffer supplemented with 4 μg/ml ADAMTS5 (Bio-Techne) for 36 h at 37°C. For comparison in immunoblots (see relevant http://proteowizard.sourceforge.net/) and proteins were identified using the Mascot search engine  searching data against the Swissprot human database (version 2018-07-18). Carbamidomethylation of cysteine was set as fixed modification, whereas methionine oxidation was set as variable. Enzyme specificity was set to trypsin, allowing a maximum of two missed cleavages. Precursor and fragment tolerance was set to 0.03 Da and 10 ppm, respectively. The Mascot.dat file results were loaded in Scaffold (Proteome Software) to validate peptide and protein identifications, to visualize and count peptide and spectra identifications. The maximum false-discovery rate was set to 0.01 for peptides and 0.05 for proteins. Only proteins identified with at least two peptides were considered for follow up analysis. Raw MS/MS files are provided online at Mendeley Data (https://data.mendeley.com/datasets/5vx85p9fjw/draft?a=fc5c7b15-55dc-479c-b1ec-9b1d3001a4b4). Protein-protein interaction network analysis of genes and proteins was performed using StringDB v11 and Cytoscape (v3.6.1). BinGO was used in Cytoscape to identify gene ontology (GO) annotations. Statistical estimation of ADAMTS5-affected proteins was performed using t test on spectral counts in Scaffold. Volcano plots were generated in Prism (v7) using t test p value (from 0.05 to 0.0000001) and spectral counts fold-change (−20 to +20). Heat maps were generated on top-50 significantly affected proteins as described in For proteomics and immunobloting analysis, control and ADAMTS5-treated rat kidneys, were extracted with 0.2% SDS, supplemented with proteinase inhibitors  10 mM EDTA, using vigorous shaking for 5 h. Low concentration SDS was used to solubilize easily extractable extracellular and cellular proteins and to avoid generic extraction of the kidney matrix by stronger detergents or denaturing buffers. Extracts were spun to clarify and protein concentration was estimated using A280. Control and ADAMTS5-treated, 0.2% SDS kidney extracts (see above) were processed using the iST Kit (PreOmics) to remove salts and detergents before liquid chromatography–tandem mass spectrometry (LC-MS/MS). iST-processed extracts were lyophilized, and each sample was dissolved in 45 μl of 100 mM triethylammonium bicarbonate buffer, reduced with 2 mM TCEP (tris-2-carboxyethyl-phosphine) and alkylated with 15 mM chloroacetamide. Protein digestion was performed with porcine trypsin (100 ng/μl in 10 mM HCl) enhanced in a microwave instrument (Discover System) for 30 min at 5 W and 60°C. Samples were dried and dissolved in 20 μl of 0.1% formic acid for LC-MS/MS analysis. Three microliters (2 μg) protein of rat kidney extract (per sample) was injected on a nanoAcquity Ultra Performance Liquid Chromatography (Waters) connected to a Q-Exactive mass spectrometer (Thermo Fisher Scientific) equipped with a Digital PicoView source (New Objective). Solvent composition at the two channels was 0.1% formic acid for channel A and 0.1% formic acid, 99.9% acetonitrile for channel B. Peptides were trapped on a Symmetry (Waters) C18 trap-column  and separated on a BEH300 (Waters) C18 column  at a flow rate of 250 nl/min using a gradient from 1 to 40% acetonitrile in 90 min. The mass spectrometer was operated in data-dependent mode (DDA), acquiring full-scan mass spectrometry spectra (350–1500 m/z) at a resolution of 70,000 at 200 m/z (mass-to-charge) after accumulation to a target value of 3,000,000, followed by high-energy collision dissociation fragmentation on the 12 most intense signals per mass spectrometry cycle. high-energy collision dissociation spectra were acquired at a resolution of 35,000 using normalized collision energy of 25 and a maximum injection time of 120 ms. Automatic gain control was set to 50,000 ions. Charge state screening was enabled. Singly and unassigned charge states were rejected. Only precursors with intensity above 8300 were selected for tandem mass spectrometry (MS/MS) (2% underfill ratio). Precursor masses previously selected for MS/MS were excluded from further selection for 30 s, and the exclusion window was set at 10 ppm. The samples were acquired using internal lock-mass calibration on m/z 371.1010 and 445.1200. Acquired raw MS/MS files were converted to Mascot Generic Format (.mgf) using ProteoWizard (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE35488) comparing tubulointerstitial transcript expression of healthy versus IgAN human kidney biopsies (n = 6) and IgAN (n = 25) biopsies used for the GSE35488 microarray , but also a clear difference in ADAMTS5 levels between IgAN patients. Thirteen IgAN biopsies had high ADAMTS5, whereas there were seven biopsies with staining levels comparable to controls  and a marginally significant negative correlation with eGFR  indicating that ADAMTS5 might be increased as kidney function declines in IgAN, causing reduction in glomerular filtration (eGFR) and increase in urine protein concentration  versus IgAN kidney biopsies from patients with clinically proven IgAN. The semiquantitative analysis of all ADAMTS5 immunostaining performed is summarized in controls . When wecontrols , we notentration . We thenntration . MEST-C ntration . The MESntration , suggestntration . Most ADntration .+ cells were present in healthy subjects , likely tubule epithelial cells undergoing mesenchymal transformation to vimentin+ myofibroblasts . To our surprise, ADAMTS5 is robustly enriched specifically in classical monocytes (CD14high/CD16low) in comparison with every other myeloid cell type . In fact, according to the blood atlas, ADAMTS5 is one of just 18 classical monocyte-enriched genes . We then used simple protein-protein interaction network mapping of ADAMTS5 first-neighbor classical monocyte-enriched genes, thus genes directly associated with ADAMTS5 from the classical monocyte-elevated gene signature via a single edge . Spectral counting was used to estimate changes in kidney proteins after ADAMTS5 incubation. . The main GO of proteins decreased by ADAMTS5 was, perhaps predictably, “Extracellular Region”  kidney extracts . ProteomADAMTS5 affected multiple kidney ECM proteins , did not reduce the immunoreactivity of IgA or C3 detected in mesangial cell lysates. Instead, pretreatment of jacalin-purified IgA with ADAMTS5 for 18 h before adding on mesangial cells , reduced IgA and C3 immunoreactivity, in either vehicle or ADAMTS5-treated cells. Thus, ADAMTS5 enzymatic activity on jacalin-purified IgA and copurified proteins affected their interaction with mesangial cells. The protein or proteins  that might be involved in binding of IgA immune complexes with mesangial cells are currently unknown, but it is likely that ADAMTS5 proteolysis will not be limited to FN1 and C3. Incubation of jacalin-purified IgA with ADAMTS5 did not affect activation of mesangial cells, at least in terms of IL-6  with or without ADAMTS5 . Followi of IL-6  that was of IL-6 , indicat of IL-6 . Thus, tWe then used a commercially available complement pathway activity kit to examine whether in vitro digestion of C3 by ADAMTS5 alters its bioactivity. Incubation of jacalin-purified IgA with ADAMTS5 led to a significant reduction in classical pathway activation, whereas the lectin pathway was reduced but inconsistent . AlternaFinally, tissue staining revealed colocalization of C3 (blue) with ADAMTS5 (red) in IgAN biopsies in areas of inflammatory infiltration and matrix  remodelling . This isCollectively, we demonstrate the increase of ADAMTS5 in IgAN kidney biopsies in areas with inflammatory infiltration and matrix remodelling. ADAMTS5 is likely expressed in IgAN as well as other kidney pathologies, by infiltrating monocytes and might play a role in matrix turnover and scarring. The likely association of ADAMTS5 with infiltrating monocytes might explain the variable degree of immunostaining observed in different biopsies. Variable infiltration of peripheral monocytes in IgAN kidneys is not surprising and is related to differences in inflammatory and pathological processes as well as long-term outcome variability observed in IgAN patients , 40. TubThe expression of ADAMTS5 in kidney inflammation is unknown and perhaps more surprisingly, the expression of ADAMTS5 by immune cells in general has not been studied in depth. The function of ADAMTS5 in inflammation might receive increased attention, given that recent immune cell RNA-sequencing datasets show increased expression of the enzyme specifically in classical monocytes –27. GeneThe role of ADAMTS5 in C3 fragmentation needs to be investigated further in the context of IgAN inflammation and complement activation in vivo, given the importance of C3 in IgAN and inflammation in general. C3 proteolysis might generate active complement fragments (such as C3A) or render complement proteins inactive. For instance, although the possible function of ADAMTS5 on C3 is unknown, the ability of leukocyte elastase to process complement is known since the late 1970s , whereasADAMTS5 might be a therapeutic target in kidney disease given that inflammatory kidney scarring is associated with loss of renal function. Pharmacologically, it might be more feasible to target a proteolytic enzyme rather than the entire inflammatory process, so targeting ADAMTS5 as a possible antiremodelling therapy could be relevant in IgAN and other kidney diseases with an inflammatory and scarring component. Indeed, the enzyme was not unique to IgAN as it was also expressed in membranous nephropathy and focal segmental glomerulosclerosis. The expression of ADAMTS5 in other kidney diseases is not surprising given that the enzyme is apparently enriched in classical monocytes. To this end, the potential renal and IgAN bioactivity of ADAMTS5 needs to be investigated in animal models focusing on kidney matrix remodelling and proteolysis, as enzymatic activity in vivo is likely to be different from the test-tube and cell culture experiments presented in this study. Although matrix proteolysis drugs are not yet mainstream, ADAMTS5 blocking is currently examined as a possible treatment in arthritis .The possible inflammatory matrix remodelling role of ADAMTS5 in IgAN and kidney inflammation as well as its ability to fragment C3 is a promising area for future translational work. The unexpected enrichment of ADAMTS5 in monocytes together with evidence for its involvement in human organ disease warrant further investigation in diseases with inflammatory and fibrotic phenotypes.Data Supplement


The pathophysiology of heart failure with preserved ejection fraction (HFpEF) remains incompletely defined. We aimed to characterize HFpEF compared to heart failure with reduced ejection fraction (HFrEF) and asymptomatic hypertensive or non-hypertensive controls.Prospective, observational study of 234 subjects  who underwent echocardiography, cardiovascular magnetic resonance imaging (CMR), plasma biomarker analysis (panel of 22) and 6-minute walk testing (6MWT). The primary end-point was the composite of all-cause mortality and/or HF hospitalization.Compared to controls both HF groups had lower exercise capacity, lower left ventricular (LV) EF, higher LV filling pressures , more right ventricular (RV) systolic dysfunction, more focal and diffuse fibrosis and higher levels of all plasma markers. LV remodeling (mass/volume) was different between HFpEF  and HFrEF ; p<0.0001. Compared to controls, HFpEF was characterized by (mild) reductions in LVEF, more myocardial fibrosis, LA remodeling/dysfunction and RV dysfunction. HFrEF patients had lower LVEF, increased LV volumes, greater burden of focal and diffuse fibrosis, more RV remodeling, lower LAEF and higher LA volumes compared to HFpEF. Inflammatory/fibrotic/renal dysfunction plasma markers were similarly elevated in both HF groups but markers of cardiomyocyte stretch/damage  were higher in HFrEF compared to HFpEF; p<0.0001. Focal fibrosis was associated with galectin3, GDF-15, MMP-3, MMP-7, MMP-8, BNP, pro-BNP and NTproANP; p<0.05. Diffuse fibrosis was associated with GDF-15, Tenascin-C, MMP-2, MMP-3, MMP-7, BNP, proBNP and NTproANP; p<0.05. Composite event rates (median 1446 days follow-up) did not differ between HFpEF and HFrEF (Log-Rank p = 0.784).HFpEF is a distinct pathophysiological entity compared to age- and sex-matched HFrEF and controls. HFpEF and HFrEF are associated with similar adverse outcomes. Inflammation is common in both HF phenotypes but cardiomyocyte stretch/stress is greater in HFrEF. Heart failure with preserved ejection fraction (HFpEF) represents a growing yet incompletely understood clinical entity. While hHowever, in-depth phenotyping of such HF groups with integrated CMR and extensive plasma biomarker profiling has yet to be performed. In this prospective, observational study, we compared CMR, echocardiography and circulating biomarkers between HFpEF, HFrEF and controls with the aim of gaining pathophysiological insights into the HFpEF syndrome. We further assessed whether clinical outcomes differed across the three groups.1] <30% predicted or forced vital capacity [FVC] <50% predicted), estimated glomerular filtration rate (eGFR) <30 ml/min/m2 and standard contraindications to CMR[All subjects were recruited at a single tertiary cardiac centre as part of a prospective, observational, cohort study. The inclusion criteria for the HF groups were clinical or radions to CMR, 7. At tns to CMR, 9. Furtns to CMR, 10.Since the primary focus of our study was phenotyping HFpEF, we enrolled consecutively approached patients. HFrEF and control subjects were then recruited towards the end of the study. While it is well recognised from epidemiological data, 8, 11 tAsymptomatic controls without known cardiac disease were recruited. Controls were recruited through advertising and none had been referred for a clinical CMR scan. Fourteen volunteers had also served as healthy controls in another study at our centre. We did All subjects underwent comprehensive clinical assessment, blood sampling, TTE and CMR, completed the Minnesota Living with Heart Failure (MLHF) questionnaire and six-At recruitment, blood was sampled for B-type natriuretic peptide , haematocrit, haemoglobin, troponin-I  and renal function. Residual supernatant plasma was stored at -80°C in cryotubes prior to batch analysis at a later stage using a Luminex® bead-based multiplex assay, enablin, pro-BNP, N-terminal pro-atrial natriuretic peptide (NTpro-ANP), renin, myeloperoxidase, highly-sensitive C-reactive protein (hs-CRP), tumour necrosis factor receptor-1 (TNFR-1), interleukin-6, cystatin-C and neutrophil gelatinase-associated lipocalin (NGAL). Plasma biomarkers  were further categorized as either surrogates of myocardial interstitial fibrosis, LV cardiomyocyte stress/damage, myocardial hypertrophy, inflammation/oxidative stress, atrial wall stress/stretch or markers of renal dysfunction[The following plasma biomarkers implicated in HF and also recently evaluated using the same assay in the TOPCAT HFpEF clinical trial, 19 weresfunction–22. The Echocardiography was performed by accredited sonographers in accordance with American Society of Echocardiography guidelines using an iE33 system . LVEF waAll scans were performed on a 3-Tesla platform  with an 18-channel cardiac coil, as reported previously, 7. ThisCVI42 software  was used for analysis, performed by a single operator (PK) blinded to clinical data. Papillary muscles and trabeculations were excluded from cavity volume when deriving ventricular volumes, EF and LV mass[2. Volumetric and mass data were indexed to body surface area.d LV mass and our d LV mass, 25, excd LV mass was defiLGE images were analyzed qualitatiavely for focal fibrosis, categorized as either absent or present and further quantified accordingly. The midAll subjects were followed up for the primary endpoint which was the composite of all-cause mortality or hospitalization for HF . Secondary outcomes were all-cause mortality and HF hospitalization. Outcome data were obtained from hospital records. In patients with multiple events, the time to first event was used as the censored outcome.SPSS v22 was used for statistical analyses. Normality for continuous data was assessed using the Shapiro-Wilk test, histograms and Q-Q plots. Normally distributed data are expressed as mean ±SD. Non-parametric data are expressed as median (25–75% IQR). Categorical data are expressed as absolute numbers or percentages. For comparison of normally distributed data between the 3 groups, the one-way ANOVA with Bonferroni correction was used. For comparison of non-normally distributed data, the Kruskal-Wallis test was employed. The Chi-square or Mann-Whitney U tests were to compare categorical data, as appropriate. For additional between group comparisons, multiple linear regression was used to adjust for clinical variables, where the dependent variable was the log transformed plasma biomarker of interest and independent variables included age, gender, body mass index (BMI) and co-morbidities [diabetes, hypertension, lung disease, atrial fibrillation (AF), chronic kidney disease (CKD), CAD). Binary logistic regression was undertaken with the same adjustments to test for differences in the presence of elevated troponin-I levels between HF groups. Spearman’s rank correlations were performed to detect any important associations with NTpro-ANP. Kaplan-Meier analysis was undertaken to calculate event rates. The Log-Rank test was used to test differences in survival curves. CMR assessments of intra-observer and inter-observer variability were undertaken at least 4 weeks apart (by PK and JRA), on a subset of 10 randomly selected patients .Fig 1. CMR was not performed due to the presence of pacemakers and such subjects were excluded from the final analysis in 2 out of 51 confirmed HFrEF and 7 out of 182 confirmed HFpEF. All subjects were recruited over a period of 26 months. The final participant was enrolled in April 2015. The final cohort comprised 234 subjects: HFpEF n = 140; HFrEF n = 46 and controls n = 48. The majority of HF patients were diagnosed and participated in the study as out-patients . All HF patients participating as in-patients had clinical evidence of heart failure  at the time of their study visit and were on or had been receiving concomitant intravenous diuretic therapy.The overall study consort diagram including reasons for exclusion are detailed in Tables 3. All 3 groups were evenly matched for age and sex distribution.The baseline data are summarized in ).Hypertensive controls were older , had a higher proportion of smokers, hypercholesterolaemia and exhibited lower exercise capacity (360 (325–423) vs 410 (378–456)m, p = 0.007) compared to non-hypertensive controls. The presence of hypertension correlated with the finding of focal fibrosis in controls . Focal fibrosis  was observed in a small minority of hypertensive controls (18%) and absent in non-hypertensive controls. There were no other significant clinical, imaging or plasma biomarker differences between the control groups . Exercise capacity was markedly worse in HF patients.Table 2).Compared to HFrEF, patients with HFpEF had less CAD, higher body mass index, less severe diastolic dysfunction (i.e. lower BNP and E/E’), more atrial fibrillation (AF) (31% versus 20%) and lower 6MWT distance . The MLHF score tended to be higher in HFpEF (p = 0.089) and NYHA class was similar between groups. Abnormal chest X-ray findings were similarly prevalent in both HF groups . LV volumes were similar but HFpEF patients exhibited higher LV mass and more concentric remodeling (higher mass/volume ratio). In comparison to HFpEF, patients with HFrEF had marked reductions in LVEF and substantially higher LV volumes and mass but with a reduction in mass/volume ratio indicative of adverse eccentric remodeling. Examples of typical CMR LV characteristics  of the HF groups are illustrated in RVEF was lower in HFrEF compared to the other groups. RVD was more prevalent in HFrEF (46%) compared to HFpEF (19%) and was also associated with greater remodeling (increased RV end-systolic volumes) compared to HFpEF and controls.Across the cohort and irrespective of cardiac rhythm, HF patients had higher LA volumes and a greater proportion of dilated atria (p<0.0001). In sinus rhythm, LAEF was lower in HF patients compared to controls and lowest in HFrEF (p<0.0001). LA conduit function was depressed in HFrEF compared to HFpEF or controls.Qualitatively, focal fibrosis was more prevalent in both HF groups compared to controls and was commonest in HFrEF . Likewise, similar trends were observed for ischemic  and non-ischemic fibrosis , p<0.0001 for both. A small subset of HF patients exhibited both patterns of focal fibrosis: HFpEF n = 4 and HFrEF n = 5. The size of MI expressed as a percentage of LV mass, was larger in HFrEF  compared to HFpEF. However, there was no significant difference in the extent of non-ischemic fibrosis (p = 0.179) between HF groups. Diffuse fibrosis  was also higher in both HF groups compared to controls and greatest in HFrEF: controls , HFpEF , HFrEF ; p<0.0001.S6 Table. All intra-observer agreements were excellent (co-eficient of variations <10%) and universally better than for inter-observer agreements. The majority of inter-observer agreements remained excellent albeit LV end-systolic volumes, RV end-systolic volumes and RV ejection fraction fared worse (but still good).Data quantification for all CMR parameters are shown in Fig 3) and Troponin-I. BNP, pro-BNP and NTpro-ANP levels were highest in HFrEF. After adjusting for potential confounders including age, sex, BMI, diabetes, hypertension, lung disease, AF and CKD, these differences (or similarities) in plasma biomarkers between HFpEF and HFrEF persisted ; p<0.05 for all. ECV (diffuse fibrosis) also correlated with plasma interstitial fibrotic markers  and cardiomyocyte stress/stretch ; p<0.05 for all. The results of correlations of imaging parameters reflecting fibrosis with plasma biomarkers are shown in S1 Fig). Troponin elevations were only observed in the HF cohorts: HFpEF 24%; HFrEF 37%. Hs-CRP levels were highest in HFpEF but not significantly different compared to HFrEF (p = 0.082).NTpro-ANP strongly correlated with: BNP r = 0.675; minimal LA volume indexed (LAVImin) r = 0.608; LAVImax r = 0.580; LAEF r = -0.506; p<0.0001 for all .Following exclusion of HF subjects with known CAD or with evidence of MI on LGE , the higher prevalence of non-ischemic fibrosis in HFrEF compared to HFpEF remained . Furthermore, the trends for key imaging differences in these sub-groups between HFrEF and HFpEF remained the same with respect to LVEF, LV remodeling patterns, RVD, LA dysfunction and iECV. Likewise, higher levels of cardiomyocyte stress/damage plasma biomarkers were again observed in HFrEF relative to HFpEF , the proportion of events were similar for HFpEF  compared to HFrEF ; p = 0.606. The event rates and time to first events did not differ significantly between HFrEF and HFpEF for both primary (Log-Rank p = 0.784) and secondary outcomes . Kaplan-Meier survival curves stratified according to both HF groups are shown in Our phenotyping study provides important insights into the clinical and pathophysiological profiles of HFpEF relative to HFrEF and controls. Firstly, our study re-affirms the clinical heterogeniety of HFpEF and its association with adverse outcomes and exercise incapacity. Secondly, striking differences in CMR parameters, underscored by plasma biomarkers were seen in both HF groups compared to controls. Thirdly, imaging differences of cardiac structure and function were clearly evident between HFpEF and HFrEF at the chamber levels  as well as at the LV tissue level . Finally, plasma markers of inflammation, fibrosis, myocardial hypertrophy and renal dysfunction were consistently elevated in both HF phenotypes but only markers of cardiomyocyte damage/loss were higher in HFrEF compared to HFpEF.Our HFpEF cohort was characterized by a high prevalence of both cardiovascular  and non-cardiovascular disease , consistent with prevailing literature. In addiAs expected we observed changes in LV structure and function (systolic and diastolic) across both HF cohorts compared to controls. However, the pattern of these changes was markedly different between HFpEF and HFrEF. The lower LVEF in HFpEF  compared to controls has previously been observed with TTE and was thought to reflect mildly reduced contractile function, subtle systolic abnormalities and impaired longitudinal function. In our In agreement with previous reports, 3, we aRecently, alternative paradigms for HFpEF and HFrEF have been proposed, postulaHowever, the data supporting the differential role of inflammation and cardiomyocyte damage in HFpEF and HFrEF has been conflicting to. While some studies, 33 haveDiastolic dysfunction noted in HF is primarily governed by myocardial hypertrophy (and stiffness), which in turn is regulated at the tissue level by alterations in cardiomyocytes and the extracellular matrix. Given tA wide range of prevalence for RVD, primarily derived from TTE data has previously been reported in HFpEF (4–44%), utilizing variable definitions of HFpEF e.g. LVEF ≥45% and different diagnostic thresholds for RVD , 38. TheOur study confirms that RVD is present in a significant minority of HFpEF and is also more prevalent in HFrEF compared to HFpEF, based upon our own internal reference controls. RVD may be part of the natural aetiological profile in HFpEF whereby biventricular remodeling often co-exists, even in early stages or as a In our study, compared to controls, both HF groups displayed more adverse LA remodeling and dysfunction, irrespective of AF. Our findings are further supported by the differential levels of NTpro-ANP  observed across all groups and its strong correlations with LAEF and LA volumes.Our work is additive to the growing evidence base implicating these parameters in HF. ImpaireHFpEF has been the subject of ongoing debate as to whether it truly exists or it is just a collection of co-morbidities in elderly subjects that ultimately drive symptoms and outcomes, 3. Our Our group has previously shown that in The structural and functional changes observed in our HFpEF cohort may also carry prognostic relevance and have ramifications for future clinical trial design and therapies. Worse outcomes have been previously shown in the presence of adverse LV remodeling, focal fWe tested an extensive array of biomarkers reflecting individual domains which provides a more unbiased approach to discriminating key pathophysiological differences between our cohorts. Furthermore, such changes in biomarker profiles corroborate the CMR findings. While imaging parameters have been used to characterise HFpEF previously, these have largely been from echocardiographic data. Prior CMR studies in this setting have not only been sparse but largely confined to assessment of individual characteristics such as the LV, RV or diffuse fibrosis and lacking controls or HFrEF groups for comparison. Our study is the first to undertake a comprehensive and combined assessment of all of the above parameters as well as quantifying focal fibrosis, using a novel metric of diffuse fibrosis (iECV) and undertaking RV and LA volumetric and EF measurements across all 3 groups.This is a single centre, observational study with possible selection bias. Therefore, the results should be confirmed in additional populations. We do not have additional information regarding the duration of HF. A small proportion of subjects did not undergo CMR due to the presence of pacemakers. At the time of study conduct, our centre was not implanting CMR conditional devices. We recognize that the unequal group sizes and higher number of patients in the HFpEF group are potentially confounding. However, we minimized the effects of age and sex by matching for these variables between the groups. Furthermore, we accounted for additional comorbidities in our statistical analysis. We deliberately chose a higher HFpEF sample size in our study since HFpEF is widely recognized to be a more heterogenous entity relative to HFrEF. We did Coronary angiography was only performed if clinically indicated. Furthermore, the HFrEF group had a higher proportion who underwent coronary angiography (24/46) compared to HFpEF (27/140). Therefore, we cannot exclude a higher prevalence of undiagnosed CAD in the HFpEF group which may account for some differences in plasma biomarker levels between the groups. Prior literature has consistently revealed a higher comorbidity burden in HFpEF compared to HFrEF, 11. IndWe measured Troponin-I with a validated assay routinely used by our institution at the time of our study. We do recognise however that this assay has been superceded by more highly sensitive measures of troponin-I with lower limits of detection and which have shown increased troponin-I levels above the 99th percentile upper reference limit in the majority of HF patients–62. UtilStudy participation as in- or out-patients with differing fluid status, or recruitment of in-patient HF patients who were receiving or had been on preceding intravenous diuretic therapies may alternatively have contributed to differing plasma biomarker levels. Patient enrolment into our study had already been completed by the time of publication of the latest ESC diagnostic HF guidelines in 2016. All patCompared to HFpEF, HFrEF has worse LV, LA and RV contractile function and more prevalent fibrosis . Both HFpEF and HFrEF are associated with similar adverse outcomes. While inflammation is common in both HF phenotypes, cardiomyocyte stretch/stress is greater in HFrEF suggesting that HFpEF is a distinct clinical entity.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S6 Table(DOCX)Click here for additional data file.S7 Table(DOCX)Click here for additional data file.S8 Table(DOCX)Click here for additional data file.S9 Table(DOCX)Click here for additional data file.S10 Table(DOCX)Click here for additional data file.S11 Table(DOCX)Click here for additional data file.S1 FigScatter plots illustrating the relationship between NTpro-ANP and: A) BNP B) minimum left atrium volume indexed–LAVImin C) maximum left atrium volume indexed—LAVImax D) left atrial ejection fraction.(TIFF)Click here for additional data file.S2 FigSurvival curves stratified according to heart failure groups for the primary endpoint: composite of all-cause mortality or hospitalization for HF (left panel); all-cause mortality (middle panel); HF hospitalization (right panel).(TIFF)Click here for additional data file.


The advancement of science, as outlined by Popper and Kuhn, is largely qualitative, but with bibliometric data, it is possible and desirable to develop a quantitative picture of scientific progress. Furthermore, it is also important to allocate finite resources to research topics that have the growth potential to accelerate the process from scientific breakthroughs to technological innovations. In this paper, we address this problem of quantitative knowledge evolution by analyzing the APS data sets from 1981 to 2010. We build the bibliographic coupling and co-citation networks, use the Louvain method to detect topical clusters (TCs) in each year, measure the similarity of TCs in consecutive years, and visualize the results as alluvial diagrams. Having the predictive features describing a given TC and its known evolution in the next year, we can train a machine learning model to predict future changes of TCs, i.e., their continuing, dissolving, merging, and splitting. We found the number of papers from certain journals, the degree, closeness, and betweenness to be the most predictive features. Additionally, betweenness increased significantly for merging events and decreased significantly for splitting events. Our results represent the first step from a descriptive understanding of the science of science (SciSci), towards one that is ultimately prescriptive. We all become scientists because we want to create an impact and make a difference to the lives of those around us and also to the many generations that are to come. We all strive to make choices in the problems we study, but not all choices lead to breakthroughs. There is actually much more about scientific breakthroughs that we can try to understand. For one, science is an ecosystem of scholars, ideas, and papers published. In this ecosystem, scientists can form strongly interacting groups over a particular period to solve specific problems, but later drift apart as their interests diverge, or due to the availability or paucity of funds, or other factors. The evolution of these problem driven groups is more or less completely documented by the papers published as outcomes of their research. By analyzing groups of closely related papers, researchers could extract rich information about knowledge processes ,2,3,4. TIn a recent paper, Liu et al. demonstrated the utility of visualizing and analyzing scientific knowledge evolution for physics at the aggregated mesoscale through the use of alluvial diagrams . In thisGiven the recent successes in the area of machine learning and artificial intelligence in a variety of prediction problems ,20, as wIn this study, we extract groups ) from the bibliographic coupling networks (BCNs) and independently from the co-citation networks (CNs) for the period 1981–2010. Next, the GED method is utilized to label four types of evolution events (changes of TCs): continuing, dissolving, merging, and splitting. Then, we use an auto-adaptive mechanism to find the most predictive machine learning model, together with its parameters for each network. Additionally, two scenarios were considered for each network: when the number of events of each kind is imbalanced  and balanced by equal sampling. In general, the prediction quality was satisfactory and good for all event types, with F-measures substantially exceeding 0.5. Such values are significantly greater than the baseline F-measures of 0.14–0.21 for both networks. The feature ranking tells us that the most informative features are context based like the number of PRE, PRB, and RMP papers belonging to the group and the structural features like the degree, closeness, and betweenness. While looking more carefully at the betweenness of papers from two merging TCs, we found significantly higher betweenness for papers that are linked across these two TCs than those connected inside the TCs. No such enhancement in betweenness was found for continuing TCs, while a significant decrease in average betweenness was found for splitting TCs. In summary, our findings suggest that evolutionary events in the landscape of physics research can be predicted accurately using various machine learning models, and understanding this predictive power in terms of important features is a worthwhile future research direction.The entire analytical process consists of several steps that are primarily defined by the group evolution prediction (GEP) framework. First, the bibliographic coupling network (BCN) and co-citation network (CN) are extracted from the references placed in the papers from a given time window , and this is carried out separately for each period.As a result, we get a time series of BCNs/CNs. Next, paper groups called topical clusters (TCs) are extracted using the Louvain clustering methods, independently for each BCN/CN in the time series. Each group is described by the set of predictive features. Having TCs for consecutive periods, we were able to identify changes in TC evolution using the group evolution discovery (GED) method that appropriately labels the TC changes; see below.Independently, the features’ ranking and its validation were performed to find the most valuable TC measures. Based on this ranking, a structural measure node betweenness was selected for the more in-depth studies as the early signal for splitting or merging. The above-mentioned steps are summarized and visualized in The group evolution prediction (GEP) method is the first generic approach for the prediction of the evolution of groups ; in our w common references, the BCN edge between them would have a weight of w. For example, Papers 1 and 2 in N papers n papers In the BCN and CN, nodes represent papers, and undirected but weighted edges denote the bibliographic coupling strengths and co-citation strengths, respectively. That is, if two papers share ong them .There are many approaches to community detection, including modularity based algorithms, hierarchical clustering, non-negative matrix factorization, principal component analysis, link partitioning, and others . In thist, built its BCN, extracting the community structure with sizes t into n pseudo-communities of the same sizes, to remove any potential size effects. The results showed that the papers in the same community significantly focused on a small number of PACS numbers compared with a null model; see To verify that the communities extracted were really focused on closely related questions, we checked the Physics and Astronomy Classification Scheme (PACS) numbers of members of the communities. This cross-validation was independent of network structure; therefore, it provided more evidence for the robustness of TCs. In our study, we only used the first two digits of the PACS numbers, as a balance between accuracy and coverage. To test whether the PACS numbers appearing in the communities could have occurred due to randomness, we chose one year t and To analyze the evolution of TCs, we needed to match them from consecutive years. The set of cited papers to a large extent overlapped year-by-year, so for the CN, we could use the regular approach proposed together with the GED method; see below and Brodka et al. . For BCNt and Here, the TCs at u et al. .t in another group t  method  was usedEquation ) and incEquation ). The inr papers . The inct and If both inclusions (CN) or both intimacy indices (BCN) are greater than the percentage thresholds alpha and beta (the only parameters in this method), the method labels the event continuing. If at least one inclusion or one intimacy index exceeds one of the thresholds, the splitting and merging events considered, the proper event is assigned depending on the number of similar groups in Rankings of the most prominent features were obtained by repeating the feature selection 1000 times using a basic evolutionary algorithm , as propWe begin with studying how scientific knowledge evolved in terms of communities of research papers and how these communities changed over time. There were several studies on the evolution of knowledge within the set of whole journals , which wAfter building BCN and CN, the Louvain method was used to extract the community structures. By checking the Physics and Astronomy Classification Scheme (PACS) numbers of the papers in these communities, we showed that the BCN communities were meaningful and reflected the real structure of the scientific communities. These results suggested that the papers in the same community were very similar to each other in terms of research topic. These results suggest papers in the same community had high similarity to each other in terms of research topic. The method and results of the validation are briefly reviewed in the Methods Section; the interested reader is referred to Liu et al. for details . For theIn Continuing: A research field is said to be continuing when the problems identified and solutions obtained from one year to another are of an incremental nature. It is likely to correspond to the repeated hypothesis testing picture of the progress of science proposed by Karl Popper . TherefoDissolving: A research field is thought to disappear in the following year if the problems are solved or abandoned, and no new significant work is done after this. For the CN, we will find a group of papers that are cited up to a given year, but receiving very few new citations afterwards. In the BCN, no new relevant papers are published in the field; hence, the reference chain terminates.Splitting: A research field splits in the following year, when the community of scientists who used to work on the same problems starts to form two or more sub-communities, which are more and more distant from one another. In terms of the CN, we will find a group of papers that are almost always cited together up till a given year, breaking up into smaller and disjoint groups of papers that are cited together in the next year. In the BCN, we will find the transition between new papers citing a group of older papers to new papers citing only a part of this reference group.Merging: Multiple research fields are considered to have merged in the following year when the previously disjoint communities of scientists found a mutual interest in each other’s field so that they solve the problems in their own domain using methods from another domain. In the CN, we find previously distinct groups of papers that are cited together by papers published after a given year. In the BCN, newly published papers will form a group commonly citing several previously disjoint groups of older papers.The GED method takes into account the size and the similarity between groups (TCs) in the consecutive time frames in order to label groups’ changes (assign event type). There are four events considered in this work:The GED method has two main parameters , which are the levels of inclusion that groups in the consecutive years have to cross in order to be considered as matching groups. We applied the GED method with a wide range of these parameters from 5 to 100%. The characteristics of the considered networks required us to set the alpha and beta thresholds to very low values, i.e., 30% for the BCN and 10% for the CN; see The machine learning approach to prediction requires dividing the data into two parts: the training dataset and test dataset. The training data are used to train the classifier, which can then label events in the test data. The labeled values are compared with the event labels, and the prediction performance is calculated. More than 450 observations were used to train the classifiers. Each observation contained 77 normalized features  divided into three categories: microscopic features , mesoscopic features , and macroscopic features . Mesoscopic features calculated for individual nodes are commonly aggregated for all nodes from the group, e.g., average node degree or betweenness in the group. See The predicted output variable (event labels) had an imbalanced distribution. Commonly, classifiers tend to focus on the dominant event type (class), which is very well predicted, but at the expense of the minority event types. For the imbalanced BCN dataset, the best performance was achieved with the attribute selected classifier (with the SMOas a base classifier), which performed feature selection . The perBy balancing the imbalanced training datasets , we forced the classifiers to pay more attention to the features rather than to the number of occurrences of the particular majority event type. Please note that the test set was untouched, i.e., left imbalanced. As a result of balancing datasets, the previously minor event types  were predicted much better, but with a significant drop in performance of the continuing event classification. More importantly, by balancing the datasets, we increased the overall prediction quality by over 20%. For the balanced BCN dataset, the best performance was achieved by means of the boosting based classifier AdaBoost with Bayes net as the base model. The percentage of the correctly classified instances was 62.0%, and the average F-measure was 0.61. The biggest sources of errors were merging events, which were wrongly classified as continuing and dissolving, as well as continuing wrongly classified as splitting. The best classified event was dissolving , followed by the splitting event . For the balanced CN dataset, the attribute selected classifier  the noise in the data, as well as to provide some explanations about phenomena . By repeHaving the list of best predictive features , we can In reality, there are always links between TCs, and the numbers and strengths of these links fluctuate over time. To develop a more quantitative description of the merging events outlined in Let us consider the part of the BCN associated with the TCs. For example, for 1999.01 and 1999.02, we can see from a and the rest of the papers as 1999.01b. For more detail analysis, we will divide 1999.01a and 1999.01b into The natural question we then ask is: are the betweennesses of the 164 papers in 1999.01 that are coupled to 1999.02 larger, equal to, or smaller than the betweenness of the rest of the 1685 papers in 1999.01 not coupled to 1999.02? Alternatively, if we think of the 164 papers as randomly sampled from the 1849 papers in 1999.01, are we sampling the 164 betweenness in an unbiased fashion? To distinguish the different parts of the TC, we call all papers in 1999.01 that have coupling with papers in 1999.02 as 1999.016 times and measured the quartiles of these samples. When we draw random samples from a TC, the 25th, 50th, and 75th percentiles depend on the size of the TC. There as more variability in these quartiles in smaller samples than in larger samples. Therefore, in the test for statistical significance, the observed quartile had to be tested against different null model quartiles for samples of different sizes. To do this, we drew samples with a range of sizes from the same set of betweenness and, for a given quartile , fit the minimum quartile value against the sample size to a cubic spline and the maximum quartile value against sample size to a different cubic spline. With these two cubic splines, we could then check whether the observed quartile value for a sample of size n was more than or less than the null model minimum or maximum using cubic spline interpolation. From the histograms shown in p < 10−6, which means the papers in In a and b in the TC to have thinned out to the point that the modularity Q of the whole is lower than the modularities a and b. Fortunately, for the When a TC splits into two in the next year, we expect the links between two parts Just to be safe, we also checked the betweenness quartiles of Although a small part split off from each of 1999.11 and 1999.12, the main event associated with the two TCs was a symmetric merging. Looking again into the relevant parts of the BCN, we found 299 out of 1014 papers in 1999.11 coupled to 347 out of 988 papers in 1999.12, and we called them 9.12 see i,k. TherSo far, we have learned that a decrease in betweenness within a TC signals a possible split, whereas an increase in betweenness of the part of the TC coupled to another TC signals a merger between the two TCs. For this story to be consistent, we must not see these signals in the continuing events However, as we can conclude from What about splitting then? A TC is likely to split into two if at least one of two parts has reduced betweenness. We see in During the past two decades, researchers have made many efforts to understand the system of science. Many problems have been solved; however, the understanding of interactions between different fields is still limited. Investigating the temporal network  and its community structures, we were able to measure and quantify the complex interaction between different fields, particularly in physics, over time. Naturally, we would like to have a predictive power based on this picture. However, the correlation between network structure and evolution events is nonlinear and complex. Therefore, we turned to machine learning techniques, which have shown a great power to solve predictive problems that are hard to solve using traditional statistical methods. To our knowledge, this is the first study that utilized both machine learning and network science approaches to predict the future of science at the community level.To be able to identify changes in TCs, we needed to define time windows used for network creation and community detection. The natural choice for bibliographical data was the usage of single years, since the publishing process may last many months. Obviously, another detail may be considered like multiple years, e.g., two or five years. In our approach, i.e., both for BCN and CN, every citation had the same importance. However, there were some other concepts like fractional counting of citations . It assuWe decided to analyze more in-depth only one feature describing the structural profile of TCs, namely node betweenness. It was primarily caused by the limited amount of resources and the complexity of the analyses. The entire process required much human assistance and could not have been easily automated. In our experiments, we utilized the raw, imbalanced or artificially flattened, balanced datasets. However, depending on the study purpose, we could bias some classes we were more interested in, e.g., split. This could be achieved either by means of appropriate balancing (sampling for the learning set or reformulating the problem into the binary question: Is split expected (true) or not ?) As of now, the betweenness analysis was still limited to several case studies; in the future, a more rigorous framework would be desired. The idea of analyzing science by the discovery of knowledge changes is general and can be applied to all bibliographical data containing citations. We focused solely on APS journals; however, also papers indexed by PubMed, Web of Science, or Google Scholar may be studied.


ASM−/− and wild-type (WT) mice received 50 μg endotoxin-free murine TNF intravenously alone or in combination with the pan-caspase inhibitor carbobenzoxy-valyl-alanyl-aspartyl-[O-methyl]-fluoromethylketone (zVAD) and were ventilated at low tidal volume while lung mechanics were followed. Blood pressure was stabilized by intra-arterial fluid support, and body temperature was kept at 37°C to delay lethal shock and to allow investigation of blood gases, lung histopathology, proinflammatory mediators, and microvascular permeability 6 hours after TNF application. Besides the lungs, also the kidneys and liver were examined. TNF elicited the release of inflammatory mediators and a high mortality rate, but failed to injure the lungs, kidneys, or liver of healthy mice significantly within 6 hours. Mortality in WT mice was most likely due to sepsis-like shock, as indicated by metabolic acidosis, high procalcitonin levels, and cardiovascular failure. ASM−/− mice were protected from TNF-induced hypotension and reflex tachycardia and also from mortality. In WT mice, intravenous exogenous TNF does not cause organ injury but induces a systemic inflammatory response with cardiovascular failure, in which the ASM plays a role.Tumor necrosis factor (TNF) is a well-known mediator of sepsis. In many cases, sepsis results in multiple organ injury including the lung with acute respiratory distress syndrome (ARDS). More than 20-year-old studies have suggested that TNF may be directly responsible for organ injury during sepsis. However, these old studies are inconclusive, because they relied on human rather than conspecific TNF, which was contaminated with endotoxin in most studies. In this study, we characterized the direct effects of intravenous murine endotoxin-free TNF on cardiovascular functions and organ injury in mice with a particular focus on the lungs. Because of the relevance of the acid sphingomyelinase in sepsis, ARDS, and caspase-independent cell death, we also included acid sphingomyelinase-deficient (ASM In The objective of the current study was to use a high dose of highly purified conspecific TNF to investigate the direct effects of TNF on organ injury and cardiovascular functions with a particular emphasis on the lung. In order to delay lethal shock and to obtain clinically relevant results, mice were ventilated in a “mouse intensive care unit” (MICU), which facilitates monitoring and stabilization of body temperature and blood pressure for several hours , 28. The−/− mice. Surprisingly, however, TNF—despite being highly effective in producing shock—failed to cause significant lung, kidney, or liver injury in WT mice.In line with our hypothesis, TNF caused a systemic inflammatory response including lethal cardiovascular failure, which was absent in the ASM−/−) mice aged 8 to 10 weeks, weighing 25 to 30 grams. The experimental protocols were in accordance with the German animal protection law and approved by regional governmental authorities .Experiments were performed with female and male C57BL/6x129/SvEv wild-type (WT) and ASM-deficient (ASMμg/kg) and ventilated with low tidal volume (VT) as described [μL NaCl was injected i.v. to stabilize cardiovascular functions. Another 15 min later, mice received 50 μg of endotoxin-free  murine TNF  in 100 μL of NaCl followed by 6 h of ventilation. In addition, two groups were treated with the general caspase inhibitor zVAD , which was suspended in DMSO at 125 mg/mL and further diluted with 0.9% NaCl to 2.5 mg/mL. Based on the study by Cauwels et al. [μg zVAD after 30 min of ventilation in 100 μL of 0.9% NaCl and 100 μg zVAD together with 1 mg bovine serum albumin (BSA) after 60 min of ventilation in 140 μL total volume  and WT+TNF (n = 5)) and TNF and zVAD (ASM−/−+zVAD/TNF (n = 5) and WT+zVAD/TNF (n = 5)); control groups were ventilated for 405 min and received saline or saline+DMSO instead of TNF or zVAD, respectively (ASM−/− sham (n = 6) and WT sham (n = 5)). A catheter for measurement of the mean arterial pressure (MAP) and fluid support was inserted into the carotid artery. In order to stabilize blood pressure, 0.9% NaCl (200 μL/h) was infused intra-arterially (i.a.) and body temperature was stabilized at 37°C . The heart frequency (HF) was calculated from the electrocardiogram . Mice were sacrificed by exsanguination via the arterial line. Blood samples were analyzed by blood gas analysis . Lungs were dissected, lavaged, and processed as described [Mice were anesthetized intraperitoneally with pentobarbital sodium (75 mg/kg) and fentanyl  were quantified in plasma and BAL fluid with enzyme-linked immunosorbent assays (ELISA) . The ratio of BSA in plasma and BAL fluid was calculated to determine microvascular permeability. Cell death was assessed by quantifying mono- and oligonucleosomes by ELISA . The assay was performed with 30 mg of homogenized frozen lung tissue according to the manufacturer's instructions. The lysis buffer was measured as blank, which was subtracted from the results. The cell death ratio was calculated as % per mg lung tissue relative to a 100% positive control provided with the assay.μm followed by hematoxylin and eosin (HE) staining. For the lung, a scoring system for experimental lung injury was used [μm, stained with periodic acid-Schiff (PAS), and counterstained with hematoxylin. The tubular injury was scored on a scale of 0–4: 0 = normal histology, 1 = mild dilatation, 2 = flattened epithelial cells and loss of brush border, 3 = mild vacuolization of tubular cytoplasm (<5 vacuoles/tubule) and tubular cell necrosis/apoptosis, and 4 = strong vacuolization of tubular cytoplasm (>5 vacuoles/tubule) and tubular cell necrosis/apoptosis. The total score is the calculated average of all tubular scores. A total of 100 tubuli per section were evaluated. Histopathology was evaluated in a blinded manner.The left lung was filled with 4% buffered formaldehyde. The lung, kidneys, and part of the median lobe of the liver were fixed in 4% buffered formaldehyde and embedded in paraffin. The lungs and livers were cut in sections of 3 was used . 10 fielμm paraffin kidney sections. Quantification of TUNEL staining was performed by counting the positively stained nuclei in 20 randomly selected visual fields at original magnification ×100 in the renal cortex.TUNEL staining (Roche) was performed according to the manufacturer's instructions with 1 Denatured urine samples were separated by electrophoresis in 10% SDS-PA gels, transferred to nitrocellulose membranes, blocked with 2% BSA in Tween Tris-buffered saline (TTBS) buffer, washed with TTBS, and incubated with a rabbit polyclonal anti-kidney injury molecule- (KIM-) 1 antibody  diluted to 1 : 1000 in TTBS overnight at 4°C. The KIM-1 antibody was detected using a HRP-conjugated goat anti-rabbit IgG  antibody and was visualized by enhanced chemiluminescence.A nitric oxide (NO) quantification assay  was used to measure total NO in blood plasma samples. For this purpose, endogenous nitrate was converted to nitrite by nitrate reductase. The total nitrite was quantified as a chromophoric azo-derivative of the Griess reagent at 550 nm.t-test and FDR correction or the Tukey posttest. Percentage data were transformed using the arcsine transformation prior to analysis. Nonparametric data, except for survival curves, were analyzed with the Kruskal-Wallis test followed by Dunn's posttest. Survival curves were compared with the Kaplan-Meier estimator. For this analysis, the groups TNF and zVAD/TNF, which showed no difference in survival, were pooled for each genotype, resulting in n = 10. Correlation was evaluated with Pearson's correlation coefficient. Statistical analyses were carried out with GraphPad Prism 5.0  or SAS 9.4 software . A p value < 0.05 was considered significant. Data are shown as mean + SEM with n = 6 in the group ASM−/− sham and n = 5 in all other groups. In all experiments, posttests were performed by comparing equally treated groups of different genotypes as well as all groups within the same genotype with each other.Time courses were analyzed by the mixed model procedure. The Bartlett test was used to check for equal variances. Box-Cox transformation was performed to achieve homoscedasticity, when suitable. Normal distribution of the residuals was validated with the Shapiro-Wilk test. Univariate data were analyzed with one-way analysis of variance, followed by the μL in total over 405 min , but there was no significant increase in elastance due to TNF or TNF+zVAD. Tissue damping (G) (aw)  was in a physiologically normal range in all groups . For unkping (G)  and airw(G) (aw)  remained2/FiO2 ratio of around 500 mmHg (66.7 kPa) and the mean pCO2 of around 38 mmHg (5.1 kPa) indicated unimpaired gas exchange in all experimental groups and provided further evidence that TNF did not harm the lung  BAL levels only in ASM−/− mice , which increased the injury score in ASM−/− sham compared to WT sham mice insignificantly   was generally low due to the anesthesia with pentobarbital but remained constant in controls . In cont−/− sham mice, and NO levels were significantly increased by TNF treatment, but there was no difference between TNF-treated ASM−/− and WT mice . In earlier experimental studies, in which TNF had an injurious effect, small amounts of endotoxin were present [One explanation for the lack of lung injury in this study might be the use of endotoxin-free TNF  in WT mice  induced death more rapidly in the present study compared to the previous two studies in which 15-25 μg TNF was used [In the present study, not only the fluid support but particularly the stabilization of body temperature may have influenced the outcome, as it prevented hypothermia, which corresponds with fever in mice, and might have prevented a more rapid death of TNF-treated mice. Despite these measures, the very high TNF dose (50 was used , wherefoWe conclude from our data that circulating TNF alone is not sufficient to increase vascular permeability in the lungs or to cause organ injury, which probably requires additional mediators. The present study suggests that the ASM plays an important role in the development of TNF-induced hypotension and thereby promotes mortality. Therefore, pharmacological inhibition of the ASM as approach to prevent lethal cardiovascular failure in septic shock should be further investigated.