dataesr/scientific-paragraphs-categorization huggingface dataset:
Pharmacology of Matrix Metalloproteinases mechanistic classes of catalysis: metallo, serine, cysteine, aspartic acid, and threonine proteases  (Puente et al. , 2003; Wang et al. , 2021)  (Fig. 1 ); see the MEROPS database (https://www. ebi. ac. uk/merops/) for the lists of all known proteases  (Rawlings et al. , 2010). Proteases can selectively process, cleave, or degrade all proteins, and our understanding of this web of interactions known as the protease web  (Fortelny et al. , 2014)  remains largely uncharacterized. In this review, we will focus on matrix metalloproteinases (MMPs) and present our current understanding of their biologic functions and roles in diseases, examine biochemical tools to study their functions, and discuss past and future clinical trials of MMP inhibitors. Precisely, we start with a historical perspective of MMPs (section II).
Protease Classes Threonine (  16 )  Aspartic (21)  Cysteine (  136 )  Metallo (148)  Serine (152) Fig. 1. Types of protease classes. The 473 human proteases are shown by their mechanism of catalysis. There are 152 serine proteases, 148 metalloproteases, 136 cysteine proteases, 21 aspartic proteases, and 16 threonine proteases. For the full list, see the MEROPS database (https://www. ebi. ac. uk/merops/). deep cavity and that its composition can be quite identical among its members, reaching up to 86% of similarity for  MMP3 and -10 (Lovejoy et al. , 1994; Laronha and Caldeira, 2020). Although their active sites located in the catalytic domain are highly conserved  (Overall and L opez-Ot ın, 2002) , the 23 members of the MMP family present various domains and exhibit some structural diversity (Fig.
In general, two main pathways are responsible for ECM degradation: an intracellular and an extracellular pathway  (Dufour and Overall, 2015). For the intracellular processing, the ECM is degraded in the phagolysosome by lysosomal proteases, whereas the extracellular remodeling is performed by secreted proteases  (Dufour and Overall, 2015). One misconception from the literature is that MMPs are the main acting proteases implicated in ECM degradation, especially collagens and proteoglycans. In reality, multiple proteases other than the MMPs regulate ECM msl. ubc. ca) (Fortelny et al. , 2015). TopFIND integrates information from the UniProt knowledgebase (Uni-ProtKB), MEROPS peptidase database, and experimental terminomics studies of eight species, including (Homo sapiens, Mus musculus, Rattus norvegicus, Arabidopsis thaliana, Saccharomyces cerevisiae, and Escherichia coli).
B. Non-ECM MMP Substrates N-terminomics and proteomics techniques were used to profile hundreds of cleavage sites in proteomes associated with MMP activity and identified numerous MMP substrates unrelated to the ECM  (Butler and Overall, 2009; Starr et al. , 2012a; Dufour and Overall, 2013, 2015; Bellac et al. , 2014; Mallia-Milanes et al. , 2018). Data obtained from these techniques have resulted in an expansion and restructuring of our understanding of MMP biology, yet it is still underappreciated in the literature. Bioinformatics tools are essential for handling big data, and multiple programs have been developed to integrate these large datasets, such as MEROPS  (Rawlings et al. , 2010)  and TopFIND  (Lange and Overall, 2011; Fortelny et al. , 2015). In MEROPS (https://www. ebi. ac. uk/merops/), over 1 million proteases, $92,000 cleavage sites, and $6,500 peptidaseinhibitor interactions are included (as of April 2022).
, 2016). Another selective MT1-MMP inhibitor, DX-2400, was developed using a human antigen-binding fragment (Fab) displaying phage library  (Devy et al. , 2009). Using a breast cancer xenograft model with MDA-MD-231 cells, DX-2400 was demonstrated to reduce tumor growth, invasion, and metastasis  (Devy et al. , 2009). In the intravenous injection of the metastatic model using mouse B16F1 melanoma cells, DX-2400 was shown to diminish the number of lung nodules and reduce metastasis  (Devy et al. , 2009). Additionally, DX-2400 was demonstrated to reduce tumor growth in the 4T1 and E0771 syngeneic BALB/c mouse model, reducing TGFb and SMAD2/3 signaling  (Ager et al. , 2015). Currently, BT1718, a hemopexin-binding MT1-MMP selective inhibitor developed by Bicycle Therapeutics containing a bicyclic peptide, a linker, and a toxin moiety is in clinical trials for the treatment of solid tumors, projected to end in 2022 (ClinicalTrials.
gov identifier: NCT03486730). BT1718 was demonstrated to be effective in reducing tumor volume in xenograft mouse models using HT-1080 cells  (Harrison et al. , 2017). Recent and ongoing MMP clinical trials include the testing of andecaliximab (GS-5745), an MMP9 humanized monoclonal antibody designed for the treatment of cancer and inflammatory diseases by Gilead Sciences  (Marshall et al. , 2015). In a phase III study, andecaliximab in combination with modified oxaliplatin, leucovorin, and fluorouracil (mFOLFOX6) were recently tested in patients with gastric or recurrent gastroesophageal junction (GEJ) adenocarcinoma (Clinical-Trials. gov identifier: NCT01803282); however, the addition of andecaliximab to mFOLFOX6 did not improve overall survival of patients  (Shah et al. , 2021). Importantly, there was no associated toxicity in patients that took andecaliximab  (Shah et al.
, 2018 ). Andecaliximab has also been evaluated in combination with gemcitabine and nab-paclitaxel in patients with advanced pancreatic adenocarcinoma and was well tolerated  (Bendell et al. , 2020). There is also an ongoing recruitment of patients for the evaluation of andecaliximab as a treatment of glioblastoma (ClinicalTrials. gov identifier: NCT03631836). Andecaliximab has also been tested in inflammatory diseases such as RA (ClinicalTrials. gov identifier: NCT02176876), UC (ClinicalTrials. gov identifier: NCT02520284), CD (ClinicalTrials. gov identifier: NCT02405442), and chronic obstructive pulmonary disease (ClinicalTrials. gov identifier: NCT02077465). In patients with RA, andecaliximab was administered as three infusions over 29 days and was generally safe and well tolerated  (Gossage et al.
, 2018). However, only 15 patients were given andecaliximab and three were given the placebo; therefore, studies in larger patient cohorts and also of increased treatment duration are needed to better characterize the efficacy of andecaliximab  (Gossage et al. , 2018)  (ClinicalTrials. gov identifier: NCT02176876). In a phase II/III study in patients with moderately to severely active UC, subjects were randomized (1:1:1) to receive placebo, 150 mg andecaliximab every 2 weeks, or 150 mg andecaliximab weekly via subcutaneous administration  (Sandborn et al. , 2018). Although andecaliximab was well tolerated, 8 weeks of treatment with 150 mg andecaliximab did not induce clinical remission or response in patients with UC, and the trial was terminated early  (Sandborn et al. , 2018) (ClinicalTrials.
gov identifier: NCT02520284). In a phase II study in patients with moderately to severely active CD, subjects were randomized (1:2:2:2) to receive subcutaneous injections of placebo weekly, 150 mg andecaliximab every 2 weeks, 150 mg andecaliximab weekly, or 300 mg andecaliximab weekly  (Schreiber et al. , 2018). Similar to the patients with UC, andecaliximab was well tolerated in the patients with CD, but no clinically meaningful symptomatic or endoscopic response was observed, and the trial was terminated due to lack of efficacy  (Schreiber et al. , 2018)  (ClinicalTrials. gov identifier: NCT02405442). Another interesting strategy is to use the biologic functions of MMPs as a treatment  (Gabrielli et al. , 2009). For example, the combination of FCX-013 and veledimex is currently being used in clinical trials for the treatment of localized scleroderma, a disease that hardens the skin and connective tissues  (Gabrielli et al.
, 2009) (ClinicalTrials. gov identifier: NCT03740724). This approach uses FCX-013, a human fibroblast genetically modified using a lentivirus vector encoding for MMP1, whose expression is dependent on an agonist. This therapy consists of injecting FCX-013 subcutaneously at the location of the fibrotic lesions. The goal is that the MMP1 producing fibroblasts will break down excess collagen accumulation. In combination with FCX-013, patients will also orally take veledimex (INXN-1001) to induce MMP1 from the injected cells. Veledimex (INXN-1001) is an oral activator and promoter for the gene therapy system  (Chiocca et al. , 2019). Once the fibrosis is resolved, the patient stops taking veledimex (INXN-1001), which stops MMP1 production. Therefore, the biologic activity of certain MMPs could also potentially be used as a therapeutic to resolve fibrosis in specific diseases  (Leong et al.
, 2006; Schuerle et al. , 2016). Recently and in the same vein of FRET biosensors, a sensor relying on a distance-dependent magnetic resonance tuning (MRET) has been designed; the variation of distance between a paramagnetic enhancer and a superparamagnetic quencher induced the appearance of magnetic resonance imaging signal, linked to the proteolytic activity of MMP2 in vivo  (Choi et al. , 2017). Optical-based sensors can also be conjugated with other techniques such as photoacoustic imaging, which A B C Fig. 8. Design of substrate-based sensors from a minimal peptide sequence of six amino acids incorporating a proline in P 3 ' position. The peptide sequence corresponds to a fluorogenic substrate crystallized within the catalytic cleft of an inactive mutant of MMP9 (PDB: 4IJI)  (Tranchant et al.
, 2006; Rosenblum et al. , 2007). Mutant MMP9 lacking the O-glycosylated linker region (OG domain) diminished MMP9's ability to increase cell migration  (Dufour et al. , 2008). Lack of Fig. 9. Activity-based probes (ABPs) directed to the MMP catalytic cleft. From the MMP catalytic domain (PDB: 4IJI)  (Tranchant et al. , 2014)  in surface representation, three regions were distinguished: the S subsites marked in purple, the S 1 ' subsite in blue, and the primed subsites in green. To each subsite is associated an ABP capable of crosslinking it. Probe A targets the S subsites; probe B, D, and E the S' subsites; and probe C the S 1 ' subsite. For each probe, the zinc-chelating group is marked in magenta, the photolabile group or reactive electrophile is associated to the color of site within which it reacts, and the analytical tag is in red.
In this study, we assessed the hypothesis that chromosomal and plasmid-encoded genes have different interaction levels based on the host species, PPI context and PPI connectivity. Using a core dataset of 4,363 bacterial samples, we examined PPI network connectivity using PPI data for all chromosomal proteins and for plasmid-encoded ones. Importantly, HGT is not strongly affected by gene sequence composition and features  (Porse et al 2018) , supporting our simple classification of genes as present or absent here. We examined the changes in PPI network structure based on changes in indirect connections when plasmid-encoded proteins enter a host's chromosomal PPI network. We identified distinctive features associated with plasmid-encoded proteins and their PPIs, assessed their covariation, and thus provide a pilot study of plasmid-related PPIs s across all bacteria.
Protein-protein interaction data extraction We retrieved PPI information for all available valid bacterial genomes (n=4,445) from STRING database v12  (Szklarczyk et al 2021)  with R v4. 0. 1 (R Core Team 2021) and RStudio v2022. 2. 3. 492 (RStudio Team 2022) via Bioconductor v3. 11  (Ihaka & Gentleman 1996)  and packages Rentrez v1. 2. 2 (Winter 2017), STRINGdb v2. 0. 1  (Szklarczyk et al 2018) , and StringR v1. 4. 0 (Wickham 2019) (Figure  1 ). Of the initial 4,445 potential samples listed, 22 were not accessible and four had more >300,000 PPIs, suggesting potential accuracy issues and so they were excluded from analysis. The valid data (n=4,419 samples) was processed and collated using Dplyr v1. 0. 8  (Wickham et al 2002)   (Chen 2022 ) and Xlsx v0. 6. 0. The total number of unique protein names in the entire dataset was 9,551,828, though the true number of gene clusters is likely smaller due to name inaccuracy and redundancy.
Using a STRINGdb score threshold of >400, we used this data to compute the numbers of unique proteins and pairwise PPIs on chromosomes: this score threshold of >400 was used throughout this study. These 4,419 post-QC samples had 3,628. 5±1,710. 0 (mean±SD) proteins and a mean of 57,105. 8±34,698. 6 PPIs each. Not all data was accessible for 56 samples among these 4,419, leaving 4,363 samples. This and subsequent data below were visualised with R packages ggplot2 v3. 3. 5  (Wickham 1996 ) and ggrepel v0. 9. 1  (Slowikowski 2021). All reported p values were corrected for multiple testing using the Benjamini-Hochberg approach in R. The accuracy of gene names in annotation is a pervasive issue where previous work has shown ~46±9% of genes can be functionally assigned  (Zhou et al 2021). To explore that limiting factor here, we examined the frequency of four-digit (short) gene names to longer ones with five or more digits, after removing any instances of names ending in an underscore or dash or dot followed by any digit.
Identification of plasmid-encoded genes and interactions across bacteria To get all available plasmid-encoded genes, we collated 32,839 plasmids: 27,939 from  Brooks et al (2019)  and 6,642 from PLSDB (v2020_11_19, Galata et al 2019) and retrieved gene annotation for each plasmid using Genbankr v1. 16. 0 (Figure  1 ). We focused on 16,383 with between two and 640 annotated genes (<640 to avoid chromosome-related contigs). We verified that the plasmids were annotated as plasmids, including 12 with lengths >2 Mb for Ralstonia solanacearum and Rhizobium gallicum. A gene was defined as plasmid-encoded if it had been found on a plasmid originating in the same species, yielding two categories for genes: plasmid-encoded or chromosomal. These plasmids had a median of 12 unique annotated genes per plasmid and had 8,941 unique plasmid-encoded genes across 98,534 unique sample-gene associations (Table  S1 , see full table FigShare doi: https://doi.
971). We also examined the covariation of samples with the PPI levels per gene for 481 samples and 2,363 plasmid-encoded genes where the input data was the number of PPIs per protein per sample using NMF as above where each protein had at least 30 PPIs across the sample and each sample had at least ten PPIs in total. The smaller dataset was due to a number of samples and genes with zero plasmid-related PPIs. The optimal rank was determined as above, and an estimate of seven groups (ranks) was obtained (cophenetic correlation coefficient maximised at 0. 9856).
Plasmid-encoded proteins are rare and collectively interact with diverse chromosomal proteins We examined the samples' 3,023 unique plasmid-encoded proteins to identify PPIs where one or both proteins were plasmid-related (termed plasmid-related PPIs here, see data at FigShare doi: https://doi. org/10. 6084/m9. figshare. 19672527) to distinguish them from chromosomal PPIs where no plasmid-encoded proteins were found, and we also tracked the rarer category of plasmid-related PPIs where both protein pairs were plasmid-encoded (see data per sample at shorturl. at/mtFNZ). Overall, as a percentage of all PPIs, plasmid-related PPIs constituted a median of 0. 35±4. 76% (mean±SD) PPIs per sample, including 0. 02% of all PPIs exclusively involving plasmid-encoded proteins. 3,874 samples had zero detected plasmid-related PPIs, 489 samples had at least one plasmid-related PPI (two samples had one plasmid-encoded protein with no PPIs).
Discussion Bacterial genome structure has been shaped by HGT more so than gene duplication in many species, such as E. coli  (Lercher & Pál 2008 , Price et al 2008). Mobile plasmid-encoded genes can endow host cells with new phenotypes, including those linked to changed adhesion, AMR and virulence  (Lipworth et al 2022) , and their fitness effects are associated with specific genetic, regulatory and protein interactions  (Hall et al 2017). To further our understanding of how new plasmid-encoded proteins can affect the structure and signalling bacterial PPI networks, we compared the PPI profiles of plasmidlinked and chromosomal proteins across 4,363 representative samples of well-characterised bacteria using data from StringDB to find 491 samples with evidence of plasmid-encoded genes within the same species.
Data for Figure  S16  The numbers of chromosomal & plasmid genes for 343 as PDF image files. https://doi. org/10. 6084/m9. figshare. 20230299 Large datasets available from this study on FigShare, showing the item, description and the corresponding DOI link. See the Supplementary Data for more. The code associated with this paper is available on Github at https://github. com/downingtim/mobilome_2022 and https://github. com/arahm/HomologyLive and data files are there at https://figshare. com/projects/Bacteria_mobilome_2022/136720.
AcknowledgementsThe authors thanks Min Jie Lee for certain R code contributions. Author Contributions TD -Conceptualization, Methodology, Software, Formal analysis, Investigation, Data Curation, Writing -Original Draft, Writing -Review & Editing, Visualisation, Project administration. AR -Methodology, Software, Formal analysis, Writing -Review & Editing. All authors reviewed the manuscript. Data for Figure S5 Presence-absence data with bacteria as rows and genes as columns https://doi. org/10. 6084/m9. figshare. 19525471 Figure S8 NMF heatmap of the metasample clustering of plasmid genes https://doi. org/10. 6084/m9. figshare. 19525492 Figure S9 NMF heatmap of the mixture coefficient for the plasmid-encoded genes https://doi. org/10. 6084/m9. figshare. 19525591 Figure S12 NMF heatmap of the metasample clustering of plasmid PPI rates https://doi.
, 2012). The PMIP3 LGM simulations have also been analyzed, however not regionally, with results highlighting large intermodel differences in annual mean, minimum and maximum Antarctic sea-ice areas, and suggesting most PMIP3 models underestimate austral winter sea-ice cover in comparison to proxy data  (Sime et al. , 2016; Marzocchi and Jansen, 2017). Therefore, a regional analysis of seasonal LGM sea-ice simulated by PMIP3 models is lacking. Furthermore, no seasonal sea-ice analysis of PMIP4 simulations under LGM boundary conditions has yet been performed. Here, we assess the minimum and maximum SO sea-ice extent as simulated in LGM PMIP3 and PMIP4 experiments. To better assess intramodel versus intermodel variability, a suite of LGM sensitivity experiments performed with the LOVECLIM model of intermediate complexity are also included.
LGM numerical simulations In this study, we include all PMIP3 and PMIP4 LGM simulations which provide sea-ice variables in the PMIP3 and PMIP4 database (Table  1 ). Each LGM simulation follows either the PMIP3 or PMIP4 protocol  (Braconnot and Kageyama, 2015; Kageyama et al. , 2017). The PMIP3 protocol calls for all models to use the same ice sheet reconstruction, while the PMIP4 protocol allows the use of either the original PMIP3 ice sheet to facilitate comparison with Clim. Past, 18, 845-862, 2022  https://doi. org/10. 5194/cp-18-845-2022 earlier simulations, or one of the newer reconstructions ICE-6G_C  (Argus et al. , 2014; Peltier et al. , 2015)  and GLAC-1D  (Ivanovic et al. , 2016). In total, data from eight models were obtained for PMIP3 and from six models for PMIP4 (Table  1 ). Three PMIP3 models (CCSM4, GISS-E2-R, MPI-ESM-P) submitted two different simulations.
These simulations differed because of a difference in the initial state (CCSM4), or small changes in the physics of the model (GISS-E2-R and MPI-ESM-P). Following  Sime et al. (2016) , we chose to average the simulations for the models that submitted two LGM runs, yielding one output per model. We also include three additional LGM simulations performed with the earth system model of intermediate complexity, LOVECLIM  (Goosse et al. , 2010). LOVE-CLIM consists of an ocean general circulation model, a dynamic-thermodynamic sea-ice model, coupled to a quasigeostrophic atmospheric model, a dynamic vegetation model and a carbon cycle model  (Goosse et al. , 2010)  These two simulations will be referred to as the "LOVECLIM sensitivity runs". They were chosen because they provided the best model-data fit against a range of paleo-proxy records, including phosphate, δ 13 C, radiocarbon ventilation ages, and neodymium isotopic signature  (Menviel et al.
, 2017 (Menviel et al. , , 2020)) , thus indicating an appropriate oceanic circulation representation. The three LOVECLIM simulations can provide information on the impact of oceanic circulation differences on SO sea-ice and SST, and thus allow us to assess intramodel versus intermodel differences. To facilitate the comparison, we used bilinear interpolation to standardize each model to a 1 • × 1 • grid with the CDO software (Climate Data Operators,  Schulzweida et al. , 2014).
Proxy data The numerical simulations are compared to a compilation of 149 proxy records covering the LGM (see Table  S1  in the Supplement,  Allen et al. , 2011; Benz et al. , 2016; Ferry et al. , 2015; Gersonde et al. , 2005; Ghadi et al. , 2020; Nair et al. , 2019; Xiao et al. , 2016). Quantitative SST was reconstructed at 138 locations, proxies for winter sea-ice presence or concentration were available at 149 locations and proxies for summer sea-ice presence were available at 132 locations. The SSTs were derived from diatom-based transfer functions  (Crosta et al. , 1998; Esper and Gersonde, 2014a)  while winter and summer sea-ice extents were derived either from the relative abundance of sea-ice indicator diatoms, or the Fragilariopsis curta group and F. obliquecostata  (Gersonde et al.
The MMM sea-ice edge is simulated at ∼ 51. 5 • S with a standard deviation of 0. 5 • north and 1 • south. The PMIP3, PMIP4 and LOVECLIM models thus simulate similar MMM sea-ice edge locations between 51 and 51. 5 • S. There are slight regional differences due to a few models displaying a different sea-ice edge in a particular sector, however, the zonally averaged standard deviations produce similar values for PMIP3 and PMIP4. With only two similar models included in the LOVECLIM sensitivity MMM, the standard deviation is much smaller. We next compare the simulations with proxy data of seaice presence or absence. A relative abundance of F. obliquecostata greater than 3 % (Fig. 1  blue points) indicates the presence of sea-ice, relative abundances of F. obliquecostata between 1 % and 3 % suggest the possible presence of seaice (Fig.
1  black points), and core locations with a relative abundance of F. obliquecostata < 1 % indicate ice-free conditions (Fig. 1 red points ). We use these data points to calculate a model data sea-ice agreement percentage, based on whether the model simulation correctly or incorrectly simulates the sea-ice state at the location of the proxy data. For these calculations, we characterize the possible presence of sea-ice (1 %-3 % [F. obliquecostata]) as ice-free. All three MMMs for austral winter display a model data sea-ice agreement between 83 % and 84 % (Fig. 1 , Table  3 ). Of the proxy data points 4 indicate the presence of sea-ice extending past 50 • S, all located within the Atlantic sector and into the westernmost edge of the Indian sector. Both the PMIP3 and PMIP4 MMMs simulate this feature as their sea-ice edge extends equatorward of 50.