Biofilms are communities of microbes embedded in a matrix of extracellular polymeric substances, largely polysaccharides. Multiple types of extracellular polymeric substances can be produced by a single bacterial strain. The distinct polymer components of biofilms are known to provide chemical protection, but little is known about how distinct extracellular polysaccharides may also protect biofilms against mechanical stresses such as shear or phagocytic engulfment. Decades-long infections of Pseudomonas. aeruginosa biofilms in the lungs of cystic fibrosis patients are natural models for studies of biofilm fitness under pressure from antibiotics and the immune system. In cystic fibrosis infections, production of the extracellular polysaccharide alginate has long been known to increase with time and to chemically protect biofilms. More recently, it is being recognized that chronic cystic fibrosis infections also evolve to increase production of another extracellular polysaccharide, Psl; much less is known about Psl’s protective benefits to biofilms. We use oscillatory bulk rheology, on biofilms grown from longitudinal clinical isolates and from genetically-manipulated lab strains, to show that increased Psl stiffens biofilms and increases biofilm toughness, which is the energy cost to cause the biofilm to yield mechanically. Further, atomic force microscopy measurements reveal greater intercellular cohesion for higher Psl expression. Of the three types of extracellular polysaccharides produced by P. aeruginosa, only Psl increases the stiffness. Stiffening by Psl requires CdrA, a protein that binds to mannose groups on Psl and is a likely cross-linker for the Psl components of the biofilm matrix. We compare the elastic moduli of biofilms to the estimated stresses exerted by neutrophils during phagocytosis, and infer that increased Psl could confer a mechanical protection against phagocytic clearance.

Trypanosoma brucei PRMT7 (TbPRMT7) is a protein arginine methyltransferase (PRMT) that strictly monomethylates various substrates, thus classifying it as a type III PRMT. However, the molecular basis of its unique product specificity has remained elusive. Here, we present the structure of TbPRMT7 in complex with its cofactor product S-adenosyl-L-homocysteine (AdoHcy) at 2.8 Å resolution and identify a glutamate residue critical for its monomethylation behavior. TbPRMT7 comprises the conserved methyltransferase and β-barrel domains, an N-terminal extension, and a dimerization arm. The active site at the interface of the N-terminal extension, methyltransferase, and β-barrel domains is stabilized by the dimerization arm of the neighboring protomer, providing a structural basis for dimerization as a prerequisite for catalytic activity. Mutagenesis of active-site residues highlights the importance of Glu181, the second of the two invariant glutamate residues of the double E loop that coordinate the target arginine in substrate peptides/proteins and that increase its nucleophilicity. Strikingly, mutation of Glu181 to aspartate converts TbPRMT7 into a type I PRMT, producing asymmetric dimethylarginine (ADMA). Isothermal titration calorimetry (ITC) using a histone H4 peptide showed that the Glu181Asp mutant has markedly increased affinity for monomethylated peptide with respect to the WT, suggesting that the enlarged active site can favorably accommodate monomethylated peptide and provide sufficient space for ADMA formation. In conclusion, these findings yield valuable insights into the product specificity and the catalytic mechanism of protein arginine methyltransferases and have important implications for the rational (re)design of PRMTs.crystal structure | enzyme catalysis | PRMT | histone methylation | epigenetics P osttranslational modifications of proteins can affect their structure, catalytic activity, and molecular interactions (1). Methylation of the guanidino group of arginine residues represents a prominent subset of these reactions (2). Histone arginine methylation is associated with gene silencing and activation (3); the modification of arginine residues in a variety of nonhistone proteins, including splicing and transcription factors, can regulate their activity (4, 5).Most of the enzymes that catalyze arginine methylation are designated protein arginine methyltransferases (PRMTs) and require the cofactor S-adenosyl-L-methionine (AdoMet) as the methyl donor (6). Four types of arginine methylation products havedimethylarginine (SDMA), and δ-N G -monomethylarginine (6, 7). Accordingly, PRMTs can be categorized into four groups: Type I PRMTs catalyze ADMA formation, type II PRMTs catalyze SDMA formation, type III PRMTs catalyze MMA formation, and type IV PRMTs catalyze δ-N G -monomethylarginine formation. Type I, II, and III PRMTs are widely distributed in nature whereas type IV PRMTs seem to be limited to yeasts and plants (8). Interestingly, whereas type I and II enzymes catalyze MMA production in addition ...

In the family of protein arginine methyltransferases (PRMTs) that predominantly generate either asymmetric or symmetric dimethylarginine (SDMA), PRMT7 is unique in producing solely monomethylarginine (MMA) products. The type of methylation on histones and other proteins dictates changes in gene expression, and numerous studies have linked altered profiles of methyl marks with disease phenotypes. Given the importance of specific inhibitor development, it is crucial to understand the mechanisms by which PRMT product specificity is conferred. We have focused our attention on active-site residues of PRMT7 from the protozoan Trypanosoma brucei. We have designed 26 single and double mutations in the active site, including residues in the Glu-Xaa 8 -Glu (double E) loop and the Met-Gln-Trp sequence of the canonical Thr-His-Trp (THW) loop known to interact with the methyl-accepting substrate arginine. Analysis of the reaction products by high resolution cation exchange chromatography combined with the knowledge of PRMT crystal structures suggests a model where the size of two distinct subregions in the active site determines PRMT7 product specificity.

Edited by John M. DenuProzymes are catalytically inactive enzyme paralogs that dramatically stimulate the function of weakly active enzymes through complex formation. The two prozymes described to date reside in the polyamine biosynthesis pathway of the human parasite Trypanosoma brucei, an early branching eukaryote that lacks transcriptional regulation and regulates its proteome through posttranscriptional and posttranslational means. Arginine methylation is a common posttranslational modification in eukaryotes catalyzed by protein arginine methyltransferases (PRMTs) that are typically thought to function as homodimers. We demonstrate that a major T. brucei PRMT, TbPRMT1, functions as a heterotetrameric enzyme-prozyme pair. The inactive PRMT paralog, TbPRMT1 PRO , is essential for catalytic activity of the TbPRMT1 ENZ subunit. Mutational analysis definitively demonstrates that TbPRMT1 ENZ is the cofactor-binding subunit and carries all catalytic activity of the complex. Our results are the first demonstration of an obligate heteromeric PRMT, and they suggest that enzyme-prozyme organization is expanded in trypanosomes as a posttranslational means of enzyme regulation.

Background: Plant homeodomain (PHD) fingers are central "readers" of histone post-translational modifications (PTMs) with > 100 PHD finger-containing proteins encoded by the human genome. Many of the PHDs studied to date bind to unmodified or methylated states of histone H3 lysine 4 (H3K4). Additionally, many of these domains, and the proteins they are contained in, have crucial roles in the regulation of gene expression and cancer development. Despite this, the majority of PHD fingers have gone uncharacterized; thus, our understanding of how these domains contribute to chromatin biology remains incomplete. Results: We expressed and screened 123 of the annotated human PHD fingers for their histone binding preferences using reader domain microarrays. A subset (31) of these domains showed strong preference for the H3 N-terminal tail either unmodified or methylated at H3K4. These H3 readers were further characterized by histone peptide microarrays and/or AlphaScreen to comprehensively define their H3 preferences and PTM cross-talk. Conclusions: The high-throughput approaches utilized in this study establish a compendium of binding information for the PHD reader family with regard to how they engage histone PTMs and uncover several novel reader domainhistone PTM interactions (i.e., PHRF1 and TRIM66). This study highlights the usefulness of high-throughput analyses of histone reader proteins as a means of understanding how chromatin engagement occurs biochemically.

Protein arginine methyltransferases (PRMTs) are found in a wide variety of eukaryotic organisms and can regulate gene expression, DNA repair, RNA splicing, and stem cell biology. In mammalian cells, nine genes encode a family of sequence-related enzymes; six of these PRMTs catalyze the formation of ω-asymmetric dimethyl derivatives, two catalyze ω-symmetric dimethyl derivatives, and only one (PRMT7) solely catalyzes ω-monomethylarginine formation. Purified recombinant PRMT7 displays a number of unique enzymatic properties including a substrate preference for arginine residues in R-X-R motifs with additional flanking basic amino acid residues and a temperature optimum well below 37 °C. Evidence has been presented for crosstalk between PRMT7 and PRMT5, where methylation of a histone H4 peptide at R17, a PRMT7 substrate, may activate PRMT5 for methylation of R3. Defects in muscle stem cells (satellite cells) and immune cells are found in mouse Prmt7 homozygous knockouts, while humans lacking PRMT7 are characterized by significant intellectual developmental delays, hypotonia, and facial dysmorphisms. The overexpression of the PRMT7 gene has been correlated with cancer metastasis in humans. Current research challenges include identifying cellular factors that control PRMT7 expression and activity, identifying the physiological substrates of PRMT7, and determining the effect of methylation on these substrates.

Arginine methylation on histones is a central player in epigenetics and in gene activation and repression. Protein arginine methyltransferase (PRMT) activity has been implicated in stem cell pluripotency, cancer metastasis, and tumorigenesis. The expression of one of the nine mammalian PRMTs, PRMT5, affects the levels of symmetric dimethylarginine (SDMA) at Arg-3 on histone H4, leading to the repression of genes which are related to disease progression in lymphoma and leukemia. Another PRMT, PRMT7, also affects SDMA levels at the same site despite its unique monomethylating activity and the lack of any evidence for PRMT7-catalyzed histone H4 Arg-3 methylation. We present evidence that PRMT7-mediated monomethylation of histone H4 Arg-17 regulates PRMT5 activity at Arg-3 in the same protein. We analyzed the kinetics of PRMT5 over a wide range of substrate concentrations. Significantly, we discovered that PRMT5 displays positive cooperativity in vitro, suggesting that this enzyme may be allosterically regulated in vivo as well. Most interestingly, monomethylation at Arg-17 in histone H4 not only raised the general activity of PRMT5 with this substrate, but also ameliorated the low activity of PRMT5 at low substrate concentrations. These kinetic studies suggest a biochemical explanation for the interplay between PRMT5- and PRMT7-mediated methylation of the same substrate at different residues and also suggest a general model for regulation of PRMTs. Elucidating the exact relationship between these two enzymes when they methylate two distinct sites of the same substrate may aid in developing therapeutics aimed at reducing PRMT5/7 activity in cancer and other diseases.

diluted with different concentrations and detected with either anti-H3P16oh or anti-Biotin antibody. k, Immunoblots of lysates from 293T cells with indicated treatment condition for 12h. Extended Data Fig. 4 H3P16oh regulates H3K4me3 and displays better affinity to KDM5A PHD3 Extended data fig 4.pdf a, Immunoblots for lysates from indicated cells with DMOG or hypoxia(0.5% O 2 ) treatment for 12h. b, Immunoblots for 293T cell lysates transfected with indicated plasmids followed with DMOG or hypoxia (1% O 2 ) treatment for 12h. c, Immunoprecipitation and immunoblots of lysates from MDA-MB-231 cells transduced with EGLN2 shCtrl or sh325 and sh327. d, Biotin-pull down assay between biotin beads or indicated biotinylated peptide with GST-KDM5A PHD1 . e, Titration curves and fitting curves of indicated H3 peptides titrated into His-KDM5A PHD3 . f, Left panel, NMR 1 H-15 N heteronuclear single quantum correlation (HSQC) spectrum of apo 15 N-labeled PHD3 domain. Right panel, overlay of NMR 1 H-15 N HSQC spectra of 0.125 mM 15 Nlabeled KDM5A PHD3 in the presence of 1.80 mM H3K4me3 (cyan) and 1.80 mM H3K4me3 P16oh (red). g, MS identification of peptides: HCD mass spectrum of 16 Da H3K4me3 P16oh peptide (Bottom panel) and H3K4me3 peptide (top panel). h, Enhanced binding of H3K4me3 P16oh peptides with KDM5A PHD3 compared to H3K4me3 peptides. The MS of [M+4H] 5+ and [M+4H] 6+ ions were shown. Left, peptide mixture used as input for KDM5A PHD3 -binding experiment; right, KDM5A PHD3 -bound products. i, NMR 1 H-15 N heteronuclear multiple quantum coherence (HMQC) spectra overlays between apo KDM5A PHD3 (black) and 0.1 mM KDM5A PHD3 in the presence of 0.45 mM H3K4me3 (cyan) and H3K4me3 P16oh (red) peptides, pH=6. j, Zoomed-in region of the two peptide-bound KDM5A PHD3 that highlights residues K53 and K54 with the most significant chemical shift differences between the two states. Extended Data Fig. 5 Correlation and overlap of CUT&RUN data in breast cancer cells Extended data fig 5.pdf a, Scatter plot showing the correlation of CUT&RUN replication samples (Rabbit1/2/3). b, Immunoblots for lysates from MDA-MB-231 cells with EGLN2 depletion by different systems. c, Heatmap and averaged plots showing the H3P16oh CUT&RUN signals genome-wide (over ±5 kb) in MDA-MB-231 cells with EGLN2 sgCtrl or sg13 and sg14 with inducible CRISPR system. d-f, Scatter plot showing the correlation of indicated CUT&RUN replication samples . g, Heatmap showing CUT&RUN peaks of indicated genes that overlapped with of H316Poh peaks on genome wide scale (over ±5 kb). h, Hierarchical clustering of the indicated CUT&RUN datasets based on similarity, with the pairwise Pearson correlation coefficients labeled in the table and depicted by varying color intensities. i, Principal component analysis (PCA) of indicated CUT&RUN datasets. j, Venn diagram showing H3K4me3 solo peaks (n=8392) and common peaks with H3P16oh (n=6651). k-l, GREAT (Genomic Regions Enrichment of Annotations Tool) analysis of function of H3K4me3 solo peaks (k) and common peaks with H3P16oh (l). ...