Computational predictions of common transcription factor binding sites on the genes of proline metabolism in plants

Proline, an imino acid, has been well documented to be associated with the stress response induced by abiotic factors such asdrought, cold and salinity in plants and biotic factors such as bacterial and fungal attacks. However, the regulatory mechanismscontrolling proline metabolism, intercellular and intracellular transport and connections of proline to other metabolic pathways arepoorly understood. F-MATCH analysis combined with composite module analysis (CMA) revealed that the binding sites matchingmatrices for O2 and OCSBF-1 were overrepresented in the promoters of differentially expressed proline metabolism genes. Thepresence of MYBAS1 consensus binding sites occurring in combination with O2 and OCSBF1 in the promoters of genes of prolinebiosynthesis pathway and SBF1 and GT1 consensus binding sites occurring in combination with O2 and OCSBF1 in the promotersof proline catabolic pathway genes suggest their involvement in modulation of proline metabolism and its accumulation in plants.     

An Unusual Mode of Galactose Recognition by a Family 32 Carbohydrate-Binding Module

Carbohydrate-binding modules (CBMs) are ancillary modules commonly associated with carbohydrate-active enzymes (CAZymes) that function to mediate the adherence of the parent enzyme to its carbohydrate substrates. CBM family 32 (CBM32) is one of the most diverse CBM families, whose members are commonly found in bacterial CAZymes that modify eukaryotic glycans. One such example is the putative μ-toxin, CpGH84A, of the family 84 glycoside hydrolases, which comprises an N-terminal putative β-N-acetylglucosaminidase catalytic module and four tandem CBM32s. Here, we report a unique mode of galactose recognition by the first CBM32, CBM32-1 from CpGH84A. Solution NMR-based analyses of CpGH84A CBM32-1 indicate a divergent subset of residues, located in ordered loops at the apex of the CBM, conferring specificity for the galacto-configured sugars galactose, GalNAc, and LacNAc that differs from those of the canonical galactose-binding CBM32s. This study showcases the impressive variability in ligand binding by this CBM family and offers insight into the growing role of these modules in the interaction of CAZymes with eukaryotic glycans.     

Expression of functional Bacillus SpoIISAB toxin–antitoxin modules in Escherichia coli

SpoIISA and SpoIISB proteins from Bacillus subtilis belong to a recently described bacterial programmed-cell death system. The current work demonstrates that the toxin–antitoxin module is also functional in Escherichia coli cells, where the expression of SpoIISA toxin leads to transient growth arrest coupled with cell lysis, and SpoIISA-induced death can be prevented by coexpression of its cognate antitoxin, SpoIISB. Escherichia coli cells appear to be able to escape the SpoIISA killing by activation of a specific, as yet unidentified protease that cleaves out the cytosolic part of the protein. Analysis of the toxic effects of the transmembrane and cytosolic portions of SpoIISA showed that neither of them separately can function as a toxin; therefore, both parts of the protein have to act in concert to exert the killing. This work also identifies genes encoding putative homologues of SpoIISA and SpoIISB proteins on chromosomes of other Bacilli species. The SpoIISA-like proteins from Bacillus anthracis and Bacillus cereus were shown to manifest the same effect on the viability of E. coli as their homologue from B. subtilis . Moreover, expression of the proposed spoIISB -like gene rescues E. coli cells from death induced by the SpoIISA homologue.     

Identifying responsive functional modules from protein-protein interaction network

Proteins interact with each other within a cell, and those interactions give rise to the biological function and dynamical behavior of cellular systems. Generally, the protein interactions are temporal, spatial, or condition dependent in a specific cell, where only a small part of interactions usually take place under certain conditions. Recently, although a large amount of protein interaction data have been collected by high-throughput technologies, the interactions are recorded or summarized under various or different conditions and therefore cannot be directly used to identify signaling pathways or active networks, which are believed to work in specific cells under specific conditions. However, protein interactions activated under specific conditions may give hints to the biological process underlying corresponding phenotypes. In particular, responsive functional modules consist of protein interactions activated under specific conditions can provide insight into the mechanism underlying biological systems, e.g. protein interaction subnetworks found for certain diseases rather than normal conditions may help to discover potential biomarkers. From computational viewpoint, identifying responsive functional modules can be formulated as an optimization problem. Therefore, efficient computational methods for extracting responsive functional modules are strongly demanded due to the NP-hard nature of such a combinatorial problem. In this review, we first report recent advances in development of computational methods for extracting responsive functional modules or active pathways from protein interaction network and microarray data. Then from computational aspect, we discuss remaining obstacles and perspectives for this attractive and challenging topic in the area of systems biology.     

A complete map of the Calcium/calmodulin-dependent protein kinase kinase 2 (CAMKK2) signaling pathway

Calcium/calmodulin-dependent protein kinase kinase 2 (CAMKK2) is a serine/threonine-protein kinase belonging to the Ca²⁺/calmodulin-dependent protein kinase subfamily. CAMKK2 has an autocatalytic site, which gets exposed when Ca²⁺/calmodulin (CAM) binds to it. This results in autophosphorylation and complete activation of CAMKK2. The three major known downstream targets of CAMKK2 are 5′-adenosine monophosphate (AMP)-activated protein kinase (AMPKα), calcium/calmodulin-dependent protein kinase 1 (CAMK1) and calcium/calmodulin-dependent protein kinase 4 (CAMK4). Activation of these targets by CAMKK2 is important for the maintenance of different cellular and physiological processes within the cell. CAMKK2 is found to be important in neuronal development, bone remodeling, adipogenesis, and systemic glucose homeostasis, osteoclastgensis and postnatal myogensis. CAMKK2 is reported to be involved in pathologies like Duchenne muscular dystrophy, inflammation, osteoporosis and bone remodeling and is also reported to be overexpressed in prostate cancer, hepatic cancer, ovarian and gastric cancer. CAMKK2 is involved in increased cell proliferation and migration through CAMKK2/AMPK pathway in prostate cancer and activation of AKT in ovarian cancer. Although CAMKK2 is a molecule of great importance, a public resource of the CAMKK2 signaling pathway is currently lacking. Therefore, we carried out detailed data mining and documentation of the signaling events associated with CAMKK2 from published literature and developed an integrated reaction map of CAMKK2 signaling. This resulted in the cataloging of 285 reactions belonging to the CAMKK2 signaling pathway, which includes 33 protein–protein interactions, 74 post-translational modifications, 7 protein translocation events, and 22 activation/inhibition events. Besides, 124 gene regulation events and 25 activator/inhibitors involved in CAMKK2 activation were also cataloged. The CAMKK2 signaling pathway map data is made freely accessible through WikiPathway database (https://www.wikipathways.org/index.php/Pathway:WP4874). We expect that data on a signaling map of CAMKK2 will provide the scientific community with an improved platform to facilitate further molecular as well as biomedical investigations on CAMKK2 and its utility in the development of biomarkers and therapeutic targets.Electronic supplementary materialThe online version of this article (10.1007/s12079-020-00592-1) contains supplementary material, which is available to authorized users.     

The nature of the carbohydrate binding module determines the catalytic efficiency of xylanase Z of Clostridium thermocellum

Xylanase Z of Clostridium thermocellum exists as a complex in the cellulosome with N-terminus feruloyl esterase, a carbohydrate binding module (CBM6) and a dockerin domain. To study the role of the binding modules on the activity of XynZ, different variants with the CBM6 attached to the catalytic domain at its C-terminal (XynZ-CB) and N-terminal (XynZ-BC), and the CBM22 attached at N-terminus (XynZ-B′C) were expressed in Escherichia coli at levels around 30% of the total cell proteins. The activities of XynZ-BC, XynZ-CB and XynZ-B′C were 4200, 4180 and 20,700 U μM⁻¹ against birchwood xylan, respectively. Substrate binding studies showed that in case of XynZ-BC and XynZ-CB the substrate birchwood xylan remaining unbound were 51 and 52%, respectively, whereas in the case of XynZ-B′C the substrate remaining unbound was 39% under the assay conditions used. The molecular docking studies showed that the binding site of CBM22 in XynZ-B′C is more exposed and thus available for substrate binding as compared to the tunnel shape binding pocket produced in XynZ-BC and thus hindering the substrate binding. The substrate binding data for the two constructs are in agreement with this explanation.     

DOMINO: a network‐based active module identification algorithm with reduced rate of false calls

Algorithms for active module identification (AMI) are central to analysis of omics data. Such algorithms receive a gene network and nodes'' activity scores as input and report subnetworks that show significant over‐representation of accrued activity signal (“active modules”), thus representing biological processes that presumably play key roles in the analyzed conditions. Here, we systematically evaluated six popular AMI methods on gene expression and GWAS data. We observed that GO terms enriched in modules detected on the real data were often also enriched on modules found on randomly permuted data. This indicated that AMI methods frequently report modules that are not specific to the biological context measured by the analyzed omics dataset. To tackle this bias, we designed a permutation‐based method that empirically evaluates GO terms reported by AMI methods. We used the method to fashion five novel AMI performance criteria. Last, we developed DOMINO, a novel AMI algorithm, that outperformed the other six algorithms in extensive testing on GE and GWAS data. Software is available at https://github.com/Shamir‐Lab.     

The efficacy of the antibacterial peptide,pyrrhocoricin, is finely regulated by its amino acid residues and active domains

Summary Pyrrhocoricin, a highly active antibacterial peptide isolated from insects, inhibits chaperone-assisted protein folding via binding to the 70 kDa heat shock protein DnaK with its amino terminal half. The C-terminus functions as an intracellular delivery module. In the current study, chimeras consisting of the putative functional units of pyrrhocoricin and a related peptide, drosocin, were made, and it was found that some mixed and matched sequences retained their ability to killEscherichia coli, Salmonella typhimurium andAgrobacterium tumefaciens. While pyrrhocoricin appeared to have a more universal pharmacophore, drosocin featured a more robust intracellular delivery unit. We also identified the minimal length of pyrrhocoricin that is needed to efficiently kill bacteria. While for activity againstS. typhimurium the peptide could not be shortened, againstE. coli it was sufficient to have a Vall-Ile16 amino-terminal fragment. Although Vall was not part of the Asp2-Pro 10 pharmacophore (it could be replaced with other residues), it could not be eliminated and apparently played an important role in defining the activity of the peptide. Indeed, when Val1 was replaced with lysine, not only the efficacy of pyrrhocoricin to kill the sensitive strains increased significantly, resulting in the most active antimicrobial peptide against some clinical strains ever made, but the modified peptide was also able to killPseudomonas aeruginosa, an originally unresponsive bacterium in the low μg ml⁻¹ concentration range. However, this substitution likely influenced the interaction with bacterial membranes rather than that with the target protein, and therefore the dominant mode of action of the Lysl-pyrrhocoricin peptide may feature membrane disintegration instead of DnaK inhibition.