Roles of Nicotinamide Adenine Dinucleotide (NAD+) in Biological Systems

Genomic diversity across the Rickettsia and 'Candidatus Megaira' genera and proposal of genus status for the Torix group

Members of the bacterial genus Rickettsia were originally identified as causative agents of vector-borne diseases in mammals. However, many Rickettsia species are arthropod symbionts and close relatives of 'Candidatus Megaira', which are symbiotic associates of microeukaryotes. Here, we clarify the evolutionary relationships between these organisms by assembling 26 genomes of Rickettsia species from understudied groups, including the Torix group, and two genomes of 'Ca. Megaira' from various insects and microeukaryotes. Our analyses of the new genomes, in comparison with previously described ones, indicate that the accessory genome diversity and broad host range of Torix Rickettsia are comparable to those of all other Rickettsia combined. Therefore, the Torix clade may play unrecognized roles in invertebrate biology and physiology. We argue this clade should be given its own genus status, for which we propose the name 'Candidatus Tisiphia'.

ADP-Ribosylation as Post-Translational Modification of Proteins: Use of Inhibitors in Cancer Control

Among the post-translational modifications of proteins, ADP-ribosylation has been studied for over fifty years, and a large set of functions, including DNA repair, transcription, and cell signaling, have been assigned to this post-translational modification (PTM). This review presents an update on the function of a large set of enzyme writers, the readers that are recruited by the modified targets, and the erasers that reverse the modification to the original amino acid residue, removing the covalent bonds formed. In particular, the review provides details on the involvement of the enzymes performing monoADP-ribosylation/polyADP-ribosylation (MAR/PAR) cycling in cancers. Of note, there is potential for the application of the inhibitors developed for cancer also in the therapy of non-oncological diseases such as the protection against oxidative stress, the suppression of inflammatory responses, and the treatment of neurodegenerative diseases. This field of studies is not concluded, since novel enzymes are being discovered at a rapid pace.

Nicotinamide Inhibits Aphid Fecundity and Impacts Survival

Nicotinamide (NAM) alters behavior in C. elegans and Drosophila, serving as an agonist of TRPV channels affecting sensory neurons and mimicking the mode of action of insecticides used to control phloem-feeding insects. The impact of NAM on green peach aphid (Myzus persicae) behaviors was assessed in artificial diet assays and foliar applications to Arabidopsis plants. Aphids feeding on artificial diets supplemented with NAM impaired stylet movement causing feeding interruptions and ultimately starvation and death. Aphid feeding behaviors were negatively impacted on NAM sprayed plants at concentrations as low as 2.5 mM leading to increased mortality. In choice assays with NAM sprayed leaves aphids showed clear preference for untreated control leaves. NAM is an intermediate in the NAD salvage pathway that should accumulate in nicotinamidase (nic) mutants. LC-MS analysis showed NAM accumulates 60-fold in nic-1-1 Arabidopsis mutants as compared with Col-0. Aphid reproductive potential was significantly decreased on nic-1-1 mutant plants, resulting in a smaller colony size and arrested population development. The results support the hypothesis that dietary NAM causes behavioral changes in aphids, including altered feeding, reduced reproduction, and increased mortality. NAM is thought to bind to TRPV channels causing overstimulation of sensory neurons in the aphid feeding apparatus.

Identification of coenzyme-binding proteins with machine learning algorithms

The coenzyme-binding proteins play a vital role in the cellular metabolism processes, such as fatty acid biosynthesis, enzyme and gene regulation, lipid synthesis, particular vesicular traffic, and β-oxidation donation of acyl-CoA esters. Based on the theory of Star Graph Topological Indices (SGTIs) of protein primary sequences, we proposed a method to develop a first classification model for predicting protein with coenzyme-binding properties. To simulate the properties of coenzyme-binding proteins, we created a dataset containing 2897 proteins, among 456 proteins functioned as coenzyme-binding activity. The SGTIs of peptide sequence were calculated with Sequence to Star Network (S2SNet) application. We used the SGTIs as inputs to several classification techniques with a machine learning software - Weka. A Random Forest classifier based on 3 features of the embedded and non-embedded graphs was identified as the best predictive model for coenzyme-binding proteins. This model developed was with the true positive (TP) rate of 91.7%, false positive (FP) rate of 7.6%, and Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.971. The prediction of new coenzyme-binding activity proteins using this model could be useful for further drug development or enzyme metabolism researches.