SARM1 (sterile alpha and TIR motif containing 1) is responsible for depletion of nicotinamide adenine dinucleotide in its oxidized form (NAD+) during Wallerian degeneration associated with neuropathies. Plant nucleotide-binding leucine-rich repeat (NLR) immune receptors recognize pathogen effector proteins and trigger localized cell death to restrict pathogen infection. Both processes depend on closely related Toll/interleukin-1 receptor (TIR) domains in these proteins, which, as we show, feature self-association–dependent NAD+ cleavage activity associated with cell death signaling. We further show that SARM1 SAM (sterile alpha motif) domains form an octamer essential for axon degeneration that contributes to TIR domain enzymatic activity. The crystal structures of ribose and NADP+ (the oxidized form of nicotinamide adenine dinucleotide phosphate) complexes of SARM1 and plant NLR RUN1 TIR domains, respectively, reveal a conserved substrate binding site. NAD+ cleavage by TIR domains is therefore a conserved feature of animal and plant cell death signaling pathways.

Automated signal recognition software is increasingly used to extract species detection data from acoustic recordings collected using autonomous recording units (ARUs), but there is little practical guidance available for ecologists on the application of this technology. Performance evaluation is an important part of employing automated acoustic recognition technology because the resulting data quality can vary with a variety of factors. We reviewed the bioacoustic literature to summarize performance evaluation and found little consistency in evaluation, metrics employed, or terminology used. We also found that few studies examined how score threshold, i.e., cutoff for the level of confidence in target species classification, affected performance, but those that did showed a strong impact of score threshold on performance. We used the lessons learned from our literature review and best practices from the field of machine learning to evaluate the performance of five readily-available automated signal recognition programs. We used the Common Nighthawk (Chordeiles minor) as our model species because it has simple, consistent, and frequent vocalizations. We found that automated signal recognition was effective for determining Common Nighthawk presence-absence and call rate, particularly at low score thresholds, but that occupancy estimates from the data processed with recognizers were consistently lower than from data generated by human listening and became unstable at high score thresholds. Of the five programs evaluated, our convolutional neural network (CNN) recognizer performed best, with recognizers built in Song Scope and MonitoR also performing well. The RavenPro and Kaleidoscope recognizers were moderately effective, but produced more false positives than the other recognizers. Finally, we synthesized six general recommendations for ecologists who employ automated signal recognition software, including what to use as a test benchmark, how to incorporate score threshold, what metrics to use, and how to evaluate efficiency. Future studies should consider our recommendations to build a body of literature on the effectiveness of this technology for avian research and monitoring. Recommandations pour l'évaluation des performances de reconnaissance acoustique et application à cinq programmes courants de reconnaissance automatisée de signaux sonores RÉSUMÉ. Les logiciels de reconnaissance automatisée de signaux sonores sont de plus en plus utilisés pour extraire les données de détection des espèces d'enregistrements acoustiques récoltés au moyen d'unités d'enregistrement autonomes (ARU en anglais), mais il existe peu d'instructions pratiques sur l'utilisation de cette technologie pour les écologistes. L'évaluation de la performance est une étape importante dans l'utilisation d'une technologie de reconnaissance acoustique automatisée parce que la qualité des résultats peut varier en fonction de divers facteurs. Nous avons passé en revue la littérature sur la bioacoustique afin de résumer les critères d'évaluation de ...

Background: The glucose-methanol-choline (GMC) superfamily is a large and functionally diverse family of oxidoreductases that share a common structural fold. Fungal members of this superfamily that are characterised and relevant for lignocellulose degradation include aryl-alcohol oxidoreductase, alcohol oxidase, cellobiose dehydrogenase, glucose oxidase, glucose dehydrogenase, pyranose dehydrogenase, and pyranose oxidase, which together form family AA3 of the auxiliary activities in the CAZy database of carbohydrate-active enzymes. Overall, little is known about the extant sequence space of these GMC oxidoreductases and their phylogenetic relations. Although some individual forms are well characterised, it is still unclear how they compare in respect of the complete enzyme class and, therefore, also how generalizable are their characteristics. Results: To improve the understanding of the GMC superfamily as a whole, we used sequence similarity networks to cluster large numbers of fungal GMC sequences and annotate them according to functionality. Subsequently, different members of the GMC superfamily were analysed in detail with regard to their sequences and phylogeny. This allowed us to define the currently characterised sequence space and show that complete clades of some enzymes have not been studied in any detail to date. Finally, we interpret our results from an evolutionary perspective, where we could show, for example, that pyranose dehydrogenase evolved from aryl-alcohol oxidoreductase after a change in substrate specificity and that the cytochrome domain of cellobiose dehydrogenase was regularly lost during evolution. Conclusions: This study offers new insights into the sequence variation and phylogenetic relationships of fungal GMC/AA3 sequences. Certain clades of these GMC enzymes identified in our phylogenetic analyses are completely uncharacterised to date, and might include enzyme activities of varying specificities and/or activities that are hitherto unstudied.

The N-terminal Toll/interleukin-1 receptor/resistance protein (TIR) domain has been shown to be both necessary and sufficient for defense signaling in the model plants flax and Arabidopsis. In examples from these organisms, TIR domain self-association is required for signaling function, albeit through distinct interfaces. Here, we investigate these properties in the TIR domain containing resistance protein RPV1 from the wild grapevine Muscadinia rotundifolia. The RPV1 TIR domain, without additional flanking sequence present, is autoactive when transiently expressed in tobacco, demonstrating that the TIR domain alone is capable of cell-death signaling. We determined the crystal structure of the RPV1 TIR domain at 2.3 Å resolution. In the crystals, the RPV1 TIR domain forms a dimer, mediated predominantly through residues in the αA and αE helices (“AE” interface). This interface is shared with the interface discovered in the dimeric complex of the TIR domains from the Arabidopsis RPS4/RRS1 resistance protein pair. We show that surface-exposed residues in the AE interface that mediate the dimer interaction in the crystals are highly conserved among plant TIR domain-containing proteins. While we were unable to demonstrate self-association of the RPV1 TIR domain in solution or using yeast 2-hybrid, mutations of surface-exposed residues in the AE interface prevent the cell-death autoactive phenotype. In addition, mutation of residues known to be important in the cell-death signaling function of the flax L6 TIR domain were also shown to be required for RPV1 TIR domain mediated cell-death. Our data demonstrate that multiple TIR domain surfaces control the cell-death function of the RPV1 TIR domain and we suggest that the conserved AE interface may have a general function in TIR-NLR signaling.

Ancestral sequence reconstruction is a technique that is gaining widespread use in molecular evolution studies and protein engineering. Accurate reconstruction requires the ability to handle appropriately large numbers of sequences, as well as insertion and deletion (indel) events, but available approaches exhibit limitations. To address these limitations, we developed Graphical Representation of Ancestral Sequence Predictions (GRASP), which efficiently implements maximum likelihood methods to enable the inference of ancestors of families with more than 10,000 members. GRASP implements partial order graphs (POGs) to represent and infer insertion and deletion events across ancestors, enabling the identification of building blocks for protein engineering. To validate the capacity to engineer novel proteins from realistic data, we predicted ancestor sequences across three distinct enzyme families: glucose-methanol-choline (GMC) oxidoreductases, cytochromes P450, and dihydroxy/sugar acid dehydratases (DHAD). All tested ancestors demonstrated enzymatic activity. Our study demonstrates the ability of GRASP (1) to support large data sets over 10,000 sequences and (2) to employ insertions and deletions to identify building blocks for engineering biologically active ancestors, by exploring variation over evolutionary time.