Are Myxobacteria intelligent?

Artificial intelligence vs. evolving super-complex tumor intelligence: critical viewpoints

Recent developments in various domains have led to a growing interest in the potential of artificial intelligence to enhance our lives and environments. In particular, the application of artificial intelligence in the management of complex human diseases, such as cancer, has garnered significant attention. The evolution of artificial intelligence is thought to be influenced by multiple factors, including human intervention and environmental factors. Similarly, tumors, being heterogeneous and complex diseases, continue to evolve due to changes in the physical, chemical, and biological environment. Additionally, the concept of cellular intelligence within biological systems has been recognized as a potential attribute of biological entities. Therefore, it is plausible that the tumor intelligence present in cancer cells of affected individuals could undergo super-evolution due to changes in the pro-tumor environment. Thus, a comparative analysis of the evolution of artificial intelligence and super-complex tumor intelligence could yield valuable insights to develop better artificial intelligence-based tools for cancer management.

Neurons learn by predicting future activity

Understanding how the brain learns may lead to machines with human-like intellectual capacities. It was previously proposed that the brain may operate on the principle of predictive coding. However, it is still not well understood how a predictive system could be implemented in the brain. Here we demonstrate that the ability of a single neuron to predict its future activity may provide an effective learning mechanism. Interestingly, this predictive learning rule can be derived from a metabolic principle, where neurons need to minimize their own synaptic activity (cost), while maximizing their impact on local blood supply by recruiting other neurons. We show how this mathematically derived learning rule can provide a theoretical connection between diverse types of brain-inspired algorithms, thus, offering a step toward development of a general theory of neuronal learning. We tested this predictive learning rule in neural network simulations and in data recorded from awake animals. Our results also suggest that spontaneous brain activity provides “training data” for neurons to learn to predict cortical dynamics. Thus, the ability of a single neuron to minimize surprise: i.e. the difference between actual and expected activity, could be an important missing element to understand computation in the brain.

Predictive Neuronal Adaptation as a Basis for Consciousness

Being able to correctly predict the future and to adjust own actions accordingly can offer a great survival advantage. In fact, this could be the main reason why brains evolved. Consciousness, the most mysterious feature of brain activity, also seems to be related to predicting the future and detecting surprise: a mismatch between actual and predicted situation. Similarly at a single neuron level, predicting future activity and adapting synaptic inputs accordingly was shown to be the best strategy to maximize the metabolic energy for a neuron. Following on these ideas, here we examined if surprise minimization by single neurons could be a basis for consciousness. First, we showed in simulations that as a neural network learns a new task, then the surprise within neurons (defined as the difference between actual and expected activity) changes similarly to the consciousness of skills in humans. Moreover, implementing adaptation of neuronal activity to minimize surprise at fast time scales (tens of milliseconds) resulted in improved network performance. This improvement is likely because adapting activity based on the internal predictive model allows each neuron to make a more "educated" response to stimuli. Based on those results, we propose that the neuronal predictive adaptation to minimize surprise could be a basic building block of conscious processing. Such adaptation allows neurons to exchange information about own predictions and thus to build more complex predictive models. To be precise, we provide an equation to quantify consciousness as the amount of surprise minus the size of the adaptation error. Since neuronal adaptation can be studied experimentally, this can allow testing directly our hypothesis. Specifically, we postulate that any substance affecting neuronal adaptation will also affect consciousness. Interestingly, our predictive adaptation hypothesis is consistent with multiple ideas presented previously in diverse theories of consciousness, such as global workspace theory, integrated information, attention schema theory, and predictive processing framework. In summary, we present a theoretical, computational, and experimental support for the hypothesis that neuronal adaptation is a possible biological mechanism of conscious processing, and we discuss how this could provide a step toward a unified theory of consciousness.

In silico characterization of a novel putative aerotaxis chemosensory system in the myxobacterium, Corallococcus coralloides

Background

An efficient signal transduction system allows a bacterium to sense environmental cues and then to respond positively or negatively to those signals; this process is referred to as taxis. In addition to external cues, the internal metabolic state of any bacterium plays a major role in determining its ability to reside and thrive in its current environment. Similar to external signaling molecules, cytoplasmic signals are also sensed by methyl-accepting chemotaxis proteins (MCPs) via diverse ligand binding domains. Myxobacteria are complex soil-dwelling social microbes that can perform a variety of physiologic and metabolic activities ranging from gliding motility, sporulation, biofilm formation, carotenoid and secondary metabolite biosynthesis, predation, and slime secretion. To live such complex lifestyles, they have evolved efficient signal transduction systems with numerous one- and two-component regulatory system along with a large array of chemosensory systems to perceive and integrate both external and internal cues.

Results

Here we report the in silico characterization of a putative energy taxis cluster, Cc-5, which is present in only one amongst 34 known and sequenced myxobacterial genomes, Corallococcus coralloides. In addition, we propose that this energy taxis cluster is involved in oxygen sensing, suggesting that C. coralloides can sense (either directly or indirectly) and then respond to changing concentrations of molecular oxygen.

Conclusions

This hypothesis is based on the presence of a unique MCP encoded in this gene cluster that contains two different oxygen-binding sensor domains, PAS and globin. In addition, the two monooxygenases encoded in this cluster may contribute to aerobic respiration via ubiquinone biosynthesis, which is part of the cytochrome bc1 complex. Finally, we suggest that this cluster was acquired from Actinobacteria, Gammaproteobacteria or Cyanobacteria. Overall, this in silico study has identified a potentially innovative and evolved mechanism of energy taxis in only one of the myxobacteria, C. coralloides.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-5151-6) contains supplementary material, which is available to authorized users.

Viruses and mobile elements as drivers of evolutionary transitions

The history of life is punctuated by evolutionary transitions which engender emergence of new levels of biological organization that involves selection acting at increasingly complex ensembles of biological entities. Major evolutionary transitions include the origin of prokaryotic and then eukaryotic cells, multicellular organisms and eusocial animals. All or nearly all cellular life forms are hosts to diverse selfish genetic elements with various levels of autonomy including plasmids, transposons and viruses. I present evidence that, at least up to and including the origin of multicellularity, evolutionary transitions are driven by the coevolution of hosts with these genetic parasites along with sharing of ‘public goods’. Selfish elements drive evolutionary transitions at two distinct levels. First, mathematical modelling of evolutionary processes, such as evolution of primitive replicator populations or unicellular organisms, indicates that only increasing organizational complexity, e.g. emergence of multicellular aggregates, can prevent the collapse of the host–parasite system under the pressure of parasites. Second, comparative genomic analysis reveals numerous cases of recruitment of genes with essential functions in cellular life forms, including those that enable evolutionary transitions.

This article is part of the themed issue ‘The major synthetic evolutionary transitions’.

The Chemical Ecology of Predatory Soil Bacteria

The study of natural products is entering a renaissance, driven by the discovery that the majority of bacterial secondary metabolites are not produced under standard laboratory conditions. Understanding the ecological role of natural products is key to efficiently directing our screening efforts, and to ensuring that each screen efficiently captures the full biosynthetic repertoire of the producing organisms. Myxobacteria represent one of the most common and diverse groups of bacteria, with roughly 2500 strains publically available. Fed largely through predation, the myxobacteria have developed a large repertoire of natural products that target other microorganisms, including bacteria and fungi. Many of these interactions can be observed in predation assays, providing direct evidence for environmental interactions. With a focus on Myxococcus xanthus, this review will highlight how recent advances in myxobacteria are revealing the chemical ecology of bacterial natural products.