Self-Organization and Information Processing: from Basic Enzymatic Activities to Complex Adaptive Cellular Behavior

One of the main aims of current biology is to understand the origin of the molecular organization that underlies the complex dynamic architecture of cellular life. Here, we present an overview of the main sources of biomolecular order and complexity spanning from the most elementary levels of molecular activity to the emergence of cellular systemic behaviors. First, we have addressed the dissipative self-organization, the principal source of molecular order in the cell. Intensive studies over the last four decades have demonstrated that self-organization is central to understand enzyme activity under cellular conditions, functional coordination between enzymatic reactions, the emergence of dissipative metabolic networks (DMN), and molecular rhythms. The second fundamental source of order is molecular information processing. Studies on effective connectivity based on transfer entropy (TE) have made possible the quantification in bits of biomolecular information flows in DMN. This information processing enables efficient self-regulatory control of metabolism. As a consequence of both main sources of order, systemic functional structures emerge in the cell; in fact, quantitative analyses with DMN have revealed that the basic units of life display a global enzymatic structure that seems to be an essential characteristic of the systemic functional metabolism. This global metabolic structure has been verified experimentally in both prokaryotic and eukaryotic cells. Here, we also discuss how the study of systemic DMN, using Artificial Intelligence and advanced tools of Statistic Mechanics, has shown the emergence of Hopfield-like dynamics characterized by exhibiting associative memory. We have recently confirmed this thesis by testing associative conditioning behavior in individual amoeba cells. In these Pavlovian-like experiments, several hundreds of cells could learn new systemic migratory behaviors and remember them over long periods relative to their cell cycle, forgetting them later. Such associative process seems to correspond to an epigenetic memory. The cellular capacity of learning new adaptive systemic behaviors represents a fundamental evolutionary mechanism for cell adaptation.This work was supported by the University of Basque Country UPV/EHU and Basque Center of Applied Mathematics, grant US18/2

Evidence of conditioned behavior in amoebae

Associative memory is the main type of learning by which complex organisms endowed with evolved nervous systems respond efficiently to certain environmental stimuli. It has been found in different multicellular species, from cephalopods to humans, but never in individual cells. Here we describe a motility pattern consistent with associative conditioned behavior in the microorganism Amoeba proteus. We use a controlled direct-current electric field as the conditioned stimulus, and a specific chemotactic peptide as the unconditioned stimulus. The amoebae are capable of linking two independent past events, generating persistent locomotion movements that can prevail for 44 min on average. We confirm a similar behavior in a related species, Metamoeba leningradensis. Thus, our results indicate that unicellular organisms can modify their behavior during migration by associative conditioning.We would like to thank Dr. Andrew Goodkov from the Institute of Cytology (Russian Academy of Science) St. Petersburg, Russia, for valuable advices related to Amoeba organisms, Laura Perez Gomez and Luis Rojo Garcia for their assistance designing Fig. 1 and the AutoCAD 3D model, A-M Perez Biedermann for her valuable contribution in our study, Jose Gonzalez Romero and Jose Miguel Perez Perez from the Institute of Parasitology and Biomedicine "Lopez-Neyra" for their technical assistance. In addition, we thank Maria Calleja-Felipe for her valuable help in the peptide gradient experiments. This work was supported by a grant of the University of Basque Country (UPV/EHU), GIU17/066, the Basque Government grant IT974-16, and by the UPV/EHU and Basque Center of Applied Mathematics, grant US18/21"

Computational and Experimental Evaluation of the Immune Response of Neoantigens for Personalized Vaccine Design

In the last few years, the importance of neoantigens in the development of personalized antitumor vaccines has increased remarkably. In order to study whether bioinformatic tools are effective in detecting neoantigens that generate an immune response, DNA samples from patients with cutaneous melanoma in different stages were obtained, resulting in a total of 6048 potential neoantigens gathered. Thereafter, the immunological responses generated by some of those neoantigens ex vivo were tested, using a vaccine designed by a new optimization approach and encapsulated in nanoparticles. Our bioinformatic analysis indicated that no differences were found between the number of neoantigens and that of non-mutated sequences detected as potential binders by IEDB tools. However, those tools were able to highlight neoantigens over non-mutated peptides in HLA-II recognition (p-value 0.03). However, neither HLA-I binding affinity (p-value 0.08) nor Class I immunogenicity values (p-value 0.96) indicated significant differences for the latter parameters. Subsequently, the new vaccine, using aggregative functions and combinatorial optimization, was designed. The six best neoantigens were selected and formulated into two nanoparticles, with which the immune response ex vivo was evaluated, demonstrating a specific activation of the immune response. This study reinforces the use of bioinformatic tools in vaccine development, as their usefulness is proven both in silico and ex vivo.This work was supported by Basque Government funding (IT456-22; IT1448-22, IT693-22 and IT1524-22; ONKOVAC 2021111042), as well as by the UPV/EHU (GIU20/035; US21/27; US18/21; PIF18/295) and Basque Center of Applied Mathematics (US21/27 and US18/21)

Stability of SARS-CoV-2 spike antigens against mutations

Modern health care needs preventive vaccines and therapeutic treatments with stability against pathogen mutations to cope with current and future viral infections. At the beginning of the COVID-19 pandemic, our analytic and predictive tool identified a set of eight short SARS-CoV-2 S-spike protein epitopes that had the potential to persistently avoid mutation. Here a combination of genetic, Systems Biology and protein structure analyses confirm the stability of our identified epitopes against viral mutations. Remarkably, this research spans the whole period of the pandemic, during which 93.9% of the eight peptides remained invariable in the globally predominant 43 circulating variants, including Omicron. Likewise, the selected epitopes are conserved in 97% of all 1,514 known SARS-CoV-2 lineages. Finally, experimental analyses performed with these short peptides showed their specific immunoreactivity. This work opens a new perspective on the design of next-generation vaccines and antibody therapies that will remain reliable against future pathogen mutations.Dr. Lozano-Perez acknowledges the European Commission ERDF/FEDER Operational Program 'Murcia' CCI No. 2007ES161PO001 (Project No. 14-20/20). Miodrag Grbic acknowledges support from the NSERC Discovery grant (Canada). This work also has received funding from the Department of Education of the Basque Government via the Consolidated Research Group MATH MODE (IT1456-22). Besides, Ildefonso Martinez De la Fuente and Iker Malaina were supported by the UPV/EHU and Basque Center of Applied Mathematics, grant US21/27N