Clemente F. Arias’s research while affiliated with Margarita Salas Center for Biological Research and other places

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Publications (39)


Application of control theory to biological regulation. A Block diagram of a typical engineering control system. B Regulation of blood glucose by insulin. Pancreatic β\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta $$\end{document}-cells continuously monitor blood glucose levels and react to hyperglycemia by secreting insulin into the bloodstream. Insulin targets a variety of tissues across the body, including muscle, liver, and adipose tissue, promoting the uptake, storage, and utilization of glucose, thus reducing the concentration of glucose in the blood. C In terms of control theory, pancreatic β\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta $$\end{document}-cells act simultaneously as sensors, detecting whether glucose levels exceed a critical threshold (the set point), and controllers, using insulin to orchestrate a coordinated systemic response aimed at lowering blood glucose through its action on a variety of actuators scattered throughout the organism. This mode of action defines a feedback mechanism that brings the system toward its predefined set point
Stock and flow representation of simple homeostatic systems. A–B Basic elements of system’s dynamics models. Stocks represent variables and solid arrows indicate inflows (positive flows) and outflows (negative flows), processes that increase and decrease the value of variables respectively (A). Biflows allow for either positive or negative flows from a stock. The direction of the flow is determined by the biflow’s sign (B). C–D Demand-driven homeostatic systems. The value of the regulated variable changes under the influence of an unregulated external outflow. Control signals may balance this effect by upregulating (C) or inhibiting (D) a homeostatic inflow. In turn, the value of the regulated variable is used by controllers to increase or decrease the levels of the control signal. This effect is modeled as a biflow to indicate that it may be positive or negative depending on the particular expression used to model the dynamics of the control signal. E–F Supply-driven homeostatic systems. In these models, the value of the regulated variable is determined by the balance between an external inflow and a homeostatic flow, inhibited (E) or upregulated (F) by the control signal. Controllers and actuators are implicitly considered through their resultant influence on the dynamics of the system. Dashed arrows represent the flow of information. Blunt and pointed arrows indicate inhibition and upregulation respectively. H: homeostatic flow; E: external flow
Examples of supply- and demand- driven homeostatic systems. A, B Demand-driven homeostatic mechanisms equivalent to the abstract models shown in Fig. 2C, D. C, D Supply-driven homeostatic mechanisms analogous to the models in Fig. 2. E, F. For simplicity, actuators and controllers are not shown. Flows regulated by controllers are shown in green, and those governed by actuators in yellow. External flows are shown in blue. Dashed arrows represent the flow of information. Blunt and pointed arrows indicate inhibition and upregulation respectively
Models of blood glucose regulation by insulin. A Steady-state value of blood glucose as a function of glucose uptake according to Eq. 11 for different rates of insulin secretion (upper) and degradation (lower). Dashed lines correspond to the unregulated system. B Steady-state value of blood glucose as a function of glucose consumption according to model 11 for different rates of insulin secretion (upper) and degradation (lower). C Diagram of a supply-driven model of insulin regulation including the paracrine effect of insulin on its own secretion. D Steady-state value of blood glucose as a function of glucose uptake according to model 12 for different rates of insulin secretion (left) and degradation (right). Parameter values: δG=0.6\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\delta _G=0.6$$\end{document} and μ=0.1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mu =0.1$$\end{document}. Scenarios of insulin secretion: sS=1,2,4\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s_S=1,2,4$$\end{document} and dS=2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$d_S=2$$\end{document}. Scenarios of insulin degradation: dS=1,2,4\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$d_S=1,2,4$$\end{document} and sS=2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s_S=2$$\end{document}. Dashed lines in A and B correspond to the unregulated model (sS=0\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s_S=0$$\end{document}, dS=0\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$d_S=0$$\end{document}, and μ=0\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mu =0$$\end{document}). Parameters have been arbitrarily chosen to illustrate the dynamics of the models
Regulation of intracellular oxygen homeostasis by Hypoxia-inducible factors (HIFs) according to model 13. A Steady-state expression of HIF-α\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha $$\end{document} as a function of extracellular oxygen levels for different values of HIF-α\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha $$\end{document} synthesis (upper) and degradation (lower). B Steady-state value of intracellular oxygen as a function of extracellular oxygen availability for different values of HIF-α\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha $$\end{document} synthesis (upper) and degradation (lower). C Steady-state expression of HIF-α\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha $$\end{document} as a function of respiration rate for different values of HIF-α\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha $$\end{document} synthesis (upper) and degradation (lower). D Steady-state value of intracellular oxygen as a function of respiration rate for different values of HIF-α\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha $$\end{document} synthesis (upper) and degradation (lower). Dashed lines correspond to the unregulated system. Parameter values: ρ=10\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\rho =10$$\end{document}, λ=2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\lambda =2$$\end{document}, and τ=0.9\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau =0.9$$\end{document}. Scenarios of HIF-α\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha $$\end{document} synthesis: sH=0,100,200,300\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s_H = 0, 100, 200, 300$$\end{document} and dH=0.01\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$d_H = 0.01$$\end{document}. Scenarios of HIF-α\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha $$\end{document} degradation: sH=200\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s_H=200$$\end{document}, and dH=0.01,0.02,0.03\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$d_H=0.01,0.02, 0.03$$\end{document}. Parameters have been arbitrarily chosen to illustrate the dynamics of the models
A functional approach to homeostatic regulation
  • Article
  • Full-text available

December 2024

Biology Direct

Clemente F. Arias

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Cristina Fernández-Arias

In this work, we present a novel modeling framework for understanding the dynamics of homeostatic regulation. Inspired by engineering control theory, this framework incorporates unique features of biological systems. First, biological variables often play physiological roles, and taking this functional context into consideration is essential to fully understand the goals and constraints of homeostatic regulation. Second, biological signals are not abstract variables, but rather material molecules that may undergo complex turnover processes of synthesis and degradation. We suggest that the particular nature of biological signals may condition the type of information they can convey, and their potential role in shaping the dynamics and the ultimate purpose of homeostatic systems. We show that the dynamic interplay between regulated variables and control signals is a key determinant of biological homeostasis, challenging the necessity and the convenience of strictly extrapolating concepts from engineering control theory in modeling the dynamics of homeostatic systems. This work provides a simple, unified framework for studying biological regulation and identifies general principles that transcend molecular details of particular homeostatic mechanisms. We show how this approach can be naturally applied to apparently different regulatory systems, contributing to a deeper understanding of homeostasis as a fundamental process in living systems.

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Predicting and Explaining with Models: A Few Remarks on Mathematical Immunology

August 2024

Clemente F. Arias

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In this work, we discuss the limitations of mathematical models to provide explanations and make useful predictions in Biology. As a benchmark to assess the utility of models, we first create two toy systems that mimic the individual behavior of T cells during immune responses. We use these microscopic toy systems to simulate the process that is usually followed in mathematical immunology. We formulate macroscopic models that simulate the dynamics of the toy systems and analyze their explicative power. The rationale for our approach is that: (i) the microscopic causes of the observed behavior are (by definition) perfectly known, which allows evaluating the performance of the macroscopic models; and (ii) the predictions made by these models can easily be tested by comparing them with numerical simulations of the toy systems they intend to simulate. By using this strategy, we show that models can reproduce an observed behavior while being incompatible with the biological rules that produce that behavior. Therefore, the ability of a mathematical model to describe a given dynamics does not imply that it can explain the causes underlying it. We also show that a mechanistic approach is unnecessary to provide valuable insight into immunological problems. Macroscopic models of an utterly phenomenological nature can be used to predict important aspects of immune responses using virtually no information about the microscopic details of the interactions between effector immune cells and pathogens.


A functional approach to homeostatic regulation

May 2024

In this work, we present a novel modeling framework for understanding the dynamics of homeostatic regulation. Inspired by engineering control theory, this framework incorporates unique features of biological systems. First, biological variables often play physiological roles, and taking this functional context into consideration is essential to fully understand the goals and constraints of homeostatic regulation. Second, biological signals are not abstract variables, but rather material molecules that may undergo complex turnover processes of synthesis and degradation. We suggest that the particular nature of biological signals may condition the type of information they can convey, and their potential role in shaping the dynamics and the ultimate purpose of homeostatic systems. We show that the dynamic interplay between regulated variables and control signals is a key determinant of biological homeostasis, challenging the necessity and the convenience of strictly extrapolating concepts from engineering control theory in modeling the dynamics of homeostatic systems. This work provides an alternative, unified framework for studying biological regulation and identifies general principles that transcend molecular details of particular homeostatic mechanisms. We show how this approach can be naturally applied to apparently different regulatory systems, contributing to a deeper understanding of homeostasis as a fundamental process in living systems.


Figure 1. Prevailing model of HIFs regulation. A,B) HIF-α is currently assumed to follow a bimodal mode of expression, determined by extracellular oxygen tensions. In normoxia, the rate of PHD-mediated degradation suffices to maintain HIF-α expression at residual levels (A). The absence of oxygen restricts the activity of the PHDs, leading to the stabilization of HIF-α and enabling the activation of target genes (B). C) Dependence of HIF-α expression on extracellular oxygen tensions as observed in HeLa cells.
Figure 5. Homeostatic response to changes in metabolic supply. A) Dependence of HIF-α expression on the input of glucose, fatty acids, and anaplerotic substrates. B) Saturation of the NAD+/NADH cycle with electrons as a function of metabolic supply. The shaded region indicates saturations above 50% threshold that defines hypoxic stress. C,D) Downregulation of oxidative decarboxylation (C) and the TCA cycle (D) by HIFs metabolic supply. E,F) Upregulation of glycolysis (E) and fermentation (F) by HIFs. G) Lactate production as a function of metabolic supply for three different conditions of oxygen availability. These results correspond to numerical simulations of our model for a fixed value of oxygen uptake (see Supplementary Material). Axis units are arbitrary and the values are shown for illustrative purposes. Solid and dashed lines correspond to simulations with and without HIFs regulation respectively.
Redefining the role of Hypoxia-inducible factors (HIFs) in oxygen homeostasis

Hypoxia-inducible factors (HIFs) are key regulators of intracellular oxygen homeostasis. The marked increase in HIFs activity in hypoxia as compared to normoxia, together with their transcriptional control of primary metabolic pathways, motivated the widespread view of HIFs as responsible for the cell’s metabolic adaptation to hypoxic stress. In this work, we suggest that this prevailing model of HIFs regulation is misleading. We propose an alternative model focused on understanding the dynamics of HIFs’ activity within its physiological context. Our model suggests that HIFs would not respond to but rather prevent the onset of hypoxic stress by regulating the traffic of electrons between catabolic substrates and oxygen. The explanatory power of our approach is patent in its interpretation of the Warburg effect, the tendency of tumor cells to favor anaerobic metabolism over respiration, even in fully aerobic conditions. This puzzling behavior is currently considered as an anomalous metabolic deviation. Our model predicts the Warburg effect as the expected homeostatic response of tumor cells to the abnormal increase in metabolic demand that characterizes malignant phenotypes. This alternative perspective prompts a redefinition of HIFs’ function and underscores the need to explicitly consider the cell’s metabolic activity in understanding its responses to changes in oxygen availability.


Figure 1. Model of cellular automata in a compact bone. (A) A scheme of a compact bone, where the osseous tissue is mostly arranged in cylinders of concentric lamellae (osteons) around a central canal (Haversian canal) containing blood vessels. Osteocytes are disposed radially, converging towards Haversian canals. They are regularly placed throughout the mineralized matrix in cavities named lacunae and are connected to each other by means of thin cytoplasmic processes termed canaliculi. In this manner, osteocytes make up a highly interconnected network. (B) Empirical evidence suggests that osteocytes can be in two different states regarding sclerostin production. Active and inactive osteocytes appear stained in different colors in anatomical preparation [8]. (C) Based on A and B, we model the interactions between osteocytes within the framework of cellular automata assuming that osteocytes are disposed in a two-dimensional region, and that they may be active (in white, and label them as 1) or inactive (in black, and label them as 0) depending on whether they produce sclerostin, a protein that inhibits the starting of bone remodeling. The cellular automata model assumes that osteocytes can change their stage depending on the number of neighboring cells that are active at any time. Adapted from [8,9].
Figure 3. Simultaneous remodeling effects induced by different transition rules. (A,B) Numerical simulations of the models considered are discussed for different choices of transition rules. The matrices represent the time evolution of osteoblasts (in abscises). Each black point indicates the activation of an osteoblast at a particular time. (C) Number of activations of each osteoblast after 1000 iterations. More activations result in a faster scan of the bone. (D) Mean number of osteoblasts that are simultaneously active. This is another indicator of the dynamics of the mechanism of bone scanning: many active osteoblasts may result in bone fragility, which is not appropriate for a homeostatic mechanism of bone remodeling.
Figure 4. Robustness of admissible algorithms. (A) Transition rules ordered by the maximum number of remodeling events that occur simultaneously. (B) Period of osteoblast activation vs. distance between osteoblasts that activate at the same time. (C) Histogram of the number of valid rules that are adjacent to a valid rule. (D) Histogram of the number of invalid rules that are adjacent to a valid rule. (E) Histogram of the change in the period of osteoblast activation between adjacent transition rules. (F) Histogram of the change in the distance between active osteoblasts between adjacent rules. See text for details.
A Computational Approach to Individual Cell-Based Decision Algorithms Involved in Bone Remodeling

Mathematics

This work is concerned with bone remodeling, an intriguing and efficient biological process that ensures the optimal compliance of the human skeleton by screening and replacing any single piece of it on a recursive basis. We propose here that a class of algorithms, which are simple enough to be implemented at an individual cell level, suffices to account for the two main features of such homeostatic process: thorough screening of the whole skeleton on the one hand and destruction and subsequent replacement of any single bone piece on the other. This last process is accomplished at a microscopic scale by special groups of cells, assembled for that purpose, called Bone Multicellular Units (BMUs). Moreover, it is shown that the algorithms proposed are robust, i.e, they remain functional in a wide range of biomechanical environments, thus allowing for different remodeling rates at different places.


A new role for erythropoietin in the homeostasis of red blood cells

January 2024

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5 Citations

Communications Biology

The regulation of red blood cell (RBC) homeostasis is widely assumed to rely on the control of cell production by erythropoietin (EPO) and the destruction of cells at a fixed, species-specific age. In this work, we show that such a regulatory mechanism would be a poor homeostatic solution to satisfy the changing needs of the body. Effective homeostatic control would require RBC lifespan to be variable and tightly regulated. We suggest that EPO may control RBC lifespan by determining CD47 expression in newly formed RBCs and SIRP-α expression in sinusoidal macrophages. EPO could also regulate the initiation and intensity of anti-RBC autoimmune responses that curtail RBC lifespan in some circumstances. These mechanisms would continuously modulate the rate of RBC destruction depending on oxygen availability. The control of RBC lifespan by EPO and autoimmunity emerges as a key mechanism in the homeostasis of RBCs.


Annual production of plastics worldwide from 1950 to 2021 [5].
Global plastic polymers production by type in 2021, according to Plastics Europe [4].
Current paradigm of microbial degradation of plastics polymers.
Schematic representation of the life cycle of G. mellonella.
Beyond Microbial Biodegradation: Plastic Degradation by Galleria mellonella

November 2023

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11 Citations

One of the major environmental issues that modern civilizations are currently dealing with is the growing amount of plastic waste. Because of how they affect all forms of life, this waste is seen as a severe worldwide issue. Current methods for plastic waste disposal do not offer definitive solutions and often lead to the production of microplastics or secondary pollution. In recent years there has been a growing interest by the scientific community in the degradation of plastics by biological means, in particular the possibilities of using insects as a potential solution to the accumulation of this type of waste have been investigated. Among these, one of the most promising is undoubtedly the lepidopteran Galleria mellonella, which synthesizes the first ever discovered polyethylene degrading enzymes. In this review we propose an overview of plastic polymers production and common degradation methodologies, and analyses the current state of the art about the degradation carried out by this insect.


Plastic degradation by insect hexamerins: Near-atomic resolution structures of the polyethylene-degrading proteins from the wax worm saliva

September 2023

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17 Citations

Science Advances

Plastic waste management is a pressing ecological, social, and economic challenge. The saliva of the lepidopteran Galleria mellonella larvae is capable of oxidizing and depolymerizing polyethylene in hours at room temperature. Here, we analyze by cryo–electron microscopy (cryo-EM) G. mellonella ’s saliva directly from the native source. The three-dimensional reconstructions reveal that the buccal secretion is mainly composed of four hexamerins belonging to the hemocyanin/phenoloxidase family, renamed Demetra, Cibeles, Ceres, and a previously unidentified factor termed Cora. Functional assays show that this factor, as its counterparts Demetra and Ceres, is also able to oxidize and degrade polyethylene. The cryo-EM data and the x-ray analysis from purified fractions show that they self-assemble primarily into three macromolecular complexes with striking structural differences that likely modulate their activity. Overall, these results establish the ground to further explore the hexamerins’ functionalities, their role in vivo, and their eventual biotechnological application.


Plastic degradation by insect hexamerins: Near-atomic resolution structures of the polyethylene-degrading proteins from the wax worm saliva

September 2023

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7 Citations

Science Advances

Plastic waste management is a pressing ecological, social, and economic challenge. The saliva of the lepidopteran Galleria mellonella larvae is capable of oxidizing and depolymerizing polyethylene in hours at room temperature. Here, we analyze by cryo–electron microscopy (cryo-EM) G. mellonella’s saliva directly from the native source. The three-dimensional reconstructions reveal that the buccal secretion is mainly composed of four hexamerins belonging to the hemocyanin/phenoloxidase family, renamed Demetra, Cibeles, Ceres, and a previously unidentified factor termed Cora. Functional assays show that this factor, as its counterparts Demetra and Ceres, is also able to oxidize and degrade polyethylene. The cryo-EM data and the x-ray analysis from purified fractions show that they self-assemble primarily into three macromolecular complexes with striking structural differences that likely modulate their activity. Overall, these results establish the ground to further explore the hexamerins’ functionalities, their role in vivo, and their eventual biotechnological application.


Plastic degradation paradigms
(A) The fate of plastics in the environment. Abiotic factors oxidize and fragment plastics. Oxidized plastic (shown in blue) supports the proliferation of microbial populations that can metabolize and assimilate the molecular components of plastics, leading to the release of CO2. (B) The metabolic paradigm of microbial degradation of plastics. This framework assumes that microorganisms are capable of carrying out the first step of plastic oxidation in the absence of abiotic factors. (C) The current view of plastic degradation by insects. The ability of insects to degrade plastic is hypothesized to be mediated by the gut microbiota of the insect. The role of the insect in this scenario would be the mechanical fragmentation of plastics. These small fragments (shown in gray) would then be attacked by microorganisms present in the insect’s gut. The oxidized plastic fragments (shown in blue) would be transformed into molecules that can be assimilated by both the microbiota and the insect’s tissues. (D) A new paradigm of insect degradation of plastics. The discovery of the PE-degrading enzymes (PEases) Demetra and Ceres in wax worm saliva evidences an alternative mechanism of insect-mediated plastic degradation, in which the enzymes cause the oxidation and fragmentation of the polymer.
Alternative hypothesis for plastic degradation by biological means
(A) Polyethylene (PE), showing the polymer and the additives (colored squares) that form the PE. (B) A newly proposed mechanism, whereby plastic is broken down by PEase-mediated free radical formation, leading to an autooxidative chain reaction. (C) Classical view of plastic degradation via a direct enzymatic cut on the C-C chain.
Why have we not yet solved the challenge of plastic degradation by biological means?

March 2023

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26 Citations

The invention of fossil fuel–derived plastics changed and reshaped society for the better; however, their mass production has created an unprecedented accumulation of waste and an environmental crisis. Scientists are searching for better ways to reduce plastic waste than the current methods of mechanical recycling and incineration, which are only partial solutions. Biological means of breaking down plastics have been investigated as alternatives, with studies mostly focusing on using microorganisms to biologically degrade sturdy plastics like polyethylene (PE). Unfortunately, after a few decades of research, biodegradation by microorganisms has not provided the hoped-for results. Recent studies suggest that insects could provide a new avenue for investigation into biotechnological tools, with the discovery of enzymes that can oxidize untreated PE. But how can insects provide a solution that could potentially make a difference? And how can biotechnology revolutionize the plastic industry to stop ongoing/increasing contamination?


Citations (22)


... One study showed that EPO could influence RBC lifespan by regulating the expression of CD47 in newly formed RBCs and SIRP-α in sinusoidal macrophages. EPO can also modulate the onset and intensity of anti-RBC autoimmune responses, thereby shortening the RBC lifespan [19]. Previous research have shown that CD47 regulates the phagocytosis of macrophages, affects RBC lifespan, and modulats immune cell activation through interactions with SIRPα, TSP-1 and integrins [20]. ...

Reference:

Recent advances and clinical applications of red blood cell life span measurement
A new role for erythropoietin in the homeostasis of red blood cells

Communications Biology

... As a result, the long polymer chains are broken down into smaller monomers that will eventually be converted into biomass, water and carbon dioxide, to be subsequently excreted by insect larvae as waste (Siddiqui et al. 2024). The ability of G. mellonella larvae to degrade plastic was first noticed by Bombelli et al. (2017), since when the scientific community's interest in this topic has increased significantly, and a great many articles have been published (Boschi et al. 2023). The utilization of the greater wax moth for polymer degradation offers a promising potential solution to the global plastic pollution crisis (Boschi et al. 2023;Siddiqui et al. 2024). ...

Beyond Microbial Biodegradation: Plastic Degradation by Galleria mellonella

... In the same year, this discovery hit the news and was published in National Geographic to reach the general public, where Dr. Federica Bertocchini was acknowledged as the discoverer [12]. Later in 2022, she and her research group in Spain identified four novel waxworm saliva enzymes responsible for this degradation and named them Demetra, Cibeles, Ceres, and Cora, which are the first plastic-degrading enzymes ever isolated from an invertebrate organism [13,14]. Some other relevant events also include the introduction of the term "plastivore" to describe insect larvae or any other organism capable of using plastic as carbon feedstock [15] and the report of plastic-degrading yeasts from adult termite guts [16]. ...

Plastic degradation by insect hexamerins: Near-atomic resolution structures of the polyethylene-degrading proteins from the wax worm saliva

Science Advances

... Studies in the early 1950s revealed that many insect species could chew polyethylene film, a synthetic polymer gaining popularity at the time [65]. This observation was confirmed six decades later when wax moth larvae G. mellonella were found to chew polyethylene plastic bags [66], partially degrading the polymer, possibly due to specific salivary enzymes [67]. In their natural environment, mealworms are soil-dwellers with significant burrowing activity [68], which likely explains the intense drilling observed when they feed expanded polystyrene foam (Fig. 1). ...

Plastic degradation by insect hexamerins: Near-atomic resolution structures of the polyethylene-degrading proteins from the wax worm saliva
  • Citing Article
  • September 2023

Science Advances

... The two common methods of presenting the plastics to insects are (i) to place large numbers of the larvae directly onto an intact piece of plastic [13,15,16,19] or (ii) to use pristine MPs of one polymer type purchased directly from a manufacturer [9,14,17]. In nature, insects have a choice of resources to consume and rarely will they be exposed to plastics that have not been modified by additives such as stabilizers, flame retardants, and antioxidants [20]. Thus, experiments that feed insects MPs made from common plastic products, ideally mixed with other food items, are needed to simulate conditions in nature. ...

Why have we not yet solved the challenge of plastic degradation by biological means?

... In most of the experiments, many discrepancies still exist regarding the scientific assessment of the plastic biodegradation efficiencies of insects and their gut bacteria; thus, extensive care must be taken while interpreting the results and the desired methodology should be optimized, standardized, and universally adopted. For example, Serrano-Anton et al. (2023) recently criticized and pinpointed the reliability of bioinformatic tools for identifying bacterial sequences. Their study revealed significant limitations in current technologies, particularly "omics" studies to define microbiomes, leading to potential misinterpretation of data and false positives. ...

The virtual microbiome: A computational framework to evaluate microbiome analyses

... The existing theoretical and numerical analysis of population dynamics of cells and phages are mostly focused on explaining sustainability of their coexistence by the emergent spatial structures [Eriksen et al., 2020], diversification of viral strategies [Kimchi et al., 2024b], and synergistic effects of various defense systems of cells [Arias et al., 2022, Wu et al., 2024. To the best of our knowledge, only the recent work [Kimchi et al., 2024a] (posted on bioRxiv after the main part of our analysis was completed) analyzes the effect of various layers of defense and counter-defense systems on bacterial and viral population dynamics. ...

The coordination of anti-phage immunity mechanisms in bacterial cells

... In the same year, this discovery hit the news and was published in National Geographic to reach the general public, where Dr. Federica Bertocchini was acknowledged as the discoverer [12]. Later in 2022, she and her research group in Spain identified four novel waxworm saliva enzymes responsible for this degradation and named them Demetra, Cibeles, Ceres, and Cora, which are the first plastic-degrading enzymes ever isolated from an invertebrate organism [13,14]. Some other relevant events also include the introduction of the term "plastivore" to describe insect larvae or any other organism capable of using plastic as carbon feedstock [15] and the report of plastic-degrading yeasts from adult termite guts [16]. ...

Wax worm saliva and the enzymes therein are the key to polyethylene degradation by Galleria mellonella

... The saliva of G. mellonella larvae can overcome the biodegradation of PE via the oxidation process as the first step in degradation. Two enzymes were identified and characterized by Sanluis-Verdes et al., (2022). Hemocytes play a pivotal role in innate immunity (Wu et al., 2016). ...

Wax worm saliva and the enzymes therein are the key to polyethylene degradation by Galleria mellonella

... Then the community's policy must take account of the reinfection risk for both of residents and visitors. Actually there are transmissible diseases with a reinfectivity, including influenza (Davies et al. 1984;Hay et al. 2001;Earn et al. 2002;Price et al. 2022;Wang et al. 2022), pertussis (Hethcote 1999;van Boven et al. 2000), Lyme disease (Nadelman et al. 2012), hand, foot and mouth disease (Zhang et al. 2019), malaria (Arias et al. 2022;Rehman et al. 2022), tuberculosis (Vynnycky and Fine 1997;Horsburgh et al. 2022;Qiu et al. 2022), Ebola virus disease (MacIntyre and Chughtai 2016; Agusto 2017), chronic lung diseases (Yum et al. 2014), invasive pneumococcal disease (Lipsitch 1997), meningococcal disease (Gupta and Maiden 2001), and COVID-19 (Crawford 2022;Kumar et al. 2020;Le Page 2022;Mensah et al. 2022;Nguyen et al. 2022;Ren et al. 2022;Saad-Roy et al. 2022;Salzer et al. 2022;Shaheen et al. 2022), although the reinfectivity has been still requiring scientific researches to understand its kinetics and other nature. ...

Killing the competition: a theoretical framework for liver-stage malaria