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Phenotype robustness to environmental fluctuations is a common biological phenomenon. Although most phenotypes involve multiple proteins that interact with each other, the basic principles of how such interactome networks respond to environmental unpredictability and change during evolution are largely unknown. Here we study interactomes of 1,840 species across the tree of life involving a total of 8,762,166 protein–protein interactions. Our study focuses on the resilience of interactomes to network failures and finds that interactomes become more resilient during evolution, meaning that interactomes become more robust to network failures over time. In bacteria, we find that a more resilient interactome is in turn associated with the greater ability of the organism to survive in a more complex, variable, and competitive environment. We find that at the protein family level proteins exhibit a coordinated rewiring of interactions over time and that a resilient interactome arises through gradual change of the network topology. Our findings have implications for understanding molecular network structure in the context of both evolution and environment.

• When describing plant–animal interaction networks, sampling can be performed using plant- or animal-centred approaches. Despite known effects of sampling on network structure, how samplings affect the estimates of interaction β-diversity across networks is still unresolved. • We investigated how the sampling method affects the assessment of β-diversity of interactions, turnover and rewiring. We contrasted plant- and animal-centred sampling methods applied to pollination networks across habitats in a heterogeneous tropical landscape, the Pantanal Wetlands. We also asked whether plant traits influence the difference in interaction specialization according to sampling. • Plant-centred networks resulted in higher β-diversity of interactions in space than animal-centred networks. Turnover explained most of the β-diversity in both methods, but rewiring was proportionately more important when using the animal-centred method. While the plant-centred method indicated lower network modularity and specialization, floral traits modulated the effects of the sampling method on species-level network metrics. • Combining animal- and plant-centred approaches returned intermediate values for β-diversity of interactions and network metrics. Distinct methods may also be better suited for answering questions at different scales. Our results point out that the method choice, or combination of methods, should always reflect the appropriate scale of the factors determining the interactions being investigated.

This paper proposed a triangular inequality-based rewiring method for the rapidly exploring random tree (RRT)-Connect robot path-planning algorithm that guarantees the planning time compared to the RRT algorithm, to bring it closer to the optimum. To check the proposed algorithm’s performance, this paper compared the RRT and RRT-Connect algorithms in various environments through simulation. From these experimental results, the proposed algorithm shows both quicker planning time and shorter path length than the RRT algorithm and shorter path length than the RRT-Connect algorithm with a similar number of samples and planning time.

Synapses between engram cells are believed to be substrates for memory storage, and the weakening or loss of these synapses leads to the forgetting of related memories. We found engulfment of synaptic components by microglia in the hippocampi of healthy adult mice. Depletion of microglia or inhibition of microglial phagocytosis prevented forgetting and the dissociation of engram cells. By introducing CD55 to inhibit complement pathways, specifically in engram cells, we further demonstrated that microglia regulated forgetting in a complement- and activity-dependent manner. Additionally, microglia were involved in both neurogenesis-related and neurogenesis-unrelated memory degradation. Together, our findings revealed complement-dependent synapse elimination by microglia as a mechanism underlying the forgetting of remote memories.

Spectral measures have long been used to quantify the robustness of real-world graphs. For example, spectral radius (or the principal eigenvalue) is related to the effective spreading rates of dynamic processes (e.g., rumor, disease, information propagation) on graphs. Algebraic connectivity (or the Fiedler value), which is a lower bound on the node and edge connectivity of a graph, captures the “partitionability” of a graph into disjoint components. In this work we address the problem of modifying a given graph’s structure under a given budget so as to maximally improve its robustness, as quantified by spectral measures. We focus on modifications based on degree-preserving edge rewiring, such that the expected load (e.g., airport flight capacity) or physical/hardware requirement (e.g., count of ISP router traffic switches) of nodes remain unchanged. Different from a vast literature of measure-independent heuristic approaches, we propose an algorithm, called EdgeRewire , which optimizes a specific measure of interest directly. Notably, EdgeRewire  is general to accommodate six different spectral measures. Experiments on real-world datasets from three different domains (Internet AS-level, P2P, and airport flights graphs) show the effectiveness of our approach, where EdgeRewire  produces graphs with both (i) higher robustness, and (ii) higher attack-tolerance over several state-of-the-art methods.

Cellular metabolism is at the foundation of all biological activities. The catabolic processes that support cellular bioenergetics and survival have been well studied. By contrast, how cells alter their metabolism to support anabolic biomass accumulation is less well understood. During the commitment to cell proliferation, extensive metabolic rewiring must occur in order for cells to acquire sufficient nutrients such as glucose, amino acids, lipids and nucleotides, which are necessary to support cell growth and to deal with the redox challenges that arise from the increased metabolic activity associated with anabolic processes. Defining the mechanisms of this metabolic adaptation for cell growth and proliferation is now a major focus of research. Understanding the principles that guide anabolic metabolism may ultimately enhance ways to treat diseases that involve deregulated cell growth and proliferation, such as cancer.Cellular metabolism is rewired in proliferating cells to support their increased need for macromolecule biosynthesis. A better understanding of how cells utilize nutrients for biosynthetic pathways and how they overcome the metabolic challenges associated with high proliferation rates can lead to better control of cell proliferation and improved cancer treatments.

Macrophages are a heterogeneous population of immune cells playing several and diverse functions in homeostatic and immune responses. The broad spectrum of macrophage functions depends on both heterogeneity and plasticity of these cells, which are highly specialized in sensing the microenvironment and modify their properties accordingly. Although it is clear that macrophage phenotypes are difficult to categorize and should be seen as plastic and adaptable, they can be simplified into two extremes: a pro-inflammatory (M1) and an anti-inflammatory/pro-resolving (M2) profile. Based on this definition, M1 macrophages are able to start and sustain inflammatory responses, secreting pro-inflammatory cytokines, activating endothelial cells, and inducing the recruitment of other immune cells into the inflamed tissue; on the other hand, M2 macrophages promote the resolution of inflammation, phagocytose apoptotic cells, drive collagen deposition, coordinate tissue integrity, and release anti-inflammatory mediators. Dramatic switches in cell metabolism accompany these phenotypic and functional changes of macrophages. In particular, M1 macrophages rely mainly on glycolysis and present two breaks on the TCA cycle that result in accumulation of itaconate (a microbicide compound) and succinate. Excess of succinate leads to Hypoxia Inducible Factor 1α (HIF1α) stabilization that, in turn, activates the transcription of glycolytic genes, thus sustaining the glycolytic metabolism of M1 macrophages. On the contrary, M2 cells are more dependent on oxidative phosphorylation (OXPHOS), their TCA cycle is intact and provides the substrates for the complexes of the electron transport chain (ETC). Moreover, pro- and anti-inflammatory macrophages are characterized by specific pathways that regulate the metabolism of lipids and amino acids and affect their responses. All these metabolic adaptations are functional to support macrophage activities as well as to sustain their polarization in specific contexts. The aim of this review is to discuss recent findings linking macrophage functions and metabolism.

The level of antagonism between political groups has risen in the past years. Supporters of a given party increasingly dislike members of the opposing group and avoid intergroup interactions, leading to homophilic social networks. While new connections offline are driven largely by human decisions, new connections on online social platforms are intermediated by link recommendation algorithms, e.g., “People you may know” or “Whom to follow” suggestions. The long-term impacts of link recommendation in polarization are unclear, particularly as exposure to opposing viewpoints has a dual effect: Connections with out-group members can lead to opinion convergence and prevent group polarization or further separate opinions. Here, we provide a complex adaptive–systems perspective on the effects of link recommendation algorithms. While several models justify polarization through rewiring based on opinion similarity, here we explain it through rewiring grounded in structural similarity—defined as similarity based on network properties. We observe that preferentially establishing links with structurally similar nodes (i.e., sharing many neighbors) results in network topologies that are amenable to opinion polarization. Hence, polarization occurs not because of a desire to shield oneself from disagreeable attitudes but, instead, due to the creation of inadvertent echo chambers. When networks are composed of nodes that react differently to out-group contacts, either converging or polarizing, we find that connecting structurally dissimilar nodes moderates opinions. Overall, our study sheds light on the impacts of social-network algorithms and unveils avenues to steer dynamics of radicalization and polarization in online social networks.

Blocking Programmed Death-1 (PD-1) can reinvigorate exhausted CD8 Tcells (TEX) and improve control of chronic infections and cancer. However, whether blocking PD-1 can reprogram TEX into durable memory Tcells (TMEM) is unclear. We found that reinvigoration of TEX in mice by PD-L1 blockade caused minimal memory development. After blockade, reinvigorated TEX became reexhausted if antigen concentration remained high and failed to become TMEM upon antigen clearance. TEX acquired an epigenetic profile distinct from that of effector Tcells (TEFF) and TMEM cells that was minimally remodeled after PD-L1 blockade. This finding suggests that TEX are a distinct lineage of CD8 T cells. Nevertheless, PD-1 pathway blockade resulted in transcriptional rewiring and reengagement of effector circuitry in the TEX epigenetic landscape. These data indicate that epigenetic fate inflexibility may limit current immunotherapies.

Inducible regulatory T (iTreg) cells play a crucial role in immune suppression and are important for the maintenance of immune homeostasis. Mounting evidence has demonstrated connections between iTreg differentiation and metabolic reprogramming, especially rewiring in fatty acid oxidation (FAO). Previous work showed that butyrate, a specific type of short-chain fatty acid (SCFA) readily produced from fiber-rich diets through microbial fermentation, was critical for the maintenance of intestinal homeostasis and capable of promoting iTreg generation by up-regulating histone acetylation for gene expression as an HDAC inhibitor. Here, we revealed that butyrate could also accelerate FAO to facilitate iTreg differentiation. Moreover, butyrate was converted, by acyl-CoA synthetase short-chain family member 2 (ACSS2), into butyryl-CoA (BCoA), which up-regulated CPT1A activity through antagonizing the association of malonyl-CoA (MCoA), the best known metabolic intermediate inhibiting CPT1A, to promote FAO and thereby iTreg differentiation. Mutation of CPT1A at Arg243, a reported amino acid required for MCoA association, impaired both MCoA and BCoA binding, indicating that Arg243 is probably the responsible site for MCoA and BCoA association. Furthermore, blocking BCoA formation by ACSS2 inhibitor compromised butyrate-mediated iTreg generation andmitigation of mouse colitis. Together, we unveil a previously unappreciated role for butyrate in iTreg differentiation and illustrate butyrate–BCoA–CPT1A axis for the regulation of immune homeostasis.