GATDE: A graph attention network with diffusion-enhanced protein-protein interaction for cancer classification.

Cancer classification is crucial for effective patient treatment, and recent years have seen various methods emerge based on protein expression levels. However, existing methods oversimplify by assuming uniform interaction strengths and neglecting intermediate influences among proteins. Addressing these limitations, GATDE employs a graph attention network enhanced with diffusion on protein-protein interactions. By constructing a weighted protein-protein interaction network, GATDE captures the diversity of these interactions and uses a diffusion process to assess multi-hop influences between proteins. This information is subsequently incorporated into the graph attention network, resulting in precise cancer classification. Experimental results on breast cancer and pan-cancer datasets demonstrate that GATDE surpasses current leading methods. Additionally, in-depth case studies further validate the effectiveness of the diffusion process and the attention mechanism, highlighting GATDE's robustness and potential for real-world applications.

Generic model to unravel the deeper insights of viral infections: an empirical application of evolutionary graph coloring in computational network biology.

PURPOSE: Graph coloring approach has emerged as a valuable problem-solving tool for both theoretical and practical aspects across various scientific disciplines, including biology. In this study, we demonstrate the graph coloring's effectiveness in computational network biology, more precisely in analyzing protein-protein interaction (PPI) networks to gain insights about the viral infections and its consequences on human health. Accordingly, we propose a generic model that can highlight important hub proteins of virus-associated disease manifestations, changes in disease-associated biological pathways, potential drug targets and respective drugs. We test our model on SARS-CoV-2 infection, a highly transmissible virus responsible for the COVID-19 pandemic. The pandemic took significant human lives, causing severe respiratory illnesses and exhibiting various symptoms ranging from fever and cough to gastrointestinal, cardiac, renal, neurological, and other manifestations. METHODS: To investigate the underlying mechanisms of SARS-CoV-2 infection-induced dysregulation of human pathobiology, we construct a two-level PPI network and employed a differential evolution-based graph coloring (DEGCP) algorithm to identify critical hub proteins that might serve as potential targets for resolving the associated issues. Initially, we concentrate on the direct human interactors of SARS-CoV-2 proteins to construct the first-level PPI network and subsequently applied the DEGCP algorithm to identify essential hub proteins within this network. We then build a second-level PPI network by incorporating the next-level human interactors of the first-level hub proteins and use the DEGCP algorithm to predict the second level of hub proteins. RESULTS: We first identify the potential crucial hub proteins associated with SARS-CoV-2 infection at different levels. Through comprehensive analysis, we then investigate the cellular localization, interactions with other viral families, involvement in biological pathways and processes, functional attributes, gene regulation capabilities as transcription factors, and their associations with disease-associated symptoms of these identified hub proteins. Our findings highlight the significance of these hub proteins and their intricate connections with disease pathophysiology. Furthermore, we predict potential drug targets among the hub proteins and identify specific drugs that hold promise in preventing or treating SARS-CoV-2 infection and its consequences. CONCLUSION: Our generic model demonstrates the effectiveness of DEGCP algorithm in analyzing biological PPI networks, provides valuable insights into disease biology, and offers a basis for developing novel therapeutic strategies for other viral infections that may cause future pandemic.

Shared Proteins and Pathways of Cardiovascular and Cognitive Diseases: Relation to Vascular Cognitive Impairment.

One of the primary goals of systems medicine is the detection of putative proteins and pathways involved in disease progression and pathological phenotypes. Vascular cognitive impairment (VCI) is a heterogeneous condition manifesting as cognitive impairment resulting from vascular factors. The precise mechanisms underlying this relationship remain unclear, which poses challenges for experimental research. Here, we applied computational approaches like systems biology to unveil and select relevant proteins and pathways related to VCI by studying the crosstalk between cardiovascular and cognitive diseases. In addition, we specifically included signals related to oxidative stress, a common etiologic factor tightly linked to aging, a major determinant of VCI. Our results show that pathways associated with oxidative stress are quite relevant, as most of the prioritized vascular cognitive genes and proteins were enriched in these pathways. Our analysis provided a short list of proteins that could be contributing to VCI: DOLK, TSC1, ATP1A1, MAPK14, YWHAZ, CREB3, HSPB1, PRDX6, and LMNA. Moreover, our experimental results suggest a high implication of glycative stress, generating oxidative processes and post-translational protein modifications through advanced glycation end-products (AGEs). We propose that these products interact with their specific receptors (RAGE) and Notch signaling to contribute to the etiology of VCI.

Host cell protein networks as a novel co-elution mechanism during protein A chromatography.

Host cell proteins (HCPs) are process-related impurities of therapeutic proteins produced in for example, Chinese hamster ovary (CHO) cells. Protein A affinity chromatography is the initial capture step to purify monoclonal antibodies or Fc-based proteins and is most effective for HCP removal. Previously proposed mechanisms that contribute to co-purification of HCPs with the therapeutic protein are either HCP-drug association or leaching from chromatin heteroaggregates. In this study, we analyzed protein A eluates of 23 Fc-based proteins by LC-MS/MS to determine their HCP content. The analysis revealed a high degree of heterogeneity in the number of HCPs identified in the different protein A eluates. Among all identified HCPs, the majority co-eluted with less than three Fc-based proteins indicating a drug-specific co-purification for most HCPs. Only ten HCPs co-purified with over 50% of the 23 Fc-based proteins. A correlation analysis of HCPs identified across multiple protein A eluates revealed their co-elution as HCP groups. Functional annotation and protein interaction analysis confirmed that some HCP groups are associated with protein-protein interaction networks. Here, we propose an additional mechanism for HCP co-elution involving protein-protein interactions within functional networks. Our findings may help to guide cell line development and to refine downstream purification strategies.

Transcriptome-based network analysis reveals hub immune genes and pathways of hepatopancreas against LPS in Amphioctopus fangsiao.

The hepatopancreas is the biggest digestive organ in Amphioctopus fangsiao (A. fangsiao), but also undertakes critical functions like detoxification and immune defense. Generally, pathogenic bacteria or endotoxin from the gut microbiota would be arrested and detoxified in the hepatopancreas, which could be accompanied by the inevitable immune responses. In recent years, studies related to cephalopods immune have been increasing, but the molecular mechanisms associated with the hepatopancreatic immunity are still unclear. In this study, lipopolysaccharide (LPS), a major component of the cell wall of Gram-negative bacteria, was used for imitating bacteria infection to stimulate the hepatopancreas of A. fangsiao. To investigate the immune process happened in A. fangsiao hepatopancreas, we performed transcriptome analysis of hepatopancreas tissue after LPS injection, and identified 2615 and 1943 differentially expressed genes (DEGs) at 6 and 24 h post-injection, respectively. GO and KEGG enrichment analysis showed that these DEGs were mainly involved in immune-related biological processes and signaling pathways, including ECM-receptor interaction signaling pathway, Phagosome signaling pathway, Lysosome signaling pathway, and JAK-STAT signaling pathways. The function relationships between these DEGs were further analyzed through protein-protein interaction (PPI) networks. It was found that Mtor, Mapk14 and Atm were the three top interacting DEGs under LPS stimulation. Finally, 15 hub genes involving multiple KEGG signaling pathways and PPI relationships were selected for qRT-PCR validation. In this study, for the first time we explored the molecular mechanisms associated with hepatopancreatic immunity in A. fangsiao using a PPI networks approach, and provided new insights for understanding hepatopancreatic immunity in A. fangsiao.

SONAR Discovers RNA-Binding Proteins from Analysis of Large-Scale Protein-Protein Interactomes.

RNA metabolism is controlled by an expanding, yet incomplete, catalog of RNA-binding proteins (RBPs), many of which lack characterized RNA binding domains. Approaches to expand the RBP repertoire to discover non-canonical RBPs are currently needed. Here, HaloTag fusion pull down of 12 nuclear and cytoplasmic RBPs followed by quantitative mass spectrometry (MS) demonstrates that proteins interacting with multiple RBPs in an RNA-dependent manner are enriched for RBPs. This motivated SONAR, a computational approach that predicts RNA binding activity by analyzing large-scale affinity precipitation-MS protein-protein interactomes. Without relying on sequence or structure information, SONAR identifies 1,923 human, 489 fly, and 745 yeast RBPs, including over 100 human candidate RBPs that contain zinc finger domains. Enhanced CLIP confirms RNA binding activity and identifies transcriptome-wide RNA binding sites for SONAR-predicted RBPs, revealing unexpected RNA binding activity for disease-relevant proteins and DNA binding proteins.

Integrated analysis of gut microbiota and metabolomic profiling in colorectal cancer metastasis.

Colorectal cancer (CRC) is characterized by its heterogeneity and complex metastatic mechanisms, presenting significant challenges in treatment and prognosis. This study aimed to unravel the intricate interplay between the gut microbiota and metabolic alterations associated with CRC metastasis. By employing high-throughput sequencing and advanced metabolomic techniques, we identified distinct patterns in the gut microbiome and fecal metabolites across different CRC metastatic sites. The differential gene analysis highlighted significant enrichment in biological processes related to immune response and extracellular matrix organization, with key genes playing roles in the complement and clotting cascades, and staphylococcus aureus infections. Protein-protein interaction networks further elucidated the potential mechanisms driving CRC spread, emphasizing the importance of extracellular vesicles and the PPAR signaling pathway in tumor metastasis. Our comprehensive microbiota analysis revealed a relatively stable alpha diversity across groups but identified specific bacterial genera associated with metastatic stages. Metabolomic profiling using OPLS-DA models unveiled distinct metabolic signatures, with differential metabolites enriched in pathways crucial for cancer metabolism and immune modulation. Integrative analysis of the gut microbiota and metabolic profiles highlighted significant correlations, suggesting a complex interplay that may influence CRC progression and metastasis. These findings offer novel insights into the microbial and metabolic underpinnings of CRC metastasis, paving the way for innovative diagnostic and therapeutic strategies targeting the gut microbiome and metabolic pathways.

Simulating Weak Attacks in a New Duplication-Divergence Model with Node Loss.

A better understanding of protein-protein interaction (PPI) networks representing physical interactions between proteins could be beneficial for evolutionary insights as well as for practical applications such as drug development. As a statistical model for PPI networks, duplication-divergence models have been proposed, but they suffer from resulting in either very sparse networks in which most of the proteins are isolated, or in networks which are much denser than what is usually observed, having almost no isolated proteins. Moreover, in real networks, where a gene codes a protein, gene loss may occur. The loss of nodes has not been captured in duplication-divergence models to date. Here, we introduce a new duplication-divergence model which includes node loss. This mechanism results in networks in which the proportion of isolated proteins can take on values which are strictly between 0 and 1. To understand this new model, we apply strong and weak attacks to networks from duplication-divergence models with and without node loss, and compare the results to those obtained when carrying out similar attacks on two real PPI networks of E. coli and of S. cerevisiae. We find that the new model more closely reflects the damage caused by strong and weak attacks found in the PPI networks.

idenPC-MIIP: identify protein complexes from weighted PPI networks using mutual important interacting partner relation.

Protein complexes are key units for studying a cell system. During the past decades, the genome-scale protein-protein interaction (PPI) data have been determined by high-throughput approaches, which enables the identification of protein complexes from PPI networks. However, the high-throughput approaches often produce considerable fraction of false positive and negative samples. In this study, we propose the mutual important interacting partner relation to reflect the co-complex relationship of two proteins based on their interaction neighborhoods. In addition, a new algorithm called idenPC-MIIP is developed to identify protein complexes from weighted PPI networks. The experimental results on two widely used datasets show that idenPC-MIIP outperforms 17 state-of-the-art methods, especially for identification of small protein complexes with only two or three proteins.

Genome-wide profiling reveals pervasive transcriptional alterations in fibroblasts derived from lesional skin in vitiligo including a reduced potential to proliferate.

Fibroblasts interact with keratinocytes and melanocytes to maintain skin homeostasis. However, the impact of selective melanocyte loss on the transcriptome of fibroblasts is not fully understood. Thus, we sought to understand the genome-wide transcriptome of fibroblasts derived from non-lesional (NL) and lesional (L) dermis in patients with non-segmental vitiligo. Transcriptional profiling of NL and L fibroblasts was performed on three individuals with vitiligo using next-generation-sequencing. Functional protein-protein interaction (PPI) networks were constructed for the significantly upregulated and downregulated genes, as well as for a common set of genes that were downregulated in both fibroblasts and epidermis in L skin (identified previously). Proliferation potential of NL and L fibroblasts was assessed experimentally. Genome-wide transcriptome analysis revealed a total of 414 (282, downregulated; 132, upregulated) differentially expressed (DE)-transcripts in L as compared to NL fibroblasts. Unsupervised hierarchical clustering of DE-transcripts segregated L and NL fibroblasts into two distinct clades, despite the apparent heterogeneity in lesions of different vitiligo patients. Gene Ontology analysis of downregulated genes revealed enrichment of keratinocyte-specific biological processes such as cornification and keratinization. PPI networks constructed for the downregulated and upregulated genes revealed deregulation of several hub genes associated with cell cycle regulation and cAMP metabolism respectively. Similarly, the PPI networks constructed for 67 genes downregulated in both fibroblasts as well as epidermis of L skin revealed downregulation of hub genes including stratifin, PIK3CG and CDH1. Analysis of the in vitro proliferation potential of L fibroblasts revealed a decrease in the expression of proliferation markers Ki67, MCM6, pERK and pCDK2, a decreased S phase population and an increase in alpha-SMA and collagen expression, corroborating the downregulation of hub genes associated with proliferation identified by PPI network analysis. Our study revealed pervasive transcriptional alterations in L compared to NL fibroblasts in vitiligo. The PPI analysis suggested a reduced potential to proliferate in melanocyte-deprived lesional fibroblasts, which was validated experimentally as well.

Sequencing-based network analysis provides a core set of genes for understanding hemolymph immune response mechanisms against Poly I:C stimulation in Amphioctopus fangsiao.

Aquatic viruses can spread rapidly and widely in seawater for their high infective ability. Polyinosinic-polycytidylic acid (Poly I:C), a viral dsRNA analog, is an immunostimulant that has been proved to activate various immune responses of immune cells in invertebrate. Hemolymph is a critical site that host immune response in invertebrates, and its transcriptome information obtained from Amphioctopus fangsiao stimulated by Poly I:C is crucial for understanding the antiviral molecular mechanisms of this species. In this study, we analyzed gene expression data in A. fangsiao hemolymph tissue within 24 h under Poly I:C stimulation and found 1082 and 299 differentially expressed genes (DEGs) at 6 and 24 h, respectively. Union set (1,369) DEGs were selected for subsequent analyses. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses were carried out for identifying DEGs related to immunity. Several significant immune-related terms and pathways, such as toll-like receptor signaling pathways term, inflammatory response term, TNF signaling pathway, and chemokine signaling pathway were identified. A protein-protein interaction (PPI) network was constructed for examining the relationships among immune-related genes. Finally, 12 hub genes, including EGFR, ACTG1, MAP2K1, and other nine hub genes, were identified based on the KEGG enrichment analysis and PPI network. The quantitative RT-PCR (qRT-PCR) was used to verify the expression profile of 12 hub genes. This research provides a reference for solving the problem of high mortality of A. fangsiao and other mollusks and provides a reference for the future production of some disease-resistant A. fangsiao.

Link Prediction with Continuous-Time Classical and Quantum Walks.

Protein-protein interaction (PPI) networks consist of the physical and/or functional interactions between the proteins of an organism, and they form the basis for the field of network medicine. Since the biophysical and high-throughput methods used to form PPI networks are expensive, time-consuming, and often contain inaccuracies, the resulting networks are usually incomplete. In order to infer missing interactions in these networks, we propose a novel class of link prediction methods based on continuous-time classical and quantum walks. In the case of quantum walks, we examine the usage of both the network adjacency and Laplacian matrices for specifying the walk dynamics. We define a score function based on the corresponding transition probabilities and perform tests on six real-world PPI datasets. Our results show that continuous-time classical random walks and quantum walks using the network adjacency matrix can successfully predict missing protein-protein interactions, with performance rivalling the state-of-the-art.

Predictive Value of Gene Databases in Discovering New Biomarkers and New Therapeutic Targets in Lung Cancer.

Background and Objectives: The molecular mechanisms of lung cancer are still unclear. Investigation of immune cell infiltration (ICI) and the hub gene will facilitate the identification of specific biomarkers. Materials and Methods: Key modules of ICI and immune cell-associated differential genes, as well as ICI profiles, were identified using lung cancer microarray data from the single sample gene set enrichment analysis (ssGSEA) and weighted gene co-expression network analysis (WGCNA) in the gene expression omnibus (GEO) database. Protein-protein interaction networks were used to identify hub genes. The receiver operating characteristic (ROC) curve was used to assess the diagnostic significance of the hub genes, and survival analysis was performed using gene expression profiling interactive analysis (GEPIA). Results: Significant changes in ICI were found in lung cancer tissues versus adjacent normal tissues. WGCNA results showed the highest correlation of yellow and blue modules with ICI. Protein-protein interaction networks identified four hub genes, namely CENPF, AURKA, PBK, and CCNB1. The lung adenocarcinoma patients in the low hub gene expression group showed higher overall survival and longer median survival than the high expression group. They were associated with a decreased risk of lung cancer in patients, indicating their potential role as cancer suppressor genes and potential targets for future therapeutic development. Conclusions: CENPF, AURKA, PBK, and CCNB1 show great potential as biomarkers and immunotherapeutic targets specific to lung cancer. Lung cancer patients' prognoses are often foreseen using matched prognostic models, and genes CENPF, AURKA, PBK, and CCNB1 in lung cancer may serve as therapeutic targets, which require further investigations.

A supervised protein complex prediction method with network representation learning and gene ontology knowledge.

BACKGROUND: Protein complexes are essential for biologists to understand cell organization and function effectively. In recent years, predicting complexes from protein-protein interaction (PPI) networks through computational methods is one of the current research hotspots. Many methods for protein complex prediction have been proposed. However, how to use the information of known protein complexes is still a fundamental problem that needs to be solved urgently in predicting protein complexes. RESULTS: To solve these problems, we propose a supervised learning method based on network representation learning and gene ontology knowledge, which can fully use the information of known protein complexes to predict new protein complexes. This method first constructs a weighted PPI network based on gene ontology knowledge and topology information, reducing the network's noise problem. On this basis, the topological information of known protein complexes is extracted as features, and the supervised learning model SVCC is obtained according to the feature training. At the same time, the SVCC model is used to predict candidate protein complexes from the protein interaction network. Then, we use the network representation learning method to obtain the vector representation of the protein complex and train the random forest model. Finally, we use the random forest model to classify the candidate protein complexes to obtain the final predicted protein complexes. We evaluate the performance of the proposed method on two publicly PPI data sets. CONCLUSIONS: Experimental results show that our method can effectively improve the performance of protein complex recognition compared with existing methods. In addition, we also analyze the biological significance of protein complexes predicted by our method and other methods. The results show that the protein complexes predicted by our method have high biological significance.

Molecular architecture of LSM14 interactions involved in the assembly of mRNA silencing complexes.

The LSM domain-containing protein LSM14/Rap55 plays a role in mRNA decapping, translational repression, and RNA granule (P-body) assembly. How LSM14 interacts with the mRNA silencing machinery, including the eIF4E-binding protein 4E-T and the DEAD-box helicase DDX6, is poorly understood. Here we report the crystal structure of the LSM domain of LSM14 bound to a highly conserved C-terminal fragment of 4E-T. The 4E-T C-terminus forms a bi-partite motif that wraps around the N-terminal LSM domain of LSM14. We also determined the crystal structure of LSM14 bound to the C-terminal RecA-like domain of DDX6. LSM14 binds DDX6 via a unique non-contiguous motif with distinct directionality as compared to other DDX6-interacting proteins. Together with mutational and proteomic studies, the LSM14-DDX6 structure reveals that LSM14 has adopted a divergent mode of binding DDX6 in order to support the formation of mRNA silencing complexes and P-body assembly.

A comprehensive review and evaluation of computational methods for identifying protein complexes from protein-protein interaction networks.

Protein complexes are the fundamental units for many cellular processes. Identifying protein complexes accurately is critical for understanding the functions and organizations of cells. With the increment of genome-scale protein-protein interaction (PPI) data for different species, various computational methods focus on identifying protein complexes from PPI networks. In this article, we give a comprehensive and updated review on the state-of-the-art computational methods in the field of protein complex identification, especially focusing on the newly developed approaches. The computational methods are organized into three categories, including cluster-quality-based methods, node-affinity-based methods and ensemble clustering methods. Furthermore, the advantages and disadvantages of different methods are discussed, and then, the performance of 17 state-of-the-art methods is evaluated on two widely used benchmark data sets. Finally, the bottleneck problems and their potential solutions in this important field are discussed.

Protein interaction networks provide insight into fetal origins of chronic obstructive pulmonary disease.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a leading cause of death in adults that may have origins in early lung development. It is a complex disease, influenced by multiple factors including genetic variants and environmental factors. Maternal smoking during pregnancy may influence the risk for diseases during adulthood, potentially through epigenetic modifications including methylation. METHODS: In this work, we explore the fetal origins of COPD by utilizing lung DNA methylation marks associated with in utero smoke (IUS) exposure, and evaluate the network relationships between methylomic and transcriptomic signatures associated with adult lung tissue from former smokers with and without COPD. To identify potential pathobiological mechanisms that may link fetal lung, smoke exposure and adult lung disease, we study the interactions (physical and functional) of identified genes using protein-protein interaction networks. RESULTS: We build IUS-exposure and COPD modules, which identify connected subnetworks linking fetal lung smoke exposure to adult COPD. Studying the relationships and connectivity among the different modules for fetal smoke exposure and adult COPD, we identify enriched pathways, including the AGE-RAGE and focal adhesion pathways. CONCLUSIONS: The modules identified in our analysis add new and potentially important insights to understanding the early life molecular perturbations related to the pathogenesis of COPD. We identify AGE-RAGE and focal adhesion as two biologically plausible pathways that may reveal lung developmental contributions to COPD. We were not only able to identify meaningful modules but were also able to study interconnections between smoke exposure and lung disease, augmenting our knowledge about the fetal origins of COPD.

Transcriptome profiling based on larvae at different time points after hatching provides a core set of gene resource for understanding the immune response mechanisms of the egg-protecting behavior against Vibrio anguillarum infection in Amphioctopus fangsiao.

Mollusks have recently received increasing attention because of their unique immune systems. Mollusks such as Amphioctopus fangsiao are economically important cephalopods, and the effects of their egg-protecting behavior on the larval immune response are unclear. Meanwhile, little research has been done on the resistance response of cephalopod larvae infected with pathogenic bacteria such as Vibrio anguillarum. In this study, V. anguillarum was used to infect the primary hatching A. fangsiao larvae under different egg-protecting behaviors for 24 h, and a total of 7156 differentially expressed genes (DEGs) were identified at four time points after hatching based on transcriptome analysis. GO and KEGG enrichment analyses showed that multiple immune-related GO terms and KEGG signaling pathways were enriched. Protein-protein interaction networks (PPI networks) were used to search functional relationships between immune-related DEGs. Finally, 20 hub genes related to multiple gene functions or involved in multiple signaling pathways were identified, and their accuracy was verified using quantitative RT-PCR. PPI networks were first used to study the effects A. fangsiao larvae after infection with V. anguillarum under different egg-protecting behaviors. The results provide significant genetic resources for exploring invertebrate larval immune processes. The data lays a foundation for further study the immune response mechanisms for invertebrates after infection.

Genetics' Piece of the PI: Inferring the Origin of Complex Traits and Diseases from Proteome-Wide Protein-Protein Interaction Dynamics.

How do common and rare genetic polymorphisms contribute to quantitative traits or disease risk and progression? Multiple human traits have been extensively characterized at the genomic level, revealing their complex genetic architecture. However, it is difficult to resolve the mechanisms by which specific variants contribute to a phenotype. Recently, analyses of variant effects on molecular traits have uncovered intermediate mechanisms that link sequence variation to phenotypic changes. Yet, these methods only capture a fraction of genetic contributions to phenotype. Here, in reviewing the field, it is proposed that complex traits can be understood by characterizing the dynamics of biochemical networks within living cells, and that the effects of genetic variation can be captured on these networks by using protein-protein interaction (PPI) methodologies. This synergy between PPI methodologies and the genetics of complex traits opens new avenues to investigate the molecular etiology of human diseases and to facilitate their prevention or treatment.

Evolution of resilience in protein interactomes across the tree of life.

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.