Network rewiring is an important mechanism of gene essentiality change

Cofitness network connectivity determines a fuzzy essential zone in open bacterial pangenome

Most in silico evolutionary studies commonly assumed that core genes are essential for cellular function, while accessory genes are dispensable, particularly in nutrient-rich environments. However, this assumption is seldom tested genetically within the pangenome context. In this study, we conducted a robust pangenomic Tn-seq analysis of fitness genes in a nutrient-rich medium for Sinorhizobium strains with a canonical open pangenome. To evaluate the robustness of fitness category assignment, Tn-seq data for three independent mutant libraries per strain were analyzed by three methods, which indicates that the Hidden Markov Model (HMM)-based method is most robust to variations between mutant libraries and not sensitive to data size, outperforming the Bayesian and Monte Carlo simulation-based methods. Consequently, the HMM method was used to classify the fitness category. Fitness genes, categorized as essential (ES), advantage (GA), and disadvantage (GD) genes for growth, are enriched in core genes, while nonessential genes (NE) are over-represented in accessory genes. Accessory ES/GA genes showed a lower fitness effect than core ES/GA genes. Connectivity degrees in the cofitness network decrease in the order of ES, GD, and GA/NE. In addition to accessory genes, 1599 out of 3284 core genes display differential essentiality across test strains. Within the pangenome core, both shared quasi-essential (ES and GA) and strain-dependent fitness genes are enriched in similar functional categories. Our analysis demonstrates a considerable fuzzy essential zone determined by cofitness connectivity degrees in Sinorhizobium pangenome and highlights the power of the cofitness network in understanding the genetic basis of ever-increasing prokaryotic pangenome data.

Yosemite toad (Anaxyrus canorus) transcriptome reveals interplay between speciation genes and adaptive introgression

Genomes are heterogeneous during the early stages of speciation, with small 'islands' of DNA appearing to reflect strong adaptive differences, surrounded by vast seas of relative homogeneity. As species diverge, secondary contact zones between them can act as an interface and selectively filter through advantageous alleles of hybrid origin. Such introgression is another important adaptive process, one that allows beneficial mosaics of recombinant DNA ('rivers') to flow from one species into another. Although genomic islands of divergence appear to be associated with reproductive isolation, and genomic rivers form by adaptive introgression, it is unknown whether islands and rivers tend to be the same or different loci. We examined three replicate secondary contact zones for the Yosemite toad (Anaxyrus canorus) using two genomic data sets and a morphometric data set to answer the questions: (1) How predictably different are islands and rivers, both in terms of genomic location and gene function? (2) Are the adaptive genetic trait loci underlying tadpole growth and development reliably islands, rivers or neither? We found that island and river loci have significant overlap within a contact zone, suggesting that some loci are first islands, and later are predictably converted into rivers. However, gene ontology enrichment analysis showed strong overlap in gene function unique to all island loci, suggesting predictability in overall gene pathways for islands. Genome-wide association study outliers for tadpole development included LPIN3, a lipid metabolism gene potentially involved in climate change adaptation, that is island-like for all three contact zones, but also appears to be introgressing (as a river) across one zone. Taken together, our results suggest that adaptive divergence and introgression may be more complementary forces than currently appreciated.

Evolutionary Change in Gut Specification in Caenorhabditis Centers on the GATA Factor ELT-3 in an Example of Developmental System Drift

Cells in a developing animal embryo become specified by the activation of cell-type-specific gene regulatory networks. The network that specifies the gut in the nematode Caenorhabditis elegans has been the subject of study for more than two decades. In this network, the maternal factors SKN-1/Nrf and POP-1/TCF activate a zygotic GATA factor cascade consisting of the regulators MED-1,2 → END-1,3 → ELT-2,7, leading to the specification of the gut in early embryos. Paradoxically, the MED, END, and ELT-7 regulators are present only in species closely related to C. elegans, raising the question of how the gut can be specified without them. Recent work found that ELT-3, a GATA factor without an endodermal role in C. elegans, acts in a simpler ELT-3 → ELT-2 network to specify gut in more distant species. The simpler ELT-3 → ELT-2 network may thus represent an ancestral pathway. In this review, we describe the elucidation of the gut specification network in C. elegans and related species and propose a model by which the more complex network might have formed. Because the evolution of this network occurred without a change in phenotype, it is an example of the phenomenon of Developmental System Drift.

On quantification and maximization of information transfer in network dynamical systems

Information flow among nodes in a complex network describes the overall cause-effect relationships among the nodes and provides a better understanding of the contributions of these nodes individually or collectively towards the underlying network dynamics. Variations in network topologies result in varying information flows among nodes. We integrate theories from information science with control network theory into a framework that enables us to quantify and control the information flows among the nodes in a complex network. The framework explicates the relationships between the network topology and the functional patterns, such as the information transfers in biological networks, information rerouting in sensor nodes, and influence patterns in social networks. We show that by designing or re-configuring the network topology, we can optimize the information transfer function between two chosen nodes. As a proof of concept, we apply our proposed methods in the context of brain networks, where we reconfigure neural circuits to optimize excitation levels among the excitatory neurons.

Complexity and self-organization in the evolution of cell polarization

Cellular life exhibits order and complexity, which typically increase over the course of evolution. Cell polarization is a well-studied example of an ordering process that breaks the internal symmetry of a cell by establishing a preferential axis. Like many cellular processes, polarization is driven by self-organization, meaning that the macroscopic pattern emerges as a consequence of microscopic molecular interactions at the biophysical level. However, the role of self-organization in the evolution of complex protein networks remains obscure. In this Review, we provide an overview of the evolution of polarization as a self-organizing process, focusing on the model species Saccharomyces cerevisiae and its fungal relatives. Moreover, we use this model system to discuss how self-organization might relate to evolutionary change, offering a shift in perspective on evolution at the microscopic scale.

Dynamic rewiring of biological activity across genotype and lineage revealed by context-dependent functional interactions

BACKGROUND

Coessentiality networks derived from CRISPR screens in cell lines provide a powerful framework for identifying functional modules in the cell and for inferring the roles of uncharacterized genes. However, these networks integrate signal across all underlying data and can mask strong interactions that occur in only a subset of the cell lines analyzed.

RESULTS

Here, we decipher dynamic functional interactions by identifying significant cellular contexts, primarily by oncogenic mutation, lineage, and tumor type, and discovering coessentiality relationships that depend on these contexts. We recapitulate well-known gene-context interactions such as oncogene-mutation, paralog buffering, and tissue-specific essential genes, show how mutation rewires known signal transduction pathways, including RAS/RAF and IGF1R-PIK3CA, and illustrate the implications for drug targeting. We further demonstrate how context-dependent functional interactions can elucidate lineage-specific gene function, as illustrated by the maturation of proreceptors IGF1R and MET by proteases FURIN and CPD.

CONCLUSIONS

This approach advances our understanding of context-dependent interactions and how they can be gleaned from these data. We provide an online resource to explore these context-dependent interactions at diffnet.hart-lab.org.

Network-based machine learning approach to predict immunotherapy response in cancer patients

Immune checkpoint inhibitors (ICIs) have substantially improved the survival of cancer patients over the past several years. However, only a minority of patients respond to ICI treatment (~30% in solid tumors), and current ICI-response-associated biomarkers often fail to predict the ICI treatment response. Here, we present a machine learning (ML) framework that leverages network-based analyses to identify ICI treatment biomarkers (NetBio) that can make robust predictions. We curate more than 700 ICI-treated patient samples with clinical outcomes and transcriptomic data, and observe that NetBio-based predictions accurately predict ICI treatment responses in three different cancer types—melanoma, gastric cancer, and bladder cancer. Moreover, the NetBio-based prediction is superior to predictions based on other conventional ICI treatment biomarkers, such as ICI targets or tumor microenvironment-associated markers. This work presents a network-based method to effectively select immunotherapy-response-associated biomarkers that can make robust ML-based predictions for precision oncology.

Identifying biomarkers for response to immunotherapy in cancer remains challenging. Here, the authors develop an approach based on network biology and machine learning -NetBio- to identify molecular biomarkers of response to immunotherapy across different cancer types and cohorts.

A Rice Protein Interaction Network Reveals High Centrality Nodes and Candidate Pathogen Effector Targets

Network science identifies key players in diverse biological systems including host-pathogen interactions. We demonstrated a scale-free network property for a comprehensive rice protein–protein interactome (RicePPInets) that exhibits nodes with increased centrality indices. While weighted k-shell decomposition was shown efficacious to predict pathogen effector targets in Arabidopsis, we improved its computational code for a broader implementation on large-scale networks including RicePPInets. We determined that nodes residing within the internal layers of RicePPInets are poised to be the most influential, central, and effective information spreaders. To identify central players and modules through network topology analyses, we integrated RicePPInets and co-expression networks representing susceptible and resistant responses to strains of the bacterial pathogens Xanthomonas oryzae pv. oryzae and X. oryzae pv. oryzicola (Xoc) and generated a RIce-Xanthomonas INteractome (RIXIN). This revealed that previously identified candidate targets of pathogen transcription activator-like (TAL) effectors are enriched in nodes with enhanced connectivity, bottlenecks, and information spreaders that are located in the inner layers of the network, and these nodes are involved in several important biological processes. Overall, our integrative multi-omics network-based platform provides a potentially useful approach to prioritizing candidate pathogen effector targets for functional validation, suggesting that this computational framework can be broadly translatable to other complex pathosystems.

A Review of Recent Developments in Preparation Methods for Large-Area Perovskite Solar Cells

The recent rapid development in perovskite solar cells (PSCs) has led to significant research interest due to their notable photovoltaic performance, currently exceeding 25% power conversion efficiency for small-area PSCs. The materials used to fabricate PSCs dominate the current photovoltaic market, especially with the rapid increase in efficiency and performance. The present work reviews recent developments in PSCs’ preparation and fabrication methods, the associated advantages and disadvantages, and methods for improving the efficiency of large-area perovskite films for commercial application. The work is structured in three parts. First is a brief overview of large-area PSCs, followed by a discussion of the preparation methods and methods to improve PSC efficiency, quality, and stability. Envisioned future perspectives on the synthesis and commercialization of large-area PSCs are discussed last. Most of the growth in commercial PSC applications is likely to be in building integrated photovoltaics and electric vehicle battery charging solutions. This review concludes that blade coating, slot-die coating, and ink-jet printing carry the highest potential for the scalable manufacture of large-area PSCs with moderate-to-high PCEs. More research and development are key to improving PSC stability and, in the long-term, closing the chasm in lifespan between PSCs and conventional photovoltaic cells.

Exploring the druggable proteome of Candida species through comprehensive computational analysis

Candida albicans and non-albicans Candida spp. are major cause of systemic mycoses. Antifungal drugs such as azoles and polyenes are not efficient to successfully eradicate Candida infection owing to their fungistatic nature or low bioavailability. Here, we have adopted a comprehensive computational workflow for identification, prioritization and validation of targets from proteomes of Candida albicans and Candida tropicalis. The protocol involves identification of essential drug-target candidates using subtractive genomics, protein-protein interaction network properties and systems biology based methods. The essentiality of the novel metabolic and non-metabolic targets was established by performing in silico gene knockouts, under aerobic as well as anaerobic conditions, and in vitro drug inhibition assays respectively. Deletion of twelve genes that are involved in amino acid, secondary metabolite, and carbon metabolism showed zero growth in metabolic model under simulated conditions. The algorithm, used in this study, can be downloaded from http://pbit.bicnirrh.res.in/offline.php and executed locally.

Differential Network Analysis via Weighted Fused Conditional Gaussian Graphical Model

The development and prognosis of complex diseases usually involves changes in regulatory relationships among biomolecules. Understanding how the regulatory relationships change with genetic alterations can help to reveal the underlying biological mechanisms for complex diseases. Although several models have been proposed to estimate the differential network between two different states, they are not suitable to deal with situations where the molecules of interest are affected by other covariates. Nor can they make use of prior information that provides insights about the structures of biomolecular networks. In this study, we introduce a novel weighted fused conditional Gaussian graphical model to jointly estimate two state-specific biomolecular regulatory networks and their difference between two different states. Unlike previous differential network estimation methods, our model can take into account the related covariates and the prior network information when inferring differential networks. The effectiveness of our proposed model is first evaluated based on simulation studies. Experiment results demonstrate that our model outperforms other state-of-the-art differential networks estimation models in all cases. We then apply our model to identify the differential gene network between two subtypes of glioblastoma based on gene expression and miRNA expression data. Our model is able to discover known mechanisms of glioblastoma and provide interesting predictions.

Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients

Cancer patient classification using predictive biomarkers for anti-cancer drug responses is essential for improving therapeutic outcomes. However, current machine-learning-based predictions of drug response often fail to identify robust translational biomarkers from preclinical models. Here, we present a machine-learning framework to identify robust drug biomarkers by taking advantage of network-based analyses using pharmacogenomic data derived from three-dimensional organoid culture models. The biomarkers identified by our approach accurately predict the drug responses of 114 colorectal cancer patients treated with 5-fluorouracil and 77 bladder cancer patients treated with cisplatin. We further confirm our biomarkers using external transcriptomic datasets of drug-sensitive and -resistant isogenic cancer cell lines. Finally, concordance analysis between the transcriptomic biomarkers and independent somatic mutation-based biomarkers further validate our method. This work presents a method to predict cancer patient drug responses using pharmacogenomic data derived from organoid models by combining the application of gene modules and network-based approaches.

Toxicity assessment and comparison of the land snail's Cornu aspersum responses against CuO nanoparticles and ZnO nanoparticles

The goal of the present study was to examine the effects of ZnO NPs and CuO NPs on Cornu aspersum land snail, enlightening their cytotoxic profile. ZnO NPs and CuO NPs were synthesized and thoroughly characterized. Α series of concentrations of either ZnO NPs or CuO NPs were administered in the feed of snails for 20 days. Thereafter, neutral red retention assay was conducted, in order to estimate NRRT50 values. Subsequently, snails were fed with NPs concentrations slightly lower than the concentrations that were corresponding to the NRRT50 values, i.e. 3 mg·L^-1 ZnO NPs and 6 mg·L^-1 CuO NPs, for 1, 5, 10 and 20 days. Both NPs agglomerates were detected in hemocytes by Transmission Electron Microscopy. Moreover, both effectors resulted to toxicity in the snails' hemocytes. The latter was shown by changes in the NRRT50 values, increased reactive oxygen species (ROS) production, lipid peroxidation, DNA integrity loss, protein carbonyl content, ubiquitin conjugates and cleaved caspases conjugates levels compared to the untreated animals. Although ZnO NPs exhibited higher toxicity, as indicated by the NRRT50 values, both NPs affected similarly a wide range of the cellular parameters mentioned above. The latter parameters could constitute sensitive biomarkers in biomonitoring studies of terrestrial environment against nanoparticles.

Edgetic perturbation signatures represent known and novel cancer biomarkers

Isoform switching is a recently characterized hallmark of cancer, and often translates to the loss or gain of domains mediating protein interactions and thus, the re-wiring of the interactome. Recent computational tools leverage domain-domain interaction data to resolve the condition-specific interaction networks from RNA-Seq data accounting for the domain content of the primary transcripts expressed. Here, we used The Cancer Genome Atlas RNA-Seq datasets to generate 642 patient-specific pairs of interactomes corresponding to both the tumor and the healthy tissues across 13 cancer types. The comparison of these interactomes provided a list of patient-specific edgetic perturbations of the interactomes associated with the cancerous state. We found that among the identified perturbations, select sets are robustly shared between patients at the multi-cancer, cancer-specific and cancer sub-type specific levels. Interestingly, the majority of the alterations do not directly involve significantly mutated genes, nevertheless, they strongly correlate with patient survival. The findings (available at EdgeExplorer: “http://webclu.bio.wzw.tum.de/EdgeExplorer”) are a new source of potential biomarkers for classifying cancer types and the proteins we identified are potential anti-cancer therapy targets.

Rapid Evolution of Gained Essential Developmental Functions of a Young Gene via Interactions with Other Essential Genes

New genes are of recent origin and only present in a subset of species in a phylogeny. Accumulated evidence suggests that new genes, like old genes that are conserved across species, can also take on important functions and be essential for the survival and reproductive success of organisms. Although there are detailed analyses of the mechanisms underlying new genes' gaining fertility functions, how new genes rapidly become essential for viability remains unclear. We focused on a young retro-duplicated gene (CG7804, which we named Cocoon) in Drosophila that originated between 4 and 10 Ma. We found that, unlike its evolutionarily conserved parental gene, Cocoon has evolved under positive selection and accumulated many amino acid differences at functional sites from the parental gene. Despite its young age, Cocoon is essential for the survival of Drosophila melanogaster at multiple developmental stages, including the critical embryonic stage, and its expression is essential in different tissues from those of its parental gene. Functional genomic analyses found that Cocoon acquired unique DNA-binding sites and has a contrasting effect on gene expression to that of its parental gene. Importantly, Cocoon binding predominantly locates at genes that have other essential functions and/or have multiple gene-gene interactions, suggesting that Cocoon acquired novel essential function to survival through forming interactions that have large impacts on the gene interaction network. Our study is an important step toward deciphering the evolutionary trajectory by which new genes functionally diverge from parental genes and become essential.

Evolutionary coupling analysis identifies the impact of disease-associated variants at less-conserved sites

Genome-wide association studies have discovered a large number of genetic variants in human patients with the disease. Thus, predicting the impact of these variants is important for sorting disease-associated variants (DVs) from neutral variants. Current methods to predict the mutational impacts depend on evolutionary conservation at the mutation site, which is determined using homologous sequences and based on the assumption that variants at well-conserved sites have high impacts. However, many DVs at less-conserved but functionally important sites cannot be predicted by the current methods. Here, we present a method to find DVs at less-conserved sites by predicting the mutational impacts using evolutionary coupling analysis. Functionally important and evolutionarily coupled sites often have compensatory variants on cooperative sites to avoid loss of function. We found that our method identified known intolerant variants in a diverse group of proteins. Furthermore, at less-conserved sites, we identified DVs that were not identified using conservation-based methods. These newly identified DVs were frequently found at protein interaction interfaces, where species-specific mutations often alter interaction specificity. This work presents a means to identify less-conserved DVs and provides insight into the relationship between evolutionarily coupled sites and human DVs.

Generalised thresholding of hidden variable network models with scale-free property

The hidden variable formalism (based on the assumption of some intrinsic node parameters) turned out to be a remarkably efficient and powerful approach in describing and analyzing the topology of complex networks. Owing to one of its most advantageous property - namely proven to be able to reproduce a wide range of different degree distribution forms - it has become a standard tool for generating networks having the scale-free property. One of the most intensively studied version of this model is based on a thresholding mechanism of the exponentially distributed hidden variables associated to the nodes (intrinsic vertex weights), which give rise to the emergence of a scale-free network where the degree distribution p(k) ~ k^−γ is decaying with an exponent of γ = 2. Here we propose a generalization and modification of this model by extending the set of connection probabilities and hidden variable distributions that lead to the aforementioned degree distribution, and analyze the conditions leading to the above behavior analytically. In addition, we propose a relaxation of the hard threshold in the connection probabilities, which opens up the possibility for obtaining sparse scale free networks with arbitrary scaling exponent.

Intrinsic disorder associated with 14-3-3 proteins and their partners

Protein-protein interactions (PPIs) mediate a variety of cellular processes and form complex networks, where connectivity is achieved owing to the "hub" proteins whose interaction with multiple protein partners is facilitated by the intrinsically disordered protein regions (IDPRs) and posttranslational modifications (PTMs). Universal regulatory proteins of the eukaryotic 14-3-3 family nicely exemplify these concepts and are the focus of this chapter. The extremely wide interactome of 14-3-3 proteins is characterized by high levels of intrinsic disorder (ID) enabling protein phosphorylation and consequent specific binding to the well-structured 14-3-3 dimers, one of the first phosphoserine/phosphothreonine binding modules discovered. However, high ID enrichment also challenges structural studies, thereby limiting the progress in the development of small molecule modulators of the key 14-3-3 PPIs of increased medical importance. Besides the well-known structural flexibility of their variable C-terminal tails, recent studies revealed the strong and conserved ID propensity hidden in the N-terminal segment of 14-3-3 proteins (~40 residues), normally forming the α-helical dimerization region, that may have a potential role for the dimer/monomer dynamics and recently reported moonlighting chaperone-like activity of these proteins. We review the role of ID in the 14-3-3 structure, their interactome, and also in selected 14-3-3 complexes. In addition, we discuss approaches that, in the future, may help minimize the disproportion between the large amount of known 14-3-3 partners and the small number of 14-3-3 complexes characterized with atomic precision, to unleash the whole potential of 14-3-3 PPIs as drug targets.

Network Analysis as a Grand Unifier in Biomedical Data Science

Biomedical data scientists study many types of networks, ranging from those formed by neurons to those created by molecular interactions. People often criticize these networks as uninterpretable diagrams termed hairballs; however, here we show that molecular biological networks can be interpreted in several straightforward ways. First, we can break down a network into smaller components, focusing on individual pathways and modules. Second, we can compute global statistics describing the network as a whole. Third, we can compare networks. These comparisons can be within the same context (e.g., between two gene regulatory networks) or cross-disciplinary (e.g., between regulatory networks and governmental hierarchies). The latter comparisons can transfer a formalism, such as that for Markov chains, from one context to another or relate our intuitions in a familiar setting (e.g., social networks) to the relatively unfamiliar molecular context. Finally, key aspects of molecular networks are dynamics and evolution, i.e., how they evolve over time and how genetic variants affect them. By studying the relationships between variants in networks, we can begin to interpret many common diseases, such as cancer and heart disease.

Structure function relations in PDZ-domain-containing proteins: Implications for protein networks in cellular signalling

Protein scaffolds as essential backbones for organization of supramolecular signalling complexes are a recurrent theme in several model systems. Scaffold proteins preferentially employ linear peptide binding motifs for recruiting their interaction partners. PDZ domains are one of the more commonly encountered peptide binding domains in several proteins including those involved in scaffolding functions. This domain is known for its promiscuity both in terms of ligand selection, mode of interaction with its ligands as well as its association with other protein interaction domains. PDZ domains are subject to several means of regulations by virtue of their functional diversity. Additionally, the PDZ domains are refractive to the effect of mutations and maintain their three-dimensional architecture under extreme mutational load. The biochemical and biophysical basis for this selectivity as well as promiscuity has been investigated and reviewed extensively. The present review focuses on the plasticity inherent in PDZ domains and its implications for modular organization as well as evolution of cellular signalling pathways in higher eukaryotes.

How the evolution of multicellularity set the stage for cancer

Neoplastic growth and many of the hallmark properties of cancer are driven by the disruption of molecular networks established during the emergence of multicellularity. Regulatory pathways and molecules that evolved to impose regulatory constraints upon networks established in earlier unicellular organisms enabled greater communication and coordination between the diverse cell types required for multicellularity, but also created liabilities in the form of points of vulnerability in the network that when mutated or dysregulated facilitate the development of cancer. These factors are usually overlooked in genomic analyses of cancer, but understanding where vulnerabilities to cancer lie in the networks of multicellular species would provide important new insights into how core molecular processes and gene regulation change during tumourigenesis. We describe how the evolutionary origins of genes influence their roles in cancer, and how connections formed between unicellular and multicellular genes that act as key regulatory hubs for normal tissue homeostasis can also contribute to malignant transformation when disrupted. Tumours in general are characterised by increased dependence on unicellular processes for survival, and major dysregulation of the control structures imposed on these processes during the evolution of multicellularity. Mounting molecular evidence suggests altered interactions at the interface between unicellular and multicellular genes play key roles in the initiation and progression of cancer. Furthermore, unicellular network regions activated in cancer show high degrees of robustness and plasticity, conferring increased adaptability to tumour cells by supporting effective responses to environmental pressures such as drug exposure. Examining how the links between multicellular and unicellular regions get disrupted in tumours has great potential to identify novel drivers of cancer, and to guide improvements to cancer treatment by identifying more effective therapeutic strategies. Recent successes in targeting unicellular processes by novel compounds underscore the logic of such approaches. Further gains could come from identifying genes at the interface between unicellular and multicellular processes and manipulating the communication between network regions of different evolutionary ages.

DiffSLC: A graph centrality method to detect essential proteins of a protein-protein interaction network

Identification of central genes and proteins in biomolecular networks provides credible candidates for pathway analysis, functional analysis, and essentiality prediction. The DiffSLC centrality measure predicts central and essential genes and proteins using a protein-protein interaction network. Network centrality measures prioritize nodes and edges based on their importance to the network topology. These measures helped identify critical genes and proteins in biomolecular networks. The proposed centrality measure, DiffSLC, combines the number of interactions of a protein and the gene coexpression values of genes from which those proteins were translated, as a weighting factor to bias the identification of essential proteins in a protein interaction network. Potentially essential proteins with low node degree are promoted through eigenvector centrality. Thus, the gene coexpression values are used in conjunction with the eigenvector of the network's adjacency matrix and edge clustering coefficient to improve essentiality prediction. The outcome of this prediction is shown using three variations: (1) inclusion or exclusion of gene co-expression data, (2) impact of different coexpression measures, and (3) impact of different gene expression data sets. For a total of seven networks, DiffSLC is compared to other centrality measures using Saccharomyces cerevisiae protein interaction networks and gene expression data. Comparisons are also performed for the top ranked proteins against the known essential genes from the Saccharomyces Gene Deletion Project, which show that DiffSLC detects more essential proteins and has a higher area under the ROC curve than other compared methods. This makes DiffSLC a stronger alternative to other centrality methods for detecting essential genes using a protein-protein interaction network that obeys centrality-lethality principle. DiffSLC is implemented using the igraph package in R, and networkx package in Python. The python package can be obtained from git.io/diffslcpy. The R implementation and code to reproduce the analysis is available via git.io/diffslc.

Effects of different kinds of essentiality on sequence evolution of human testis proteins

We asked if essentiality for either fertility or viability differentially affects sequence evolution of human testis proteins. Based on murine knockout data, we classified a set of 965 proteins expressed in human seminiferous tubules into three categories: proteins essential for prepubertal survival (“lethality proteins”), associated with male sub- or infertility (“male sub-/infertility proteins”), and nonessential proteins. In our testis protein dataset, lethality genes evolved significantly slower than nonessential and male sub-/infertility genes, which is in line with other authors’ findings. Using tissue specificity, connectivity in the protein-protein interaction (PPI) network, and multifunctionality as proxies for evolutionary constraints, we found that of the three categories, proteins linked to male sub- or infertility are least constrained. Lethality proteins, on the other hand, are characterized by broad expression, many PPI partners, and high multifunctionality, all of which points to strong evolutionary constraints. We conclude that compared with lethality proteins, those linked to male sub- or infertility are nonetheless indispensable, but evolve under more relaxed constraints. Finally, adaptive evolution in response to postmating sexual selection could further accelerate evolutionary rates of male sub- or infertility proteins expressed in human testis. These findings may become useful for in silico detection of human sub-/infertility genes.

Network biology concepts in complex disease comorbidities

The co-occurrence of diseases can inform the underlying network biology of shared and multifunctional genes and pathways. In addition, comorbidities help to elucidate the effects of external exposures, such as diet, lifestyle and patient care. With worldwide health transaction data now often being collected electronically, disease co-occurrences are starting to be quantitatively characterized. Linking network dynamics to the real-life, non-ideal patient in whom diseases co-occur and interact provides a valuable basis for generating hypotheses on molecular disease mechanisms, and provides knowledge that can facilitate drug repurposing and the development of targeted therapeutic strategies.

Controllability of protein-protein interaction phosphorylation-based networks: Participation of the hub 14-3-3 protein family

Posttranslational regulation of protein function is an ubiquitous mechanism in eukaryotic cells. Here, we analyzed biological properties of nodes and edges of a human protein-protein interaction phosphorylation-based network, especially of those nodes critical for the network controllability. We found that the minimal number of critical nodes needed to control the whole network is 29%, which is considerably lower compared to other real networks. These critical nodes are more regulated by posttranslational modifications and contain more binding domains to these modifications than other kinds of nodes in the network, suggesting an intra-group fast regulation. Also, when we analyzed the edges characteristics that connect critical and non-critical nodes, we found that the former are enriched in domain-to-eukaryotic linear motif interactions, whereas the later are enriched in domain-domain interactions. Our findings suggest a possible structure for protein-protein interaction networks with a densely interconnected and self-regulated central core, composed of critical nodes with a high participation in the controllability of the full network, and less regulated peripheral nodes. Our study offers a deeper understanding of complex network control and bridges the controllability theorems for complex networks and biological protein-protein interaction phosphorylation-based networked systems.

Identifying Network Perturbation in Cancer

We present a computational framework, called DISCERN (DIfferential SparsE Regulatory Network), to identify informative topological changes in gene-regulator dependence networks inferred on the basis of mRNA expression datasets within distinct biological states. DISCERN takes two expression datasets as input: an expression dataset of diseased tissues from patients with a disease of interest and another expression dataset from matching normal tissues. DISCERN estimates the extent to which each gene is perturbed-having distinct regulator connectivity in the inferred gene-regulator dependencies between the disease and normal conditions. This approach has distinct advantages over existing methods. First, DISCERN infers conditional dependencies between candidate regulators and genes, where conditional dependence relationships discriminate the evidence for direct interactions from indirect interactions more precisely than pairwise correlation. Second, DISCERN uses a new likelihood-based scoring function to alleviate concerns about accuracy of the specific edges inferred in a particular network. DISCERN identifies perturbed genes more accurately in synthetic data than existing methods to identify perturbed genes between distinct states. In expression datasets from patients with acute myeloid leukemia (AML), breast cancer and lung cancer, genes with high DISCERN scores in each cancer are enriched for known tumor drivers, genes associated with the biological processes known to be important in the disease, and genes associated with patient prognosis, in the respective cancer. Finally, we show that DISCERN can uncover potential mechanisms underlying network perturbation by explaining observed epigenomic activity patterns in cancer and normal tissue types more accurately than alternative methods, based on the available epigenomic data from the ENCODE project.

Gene Network Rewiring to Study Melanoma Stage Progression and Elements Essential for Driving Melanoma

Metastatic melanoma patients have a poor prognosis, mainly attributable to the underlying heterogeneity in melanoma driver genes and altered gene expression profiles. These characteristics of melanoma also make the development of drugs and identification of novel drug targets for metastatic melanoma a daunting task. Systems biology offers an alternative approach to re-explore the genes or gene sets that display dysregulated behaviour without being differentially expressed. In this study, we have performed systems biology studies to enhance our knowledge about the conserved property of disease genes or gene sets among mutually exclusive datasets representing melanoma progression. We meta-analysed 642 microarray samples to generate melanoma reconstructed networks representing four different stages of melanoma progression to extract genes with altered molecular circuitry wiring as compared to a normal cellular state. Intriguingly, a majority of the melanoma network-rewired genes are not differentially expressed and the disease genes involved in melanoma progression consistently modulate its activity by rewiring network connections. We found that the shortlisted disease genes in the study show strong and abnormal network connectivity, which enhances with the disease progression. Moreover, the deviated network properties of the disease gene sets allow ranking/prioritization of different enriched, dysregulated and conserved pathway terms in metastatic melanoma, in agreement with previous findings. Our analysis also reveals presence of distinct network hubs in different stages of metastasizing tumor for the same set of pathways in the statistically conserved gene sets. The study results are also presented as a freely available database at http://bioinfo.icgeb.res.in/m3db/. The web-based database resource consists of results from the analysis presented here, integrated with cytoscape web and user-friendly tools for visualization, retrieval and further analysis.

Network Modules of the Cross-Species Genotype-Phenotype Map Reflect the Clinical Severity of Human Diseases

Recent advances in genome sequencing techniques have improved our understanding of the genotype-phenotype relationship between genetic variants and human diseases. However, genetic variations uncovered from patient populations do not provide enough information to understand the mechanisms underlying the progression and clinical severity of human diseases. Moreover, building a high-resolution genotype-phenotype map is difficult due to the diverse genetic backgrounds of the human population. We built a cross-species genotype-phenotype map to explain the clinical severity of human genetic diseases. We developed a data-integrative framework to investigate network modules composed of human diseases mapped with gene essentiality measured from a model organism. Essential and nonessential genes connect diseases of different types which form clusters in the human disease network. In a large patient population study, we found that disease classes enriched with essential genes tended to show a higher mortality rate than disease classes enriched with nonessential genes. Moreover, high disease mortality rates are explained by the multiple comorbid relationships and the high pleiotropy of disease genes found in the essential gene-enriched diseases. Our results reveal that the genotype-phenotype map of a model organism can facilitate the identification of human disease-gene associations and predict human disease progression.

Regulation rewiring analysis reveals mutual regulation between STAT1 and miR-155-5p in tumor immunosurveillance in seven major cancers

Transcription factors (TFs) and microRNAs (miRNAs) form a gene regulatory network (GRN) at the transcriptional and post-transcriptional level in living cells. However, this network has not been well characterized, especially in regards to the mutual regulations between TFs and miRNAs in cancers. In this study, we collected those regulations inferred by ChIP-Seq or CLIP-Seq to construct the GRN formed by TFs, miRNAs, and target genes. To increase the reliability of the proposed network and examine the regulation activity of TFs and miRNAs, we further incorporated the mRNA and miRNA expression profiles in seven cancer types using The Cancer Genome Atlas data. We observed that regulation rewiring was prevalent during tumorigenesis and found that the rewired regulatory feedback loops formed by TFs and miRNAs were highly associated with cancer. Interestingly, we identified one regulatory feedback loop between STAT1 and miR-155-5p that is consistently activated in all seven cancer types with its function to regulate tumor-related biological processes. Our results provide insights on the losing equilibrium of the regulatory feedback loop between STAT1 and miR-155-5p influencing tumorigenesis.

Hierarchical control of coherent gene clusters defines the molecular mechanisms of glioblastoma

Glioblastoma is a highly-aggressive and rapidly-lethal tumor characterized by resistance to therapy. Although data on multiple genes, proteins, and pathways are available, the key challenge is deciphering this information and identifying central molecular targets. Therapeutically targeting individual molecules is often unsuccessful due to the presence of compensatory and redundant pathways, and crosstalk. A systems biology approach that involves a hierarchical gene group networks analysis can delineate the coherent functions of different disease mediators. Here, we report an integrative networks-based analysis to identify a system of coherent gene modules in primary and secondary glioblastoma. Our study revealed a hierarchical transcriptional control of genes in these modules. We elucidated those modules responsible for conversion of the glioma-associated microglia/macrophages into glioma-supportive, immunosuppressive cells. Further, we identified clusters comprising mediators of angiogenesis, proliferation, and cell death for both primary and secondary glioblastomas. Data obtained for these clusters point to a possible role of transcription regulators that function as the gene modules mediators in glioblastoma pathogenesis. We elucidated a set of possible transcription regulators that can be targeted to affect the selected gene clusters at specific levels for glioblastoma. Our innovative approach to construct informative disease models may hold the key to successful management of complex diseases including glioblastoma and other cancers.

Global coordination in adaptation to gene rewiring

Gene rewiring is a common evolutionary phenomenon in nature that may lead to extinction for living organisms. Recent studies on synthetic biology demonstrate that cells can survive genetic rewiring. This survival (adaptation) is often linked to the stochastic expression of rewired genes with random transcriptional changes. However, the probability of adaptation and the underlying common principles are not clear. We performed a systematic survey of an assortment of gene-rewired Escherichia coli strains to address these questions. Three different cell fates, designated good survivors, poor survivors and failures, were observed when the strains starved. Large fluctuations in the expression of the rewired gene were commonly observed with increasing cell size, but these changes were insufficient for adaptation. Cooperative reorganizations in the corresponding operon and genome-wide gene expression largely contributed to the final success. Transcriptome reorganizations that generally showed high-dimensional dynamic changes were restricted within a one-dimensional trajectory for adaptation to gene rewiring, indicating a general path directed toward cellular plasticity for a successful cell fate. This finding of global coordination supports a mechanism of stochastic adaptation and provides novel insights into the design and application of complex genetic or metabolic networks.

A functional portrait of Med7 and the mediator complex in Candida albicans

Mediator is a multi-subunit protein complex that regulates gene expression in eukaryotes by integrating physiological and developmental signals and transmitting them to the general RNA polymerase II machinery. We examined, in the fungal pathogen Candida albicans, a set of conditional alleles of genes encoding Mediator subunits of the head, middle, and tail modules that were found to be essential in the related ascomycete Saccharomyces cerevisiae. Intriguingly, while the Med4, 8, 10, 11, 14, 17, 21 and 22 subunits were essential in both fungi, the structurally highly conserved Med7 subunit was apparently non-essential in C. albicans. While loss of CaMed7 did not lead to loss of viability under normal growth conditions, it dramatically influenced the pathogen's ability to grow in different carbon sources, to form hyphae and biofilms, and to colonize the gastrointestinal tracts of mice. We used epitope tagging and location profiling of the Med7 subunit to examine the distribution of the DNA sites bound by Mediator during growth in either the yeast or the hyphal form, two distinct morphologies characterized by different transcription profiles. We observed a core set of 200 genes bound by Med7 under both conditions; this core set is expanded moderately during yeast growth, but is expanded considerably during hyphal growth, supporting the idea that Mediator binding correlates with changes in transcriptional activity and that this binding is condition specific. Med7 bound not only in the promoter regions of active genes but also within coding regions and at the 3′ ends of genes. By combining genome-wide location profiling, expression analyses and phenotyping, we have identified different Med7p-influenced regulons including genes related to glycolysis and the Filamentous Growth Regulator family. In the absence of Med7, the ribosomal regulon is de-repressed, suggesting Med7 is involved in central aspects of growth control.

In this study, we have investigated Mediator function in the human fungal pathogen C. albicans. An initial screening of conditionally regulated Mediator subunits showed that the Med7 of C. albicans was not essential, in contrast to the situation noted for S. cerevisiae. While loss of CaMed7 did not lead to loss of viability under normal growth conditions, it dramatically influenced the pathogen's ability to grow in different carbon sources, to form hyphae and biofilms, and to colonize the gastrointestinal tracts of mice. We used location profiling to determine Mediator binding under yeast and hyphal morphologies characterized by different transcription profiles. We observed a core set of specific and common genes bound by Med7 under both conditions; this specific core set is expanded considerably during hyphal growth, supporting the idea that Mediator binding correlates with changes in transcriptional activity and that this binding is condition specific. Med7 bound not only in the promoter regions of active genes but also of inactive genes and within coding regions and at the 3′ ends of genes. By combining genome-wide location profiling, expression analyses and phenotyping, we have identified different Med7 regulons including genes related to glycolysis and the Filamentous Growth Regulator family.

An explanatory evo-devo model for the developmental hourglass

The "developmental hourglass'' describes a pattern of increasing morphological divergence towards earlier and later embryonic development, separated by a period of significant conservation across distant species (the "phylotypic stage''). Recent studies have found evidence in support of the hourglass effect at the genomic level. For instance, the phylotypic stage expresses the oldest and most conserved transcriptomes. However, the regulatory mechanism that causes the hourglass pattern remains an open question. Here, we use an evolutionary model of regulatory gene interactions during development to identify the conditions under which the hourglass effect can emerge in a general setting. The model focuses on the hierarchical gene regulatory network that controls the developmental process, and on the evolution of a population under random perturbations in the structure of that network. The model predicts, under fairly general assumptions, the emergence of an hourglass pattern in the structure of a temporal representation of the underlying gene regulatory network. The evolutionary age of the corresponding genes also follows an hourglass pattern, with the oldest genes concentrated at the hourglass waist. The key behind the hourglass effect is that developmental regulators should have an increasingly specific function as development progresses. Analysis of developmental gene expression profiles from Drosophila melanogaster and Arabidopsis thaliana provide consistent results with our theoretical predictions.

Characterizing and controlling the inflammatory network during influenza A virus infection

To gain insights into the pathogenesis of influenza A virus (IAV) infections, this study focused on characterizing the inflammatory network and identifying key proteins by combining high-throughput data and computational techniques. We constructed the cell-specific normal and inflammatory networks for H5N1 and H1N1 infections through integrating high-throughput data. We demonstrated that better discrimination between normal and inflammatory networks by network entropy than by other topological metrics. Moreover, we identified different dynamical interactions among TLR2, IL-1β, IL10 and NFκB between normal and inflammatory networks using optimization algorithm. In particular, good robustness and multistability of inflammatory sub-networks were discovered. Furthermore, we identified a complex, TNFSF10/HDAC4/HDAC5, which may play important roles in controlling inflammation, and demonstrated that changes in network entropy of this complex negatively correlated to those of three proteins: TNFα, NFκB and COX-2. These findings provide significant hypotheses for further exploring the molecular mechanisms of infectious diseases and developing control strategies.

New genes as drivers of phenotypic evolution

During the course of evolution, genomes acquire novel genetic elements as sources of functional and phenotypic diversity, including new genes that originated in recent evolution. In the past few years, substantial progress has been made in understanding the evolution and phenotypic effects of new genes. In particular, an emerging picture is that new genes, despite being present in the genomes of only a subset of species, can rapidly evolve indispensable roles in fundamental biological processes, including development, reproduction, brain function and behaviour. The molecular underpinnings of how new genes can develop these roles are starting to be characterized. These recent discoveries yield fresh insights into our broad understanding of biological diversity at refined resolution.