High-quality binary protein interaction map of the yeast interactome network

The transition to synchronization of networked systems

We study the synchronization properties of a generic networked dynamical system, and show that, under a suitable approximation, the transition to synchronization can be predicted with the only help of eigenvalues and eigenvectors of the graph Laplacian matrix. The transition comes out to be made of a well defined sequence of events, each of which corresponds to a specific clustered state. The network's nodes involved in each of the clusters can be identified, and the value of the coupling strength at which the events are taking place can be approximately ascertained. Finally, we present large-scale simulations which show the accuracy of the approximation made, and of our predictions in describing the synchronization transition of both synthetic and real-world large size networks, and we even report that the observed sequence of clusters is preserved in heterogeneous networks made of slightly non-identical systems.

The mast cells - Cytokines axis in Autism Spectrum Disorder

Autism Spectrum Disorder (ASD) is a neurodevelopmental disturbance, diagnosed in early childhood. It is associated with varying degrees of dysfunctional communication and social skills, repetitive and stereotypic behaviors. Regardless of the constant increase in the number of diagnosed patients, there are still no established treatment schemes in global practice. Many children with ASD have allergic symptoms, often in the absence of mast cell (MC) positive tests. Activation of MCs may release molecules related to inflammation and neurotoxicity, which contribute to the pathogenesis of ASD. The aim of the present paper is to enrich the current knowledge regarding the relationship between MCs and ASD by providing PPI network analysis-based data that reveal key molecules and immune pathways associated with MCs in the pathogenesis of autism. Network and enrichment analyzes were performed using receptor information and secreted molecules from activated MCs identified in ASD patients. Our analyses revealed cytokines and key marker molecules for MCs degranulation, molecular pathways of key mediators released during cell degranulation, as well as various receptors. Understanding the relationship between ASD and the activation of MCs, as well as the involved molecules and interactions, is important for elucidating the pathogenesis of ASD and developing effective future treatments for autistic patients by discovering new therapeutic target molecules.

Computational Approaches to Predict Protein-Protein Interactions in Crowded Cellular Environments

Investigating protein-protein interactions is crucial for understanding cellular biological processes because proteins often function within molecular complexes rather than in isolation. While experimental and computational methods have provided valuable insights into these interactions, they often overlook a critical factor: the crowded cellular environment. This environment significantly impacts protein behavior, including structural stability, diffusion, and ultimately the nature of binding. In this review, we discuss theoretical and computational approaches that allow the modeling of biological systems to guide and complement experiments and can thus significantly advance the investigation, and possibly the predictions, of protein-protein interactions in the crowded environment of cell cytoplasm. We explore topics such as statistical mechanics for lattice simulations, hydrodynamic interactions, diffusion processes in high-viscosity environments, and several methods based on molecular dynamics simulations. By synergistically leveraging methods from biophysics and computational biology, we review the state of the art of computational methods to study the impact of molecular crowding on protein-protein interactions and discuss its potential revolutionizing effects on the characterization of the human interactome.

Reconstructing the evolution history of networked complex systems

The evolution processes of complex systems carry key information in the systems' functional properties. Applying machine learning algorithms, we demonstrate that the historical formation process of various networked complex systems can be extracted, including protein-protein interaction, ecology, and social network systems. The recovered evolution process has demonstrations of immense scientific values, such as interpreting the evolution of protein-protein interaction network, facilitating structure prediction, and particularly revealing the key co-evolution features of network structures such as preferential attachment, community structure, local clustering, degree-degree correlation that could not be explained collectively by previous theories. Intriguingly, we discover that for large networks, if the performance of the machine learning model is slightly better than a random guess on the pairwise order of links, reliable restoration of the overall network formation process can be achieved. This suggests that evolution history restoration is generally highly feasible on empirical networks.

Resistance mechanisms of Saccharomyces cerevisiae against silver nanoparticles with different sizes and coatings

To investigate the underlying resistance mechanisms of Saccharomyces cerevisiae against Ag-NPs with different particle sizes and coatings, transcriptome sequencing (RNA-seq) technology was used to characterize the transcriptomes from S. cerevisiae exposed to 20-PVP-Ag, 100-PVP-Ag, 20-CIT-Ag and 100-CIT-Ag, respectively. The steroid biosynthesis was found as a general pathway for Ag-NPs stress responding, in which ERG6 and ERG3 were inhibited and ERG11, ERG25 and ERG5 were significantly up-regulated to resist the stress by supporting the later mutation and resistance and modulate drug efflux indirectly. The resistance mechanism of S. cerevisiae to 20-PVP-Ag seems different from that of 100-PVP-Ag, 20-CIT-Ag and 100-CIT-Ag. Under the 20-PVP-Ag, transmembrane transporter activity, transition metal ion homeostasis and oxidative phosphorylation pathway were main resistance pathways to enhance cell transport processes. While 100-PVP-Ag, 20-CIT-Ag and 100-CIT-Ag mainly impacted RNA binding, structural constituent of ribosome and ribosome pathway which can provide more energy to maintain the number and function of protein in cells. This study reveals the differences in resistance mechanisms of S. cerevisiae to Ag-NPs with different particle sizes and coatings, and explains several main regulatory mechanisms used to respond to silver stress. It will provide theoretical basis for the study of chemical risk assessment.

Methionine sulfoxide reductase 2 regulates Cvt autophagic pathway by altering the stability of Atg19 and Ape1 in Saccharomyces cerevisiae

The reversible oxidation of methionine plays a crucial role in redox regulation of proteins. Methionine oxidation in proteins causes major structural modifications that can destabilize and abrogate their function. The highly conserved methionine sulfoxide reductases protect proteins from oxidative damage by reducing their oxidized methionines, thus restoring their stability and function. Deletion or mutation in conserved methionine sulfoxide reductases leads to aging and several human neurological disorders and also reduces yeast growth on nonfermentable carbon sources. Despite their importance in human health, limited information about their physiological substrates in humans and yeast is available. For the first time, we show that Mxr2 interacts in vivo with two core proteins of the cytoplasm to vacuole targeting (Cvt) autophagy pathway, Atg19, and Ape1 in Saccharomyces cerevisiae. Deletion of MXR2 induces instability and early turnover of immature Ape1 and Atg19 proteins and reduces the leucine aminopeptidase activity of Ape1 without affecting the maturation process of Ape1. Additonally, Mxr2 interacts with the immature Ape1, dependent on Met17 present within the propeptide of Ape1 as a single substitution mutation of Met17 to Leu abolishes this interaction. Importantly, Ape1 M17L mutant protein resists oxidative stress-induced degradation in WT and mxr2Δ cells. By identifying Atg19 and Ape1 as cytosolic substrates of Mxr2, our study maps the hitherto unexplored connection between Mxr2 and the Cvt autophagy pathway and sheds light on Mxr2-dependent oxidative regulation of the Cvt pathway.

The maximum capability of a topological feature in link prediction

Networks offer a powerful approach to modeling complex systems by representing the underlying set of pairwise interactions. Link prediction is the task that predicts links of a network that are not directly visible, with profound applications in biological, social, and other complex systems. Despite intensive utilization of the topological feature in this task, it is unclear to what extent a feature can be leveraged to infer missing links. Here, we aim to unveil the capability of a topological feature in link prediction by identifying its prediction performance upper bound. We introduce a theoretical framework that is compatible with different indexes to gauge the feature, different prediction approaches to utilize the feature, and different metrics to quantify the prediction performance. The maximum capability of a topological feature follows a simple yet theoretically validated expression, which only depends on the extent to which the feature is held in missing and nonexistent links. Because a family of indexes based on the same feature shares the same upper bound, the potential of all others can be estimated from one single index. Furthermore, a feature's capability is lifted in the supervised prediction, which can be mathematically quantified, allowing us to estimate the benefit of applying machine learning algorithms. The universality of the pattern uncovered is empirically verified by 550 structurally diverse networks. The findings have applications in feature and method selection, and shed light on network characteristics that make a topological feature effective in link prediction.

Identification of common candidate genes and pathways for Spina Bifida and Wilm's Tumor using an integrative bioinformatics analysis

Spina Bifida (SB) and Wilm's Tumor (WT) are conditions, both associated with children. Several studies have shown that WT later develops in SB patients, which led us to elucidate common key genes and linked pathways of both conditions, aimed at their concurrent therapeutic management. For this, integrated bioinformatics analysis was employed. A comprehensive manual curation of genes identified 133 and 139 genes associated with SB and WT, respectively, which were used to construct a single protein-protein interaction (PPI) network. Topological parameters analysis of the network showed its scale-free and hierarchical nature. Centrality-based analysis of the network identified 116 hubs, of which, 6 were called the key genes attributed to being common between SB and WT besides being the hubs. Gene enrichment analysis of the 5 most essential modules, identified important biological processes and pathways possibly linking SB to WT. Additionally, miRNA-key gene-transcription factor (TF) regulatory network elucidated a few important miRNAs and TFs that regulate our key genes. In closing, we put forward TP53, DICER1, NCAM1, PAX3, PTCH1, MTHFR; hsa-mir-107, hsa-mir-137, hsa-mir-122, hsa-let-7d; and YY1, SOX4, MYC, STAT3; key genes, miRNAs and TFs, respectively, as the key regulators. Further, MD simulation studies of wild and Glu429Ala forms of MTHFR proteins showed that there is a slight change in MTHFR protein structure due to Glu429Ala polymorphism. We anticipate that the interplay of these three entities will be an interesting area of research to explore the regulatory mechanism of SB and WT and may serve as candidate target molecules to diagnose, monitor, and treat SB and WT, parallelly.Communicated by Ramaswamy H. Sarma.

Bioinformatics prediction and screening of viral mimicry candidates through integrating known and predicted DMI data

Domain-motif interactions (DMIs) represent transient bonds formed when a Short Linear Motif (SLiM) engages a globular domain via a compact contact interface. Understanding the mechanics of DMIs is critical for maintaining diverse regulatory processes and deciphering how various viruses hijack host cellular machinery. However, identifying DMIs through traditional in vitro and in vivo experiments is challenging due to their degenerate nature and small contact areas. Predictions often carry a high rate of false positives, necessitating rigorous in-silico validation before embarking on experimental work. This study assessed the binding energy changes in predicted SLiM instances through in-silico peptide exchange experiment, elucidating how they interact with known 3D DMI complexes. We identified a subset of potential mimicry candidates that exhibited effective binding affinities with native DMI structures, suggesting their potential to be true mimicry candidates. The identified viral SLiMs can be potential targets in developing therapeutics, opening new opportunities for innovative treatments that can be finely tuned to address the complex molecular underpinnings of various diseases. To gain a comprehensive understanding of identified DMIs, it is imperative to conduct further validation through experimental approaches.

The social and structural architecture of the yeast protein interactome

Cellular functions are mediated by protein-protein interactions, and mapping the interactome provides fundamental insights into biological systems. Affinity purification coupled to mass spectrometry is an ideal tool for such mapping, but it has been difficult to identify low copy number complexes, membrane complexes and complexes that are disrupted by protein tagging. As a result, our current knowledge of the interactome is far from complete, and assessing the reliability of reported interactions is challenging. Here we develop a sensitive high-throughput method using highly reproducible affinity enrichment coupled to mass spectrometry combined with a quantitative two-dimensional analysis strategy to comprehensively map the interactome of Saccharomyces cerevisiae. Thousand-fold reduced volumes in 96-well format enabled replicate analysis of the endogenous GFP-tagged library covering the entire expressed yeast proteome1. The 4,159 pull-downs generated a highly structured network of 3,927 proteins connected by 31,004 interactions, doubling the number of proteins and tripling the number of reliable interactions compared with existing interactome maps2. This includes very-low-abundance epigenetic complexes, organellar membrane complexes and non-taggable complexes inferred by abundance correlation. This nearly saturated interactome reveals that the vast majority of yeast proteins are highly connected, with an average of 16 interactors. Similar to social networks between humans, the average shortest distance between proteins is 4.2 interactions. AlphaFold-Multimer provided novel insights into the functional roles of previously uncharacterized proteins in complexes. Our web portal ( www.yeast-interactome.org ) enables extensive exploration of the interactome dataset.

The protein interactome of the citrus Huanglongbing pathogen Candidatus Liberibacter asiaticus

The bacterium Candidatus Liberibacter asiaticus (CLas) causes citrus Huanglongbing disease. Our understanding of the pathogenicity and biology of this microorganism remains limited because CLas has not yet been cultivated in artificial media. Its genome is relatively small and encodes approximately 1136 proteins, of which 415 have unknown functions. Here, we use a high-throughput yeast-two-hybrid (Y2H) screen to identify interactions between CLas proteins, thus providing insights into their potential functions. We identify 4245 interactions between 542 proteins, after screening 916 bait and 936 prey proteins. The false positive rate of the Y2H assay is estimated to be 2.9%. Pull-down assays for nine protein-protein interactions (PPIs) likely involved in flagellar function support the robustness of the Y2H results. The average number of PPIs per node in the CLas interactome is 15.6, which is higher than the numbers previously reported for interactomes of free-living bacteria, suggesting that CLas genome reduction has been accompanied by increased protein multi-functionality. We propose potential functions for 171 uncharacterized proteins, based on the PPI results, guilt-by-association analyses, and comparison with data from other bacterial species. We identify 40 hub-node proteins, including quinone oxidoreductase and LysR, which are known to protect other bacteria against oxidative stress and might be important for CLas survival in the phloem. We expect our PPI database to facilitate research on CLas biology and pathogenicity mechanisms.

Single-cell imaging of protein dynamics of paralogs reveals mechanisms of gene retention

Gene duplication is common across the tree of life, including yeast and humans, and contributes to genomic robustness. In this study, we examined changes in the subcellular localization and abundance of proteins in response to the deletion of their paralogs originating from the whole-genome duplication event, which is a largely unexplored mechanism of functional divergence. We performed a systematic single-cell imaging analysis of protein dynamics and screened subcellular redistribution of proteins, capturing their localization and abundance changes, providing insight into forces determining paralog retention. Paralogs showed dependency, whereby proteins required their paralog to maintain their native abundance or localization, more often than compensation. Network feature analysis suggested the importance of functional redundancy and rewiring of protein and genetic interactions underlying redistribution response of paralogs. Translation of non-canonical protein isoform emerged as a novel compensatory mechanism. This study provides new insights into paralog retention and evolutionary forces that shape genomes.

Speos: an ensemble graph representation learning framework to predict core gene candidates for complex diseases

Understanding phenotype-to-genotype relationships is a grand challenge of 21st century biology with translational implications. The recently proposed "omnigenic" model postulates that effects of genetic variation on traits are mediated by core-genes and -proteins whose activities mechanistically influence the phenotype, whereas peripheral genes encode a regulatory network that indirectly affects phenotypes via core gene products. Here, we develop a positive-unlabeled graph representation-learning ensemble-approach based on a nested cross-validation to predict core-like genes for diverse diseases using Mendelian disorder genes for training. Employing mouse knockout phenotypes for external validations, we demonstrate that core-like genes display several key properties of core genes: Mouse knockouts of genes corresponding to our most confident predictions give rise to relevant mouse phenotypes at rates on par with the Mendelian disorder genes, and all candidates exhibit core gene properties like transcriptional deregulation in disease and loss-of-function intolerance. Moreover, as predicted for core genes, our candidates are enriched for drug targets and druggable proteins. In contrast to Mendelian disorder genes the new core-like genes are enriched for druggable yet untargeted gene products, which are therefore attractive targets for drug development. Interpretation of the underlying deep learning model suggests plausible explanations for our core gene predictions in form of molecular mechanisms and physical interactions. Our results demonstrate the potential of graph representation learning for the interpretation of biological complexity and pave the way for studying core gene properties and future drug development.

A Protein-Protein Interaction Analysis Suggests a Wide Range of New Functions for the p21-Activated Kinase (PAK) Ste20

The p21-activated kinases (PAKs) are important signaling proteins. They contribute to a surprisingly wide range of cellular processes and play critical roles in a number of human diseases including cancer, neurological disorders and cardiac diseases. To get a better understanding of PAK functions, mechanisms and integration of various cellular activities, we screened for proteins that bind to the budding yeast PAK Ste20 as an example, using the split-ubiquitin technique. We identified 56 proteins, most of them not described previously as Ste20 interactors. The proteins fall into a small number of functional categories such as vesicle transport and translation. We analyzed the roles of Ste20 in glucose metabolism and gene expression further. Ste20 has a well-established role in the adaptation to changing environmental conditions through the stimulation of mitogen-activated protein kinase (MAPK) pathways which eventually leads to transcription factor activation. This includes filamentous growth, an adaptation to nutrient depletion. Here we show that Ste20 also induces filamentous growth through interaction with nuclear proteins such as Sac3, Ctk1 and Hmt1, key regulators of gene expression. Combining our observations and the data published by others, we suggest that Ste20 has several new and unexpected functions.

Functional diversification despite structural congruence in the HipBST toxin-antitoxin system of Legionella pneumophila

IMPORTANCE

Toxin-antitoxin (TA) systems are parasitic genetic elements found in almost all bacterial genomes. They are exchanged horizontally between cells and are typically poorly conserved across closely related strains and species. Here, we report the characterization of a tripartite TA system in the bacterial pathogen Legionella pneumophila that is highly conserved across Legionella species genomes. This system (denoted HipBSTLp) is a distant homolog of the recently discovered split-HipA system in Escherichia coli (HipBSTEc). We present bioinformatic, molecular, and structural analyses of the divergence between these two systems and the functionality of this newly described TA system family. Furthermore, we provide evidence to refute previous claims that the toxin in this system (HipTLp) possesses bifunctionality as an L. pneumophila virulence protein. Overall, this work expands our understanding of the split-HipA system architecture and illustrates the potential for undiscovered biology in these abundant genetic elements.

A positive statistical benchmark to assess network agreement

Current computational methods for validating experimental network datasets compare overlap, i.e., shared links, with a reference network using a negative benchmark. However, this fails to quantify the level of agreement between the two networks. To address this, we propose a positive statistical benchmark to determine the maximum possible overlap between networks. Our approach can efficiently generate this benchmark in a maximum entropy framework and provides a way to assess whether the observed overlap is significantly different from the best-case scenario. We introduce a normalized overlap score, Normlap, to enhance comparisons between experimental networks. As an application, we compare molecular and functional networks, resulting in an agreement network of human as well as yeast network datasets. The Normlap score can improve the comparison between experimental networks by providing a computational alternative to network thresholding and validation.

Vir1p, the yeast homolog of virilizer, is required for mRNA m6A methylation and meiosis

N6-Methyladenosine (m6A) is among the most abundant modifications of eukaryotic mRNAs. mRNA methylation regulates many biological processes including playing an essential role in meiosis. During meiosis in the budding yeast, Saccharomyces cerevisiae, m6A levels peak early, before the initiation of the meiotic divisions. High-throughput studies suggested, and this work confirms that the uncharacterized protein Ygl036wp interacts with Kar4p, a component of the mRNA m6A-methyltransferase complex. Protein structure programs predict that Ygl036wp folds like VIRMA/Virilizer/VIR, which is involved in mRNA m6A-methylation in higher eukaryotes. In addition, Ygl036wp contains conserved motifs shared with VIRMA/Virilizer/VIR. Accordingly, we propose the name VIR1 for budding yeast ortholog of VIRMA/Virilizer/VIR 1. Vir1p interacts with all other members of the yeast methyltransferase complex and is itself required for mRNA m6A methylation and meiosis. In the absence of Vir1p proteins comprising the methyltransferase complex become unstable, suggesting that Vir1p acts as a scaffold for the complex. The vir1Δ/Δ mutant is defective for the premeiotic S-phase, which is suppressed by overexpression of the early meiotic transcription factor IME1; additional overexpression of the translational regulator RIM4 is required for sporulation. The vir1Δ/Δ mutant exhibits reduced levels of IME1 mRNA, as well as transcripts within Ime1p's regulon. Suppression by IME1 revealed an additional defect in the expression of the middle meiotic transcription factor, Ndt80p (and genes in its regulon), which is rescued by overexpression of RIM4. Together, these data suggest that Vir1p is required for cells to initiate the meiotic program and for progression through the meiotic divisions and spore formation.

Changing course: Glucose starvation drives nuclear accumulation of Hexokinase 2 in S. cerevisiae

Glucose is the preferred carbon source for most eukaryotes, and the first step in its metabolism is phosphorylation to glucose-6-phosphate. This reaction is catalyzed by hexokinases or glucokinases. The yeast Saccharomyces cerevisiae encodes three such enzymes, Hxk1, Hxk2, and Glk1. In yeast and mammals, some isoforms of this enzyme are found in the nucleus, suggesting a possible moonlighting function beyond glucose phosphorylation. In contrast to mammalian hexokinases, yeast Hxk2 has been proposed to shuttle into the nucleus in glucose-replete conditions, where it reportedly moonlights as part of a glucose-repressive transcriptional complex. To achieve its role in glucose repression, Hxk2 reportedly binds the Mig1 transcriptional repressor, is dephosphorylated at serine 15 and requires an N-terminal nuclear localization sequence (NLS). We used high-resolution, quantitative, fluorescent microscopy of live cells to determine the conditions, residues, and regulatory proteins required for Hxk2 nuclear localization. Countering previous yeast studies, we find that Hxk2 is largely excluded from the nucleus under glucose-replete conditions but is retained in the nucleus under glucose-limiting conditions. We find that the Hxk2 N-terminus does not contain an NLS but instead is necessary for nuclear exclusion and regulating multimerization. Amino acid substitutions of the phosphorylated residue, serine 15, disrupt Hxk2 dimerization but have no effect on its glucose-regulated nuclear localization. Alanine substation at nearby lysine 13 affects dimerization and maintenance of nuclear exclusion in glucose-replete conditions. Modeling and simulation provide insight into the molecular mechanisms of this regulation. In contrast to earlier studies, we find that the transcriptional repressor Mig1 and the protein kinase Snf1 have little effect on Hxk2 localization. Instead, the protein kinase Tda1 regulates Hxk2 localization. RNAseq analyses of the yeast transcriptome dispels the idea that Hxk2 moonlights as a transcriptional regulator of glucose repression, demonstrating that Hxk2 has a negligible role in transcriptional regulation in both glucose-replete and limiting conditions. Our studies define a new model of cis- and trans-acting regulators of Hxk2 dimerization and nuclear localization. Based on our data, the nuclear translocation of Hxk2 in yeast occurs in glucose starvation conditions, which aligns well with the nuclear regulation of mammalian orthologs. Our results lay the foundation for future studies of Hxk2 nuclear activity.

Next-generation large-scale binary protein interaction network for Drosophila melanogaster

Generating reference maps of interactome networks illuminates genetic studies by providing a protein-centric approach to finding new components of existing pathways, complexes, and processes. We apply state-of-the-art methods to identify binary protein-protein interactions (PPIs) for Drosophila melanogaster. Four all-by-all yeast two-hybrid (Y2H) screens of > 10,000 Drosophila proteins result in the 'FlyBi' dataset of 8723 PPIs among 2939 proteins. Testing subsets of data from FlyBi and previous PPI studies using an orthogonal assay allows for normalization of data quality; subsequent integration of FlyBi and previous data results in an expanded binary Drosophila reference interaction network, DroRI, comprising 17,232 interactions among 6511 proteins. We use FlyBi data to generate an autophagy network, then validate in vivo using autophagy-related assays. The deformed wings (dwg) gene encodes a protein that is both a regulator and a target of autophagy. Altogether, these resources provide a foundation for building new hypotheses regarding protein networks and function.

Network medicine: an approach to complex kidney disease phenotypes

Scientific reductionism has been the basis of disease classification and understanding for more than a century. However, the reductionist approach of characterizing diseases from a limited set of clinical observations and laboratory evaluations has proven insufficient in the face of an exponential growth in data generated from transcriptomics, proteomics, metabolomics and deep phenotyping. A new systematic method is necessary to organize these datasets and build new definitions of what constitutes a disease that incorporates both biological and environmental factors to more precisely describe the ever-growing complexity of phenotypes and their underlying molecular determinants. Network medicine provides such a conceptual framework to bridge these vast quantities of data while providing an individualized understanding of disease. The modern application of network medicine principles is yielding new insights into the pathobiology of chronic kidney diseases and renovascular disorders by expanding the understanding of pathogenic mediators, novel biomarkers and new options for renal therapeutics. These efforts affirm network medicine as a robust paradigm for elucidating new advances in the diagnosis and treatment of kidney disorders.

Random search immune algorithm for community detection

Community detection is a prominent research topic in Complex Network Analysis, and it constitutes an important research field on all those areas where complex networks represent a powerful interpretation tool for describing and understanding systems involved in neuroscience, biology, social science, economy, and many others. A challenging approach to uncover the community structure in complex network, and then revealing the internal organization of nodes, is Modularity optimization. In this research paper, we present an immune optimization algorithm (opt-IA) developed to detect community structures, with the main aim to maximize the modularity produced by the discovered communities. In order to assess the performance of opt-IA, we compared it with an overall of 20 heuristics and metaheuristics, among which one Hyper-Heuristic method, using social and biological complex networks as data set. Unlike these algorithms, opt-IA is entirely based on a fully random search process, which in turn is combined with purely stochastic operators. According to the obtained outcomes, opt-IA shows strictly better performances than almost all heuristics and metaheuristics to which it was compared; whilst it turns out to be comparable with the Hyper-Heuristic method. Overall, it can be claimed that opt-IA, even if driven by a purely random process, proves to be reliable and with efficient performance. Furthermore, to prove the latter claim, a sensitivity analysis of the functionality was conducted, using the classic metrics NMI, ARI and NVI.

Recruitment of the lipid kinase Mss4 to the meiotic spindle pole promotes prospore membrane formation in Saccharomyces cerevisiae

Spore formation in the budding yeast, Saccharomyces cerevisiae, involves de novo creation of four prospore membranes, each of which surrounds a haploid nucleus resulting from meiosis. The meiotic outer plaque (MOP) is a meiosis-specific protein complex associated with each meiosis II spindle pole body (SPB). Vesicle fusion on the MOP surface creates an initial prospore membrane anchored to the SPB. Ady4 is a meiosis-specific MOP component that stabilizes the MOP-prospore membrane interaction. We show that Ady4 recruits the lipid kinase, Mss4, to the MOP. MSS4 overexpression suppresses the ady4∆ spore formation defect, suggesting that a specific lipid environment provided by Mss4 promotes maintenance of prospore membrane attachment to MOPs. The meiosis-specific Spo21 protein is an essential structural MOP component. We show that the Spo21 N terminus contains an amphipathic helix that binds to prospore membranes. A mutant in SPO21 that removes positive charges from this helix shares phenotypic similarities to ady4∆. We propose that Mss4 generates negatively charged lipids in prospore membranes that enhance binding by the positively charged N terminus of Spo21, thereby providing a mechanism by which the MOP-prospore membrane interaction is stabilized.

Evolutionary conservation of sequence motifs at sites of protein modification

Gene duplications are common in biology and are likely to be an important source of functional diversification and specialization. The yeast Saccharomyces cerevisiae underwent a whole genome duplication event early in evolution, and a substantial number of duplicated genes have been retained. We identified more than 3,500 instances where only one of two paralogous proteins undergoes post-translational modification despite having retained the same amino acid residue in both. We also developed a web-based search algorithm (CoSMoS.c.) that scores conservation of amino acid sequences based on 1011 wild and domesticated yeast isolates and used it to compare differentially-modified pairs of paralogous proteins. We found that the most common modifications - phosphorylation, ubiquitylation and acylation but not N-glycosylation - occur in regions of high sequence conservation. Such conservation is evident even for ubiquitylation and succinylation, where there is no established 'consensus site' for modification. Differences in phosphorylation were not associated with predicted secondary structure or solvent accessibility, but did mirror known differences in kinase-substrate interactions. Thus, differences in post-translational modification likely result from differences in adjoining amino acids and their interactions with modifying enzymes. By integrating data from large scale proteomics and genomics analysis, in a system with such substantial genetic diversity, we obtained a more comprehensive understanding of the functional basis for genetic redundancies that have persisted for 100 million years.

Structural basis of V-ATPase VO region assembly by Vma12p, 21p, and 22p

Vacuolar-type adenosine triphosphatases (V-ATPases) are rotary proton pumps that acidify specific intracellular compartments in almost all eukaryotic cells. These multi-subunit enzymes consist of a soluble catalytic V₁ region and a membrane-embedded proton-translocating V_O region. V_O is assembled in the endoplasmic reticulum (ER) membrane, and V₁ is assembled in the cytosol. However, V₁ binds V_O only after V_O is transported to the Golgi membrane, thereby preventing acidification of the ER. We isolated V_O complexes and subcomplexes from Saccharomyces cerevisiae bound to V-ATPase assembly factors Vma12p, Vma21p, and Vma22p. Electron cryomicroscopy shows how the Vma12-22p complex recruits subunits a, e, and f to the rotor ring of V_O while blocking premature binding of V₁. Vma21p, which contains an ER-retrieval motif, binds the V_O:Vma12-22p complex, "mature" V_O, and a complex that appears to contain a ring of loosely packed rotor subunits and the proteins YAR027W and YAR028W. The structures suggest that Vma21p binds assembly intermediates that contain a rotor ring and that activation of proton pumping following assembly of V₁ with V_O removes Vma21p, allowing V-ATPase to remain in the Golgi. Together, these structures show how Vma12-22p and Vma21p function in V-ATPase assembly and quality control, ensuring the enzyme acidifies only its intended cellular targets.

Vir1p, the Yeast Homolog of Virilizer, is Required for mRNA m 6 A Methylation and Meiosis

N ⁶ -Methyladenosine (m ⁶ A) is one of the most abundant modifications found on eukaryotic mRNAs. mRNA methylation regulates a host of biological processes including meiosis, a specialized diploid cell division program that results in the formation of haploid cells (gametes). During budding yeast meiosis, m ⁶ A levels peak early, before the initiation of the meiotic divisions. High-throughput studies and work from our lab showed that Ygl036wp, a previously uncharacterized protein interacts with Kar4p, a meiotic protein required for mRNA m ⁶ A-methylation. Ygl036wp has no discernable domains except for several intrinsically disordered regions. However, protein folding prediction tools showed that Ygl036wp folds like VIRMA/Virilizer/VIR, which is involved in mRNA m ⁶ A-methylation in higher eukaryotes. In addition, Ygl036wp has several conserved motifs shared with VIRMA/Virilizer/VIR proteins. Accordingly, we propose to call the gene VIR1 for budding yeast ortholog of VIR MA/Virilizer/VIR 1 . In support, Vir1p interacts with all other members of the yeast methyltransferase complex and is required for mRNA m ⁶ A methylation and meiosis. Vir1p is required for the stability of proteins comprising the methyltransferase complex, suggesting that Vir1p acts as a scaffold to stabilize the complex. The vir1 Δ/Δ mutant is defective for premeiotic S-phase, which is suppressed by overexpression of the early meiotic transcription factor IME1; additional overexpression of the translational regulator RIM4 is required for sporulation. Consistent with IME1 suppression, vir1 Δ/Δ exhibits a defect in the abundance of IME1 mRNA, as well as transcripts within Ime1p's regulon. Suppression by IME1 revealed a defect in the expression of the middle meiotic transcription factor, Ndt80p (and genes in its regulon), which is rescued by additional overexpression of RIM4 . Together, these data suggest that Vir1p is required for cells to initiate the meiotic program and for progression through the meiotic divisions and spore formation.

Author Summary

Ygl036wp is a previously uncharacterized protein that we propose to name Vir1p (budding yeast ortholog of VIR MA/Virilizer/VIR 1 ). Work from our lab and others initially found an interaction between Vir1p and members of the yeast mRNA methyltransferase complex (Kar4p and Mum2p). We found that Vir1p interacts with all known members of the methyltransferase complex and is required for mRNA methylation. Vir1p is required early in meiosis; vir1 Δ/Δ mutants arrest due to the reduced expression of Ime1p. Lower levels of Ime1p cause severe disruption to the meiotic transcriptome in vir1 Δ/Δ. The vir1 Δ/Δ meiotic defect can be partially suppressed by the overexpression of IME1 ; full suppression requires overexpression of both IME1 and RIM4 . Using recent advances in protein folding predictions, we found that Vir1p is a remote homolog of VIRMA/Virilizer/VIR and shares conserved motifs with the protein from other organisms. Vir1p, like VIRMA/Virilizer/VIR, stabilizes the methyltransferase complex.

A novel strategy for dynamic modeling of genome-scale interaction networks

MOTIVATION

The recent availability of omics data allows the construction of holistic maps of interactions between numerous role-playing biomolecules. However, these networks are often static, ignoring the dynamic behavior of biological processes. On the other hand, dynamic models are commonly constructed on small scales. Hence, the construction of large-scale dynamic models that can quantitatively predict the time-course cellular behaviors remains a big challenge.

RESULTS

In this study, a pipeline is proposed for the automatic construction of large-scale dynamic models. The pipeline uses a list of biomolecules and their time-course trajectories in a given phenomenon as input. First, the interaction network of the biomolecules is constructed. To state the underlying molecular events of each interaction, it is translated into a map of biochemical reactions. Next, to define the kinetics of the reactions, an ordinary differential equation (ODE) is generated for each involved biomolecule. Finally, the parameters of the ODE system are estimated by a novel large-scale parameter approximation method. The high performance of the pipeline is demonstrated by modeling the response of a colorectal cancer cell line to different chemotherapy regimens. In conclusion, Systematic Protein Association Dynamic ANalyzer constructs genome-scale dynamic models, filling the gap between large-scale static and small-scale dynamic modeling strategies. This simulation approach allows for holistic quantitative predictions which are critical for the simulation of therapeutic interventions in precision medicine.

AVAILABILITY AND IMPLEMENTATION

Detailed information about the constructed large-scale model of colorectal cancer is available in supplementary data. The SPADAN toolbox source code is also available on GitHub (https://github.com/PooyaBorzou/SPADAN).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Discovering Entities Similarities in Biological Networks Using a Hybrid Immune Algorithm

Disease phenotypes are generally caused by the failure of gene modules which often have similar biological roles. Through the study of biological networks, it is possible to identify the intrinsic structure of molecular interactions in order to identify the so-called “disease modules”. Community detection is an interesting and valuable approach to discovering the structure of the community in a complex network, revealing the internal organization of the nodes, and has become a leading research topic in the analysis of complex networks. This work investigates the link between biological modules and network communities in test-case biological networks that are commonly used as a reference point and which include Protein–Protein Interaction Networks, Metabolic Networks and Transcriptional Regulation Networks. In order to identify small and structurally well-defined communities in the biological context, a hybrid immune metaheuristic algorithm Hybrid-IA is proposed and compared with several metaheuristics, hyper-heuristics, and the well-known greedy algorithm Louvain, with respect to modularity maximization. Considering the limitation of modularity optimization, which can fail to identify smaller communities, the reliability of Hybrid-IA was also analyzed with respect to three well-known sensitivity analysis measures (NMI, ARI and NVI) that assess how similar the detected communities are to real ones. By inspecting all outcomes and the performed comparisons, we will see that on one hand Hybrid-IA finds slightly lower modularity values than Louvain, but outperforms all other metaheuristics, while on the other hand, it can detect communities more similar to the real ones when compared to those detected by Louvain.

Cost-based feature selection for network model choice

Selecting a small set of informative features from a large number of possibly noisy candidates is a challenging problem with many applications in machine learning and approximate Bayesian computation. In practice, the cost of computing informative features also needs to be considered. This is particularly important for networks because the computational costs of individual features can span several orders of magnitude. We addressed this issue for the network model selection problem using two approaches. First, we adapted nine feature selection methods to account for the cost of features. We show for two classes of network models that the cost can be reduced by two orders of magnitude without considerably affecting classification accuracy (proportion of correctly identified models). Second, we selected features using pilot simulations with smaller networks. This approach reduced the computational cost by a factor of 50 without affecting classification accuracy. To demonstrate the utility of our approach, we applied it to three different yeast protein interaction networks and identified the best-fitting duplication divergence model. Supplemental materials, including computer code to reproduce our results, are available online.

Combining LC-MS/MS profiles with network pharmacology to predict molecular mechanisms of the hyperlipidemic activity of Lagenaria siceraria stand

ETHNOPHARMACOLOGICAL RELEVANCE

Lagenaria siceraria Stand. (Family: Cucurbitaceae), popularly known as bottle gourd, is traditionally used in Ayurvedic medicine as a food plant, especially in hypertension and obesity.

AIM OF THE STUDY

Investigations were undertaken to assign novel lead combinations from this common food plant to multi-molecular modes of actions in the complex disease networks of obesity and hypertension. LC-MS/MS based metabolite screening, in-vivo high fat diet induced hyperlipidemia animal study and network pharmacology explorations of the mechanism of action for lipid lowering effects including a neighbourhood community approach for molecular combinations were performed.

MATERIAL AND METHODS

Major chemical constituents of the fruits of LS (LSFE) were analysed by HPLC-DAD-MS/MS-QTOF. Wistar albino rats (n = 36), divided into 6 groups (n = 6) received either no treatment or a high-fat diet along with LSFE or Atorvastatin. Lipid profiles and biochemical parameters were evaluated. In silico cross-validated network analyses using different databases and Cytospace were applied.

RESULTS

Profiling of LSFE revealed 18 major constituents: phenolic acids like p-Coumaric acid and Ferulic acid, the monolignolconferyl alcohol, the flavonoid glycosides hesperidin and apigenin-7-glucoside. Hyperlipidemic animals treated with LSFE (200 mg/kg, 400 mg/kg, 600 mg/kg) showed a significant improvement of their lipid profiles after 30 days of treatment. Network pharmacology analyses for the major 18 compounds revealed enrichment of the insulin and the ErbB signalling pathway. Novel target node combinations (e.g. AKR1C1, AGXT) including their connection to different pathways were identified in silico.

CONCLUSIONS

The combined in vivo and bioinformatics analyses propose that lead compounds of LSFE act in combination on relevant targets of hyperlipidemia. Perturbations of the IRS→Akt→Foxo1 cascade are predicted which suggest further clinical investigation towards development of safe natural alternative to manage hyperlipidemia.

A proteome-scale map of the SARS-CoV-2-human contactome

Understanding the mechanisms of coronavirus disease 2019 (COVID-19) disease severity to efficiently design therapies for emerging virus variants remains an urgent challenge of the ongoing pandemic. Infection and immune reactions are mediated by direct contacts between viral molecules and the host proteome, and the vast majority of these virus-host contacts (the 'contactome') have not been identified. Here, we present a systematic contactome map of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with the human host encompassing more than 200 binary virus-host and intraviral protein-protein interactions. We find that host proteins genetically associated with comorbidities of severe illness and long COVID are enriched in SARS-CoV-2 targeted network communities. Evaluating contactome-derived hypotheses, we demonstrate that viral NSP14 activates nuclear factor κB (NF-κB)-dependent transcription, even in the presence of cytokine signaling. Moreover, for several tested host proteins, genetic knock-down substantially reduces viral replication. Additionally, we show for USP25 that this effect is phenocopied by the small-molecule inhibitor AZ1. Our results connect viral proteins to human genetic architecture for COVID-19 severity and offer potential therapeutic targets.

Detection of Protein-Protein Interactions Utilizing the Split-Ubiquitin Membrane-Based Yeast Two-Hybrid System

Identifying the interactors of a protein is a key step in understanding its possible cellular function(s). Among the various methods that can be used to study protein-protein interactions (PPIs), the yeast two-hybrid (Y2H) assay is one of the most standardized, sensitive, and cost-effective in vivo methods available. The most commonly used GAL4-based Y2H system utilizes the yeast transcription factor GAL4 to detect interactions between soluble proteins. By virtue of involving a transcription factor, the protein-protein interactions occur in the nucleus. The split-ubiquitin Y2H system offers an alternative to the traditional GAL4-based Y2H system and takes advantage of the reconstitution of split-ubiquitin in the cytosol to identify interactions between two proteins. Moreover, new membranous and soluble interacting partner(s) can be identified by screening a target protein against proteins produced from a cDNA library using this system.

A comprehensive SARS-CoV-2-human protein-protein interactome reveals COVID-19 pathobiology and potential host therapeutic targets

Studying viral-host protein-protein interactions can facilitate the discovery of therapies for viral infection. We use high-throughput yeast two-hybrid experiments and mass spectrometry to generate a comprehensive SARS-CoV-2-human protein-protein interactome network consisting of 739 high-confidence binary and co-complex interactions, validating 218 known SARS-CoV-2 host factors and revealing 361 novel ones. Our results show the highest overlap of interaction partners between published datasets and of genes differentially expressed in samples from COVID-19 patients. We identify an interaction between the viral protein ORF3a and the human transcription factor ZNF579, illustrating a direct viral impact on host transcription. We perform network-based screens of >2,900 FDA-approved or investigational drugs and identify 23 with significant network proximity to SARS-CoV-2 host factors. One of these drugs, carvedilol, shows clinical benefits for COVID-19 patients in an electronic health records analysis and antiviral properties in a human lung cell line infected with SARS-CoV-2. Our study demonstrates the value of network systems biology to understand human-virus interactions and provides hits for further research on COVID-19 therapeutics.

Next-Generation Yeast Two-Hybrid Screening to Discover Protein-Protein Interactions

Yeast two-hybrid is a powerful approach to discover new protein-protein interactions. Traditional methods involve screening a target protein against a cDNA expression library and assaying individual positive colonies to identify interacting partners. Here we describe a simple approach to perform yeast two-hybrid screens of a cDNA expression library in batch liquid culture. Positive yeast cell populations are enriched under selection and then harvested en masse. Prey cDNAs are amplified and used as input for next-generation sequencing libraries for identification, quantification, and ranking.

Gene transferability and sociality do not correlate with gene connectivity

The connectivity of a gene, defined as the number of interactions a gene's product has with other genes' products, is a key characteristic of a gene. In prokaryotes, the complexity hypothesis predicts that genes which undergo more frequent horizontal transfer will be less connected than genes which are only very rarely transferred. We tested the role of horizontal gene transfer, and other potentially important factors, by examining the connectivity of chromosomal and plasmid genes, across 134 diverse prokaryotic species. We found that (i) genes on plasmids were less connected than genes on chromosomes; (ii) connectivity of plasmid genes was not correlated with plasmid mobility; and (iii) the sociality of genes (cooperative or private) was not correlated with gene connectivity.

Statistical inference links data and theory in network science

The number of network science applications across many different fields has been rapidly increasing. Surprisingly, the development of theory and domain-specific applications often occur in isolation, risking an effective disconnect between theoretical and methodological advances and the way network science is employed in practice. Here we address this risk constructively, discussing good practices to guarantee more successful applications and reproducible results. We endorse designing statistically grounded methodologies to address challenges in network science. This approach allows one to explain observational data in terms of generative models, naturally deal with intrinsic uncertainties, and strengthen the link between theory and applications.

Theoretical models and structures recovered from measured data serve for analysis of complex networks. The authors discuss here existing gaps between theoretical methods and real-world applied networks, and potential ways to improve the interplay between theory and applications.

Network reconstruction for trans acting genetic loci using multi-omics data and prior information

BACKGROUND

Molecular measurements of the genome, the transcriptome, and the epigenome, often termed multi-omics data, provide an in-depth view on biological systems and their integration is crucial for gaining insights in complex regulatory processes. These data can be used to explain disease related genetic variants by linking them to intermediate molecular traits (quantitative trait loci, QTL). Molecular networks regulating cellular processes leave footprints in QTL results as so-called trans-QTL hotspots. Reconstructing these networks is a complex endeavor and use of biological prior information can improve network inference. However, previous efforts were limited in the types of priors used or have only been applied to model systems. In this study, we reconstruct the regulatory networks underlying trans-QTL hotspots using human cohort data and data-driven prior information.

METHODS

We devised a new strategy to integrate QTL with human population scale multi-omics data. State-of-the art network inference methods including BDgraph and glasso were applied to these data. Comprehensive prior information to guide network inference was manually curated from large-scale biological databases. The inference approach was extensively benchmarked using simulated data and cross-cohort replication analyses. Best performing methods were subsequently applied to real-world human cohort data.

RESULTS

Our benchmarks showed that prior-based strategies outperform methods without prior information in simulated data and show better replication across datasets. Application of our approach to human cohort data highlighted two novel regulatory networks related to schizophrenia and lean body mass for which we generated novel functional hypotheses.

CONCLUSIONS

We demonstrate that existing biological knowledge can improve the integrative analysis of networks underlying trans associations and generate novel hypotheses about regulatory mechanisms.

The Analysis of the Power Law Feature in Complex Networks

Consensus about the universality of the power law feature in complex networks is experiencing widespread challenges. In this paper, we propose a generic theoretical framework in order to examine the power law property. First, we study a class of birth-and-death networks that are more common than BA networks in the real world, and then we calculate their degree distributions; the results show that the tails of their degree distributions exhibit a distinct power law feature. Second, we suggest that in the real world two important factors-network size and node disappearance probability-will affect the analysis of power law characteristics in observation networks. Finally, we suggest that an effective way of detecting the power law property is to observe the asymptotic (limiting) behavior of the degree distribution within its effective intervals.

Experimental guidance for discovering genetic networks through hypothesis reduction on time series

Large programs of dynamic gene expression, like cell cyles and circadian rhythms, are controlled by a relatively small "core" network of transcription factors and post-translational modifiers, working in concerted mutual regulation. Recent work suggests that system-independent, quantitative features of the dynamics of gene expression can be used to identify core regulators. We introduce an approach of iterative network hypothesis reduction from time-series data in which increasingly complex features of the dynamic expression of individual, pairs, and entire collections of genes are used to infer functional network models that can produce the observed transcriptional program. The culmination of our work is a computational pipeline, Iterative Network Hypothesis Reduction from Temporal Dynamics (Inherent dynamics pipeline), that provides a priority listing of targets for genetic perturbation to experimentally infer network structure. We demonstrate the capability of this integrated computational pipeline on synthetic and yeast cell-cycle data.

Phosphorylation of RGS regulates MAP kinase localization and promotes completion of cytokinesis

Phosphorylation of the RGS Sst2 alters its subcellular distribution, MAPK localization, and interaction with Kel1, which promotes coordination of polarized growth with completion of cytokinesis.

Yeast use the G-protein–coupled receptor signaling pathway to detect and track the mating pheromone. The G-protein–coupled receptor pathway is inhibited by the regulator of G-protein signaling (RGS) Sst2 which induces Gα GTPase activity and inactivation of downstream signaling. G-protein signaling activates the MAPK Fus3, which phosphorylates the RGS; however, the role of this modification is unknown. We found that pheromone-induced RGS phosphorylation peaks early; the phospho-state of RGS controls its localization and influences MAPK spatial distribution. Surprisingly, phosphorylation of the RGS promotes completion of cytokinesis before pheromone-induced growth. Completion of cytokinesis in the presence of pheromone is promoted by the kelch-repeat protein, Kel1 and antagonized by the formin Bni1. We found that RGS complexes with Kel1 and prefers the unphosphorylatable RGS mutant. We also found overexpression of unphosphorylatable RGS exacerbates cytokinetic defects, whereas they are rescued by overexpression of Kel1. These data lead us to a model where Kel1 promotes completion of cytokinesis before pheromone-induced polarity but is inhibited by unphosphorylated RGS binding.

Network Comparison with Interpretable Contrastive Network Representation Learning

Identifying unique characteristics in a network through comparison with another network is an essential network analysis task. For example, with networks of protein interactions obtained from normal and cancer tissues, we can discover unique types of interactions in cancer tissues. This analysis task could be greatly assisted by contrastive learning, which is an emerging analysis approach to discover salient patterns in one dataset relative to another. However, existing contrastive learning methods cannot be directly applied to networks as they are designed only for high-dimensional data analysis. To address this problem, we introduce a new analysis approach called contrastive network representation learning (cNRL). By integrating two machine learning schemes, network representation learning and contrastive learning, cNRL enables embedding of network nodes into a low-dimensional representation that reveals the uniqueness of one network compared to another. Within this approach, we also design a method, named i-cNRL, which offers interpretability in the learned results, allowing for understanding which specific patterns are only found in one network. We demonstrate the effectiveness of i-cNRL for network comparison with multiple network models and real-world datasets. Furthermore, we compare i-cNRL and other potential cNRL algorithm designs through quantitative and qualitative evaluations.

Rule-Based Pruning and In Silico Identification of Essential Proteins in Yeast PPIN

Proteins are vital for the significant cellular activities of living organisms. However, not all of them are essential. Identifying essential proteins through different biological experiments is relatively more laborious and time-consuming than the computational approaches used in recent times. However, practical implementation of conventional scientific methods sometimes becomes challenging due to poor performance impact in specific scenarios. Thus, more developed and efficient computational prediction models are required for essential protein identification. An effective methodology is proposed in this research, capable of predicting essential proteins in a refined yeast protein–protein interaction network (PPIN). The rule-based refinement is done using protein complex and local interaction density information derived from the neighborhood properties of proteins in the network. Identification and pruning of non-essential proteins are equally crucial here. In the initial phase, careful assessment is performed by applying node and edge weights to identify and discard the non-essential proteins from the interaction network. Three cut-off levels are considered for each node and edge weight for pruning the non-essential proteins. Once the PPIN has been filtered out, the second phase starts with two centralities-based approaches: (1) local interaction density (LID) and (2) local interaction density with protein complex (LIDC), which are successively implemented to identify the essential proteins in the yeast PPIN. Our proposed methodology achieves better performance in comparison to the existing state-of-the-art techniques.

Domination based classification algorithms for the controllability analysis of biological interaction networks

Deciding the size of a minimum dominating set is a classic NP-complete problem. It has found increasing utility as the basis for classifying vertices in networks derived from protein-protein, noncoding RNA, metabolic, and other biological interaction data. In this context it can be helpful, for example, to identify those vertices that must be present in any minimum solution. Current classification methods, however, can require solving as many instances as there are vertices, rendering them computationally prohibitive in many applications. In an effort to address this shortcoming, new classification algorithms are derived and tested for efficiency and effectiveness. Results of performance comparisons on real-world biological networks are reported.

An ORFeome of rice E3 ubiquitin ligases for global analysis of the ubiquitination interactome

BACKGROUND

Ubiquitination is essential for many cellular processes in eukaryotes, including 26S proteasome-dependent protein degradation, cell cycle progression, transcriptional regulation, and signal transduction. Although numerous ubiquitinated proteins have been empirically identified, their cognate ubiquitin E3 ligases remain largely unknown.

RESULTS

Here, we generate a complete ubiquitin E3 ligase-encoding open reading frames (UbE3-ORFeome) library containing 98.94% of the 1515 E3 ligase genes in the rice (Oryza sativa L.) genome. In the test screens with four known ubiquitinated proteins, we identify both known and new E3s. The interaction and degradation between several E3s and their substrates are confirmed in vitro and in vivo. In addition, we identify the F-box E3 ligase OsFBK16 as a hub-interacting protein of the phenylalanine ammonia lyase family OsPAL1-OsPAL7. We demonstrate that OsFBK16 promotes the degradation of OsPAL1, OsPAL5, and OsPAL6. Remarkably, we find that overexpression of OsPAL1 or OsPAL6 as well as loss-of-function of OsFBK16 in rice displayed enhanced blast resistance, indicating that OsFBK16 degrades OsPALs to negatively regulate rice immunity.

CONCLUSIONS

The rice UbE3-ORFeome is the first complete E3 ligase library in plants and represents a powerful proteomic resource for rapid identification of the cognate E3 ligases of ubiquitinated proteins and establishment of functional E3-substrate interactome in plants.

In vitro ve in silico analizi ile metforminin meme tümörü hücrelerinde protein profili üzerindeki etkinliği

Aim: This study aimed to uncover the varieties in protein profiles of Met in breast tumor (BT) cells by assessment of in vitro and in silico analysis. Materials and Methods: Here, the cells obtained from mastectomy patients were cultured, the effective Met-dose was determined as 25 mM through cell viability and BrdU tests. Protein identification in the breast tumor cells was implemented by employing LC-MS/MS technology. Results: The expression of SSR3, THAP3, FTH1, NEFM, ANP32A, ANP32B, KRT7 proteins was significantly decreased whereas the GARS protein increased in the 25 mM Met group compared to the Non-Met (0 mM) control group. In silico analysis, we analyzed the probable interactions of all these proteins with each other and other proteins, to evaluate the analysis of the larger protein network, and which metabolic pathway proteins are involved in. Conclusion: The stated proteomics analysis in our study proposes a better understanding of the prognosis of breast cancer and future studies to investigate the effect of metformin in this field on proteomic pathways in other sorts of cancer.

A comprehensive SARS-CoV-2-human protein-protein interactome network identifies pathobiology and host-targeting therapies for COVID-19

Physical interactions between viral and host proteins are responsible for almost all aspects of the viral life cycle and the host's immune response. Studying viral-host protein-protein interactions is thus crucial for identifying strategies for treatment and prevention of viral infection. Here, we use high-throughput yeast two-hybrid and affinity purification followed by mass spectrometry to generate a comprehensive SARS-CoV-2-human protein-protein interactome network consisting of both binary and co-complex interactions. We report a total of 739 high-confidence interactions, showing the highest overlap of interaction partners among published datasets as well as the highest overlap with genes differentially expressed in samples (such as upper airway and bronchial epithelial cells) from patients with SARS-CoV-2 infection. Showcasing the utility of our network, we describe a novel interaction between the viral accessory protein ORF3a and the host zinc finger transcription factor ZNF579 to illustrate a SARS-CoV-2 factor mediating a direct impact on host transcription. Leveraging our interactome, we performed network-based drug screens for over 2,900 FDA-approved/investigational drugs and obtained a curated list of 23 drugs that had significant network proximities to SARS-CoV-2 host factors, one of which, carvedilol, showed promising antiviral properties. We performed electronic health record-based validation using two independent large-scale, longitudinal COVID-19 patient databases and found that carvedilol usage was associated with a significantly lowered probability (17%-20%, P < 0.001) of obtaining a SARS-CoV-2 positive test after adjusting various confounding factors. Carvedilol additionally showed anti-viral activity against SARS-CoV-2 in a human lung epithelial cell line [half maximal effective concentration (EC ₅₀ ) value of 4.1 µM], suggesting a mechanism for its beneficial effect in COVID-19. Our study demonstrates the value of large-scale network systems biology approaches for extracting biological insight from complex biological processes.

From systems to structure - using genetic data to model protein structures

Understanding the effects of genetic variation is a fundamental problem in biology that requires methods to analyse both physical and functional consequences of sequence changes at systems-wide and mechanistic scales. To achieve a systems view, protein interaction networks map which proteins physically interact, while genetic interaction networks inform on the phenotypic consequences of perturbing these protein interactions. Until recently, understanding the molecular mechanisms that underlie these interactions often required biophysical methods to determine the structures of the proteins involved. The past decade has seen the emergence of new approaches based on coevolution, deep mutational scanning and genome-scale genetic or chemical-genetic interaction mapping that enable modelling of the structures of individual proteins or protein complexes. Here, we review the emerging use of large-scale genetic datasets and deep learning approaches to model protein structures and their interactions, and discuss the integration of structural data from different sources.

Compartmentalization and transporter engineering strategies for terpenoid synthesis

Microbial cell factories for terpenoid synthesis form a less expensive and more environment-friendly approach than chemical synthesis and extraction, and are thus being regarded as mainstream research recently. Organelle compartmentalization for terpenoid synthesis has received much attention from researchers owing to the diverse physiochemical characteristics of organelles. In this review, we first systematically summarized various compartmentalization strategies utilized in terpenoid production, mainly plant terpenoids, which can provide catalytic reactions with sufficient intermediates and a suitable environment, while bypassing competing metabolic pathways. In addition, because of the limited storage capacity of cells, strategies used for the expansion of specific organelle membranes were discussed. Next, transporter engineering strategies to overcome the cytotoxic effects of terpenoid accumulation were analyzed. Finally, we discussed the future perspectives of compartmentalization and transporter engineering strategies, with the hope of providing theoretical guidance for designing and constructing cell factories for the purpose of terpenoid production.

Zng1 is a GTP-dependent zinc transferase needed for activation of methionine aminopeptidase

The evolution of zinc (Zn) as a protein cofactor altered the functional landscape of biology, but dependency on Zn also created an Achilles' heel, necessitating adaptive mechanisms to ensure Zn availability to proteins. A debated strategy is whether metallochaperones exist to prioritize essential Zn-dependent proteins. Here, we present evidence for a conserved family of putative metal transferases in human and fungi, which interact with Zn-dependent methionine aminopeptidase type I (MetAP1/Map1p/Fma1). Deletion of the putative metal transferase in Saccharomyces cerevisiae (ZNG1; formerly YNR029c) leads to defective Map1p function and a Zn-deficiency growth defect. In vitro, Zng1p can transfer Zn2+ or Co2+ to apo-Map1p, but unlike characterized copper chaperones, transfer is dependent on GTP hydrolysis. Proteomics reveal mis-regulation of the Zap1p transcription factor regulon because of loss of ZNG1 and Map1p activity, suggesting that Zng1p is required to avoid a compounding effect of Map1p dysfunction on survival during Zn limitation.

Protein-protein interaction networks as miners of biological discovery

Protein-protein interactions (PPIs) form the basis of a myriad of biological pathways and mechanism, such as the formation of protein-complexes or the components of signaling cascades. Here, we reviewed experimental methods for identifying PPI pairs, including yeast two-hybrid, mass spectrometry, co-localization, and co-immunoprecipitation. Furthermore, a range of computational methods leveraging biochemical properties, evolution history, protein structures and more have enabled identification of additional PPIs. Given the wealth of known PPIs, we reviewed important network methods to construct and analyze networks of PPIs. These methods aid biological discovery through identifying hub genes and dynamic changes in the network, and have been thoroughly applied in various fields of biological research. Lastly, we discussed the challenges and future direction of research utilizing the power of PPI networks. This article is protected by copyright. All rights reserved.