Viral perturbations of host networks reflect disease etiology

A practically efficient algorithm for identifying critical control proteins in directed probabilistic biological networks

Network controllability is unifying the traditional control theory with the structural network information rooted in many large-scale biological systems of interest, from intracellular networks in molecular biology to brain neuronal networks. In controllability approaches, the set of minimum driver nodes is not unique, and critical nodes are the most important control elements because they appear in all possible solution sets. On the other hand, a common but largely unexplored feature in network control approaches is the probabilistic failure of edges or the uncertainty in the determination of interactions between molecules. This is particularly true when directed probabilistic interactions are considered. Until now, no efficient algorithm existed to determine critical nodes in probabilistic directed networks. Here we present a probabilistic control model based on a minimum dominating set framework that integrates the probabilistic nature of directed edges between molecules and determines the critical control nodes that drive the entire network functionality. The proposed algorithm, combined with the developed mathematical tools, offers practical efficiency in determining critical control nodes in large probabilistic networks. The method is then applied to the human intracellular signal transduction network revealing that critical control nodes are associated with important biological features and perturbed sets of genes in human diseases, including SARS-CoV-2 target proteins and rare disorders. We believe that the proposed methodology can be useful to investigate multiple biological systems in which directed edges are probabilistic in nature, both in natural systems or when determined with large uncertainties in-silico.

Viruses traverse the human proteome through peptide interfaces that can be biomimetically leveraged for drug discovery

We present a drug design strategy based on structural knowledge of protein-protein interfaces selected through virus-host coevolution and translated into highly potential small molecules. This approach is grounded on Vinland, the most comprehensive atlas of virus-human protein-protein interactions with annotation of interacting domains. From this inspiration, we identified small viral protein domains responsible for interaction with human proteins. These peptides form a library of new chemical entities used to screen for replication modulators of several pathogens. As a proof of concept, a peptide from a KSHV protein, identified as an inhibitor of influenza virus replication, was translated into a small molecule series with low nanomolar antiviral activity. By targeting the NEET proteins, these molecules turn out to be of therapeutic interest in a nonalcoholic steatohepatitis mouse model with kidney lesions. This study provides a biomimetic framework to design original chemistries targeting cellular proteins, with indications going far beyond infectious diseases.

Predicting host-based, synthetic lethal antiviral targets from omics data

Traditional antiviral therapies often have limited effectiveness due to toxicity and the emergence of drug resistance. Host-based antivirals are an alternative, but can cause nonspecific effects. Recent evidence shows that virus-infected cells can be selectively eliminated by targeting synthetic lethal (SL) partners of proteins disrupted by viral infection. Thus, we hypothesized that genes depleted in CRISPR knockout (KO) screens of virus-infected cells may be enriched in SL partners of proteins altered by infection. To investigate this, we established a computational pipeline predicting antiviral SL drug targets. First, we identified SARS-CoV-2-induced changes in gene products via a large compendium of omics data. Second, we identified SL partners for each altered gene product. Last, we screened CRISPR KO data for SL partners required for cell viability in infected cells. Despite differences in virus-induced alterations detected by various omics data, they share many predicted SL targets, with significant enrichment in CRISPR KO-depleted datasets. Our comparison of SARS-CoV-2 and influenza infection data revealed potential broad-spectrum, host-based antiviral SL targets. This suggests that CRISPR KO data are replete with common antiviral targets due to their SL relationship with virus-altered states and that such targets can be revealed from analysis of omics datasets and SL predictions.

Predicting host-based, synthetic lethal antiviral targets from omics data

Traditional antiviral therapies often have limited effectiveness due to toxicity and development of drug resistance. Host-based antivirals, while an alternative, may lead to non-specific effects. Recent evidence shows that virus-infected cells can be selectively eliminated by targeting synthetic lethal (SL) partners of proteins disrupted by viral infection. Thus, we hypothesized that genes depleted in CRISPR KO screens of virus-infected cells may be enriched in SL partners of proteins altered by infection. To investigate this, we established a computational pipeline predicting SL drug targets of viral infections. First, we identified SARS-CoV-2-induced changes in gene products via a large compendium of omics data. Second, we identified SL partners for each altered gene product. Last, we screened CRISPR KO data for SL partners required for cell viability in infected cells. Despite differences in virus-induced alterations detected by various omics data, they share many predicted SL targets, with significant enrichment in CRISPR KO-depleted datasets. Comparing data from SARS-CoV-2 and influenza infections, we found possible broad-spectrum, host-based antiviral SL targets. This suggests that CRISPR KO data are replete with common antiviral targets due to their SL relationship with virus-altered states and that such targets can be revealed from analysis of omics datasets and SL predictions.

Subcellular Distribution of BALF2 and the Role of Rab1 in the Formation of Epstein-Barr Virus Cytoplasmic Assembly Compartment and Virion Release

Epstein-Barr virus (EBV) replicates its genome in the nucleus and undergoes tegumentation and envelopment in the cytoplasm. We are interested in how the single-stranded DNA binding protein BALF2, which executes its function and distributes predominantly in the nucleus, is packaged into the tegument of virions. At the mid-stage of virus replication in epithelial TW01-EBV cells, a small pool of BALF2 colocalizes with tegument protein BBLF1, BGLF4 protein kinase, and the cis-Golgi marker GM130 at the perinuclear viral assembly compartment (AC). A possible nuclear localization signal (NLS) between amino acids 1100 and 1128 (C29), which contains positive charged amino acid 1113RRKRR1117, is able to promote yellow fluorescent protein (YFP)-LacZ into the nucleus. In addition, BALF2 interacts with the nucleocapsid-associated protein BVRF1, suggesting that BALF2 may be transported into the cytoplasm with nucleocapsids in a nuclear egress complex (NEC)-dependent manner. A group of proteins involved in intracellular transport were identified to interact with BALF2 in a proteomic analysis. Among them, the small GTPase Rab1A functioning in bi-directional trafficking at the ER-Golgi interface is also a tegument component. In reactivated TW01-EBV cells, BALF2 colocalizes with Rab1A in the cytoplasmic AC. Expression of dominant-negative GFP-Rab1A(N124I) diminished the accumulation of BALF2 in the AC, coupling with attenuation of gp350/220 glycosylation. Virion release was significantly downregulated by expressing dominant-negative GFP-Rab1A(N124I). Overall, the subcellular distribution of BALF2 is regulated through its complex interaction with various proteins. Rab1 activity is required for proper gp350/220 glycosylation and the maturation of EBV. IMPORTANCE Upon EBV lytic reactivation, the virus-encoded DNA replication machinery functions in the nucleus, while the newly synthesized DNA is encapsidated and transported to the cytoplasm for final virus assembly. The single-stranded DNA binding protein BALF2 executing functions within the nucleus was also identified in the tegument layer of mature virions. Here, we studied the functional domain of BALF2 that contributes to the nuclear targeting and used a proteomic approach to identify novel BALF2-interacting cellular proteins that may contribute to virion morphogenesis. The GTPase Rab1, a master regulator of anterograde and retrograde endoplasmic reticulum (ER)-Golgi trafficking, colocalizes with BALF2 in the juxtanuclear concave region at the midstage of EBV reactivation. Rab1 activity is required for BALF2 targeting to the cytoplasmic assembly compartment (AC) and for gp350/220 targeting to cis-Golgi for proper glycosylation and virion release. Our study hints that EBV hijacks the bi-directional ER-Golgi trafficking machinery to complete virus assembly.

A binary interaction map between turnip mosaic virus and Arabidopsis thaliana proteomes

Viruses are obligate intracellular parasites that have co-evolved with their hosts to establish an intricate network of protein-protein interactions. Here, we followed a high-throughput yeast two-hybrid screening to identify 378 novel protein-protein interactions between turnip mosaic virus (TuMV) and its natural host Arabidopsis thaliana. We identified the RNA-dependent RNA polymerase NIb as the viral protein with the largest number of contacts, including key salicylic acid-dependent transcription regulators. We verified a subset of 25 interactions in planta by bimolecular fluorescence complementation assays. We then constructed and analyzed a network comprising 399 TuMV-A. thaliana interactions together with intravirus and intrahost connections. In particular, we found that the host proteins targeted by TuMV are enriched in different aspects of plant responses to infections, are more connected and have an increased capacity to spread information throughout the cell proteome, display higher expression levels, and have been subject to stronger purifying selection than expected by chance. The proviral or antiviral role of ten host proteins was validated by characterizing the infection dynamics in the corresponding mutant plants, supporting a proviral role for the transcriptional regulator TGA1. Comparison with similar studies with animal viruses, highlights shared fundamental features in their mode of action.

Differentially Infiltrated Identification of Novel Diagnostic Biomarkers Associated with Immune Infiltration in Nasopharyngeal Carcinoma

BACKGROUND

The prognostic value of tumor-infiltrating immune cells has been widely studied in nasopharyngeal carcinoma (NPC). However, the role of tumor-infiltrating immune cells in the diagnosis of NPC has not been fully elucidated. Thus, tumor-infiltrating immune cell-related biomarkers in the diagnosis of NPC patients were explored in the current study.

METHODS

Gene expression profiles of NPC patients were downloaded from the Gene Expression Omnibus (GEO) database. Differentially infiltrating immune cells (DDICs) between NPC and control samples were analyzed by the CIBERSORT algorithm. Weighted gene coexpression network analysis (WGCNA) was performed to screen hub genes significantly correlated with DDIC. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of hub genes were performed with R package clusterProfiler. The diagnostic value of hub genes was evaluated by receiver operating characteristic (ROC) curves. RT-qPCR was conducted to validate the expression patterns of diagnostic markers in NPC and adjacent control tissues. The correlations between diagnostic markers and immunomodulators were analyzed using the TISIDB. The protein-protein interaction (PPI) network based on immunomodulators significantly associated with diagnostic biomarkers was constructed and visualized by STRING. The functional enrichment analysis of genes in the PPI network was analyzed by the WebGestalt online tool.

RESULTS

The abundances of memory B cells, plasma cells, follicular helper T cells, activated NK cells, M0 macrophages, M1 macrophages, M2 macrophages, resting mast cells, and activated mast cells were significantly different between NPC and control samples. Dark orange was identified as the hub module, with a total of 371 genes associated with memory B cells, plasma cells, and M0 and M1 macrophages defined as hub genes, which were enriched into immune-related biological processes and pathways. FCER2, KHDRBS2, and IGSF9 were considered diagnostic biomarkers with areas under ROC curves as 0.985, 0.978, and 0.975, respectively. Moreover, real-time reverse transcriptase-polymerase chain reaction (RT-qPCR) suggested that the expression patterns of FCER2, KHDRBS2, and IGSF9 were consistent with the results in GEO datasets. TISIDB analysis revealed that FCER2, KHDRBS2, and IGSF9 had a strong association with 8 immunoinhibitors (BTLA, CD160, CD96, LAG3, PDCD1, TIGIT, CD244, and TGFB1) and 11 immunostimulators (CD27, CD28, CD40LG, CD48, ICOS, KLRC1, KLRK1, TMIGD2, TNFRSF13C, CXCR4, and C10 or f54). The PPI network implied that these 19 immunomodulators had interactions with other 50 genes. WebGestalt analysis demonstrated that 69 genes in the PPI network were enriched into cytokine-cytokine receptor interaction, NF-kappa B signaling pathway, and pathways in cancer.

CONCLUSION

Our study identified novel diagnostic biomarkers and revealed potential immune-related mechanisms in NPC. These findings enlighten the investigation of the molecular mechanisms of tumor-infiltrating immune cells regulating NPC.

Systems Bioinformatics Reveals Possible Relationship between COVID-19 and the Development of Neurological Diseases and Neuropsychiatric Disorders

Coronavirus Disease 2019 (COVID-19) is associated with increased incidence of neurological diseases and neuropsychiatric disorders after infection, but how it contributes to their development remains under investigation. Here, we investigate the possible relationship between COVID-19 and the development of ten neurological disorders and three neuropsychiatric disorders by exploring two pathological mechanisms: (i) dysregulation of host biological processes via virus-host protein-protein interactions (PPIs), and (ii) autoreactivity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epitopes with host "self" proteins via molecular mimicry. We also identify potential genetic risk factors which in combination with SARS-CoV-2 infection might lead to disease development. Our analysis indicated that neurodegenerative diseases (NDs) have a higher number of disease-associated biological processes that can be modulated by SARS-CoV-2 via virus-host PPIs than neuropsychiatric disorders. The sequence similarity analysis indicated the presence of several matching 5-mer and/or 6-mer linear motifs between SARS-CoV-2 epitopes with autoreactive epitopes found in Alzheimer's Disease (AD), Parkinson's Disease (PD), Myasthenia Gravis (MG) and Multiple Sclerosis (MS). The results include autoreactive epitopes that recognize amyloid-beta precursor protein (APP), microtubule-associated protein tau (MAPT), acetylcholine receptors, glial fibrillary acidic protein (GFAP), neurofilament light polypeptide (NfL) and major myelin proteins. Altogether, our results suggest that there might be an increased risk for the development of NDs after COVID-19 both via autoreactivity and virus-host PPIs.

Viral hijacking mechanism in humans through protein-protein interactions

Numerous viruses have evolved mechanisms to inhibit or alter the host cell's apoptotic response as part of their coevolution with their hosts. The analysis of virus-host protein interactions require an in-depth understanding of both the viral and host protein structures and repertoires, as well as evolutionary mechanisms and pertinent biological facts. Throughout the course of a viral infection, there is constant battle for binding between virus and cellular proteins. Exogenous interfaces facilitating viral-host interactions are well known for constantly targeting and suppressing endogenous interfaces mediating intraspecific interactions, such as viral-viral and host-host connections. In these interactions, the protein-protein interactions (PPIs), are mostly shown as networks (protein interaction networks, PINs), with proteins represented as nodes and their interactions represented as edges. Host proteins with a higher degree of connectivity are more likely to interact with viral proteins. Due to technical advancements, three-dimensional interactions may now be visualized computationally utilizing molecular modeling and cryo-EM approaches. The uniqueness of viral domain repertoires, their evolution, and their activities during viral infection make viruses fascinating models for research. This chapter aims to provide readers a complete picture of the viral hijacking mechanism in protein-protein interactions.

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.

Estimating longitudinal depressive symptoms from smartphone data in a transdiagnostic cohort

BACKGROUND

Passive measures collected using smartphones have been suggested to represent efficient proxies for depression severity, but the performance of such measures across diagnoses has not been studied.

METHODS

We enrolled a cohort of 45 individuals (11 with major depressive disorder, 11 with bipolar disorder, 11 with schizophrenia or schizoaffective disorder, and 12 individuals with no axis I psychiatric disorder). During the 8-week study period, participants were evaluated with a rater-administered Montgomery-Åsberg Depression Rating Scale (MADRS) biweekly, completed self-report PHQ-8 measures weekly on their smartphone, and consented to collection of smartphone-based GPS and accelerometer data in order to learn about their behaviors. We utilized linear mixed models to predict depression severity on the basis of phone-based PHQ-8 and passive measures.

RESULTS

Among the 45 individuals, 38 (84%) completed the 8-week study. The average root-mean-squared error (RMSE) in predicting the MADRS score (scale 0-60) was 4.72 using passive data alone, 4.27 using self-report measures alone, and 4.30 using both.

CONCLUSIONS

While passive measures did not improve MADRS score prediction in our cross-disorder study, they may capture behavioral phenotypes that cannot be measured objectively, granularly, or over long-term via self-report.

Identification of viral-mediated pathogenic mechanisms in neurodegenerative diseases using network-based approaches

During the course of a viral infection, virus-host protein-protein interactions (PPIs) play a critical role in allowing viruses to replicate and survive within the host. These interspecies molecular interactions can lead to viral-mediated perturbations of the human interactome causing the generation of various complex diseases. Evidences suggest that viral-mediated perturbations are a possible pathogenic etiology in several neurodegenerative diseases (NDs). These diseases are characterized by chronic progressive degeneration of neurons, and current therapeutic approaches provide only mild symptomatic relief; therefore, there is unmet need for the discovery of novel therapeutic interventions. In this paper, we initially review databases and tools that can be utilized to investigate viral-mediated perturbations in complex NDs using network-based analysis by examining the interaction between the ND-related PPI disease networks and the virus-host PPI network. Afterwards, we present our theoretical-driven integrative network-based bioinformatics approach that accounts for pathogen-genes-disease-related PPIs with the aim to identify viral-mediated pathogenic mechanisms focusing in multiple sclerosis (MS) disease. We identified seven high centrality nodes that can act as disease communicator nodes and exert systemic effects in the MS-enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways network. In addition, we identified 12 KEGG pathways, 5 Reactome pathways and 52 Gene Ontology Immune System Processes by which 80 viral proteins from eight viral species might exert viral-mediated pathogenic mechanisms in MS. Finally, our analysis highlighted the Th17 differentiation pathway, a disease communicator node and part of the 12 underlined KEGG pathways, as a key viral-mediated pathogenic mechanism and a possible therapeutic target for MS disease.

Network-based analysis revealed significant interactions between risk genes of severe COVID-19 and host genes interacted with SARS-CoV-2 proteins

Whether risk genes of severe coronavirus disease 2019 (COVID-19) from genome-wide association study could play their regulatory roles by interacting with host genes that were interacted with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) proteins was worthy of exploration. In this study, we implemented a network-based approach by developing a user-friendly software Network Calculator (https://github.com/Haoxiang-Qi/Network-Calculator.git). By using Network Calculator, we identified a network composed of 13 risk genes and 28 SARS-CoV-2 interacted host genes that had the highest network proximity with each other, with a hub gene HNRNPK identified. Among these genes, 14 of them were identified to be differentially expressed in RNA-seq data from severe COVID-19 cases. Besides, by expression enrichment analysis in single-cell RNA-seq data, compared with mild COVID-19, these genes were significantly enriched in macrophage, T cell and epithelial cell for severe COVID-19. Meanwhile, 74 pathways were significantly enriched. Our analysis provided insights for the underlying genetic etiology of severe COVID-19 from the perspective of network biology.

The endoplasmic reticulum unfolded protein response - homeostasis, cell death and evolution in virus infections

Viruses elicit cell and organismic stress, and offset homeostasis. They trigger intrinsic, innate and adaptive immune responses, which limit infection. Viruses restore homeostasis by harnessing evolutionary conserved stress responses, such as the endoplasmic reticulum (ER) unfolded protein response (UPRER). The canonical UPRER restores homeostasis based on a cell-autonomous signalling network modulating transcriptional and translational output. The UPRER remedies cell damage, but upon severe and chronic stress leads to cell death. Signals from the UPRER flow along three branches with distinct stress sensors, the inositol requiring enzyme (Ire) 1, protein kinase R (PKR)-like ER kinase (PERK), and the activating transcription factor 6 (ATF6). This review shows how both enveloped and non-enveloped viruses use the UPRER to control cell stress and metabolic pathways, and thereby enhance infection and progeny formation, or undergo cell death. We highlight how the Ire1 axis bypasses apoptosis, boosts viral transcription and maintains dormant viral genomes during latency and persistence periods concurrent with long term survival of infected cells. These considerations open new options for oncolytic virus therapies against cancer cells where the UPRER is frequently upregulated. We conclude with a discussion of the evolutionary impact that viruses, in particular retroviruses, and anti-viral defense has on the UPRER.

Network medicine framework for identifying drug-repurposing opportunities for COVID-19

The COVID-19 pandemic has highlighted the need to quickly and reliably prioritize clinically approved compounds for their potential effectiveness for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. Here, we deployed algorithms relying on artificial intelligence, network diffusion, and network proximity, tasking each of them to rank 6,340 drugs for their expected efficacy against SARS-CoV-2. To test the predictions, we used as ground truth 918 drugs experimentally screened in VeroE6 cells, as well as the list of drugs in clinical trials that capture the medical community's assessment of drugs with potential COVID-19 efficacy. We find that no single predictive algorithm offers consistently reliable outcomes across all datasets and metrics. This outcome prompted us to develop a multimodal technology that fuses the predictions of all algorithms, finding that a consensus among the different predictive methods consistently exceeds the performance of the best individual pipelines. We screened in human cells the top-ranked drugs, obtaining a 62% success rate, in contrast to the 0.8% hit rate of nonguided screenings. Of the six drugs that reduced viral infection, four could be directly repurposed to treat COVID-19, proposing novel treatments for COVID-19. We also found that 76 of the 77 drugs that successfully reduced viral infection do not bind the proteins targeted by SARS-CoV-2, indicating that these network drugs rely on network-based mechanisms that cannot be identified using docking-based strategies. These advances offer a methodological pathway to identify repurposable drugs for future pathogens and neglected diseases underserved by the costs and extended timeline of de novo drug development.

Conquering the Nuclear Envelope Barriers by EBV Lytic Replication

The nuclear envelope (NE) of eukaryotic cells has a highly structural architecture, comprising double lipid-bilayer membranes, nuclear pore complexes, and an underlying nuclear lamina network. The NE structure is held in place through the membrane-bound LINC (linker of nucleoskeleton and cytoskeleton) complex, spanning the inner and outer nuclear membranes. The NE functions as a barrier between the nucleus and cytoplasm and as a transverse scaffold for various cellular processes. Epstein-Barr virus (EBV) is a human pathogen that infects most of the world's population and is associated with several well-known malignancies. Within the nucleus, the replicated viral DNA is packaged into capsids, which subsequently egress from the nucleus into the cytoplasm for tegumentation and final envelopment. There is increasing evidence that viral lytic gene expression or replication contributes to the pathogenesis of EBV. Various EBV lytic proteins regulate and modulate the nuclear envelope structure in different ways, especially the viral BGLF4 kinase and the nuclear egress complex BFRF1/BFRF2. From the aspects of nuclear membrane structure, viral components, and fundamental nucleocytoplasmic transport controls, this review summarizes our findings and recently updated information on NE structure modification and NE-related cellular processes mediated by EBV.

In silico Analyses of Immune System Protein Interactome Network, Single-Cell RNA Sequencing of Human Tissues, and Artificial Neural Networks Reveal Potential Therapeutic Targets for Drug Repurposing Against COVID-19

Background: There is pressing urgency to identify therapeutic targets and drugs that allow treating COVID-19 patients effectively. Methods: We performed in silico analyses of immune system protein interactome network, single-cell RNA sequencing of human tissues, and artificial neural networks to reveal potential therapeutic targets for drug repurposing against COVID-19. Results: We screened 1,584 high-confidence immune system proteins in ACE2 and TMPRSS2 co-expressing cells, finding 25 potential therapeutic targets significantly overexpressed in nasal goblet secretory cells, lung type II pneumocytes, and ileal absorptive enterocytes of patients with several immunopathologies. Then, we performed fully connected deep neural networks to find the best multitask classification model to predict the activity of 10,672 drugs, obtaining several approved drugs, compounds under investigation, and experimental compounds with the highest area under the receiver operating characteristics. Conclusion: After being effectively analyzed in clinical trials, these drugs can be considered for treatment of severe COVID-19 patients. Scripts can be downloaded at https://github.com/muntisa/immuno-drug-repurposing-COVID-19.

Protein Interaction Network Biology in Neuroscience

Mapping the intricate networks of cellular proteins in the human brain has the potential to address unsolved questions in molecular neuroscience, including the molecular basis of cognition, synaptic plasticity, long-term potentiation, learning, and memory. Perturbations to the protein-protein interaction networks (PPIN) present in neurons, glia, and other cell-types have been linked to multifactorial neurological disorders. Yet while knowledge of brain PPINs is steadily improving, the complexity and dynamic nature of the heterogeneous central nervous system in normal and disease contexts poses a formidable experimental challenge. In this review, the recent applications of functional proteomics and systems biology approaches to study PPINs central to normal neuronal function, during neurodevelopment, and in neurodegenerative disorders are summarized. How systematic PPIN analysis offers a unique mechanistic framework to explore intra- and inter-cellular functional modules governing neuronal activity and brain function is also discussed. Finally, future technological advancements needed to address outstanding questions facing neuroscience are outlined.

Human Virus Transcriptional Regulators

Viral genomes encode transcriptional regulators that alter the expression of viral and host genes. Despite an emerging role in human diseases, a thorough annotation of human viral transcriptional regulators (vTRs) is currently lacking, limiting our understanding of their molecular features and functions. Here, we provide a comprehensive catalog of 419 vTRs belonging to 20 different virus families. Using this catalog, we characterize shared and unique cellular genes, proteins, and pathways targeted by particular vTRs and discuss the role of vTRs in human disease pathogenesis. Our study provides a unique and valuable resource for the fields of virology, genomics, and human disease genetics.

Brain Disease Network Analysis to Elucidate the Neurological Manifestations of COVID-19

Although COVID-19 largely causes respiratory complications, it can also lead to various extrapulmonary manifestations resulting in higher mortality and these comorbidities are posing a challenge to the health care system. Reports indicate that 30–60% of patients with COVID-19 suffer from neurological symptoms. To understand the molecular basis of the neurologic comorbidity in COVID-19 patients, we have investigated the genetic association between COVID-19 and various brain disorders through a systems biology-based network approach and observed a remarkable resemblance. Our results showed 123 brain-related disorders associated with COVID-19 and form a high-density disease-disease network. The brain-disease-gene network revealed five highly clustered modules demonstrating a greater complexity of COVID-19 infection. Moreover, we have identified 35 hub proteins of the network which were largely involved in the protein catabolic process, cell cycle, RNA metabolic process, and nuclear transport. Perturbing these hub proteins by drug repurposing will improve the clinical conditions in comorbidity. In the near future, we assumed that in COVID-19 patients, many other neurological manifestations will likely surface. Thus, understanding the infection mechanisms of SARS-CoV-2 and associated comorbidity is a high priority to contain its short- and long-term effects on human health. Our network-based analysis strengthens the understanding of the molecular basis of the neurological manifestations observed in COVID-19 and also suggests drug for repurposing.

Crippling life support for SARS-CoV-2 and other viruses through synthetic lethality

With the rapid global spread of SARS-CoV-2, we have become acutely aware of the inadequacies of our ability to respond to viral epidemics. Although disrupting the viral life cycle is critical for limiting viral spread and disease, it has proven challenging to develop targeted and selective therapeutics. Synthetic lethality offers a promising but largely unexploited strategy against infectious viral disease; as viruses infect cells, they abnormally alter the cell state, unwittingly exposing new vulnerabilities in the infected cell. Therefore, we propose that effective therapies can be developed to selectively target the virally reconfigured host cell networks that accompany altered cellular states to cripple the host cell that has been converted into a virus factory, thus disrupting the viral life cycle.

Integrative Network Biology Framework Elucidates Molecular Mechanisms of SARS-CoV-2 Pathogenesis

COVID-19 (coronavirus disease 2019) is a respiratory illness caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Although the pathophysiology of this virus is complex and largely unknown, we employed a network-biology-fueled approach and integrated transcriptome data pertaining to lung epithelial cells with human interactome to generate Calu-3-specific human-SARS-CoV-2 interactome (CSI). Topological clustering and pathway enrichment analysis show that SARS-CoV-2 targets central nodes of the host-viral network, which participate in core functional pathways. Network centrality analyses discover 33 high-value SARS-CoV-2 targets, which are possibly involved in viral entry, proliferation, and survival to establish infection and facilitate disease progression. Our probabilistic modeling framework elucidates critical regulatory circuitry and molecular events pertinent to COVID-19, particularly the host-modifying responses and cytokine storm. Overall, our network-centric analyses reveal novel molecular components, uncover structural and functional modules, and provide molecular insights into the pathogenicity of SARS-CoV-2 that may help foster effective therapeutic design.

Identification of key pathways and genes responsible for aggressive behavior

Aggression is a complex behavior, underpinned by cross talk between several biomolecules. To date a composite molecular network of the behavioral disorder has not been constructed. The present study aims to develop the same from the system network analyses recruiting genes with empirical evidence demonstrating their role in the incidence and progression of aggression. In short, 327 genes were recruited in the study after extensive literature survey and subsequent shortlisting by sieving out the comorbidities like cancer and other pathological and physiological ailments, other languages and repeated citations. Subsequent String network analysis coalesces 275 genes in a network with 2223 edges. The developed network was then subjected to delineate modules using MCODE which via gene clustering on the basis of gene ontology segregate all genes into 14 modules. Of these, as expected top 5 modules involved entailing of neuronal signaling pathways with redundant repetitions. Finally, 10 genes (known) were picked randomly, accounting average module size, and subjected to the network analysis with 100,000 bootstrap replicates. This results in the detection of certain novel genes that lacks empirical evidence for their association with the aggression. Amongst those, most notable are genes involved in protein turnover regulation like UBC, UBA, mitogenic proteins such as Rho and Myc, transcription factors like Tp53. The findings in turn fill caveats in the molecular resolution of cross talk that underscore the development of aggressive behavior and may then be exploited as screening biomarker and/or therapeutic intervention for aggression.

SARS-CoV infection crosstalk with human host cell noncoding-RNA machinery: An in-silico approach

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Protein-protein interaction (PPI) network of the SARS-CoV and Homo sapiens was obtained.

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Based on Bioinformatic analysis, the TGF-beta signaling pathway with the largest numbers of involved genes may play key roles during SARS-CoV infection.

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An integrated network of mRNA/lncRNA/miRNA was created.

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In the network, SMAD2, SMAD3, SMAD4, SMAD7, and TGFBR1 with the highest number of interactions were identified as target hubs.

Although 70 % of the genome is transcribed to RNA in humans, only ∼2% of these transcripts are translated into proteins. The rest of the transcripts are defined as noncoding RNAs, including Long noncoding RNAs (LncRNAs) and MicroRNAs (miRNAs) that mostly function post-transcriptionally to regulate the gene expression. The outbreak of a novel coronavirus (SARS-CoV) has caused a major public health concern across the globe. The SARS-CoV is the seventh coronavirus that is known to cause human disease. There are currently no promising antiviral drugs with proven efficacy nor are there vaccines for its prevention. As of August 10, 2020, SARS-CoV has been infected more than 13 million cases in more than 213 countries, with an estimated mortality rate of ∼3 %. Thus, it is of utmost important priority to develop novel therapies for COVID-19. It is not fully investigated whether noncoding RNAs regulate signaling pathways that SARS-CoV involved in. Hence, computational analysis of the noncoding RNA interactions and determining importance of key regulatory noncoding RNAs in antiviral defense mechanisms will likely be helpful in developing new drugs to attack SARS-CoV infection. To elucidate this, we utilized bioinformatic approaches to find the interaction network of SARS-CoV/human proteins, miRNAs, and lncRNAs. We found TGF-beta signaling pathway as one of the potential interactive pathways. Furthermore, potential miRNAs/lncRNAs networks that the virus might engage during infection in human host cells have been shown. Altogether, TGF-beta signaling pathway as well as hub miRNAs, and LncRNAs involve during SARS-CoV pathogenesis can be considered as potential therapeutic targets.

Integrative Network Biology Framework Elucidates Molecular Mechanisms of SARS-CoV-2 Pathogenesis

COVID-19 (Coronavirus disease 2019) is a respiratory illness caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). While the pathophysiology of this deadly virus is complex and largely unknown, we employ a network biology-fueled approach and integrate multiomics data pertaining to lung epithelial cells-specific co-expression network and human interactome to generate Calu-3-specific human-SARS-CoV-2 Interactome (CSI). Topological clustering and pathway enrichment analysis show that SARS-CoV-2 target central nodes of host-viral network that participate in core functional pathways. Network centrality analyses discover 28 high-value SARS-CoV-2 targets, which are possibly involved in viral entry, proliferation and survival to establish infection and facilitate disease progression. Our probabilistic modeling framework elucidates critical regulatory circuitry and molecular events pertinent to COVID-19, particularly the host modifying responses and cytokine storm. Overall, our network centric analyses reveal novel molecular components, uncover structural and functional modules, and provide molecular insights into SARS-CoV-2 pathogenicity that may foster effective therapeutic design. Funding: This work was supported by the National Science Foundation (IOS-1557796) to M.S.M., and U54 ES 030246 from NIH/NIEHS to M. A. Conflict of Interest: The authors declare no competing interests. The authors also declare no financial interests.

Network Medicine Framework for Identifying Drug Repurposing Opportunities for COVID-19

The current pandemic has highlighted the need for methodologies that can quickly and reliably prioritize clinically approved compounds for their potential effectiveness for SARS-CoV-2 infections. In the past decade, network medicine has developed and validated multiple predictive algorithms for drug repurposing, exploiting the sub-cellular network-based relationship between a drug's targets and disease genes. Here, we deployed algorithms relying on artificial intelligence, network diffusion, and network proximity, tasking each of them to rank 6,340 drugs for their expected efficacy against SARS-CoV-2. To test the predictions, we used as ground truth 918 drugs that had been experimentally screened in VeroE6 cells, and the list of drugs under clinical trial, that capture the medical community's assessment of drugs with potential COVID-19 efficacy. We find that while most algorithms offer predictive power for these ground truth data, no single method offers consistently reliable outcomes across all datasets and metrics. This prompted us to develop a multimodal approach that fuses the predictions of all algorithms, showing that a consensus among the different predictive methods consistently exceeds the performance of the best individual pipelines. We find that 76 of the 77 drugs that successfully reduced viral infection do not bind the proteins targeted by SARS-CoV-2, indicating that these drugs rely on network-based actions that cannot be identified using docking-based strategies. These advances offer a methodological pathway to identify repurposable drugs for future pathogens and neglected diseases underserved by the costs and extended timeline of de novo drug development.

Network Medicine in Pathobiology

The past decade has witnessed exponential growth in the generation of high-throughput human data across almost all known dimensions of biological systems. The discipline of network medicine has rapidly evolved in parallel, providing an unbiased, comprehensive biological framework through which to interrogate and integrate systematically these large-scale, multi-omic data to enhance our understanding of disease mechanisms and to design drugs that reflect a deep knowledge of molecular pathobiology. In this review, we discuss the key principles of network medicine and the human disease network and explore the latest applications of network medicine in this multi-omic era. We also highlight the current conceptual and technological challenges, which serve as exciting opportunities by which to improve and expand the network-based applications beyond the artificial boundaries of the current state of human pathobiology.

Combined Transcriptome and Proteome Analysis of Immortalized Human Keratinocytes Expressing Human Papillomavirus 16 (HPV16) Oncogenes Reveals Novel Key Factors and Networks in HPV-Induced Carcinogenesis

Human papillomavirus (HPV)-associated cancers still remain a big health problem, especially in developing countries, despite the availability of prophylactic vaccines. Although HPV oncogenes have been intensively investigated for decades, a study applying recent advances in RNA-Seq and quantitative proteomic approaches to a precancerous model system with well-defined HPV oncogene expression alongside HPV-negative parental cells has been missing until now. Here, combined omics analyses reveal global changes caused by the viral oncogenes in a less biased way and allow the identification of novel factors and key cellular networks potentially promoting malignant transformation. In addition, this system also provides a basis for mechanistic research on novel key factors regulated by HPV oncogenes, especially those that are confirmed in vivo in cervical cancer as well as in head and neck cancer patient samples from TCGA data sets.

Although the role of high-risk human papillomaviruses (hrHPVs) as etiological agents in cancer development has been intensively studied during the last decades, there is still the necessity of understanding the impact of the HPV E6 and E7 oncogenes on host cells, ultimately leading to malignant transformation. Here, we used newly established immortalized human keratinocytes with a well-defined HPV16 E6E7 expression cassette to get a more complete and less biased overview of global changes induced by HPV16 by employing transcriptome sequencing (RNA-Seq) and stable isotope labeling by amino acids in cell culture (SILAC). This is the first study combining transcriptome and proteome data to characterize the impact of HPV oncogenes in human keratinocytes in comparison with their virus-negative counterparts. To enhance the informative value and accuracy of the RNA-Seq data, four different bioinformatic workflows were used. We identified potential novel upstream regulators (e.g., CNOT7, SPDEF, MITF, and PAX5) controlling distinct clusters of genes within the HPV-host cell network as well as distinct factors (e.g., CPPED1, LCP1, and TAGLN) with essential functions in cancer. Validated results in this study were compared to data sets from The Cancer Genome Atlas (TCGA), demonstrating that several identified factors were also differentially expressed in cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) and HPV-positive head and neck squamous cell carcinomas (HNSCs). This highly integrative approach allows the identification of novel HPV-induced cellular changes that are also reflected in cancer patients, providing a promising omics data set for future studies in both basic and translational research.

IMPORTANCE Human papillomavirus (HPV)-associated cancers still remain a big health problem, especially in developing countries, despite the availability of prophylactic vaccines. Although HPV oncogenes have been intensively investigated for decades, a study applying recent advances in RNA-Seq and quantitative proteomic approaches to a precancerous model system with well-defined HPV oncogene expression alongside HPV-negative parental cells has been missing until now. Here, combined omics analyses reveal global changes caused by the viral oncogenes in a less biased way and allow the identification of novel factors and key cellular networks potentially promoting malignant transformation. In addition, this system also provides a basis for mechanistic research on novel key factors regulated by HPV oncogenes, especially those that are confirmed in vivo in cervical cancer as well as in head and neck cancer patient samples from TCGA data sets.

Autism genes and the leukocyte transcriptome in autistic toddlers relate to pathogen interactomes, infection and the immune system. A role for excess neurotrophic sAPPα and reduced antimicrobial Aβ

Prenatal and early childhood infections have been implicated in autism. Many autism susceptibility genes (206 Autworks genes) are localised in the immune system and are related to immune/infection pathways. They are enriched in the host/pathogen interactomes of 18 separate microbes (bacteria/viruses and fungi) and to the genes regulated by bacterial toxins, mycotoxins and Toll-like receptor ligands. This enrichment was also observed for misregulated genes from a microarray study of leukocytes from autistic toddlers. The upregulated genes from this leukocyte study also matched the expression profiles in response to numerous infectious agents from the Broad Institute molecular signatures database. They also matched genes related to sudden infant death syndrome and autism comorbid conditions (autoimmune disease, systemic lupus erythematosus, diabetes, epilepsy and cardiomyopathy) as well as to estrogen and thyrotropin responses and to those upregulated by different types of stressors including oxidative stress, hypoxia, endoplasmic reticulum stress, ultraviolet radiation or 2,4-dinitrofluorobenzene, a hapten used to develop allergic skin reactions in animal models. The oxidative/integrated stress response is also upregulated in the autism brain and may contribute to myelination problems. There was also a marked similarity between the expression signatures of autism and Alzheimer's disease, and 44 shared autism/Alzheimer's disease genes are almost exclusively expressed in the blood-brain barrier. However, in contrast to Alzheimer's disease, levels of the antimicrobial peptide beta-amyloid are decreased and the levels of the neurotrophic/myelinotrophic soluble APP alpha are increased in autism, together with an increased activity of α-secretase. sAPPα induces an increase in glutamatergic and a decrease in GABA-ergic synapses creating and excitatory/inhibitory imbalance that has also been observed in autism. A literature survey showed that multiple autism genes converge on APP processing and that many are able to increase sAPPalpha at the expense of beta-amyloid production. A genetically programmed tilt of this axis towards an overproduction of neurotrophic/gliotrophic sAPPalpha and underproduction of antimicrobial beta-amyloid may explain the brain overgrowth and myelination dysfunction, as well as the involvement of pathogens in autism.

Convergent perturbation of the human domain-resolved interactome by viruses and mutations inducing similar disease phenotypes

An important goal of systems medicine is to study disease in the context of genetic and environmental perturbations to the human interactome network. For diseases with both genetic and infectious contributors, a key postulate is that similar perturbations of the human interactome by either disease mutations or pathogens can have similar disease consequences. This postulate has so far only been tested for a few viral species at the level of whole proteins. Here, we expand the scope of viral species examined, and test this postulate more rigorously at the higher resolution of protein domains. Focusing on diseases with both genetic and viral contributors, we found significant convergent perturbation of the human domain-resolved interactome by endogenous genetic mutations and exogenous viral proteins inducing similar disease phenotypes. Pan-cancer, pan-oncovirus analysis further revealed that domains of human oncoproteins either physically targeted or structurally mimicked by oncoviruses are enriched for cancer driver rather than passenger mutations, suggesting convergent targeting of cancer driver pathways by diverse oncoviruses. Our study provides a framework for high-resolution, network-based comparison of various disease factors, both genetic and environmental, in terms of their impacts on the human interactome.

The P1 gene of Cauliflower mosaic virus is responsible for breaking resistance in Arabidopsis thaliana ecotype Enkheim (En-2)

Arabidopsis thaliana ecotype En-2 is resistant to several strains of Cauliflower mosaic virus (CaMV), including strain W260, but is susceptible to strain NY8153. Resistance in En-2 is conditioned by a single, semi-dominant gene called CAR1. We constructed several recombinant infectious clones between W260 and NY8153 and evaluated their capability to infect En-2. This analysis showed that the capacity of NY8153 to break resistance in En-2 was conditioned by mutations within the CaMV gene 1, a gene that encodes a protein dedicated to cell-to-cell movement (P1), and conversely, that P1 of W260 is responsible for eliciting the plant defense response. A previous study had shown that P6 of W260 was responsible for overcoming resistance in Arabidopsis ecotype Tsu-0 and that P6 of CaMV strain CM1841 was responsible for triggering resistance. The present study now shows that a second gene of CaMV is targeted by Arabidopsis for plant immunity.

Viruses.STRING: A Virus-Host Protein-Protein Interaction Database

As viruses continue to pose risks to global health, having a better understanding of virus–host protein–protein interactions aids in the development of treatments and vaccines. Here, we introduce Viruses.STRING, a protein–protein interaction database specifically catering to virus–virus and virus–host interactions. This database combines evidence from experimental and text-mining channels to provide combined probabilities for interactions between viral and host proteins. The database contains 177,425 interactions between 239 viruses and 319 hosts. The database is publicly available at viruses.string-db.org, and the interaction data can also be accessed through the latest version of the Cytoscape STRING app.

Protein-protein interactions of human viruses

Viruses infect their human hosts by a series of interactions between viral and host proteins, indicating that detailed knowledge of such virus-host interaction interfaces are critical for our understanding of viral infection mechanisms, disease etiology and the development of new drugs. In this review, we primarily survey human host-virus interaction data that are available from public databases following the standardized PSI-MS format. Notably, available host-virus protein interaction information is strongly biased toward a small number of virus families including herpesviridae, papillomaviridae, orthomyxoviridae and retroviridae. While we explore the reliability and relevance of these protein interactions we also survey the current knowledge about viruses functional and topological targets. Furthermore, we assess emerging frontiers of host-virus protein interaction research, focusing on protein interaction interfaces of hosts that are infected by different viruses and viruses that infect multiple hosts. Finally, we cover the current status of research that investigates the relationships of virus-targeted host proteins to other comorbidities as well as the influence of host-virus protein interactions on human metabolism.

High-Quality Yeast-2-Hybrid Interaction Network Mapping

In this article, we describe a Y2H interaction mapping protocol for systematically screening medium-to-large collections of open reading frames (ORFs) against each other. The protocol is well suited for analysis of focused networks, for modules of interest, assembling genome-scale interactome maps, and investigating host-microbe interactions. The four-step high-throughput protocol has a demonstrated low false-discovery rate that is essential for producing meaningful network maps. Following the assembly of yeast expression clones from an existing ORFeome collection, we describe in detail the four-step procedure that begins with the primary screen using minipools, followed by secondary verification of primary positives, identification of candidate interaction pairs by sequencing, and a final verification step using fresh inoculants. In addition to this core protocol, aspects of network mapping and quality control will be discussed briefly. © 2018 by John Wiley & Sons, Inc.

Predicting perturbation patterns from the topology of biological networks

High-throughput technologies, offering an unprecedented wealth of quantitative data underlying the makeup of living systems, are changing biology. Notably, the systematic mapping of the relationships between biochemical entities has fueled the rapid development of network biology, offering a suitable framework to describe disease phenotypes and predict potential drug targets. However, our ability to develop accurate dynamical models remains limited, due in part to the limited knowledge of the kinetic parameters underlying these interactions. Here, we explore the degree to which we can make reasonably accurate predictions in the absence of the kinetic parameters. We find that simple dynamically agnostic models are sufficient to recover the strength and sign of the biochemical perturbation patterns observed in 87 biological models for which the underlying kinetics are known. Surprisingly, a simple distance-based model achieves 65% accuracy. We show that this predictive power is robust to topological and kinetic parameter perturbations, and we identify key network properties that can increase up to 80% the recovery rate of the true perturbation patterns. We validate our approach using experimental data on the chemotactic pathway in bacteria, finding that a network model of perturbation spreading predicts with ∼80% accuracy the directionality of gene expression and phenotype changes in knock-out and overproduction experiments. These findings show that the steady advances in mapping out the topology of biochemical interaction networks opens avenues for accurate perturbation spread modeling, with direct implications for medicine and drug development.

System pharmacogenomics application in infectious diseases

The new era in systems pharmacology has revolutionized the human biology. Its applicability, precise treatment, adequate response and safety measures fit into all the paradigm of medical/clinical practice. The importance of mathematical models in understanding the disease pathology and epideomology is now being realized. The advent of high-throughput technologies and the emergence of systems biology have resulted in the creation of systems pharmacogenomics and the focus is now on personalized medicine. However, there are some regulatory issues that need to be addresssed; are we ready for this universal adoption? This article details some of the infectious disease pharmacogenomics to the developments in this area.

Risk of Human Papillomavirus Infection in Cancer-Prone Individuals: What We Know

Human papillomavirus (HPV) infections cause a significant proportion of cancers worldwide, predominantly squamous cell carcinomas (SCC) of the mucosas and skin. High-risk HPV types are associated with SCCs of the anogenital and oropharyngeal tract. HPV oncogene activities and the biology of SCCs have been intensely studied in laboratory models and humans. What remains largely unknown are host tissue and immune-related factors that determine an individual's susceptibility to infection and/or carcinogenesis. Such susceptibility factors could serve to identify those at greatest risk and spark individually tailored HPV and SCC prevention efforts. Fanconi anemia (FA) is an inherited DNA repair disorder that is in part characterized by extreme susceptibility to SCCs. An increased prevalence of HPV has been reported in affected individuals, and molecular and functional connections between FA, SCC, and HPV were established in laboratory models. However, the presence of HPV in some human FA tumors is controversial, and the extent of the etiological connections remains to be established. Herein, we discuss cellular, immunological, and phenotypic features of FA, placed into the context of HPV pathogenesis. The goal is to highlight this orphan disease as a unique model system to uncover host genetic and molecular HPV features, as well as SCC susceptibility factors.

Genetic, Transcriptome, Proteomic, and Epidemiological Evidence for Blood-Brain Barrier Disruption and Polymicrobial Brain Invasion as Determinant Factors in Alzheimer's Disease

Diverse pathogens are detected in Alzheimer's disease (AD) brains. A bioinformatics survey showed that AD genome-wide association study (GWAS) genes (localized in bone marrow, immune locations and microglia) relate to multiple host/pathogen interactomes (Candida albicans, Cryptococcus neoformans, Bornavirus, Borrelia burgdorferri, cytomegalovirus, Ebola virus, HSV-1, HERV-W, HIV-1, Epstein-Barr, hepatitis C, influenza, Chlamydia pneumoniae, Porphyrymonas gingivalis, Helicobacter pylori, Toxoplasma gondii, Trypanosoma cruzi). These interactomes also relate to the AD hippocampal transcriptome and to plaque or tangle proteins. Upregulated AD hippocampal genes match those upregulated by multiple bacteria, viruses, fungi, or protozoa in immunocompetent cells. AD genes are enriched in GWAS datasets reflecting pathogen diversity, suggesting selection for pathogen resistance, as supported by the old age of AD patients, implying resistance to earlier infections. APOE4 is concentrated in regions of high parasitic burden and protects against childhood tropical infections and hepatitis C. Immune/inflammatory gain of function applies to APOE4, CR1, and TREM2 variants. AD genes are also expressed in the blood-brain barrier (BBB), which is disrupted by AD risk factors (age, alcohol, aluminum, concussion, cerebral hypoperfusion, diabetes, homocysteine, hypercholesterolemia, hypertension, obesity, pesticides, pollution, physical inactivity, sleep disruption, smoking) and by pathogens, directly or via olfactory routes to basal-forebrain BBB control centers. The BBB benefits from statins, NSAIDs, estrogen, melatonin, memantine, and the Mediterranean diet. Polymicrobial involvement is supported by upregulation of bacterial, viral, and fungal sensors/defenders in the AD brain, blood, or cerebrospinal fluid. AD serum amyloid-β autoantibodies may attenuate its antimicrobial effects favoring microbial survival and cerebral invasion leading to activation of neurodestructive immune/inflammatory processes, which may also be augmented by age-related immunosenescence. AD may thus respond to antibiotic, antifungal, or antiviral therapy.

Elucidation of host-pathogen protein-protein interactions to uncover mechanisms of host cell rewiring

Infectious diseases are the result of molecular cross-talks between hosts and their pathogens. These cross-talks are in part mediated by host-pathogen protein-protein interactions (HP-PPI). HP-PPI play crucial roles in infections, as they may tilt the balance either in favor of the pathogens' spread or their clearance. The identification of host proteins targeted by viral or bacterial pathogenic proteins necessary for the infection can provide insights into their underlying molecular mechanisms of pathogenicity, and potentially even single out pharmacological intervention targets. Here, we review the available methods to study HP-PPI, with a focus on recent mass spectrometry based methods to decipher bacterial-human infectious diseases and examine their relevance in uncovering host cell rewiring by pathogens.

Genetic factors affecting EBV copy number in lymphoblastoid cell lines derived from the 1000 Genome Project samples

Epstein-Barr virus (EBV), human herpes virus 4, has been classically associated with infectious mononucleosis, multiple sclerosis and several types of cancers. Many of these diseases show marked geographical differences in prevalence, which points to underlying genetic and/or environmental factors. Those factors may include a different susceptibility to EBV infection and viral copy number among human populations. Since EBV is commonly used to transform B-cells into lymphoblastoid cell lines (LCLs) we hypothesize that differences in EBV copy number among individual LCLs may reflect differential susceptibility to EBV infection. To test this hypothesis, we retrieved whole-genome sequenced EBV-mapping reads from 1,753 LCL samples derived from 19 populations worldwide that were sequenced within the context of the 1000 Genomes Project. An in silico methodology was developed to estimate the number of EBV copy number in LCLs and validated these estimations by real-time PCR. After experimentally confirming that EBV relative copy number remains stable over cell passages, we performed a genome wide association analysis (GWAS) to try detecting genetic variants of the host that may be associated with EBV copy number. Our GWAS has yielded several genomic regions suggestively associated with the number of EBV genomes per cell in LCLs, unraveling promising candidate genes such as CAND1, a known inhibitor of EBV replication. While this GWAS does not unequivocally establish the degree to which genetic makeup of individuals determine viral levels within their derived LCLs, for which a larger sample size will be needed, it potentially highlighted human genes affecting EBV-related processes, which constitute interesting candidates to follow up in the context of EBV related pathologies.

Network Analysis Reveals a Common Host-Pathogen Interaction Pattern in Arabidopsis Immune Responses

Many plant pathogens secrete virulence effectors into host cells to target important proteins in host cellular network. However, the dynamic interactions between effectors and host cellular network have not been fully understood. Here, an integrative network analysis was conducted by combining Arabidopsis thaliana protein-protein interaction network, known targets of Pseudomonas syringae and Hyaloperonospora arabidopsidis effectors, and gene expression profiles in the immune response. In particular, we focused on the characteristic network topology of the effector targets and differentially expressed genes (DEGs). We found that effectors tended to manipulate key network positions with higher betweenness centrality. The effector targets, especially those that are common targets of an individual effector, tended to be clustered together in the network. Moreover, the distances between the effector targets and DEGs increased over time during infection. In line with this observation, pathogen-susceptible mutants tended to have more DEGs surrounding the effector targets compared with resistant mutants. Our results suggest a common plant-pathogen interaction pattern at the cellular network level, where pathogens employ potent local impact mode to interfere with key positions in the host network, and plant organizes an in-depth defense by sequentially activating genes distal to the effector targets.

The RNA Exosome Syncs IAV-RNAPII Transcription to Promote Viral Ribogenesis and Infectivity

The nuclear RNA exosome is an essential multi-subunit complex that controls RNA homeostasis. Congenital mutations in RNA exosome genes are associated with neurodegenerative diseases. Little is known about the role of the RNA exosome in the cellular response to pathogens. Here, using NGS and human and mouse genetics, we show that influenza A virus (IAV) ribogenesis and growth are suppressed by impaired RNA exosome activity. Mechanistically, the nuclear RNA exosome coordinates the initial steps of viral transcription with RNAPII at host promoters. The viral polymerase complex co-opts the nuclear RNA exosome complex and cellular RNAs en route to 3' end degradation. Exosome deficiency uncouples chromatin targeting of the viral polymerase complex and the formation of cellular:viral RNA hybrids, which are essential RNA intermediates that license transcription of antisense genomic viral RNAs. Our results suggest that evolutionary arms races have shaped the cellular RNA quality control machinery.

Genomic investigation of piglet resilience following porcine epidemic diarrhea outbreaks

Porcine epidemic diarrhea virus (PEDV) belongs to the Coronaviridae family and causes malabsorptive watery diarrhea, vomiting, dehydration and imbalanced blood electrolytes in pigs. Since the 1970s, PED outbreaks have become a source of problems in pig producing countries all over the world, causing large economic losses for pig producers. Although the infection in adults is not fatal, in naïve suckling piglets mortality is close to 100%. In this study, we investigated genome-wide differences between dead and recovered suckling piglets from commercial farms after PED outbreaks. Samples from 262 animals (156 dead and 106 recovered) belonging to several commercial lines were collected from five different farms in three different countries (USA, Canada and Germany) and genotyped with the porcine 80K SNP chip. Mean F_st value was calculated in 1-Mb non-overlapping windows between dead and recovered individuals, and the results were normalized to find differences within the comparison. Seven windows with high divergence between dead and recovered were detected-five on chromosome 2, one on chromosome 4 and one on chromosome 15-in total encompassing 152 genes. Several of these genes are either under- or overexpressed in many virus infections, including Coronaviridae (such as SARS-CoV). A total of 32 genes are included in one or more Gene Ontology terms that can be related to PED development, such as Golgi apparatus, as well as mechanisms generally linked to resilience or diarrhea development (cell proliferation, ion transport, ATPase activity). Taken together this information provides a first genomic picture of PEDV resilience in suckling piglets.

Network diffusion-based analysis of high-throughput data for the detection of differentially enriched modules

A relation exists between network proximity of molecular entities in interaction networks, functional similarity and association with diseases. The identification of network regions associated with biological functions and pathologies is a major goal in systems biology. We describe a network diffusion-based pipeline for the interpretation of different types of omics in the context of molecular interaction networks. We introduce the network smoothing index, a network-based quantity that allows to jointly quantify the amount of omics information in genes and in their network neighbourhood, using network diffusion to define network proximity. The approach is applicable to both descriptive and inferential statistics calculated on omics data. We also show that network resampling, applied to gene lists ranked by quantities derived from the network smoothing index, indicates the presence of significantly connected genes. As a proof of principle, we identified gene modules enriched in somatic mutations and transcriptional variations observed in samples of prostate adenocarcinoma (PRAD). In line with the local hypothesis, network smoothing index and network resampling underlined the existence of a connected component of genes harbouring molecular alterations in PRAD.

Systems Biology-Based Investigation of Cellular Antiviral Drug Targets Identified by Gene-Trap Insertional Mutagenesis

Viruses require host cellular factors for successful replication. A comprehensive systems-level investigation of the virus-host interactome is critical for understanding the roles of host factors with the end goal of discovering new druggable antiviral targets. Gene-trap insertional mutagenesis is a high-throughput forward genetics approach to randomly disrupt (trap) host genes and discover host genes that are essential for viral replication, but not for host cell survival. In this study, we used libraries of randomly mutagenized cells to discover cellular genes that are essential for the replication of 10 distinct cytotoxic mammalian viruses, 1 gram-negative bacterium, and 5 toxins. We herein reported 712 candidate cellular genes, characterizing distinct topological network and evolutionary signatures, and occupying central hubs in the human interactome. Cell cycle phase-specific network analysis showed that host cell cycle programs played critical roles during viral replication (e.g. MYC and TAF4 regulating G0/1 phase). Moreover, the viral perturbation of host cellular networks reflected disease etiology in that host genes (e.g. CTCF, RHOA, and CDKN1B) identified were frequently essential and significantly associated with Mendelian and orphan diseases, or somatic mutations in cancer. Computational drug repositioning framework via incorporating drug-gene signatures from the Connectivity Map into the virus-host interactome identified 110 putative druggable antiviral targets and prioritized several existing drugs (e.g. ajmaline) that may be potential for antiviral indication (e.g. anti-Ebola). In summary, this work provides a powerful methodology with a tight integration of gene-trap insertional mutagenesis testing and systems biology to identify new antiviral targets and drugs for the development of broadly acting and targeted clinical antiviral therapeutics.

Infectious diseases result in millions of deaths and cost billions of dollars annually. Hence, there is urgency for developing more innovative and effective antiviral therapeutics. In this study, we used libraries of randomly mutagenized cells to discover cellular genes that are essential for the replication of 10 distinct cytotoxic mammalian viruses. We herein reported over 700 candidate cellular genes, over 20% of which were independently selected by multiple viruses in one or more cell types. Using systems biology-based analysis, we found that host genes associated with viral replication tended to occupy central hubs in the human protein interactome and to be ancient genes with low evolutionary rates, compared to non-virus-associated genes. Cell cycle phase-specific sub-network analysis showed that host cell cycle program played important roles during viral replication by regulating specific cell cycle phases. Moreover, we presented novel evidences to suggest that host genes supporting viral replication were frequently implicated in Mendelian and orphan diseases, or played critical roles in cancer. Importantly, we found approximately 110 new putative druggable antiviral targets by merging genome-wide gene-trap insertional mutagenesis, drug-gene network, and bioinformatics data. Furthermore, we have demonstrated the use of a computable representation of genetic testing to effectively identify new potential antiviral indications for existing drugs. In summary, this study presents new and important methodologies for developing broadly active antiviral therapeutics.

Comparative analysis of housekeeping and tissue-specific driver nodes in human protein interaction networks

Background

Several recent studies have used the Minimum Dominating Set (MDS) model to identify driver nodes, which provide the control of the underlying networks, in protein interaction networks. There may exist multiple MDS configurations in a given network, thus it is difficult to determine which one represents the real set of driver nodes. Because these previous studies only focus on static networks and ignore the contextual information on particular tissues, their findings could be insufficient or even be misleading.

Results

In this study, we develop a Collective-Influence-corrected Minimum Dominating Set (CI-MDS) model which takes into account the collective influence of proteins. By integrating molecular expression profiles and static protein interactions, 16 tissue-specific networks are established as well. We then apply the CI-MDS model to each tissue-specific network to detect MDS proteins. It generates almost the same MDSs when it is solved using different optimization algorithms. In addition, we classify MDS proteins into Tissue-Specific MDS (TS-MDS) proteins and HouseKeeping MDS (HK-MDS) proteins based on the number of tissues in which they are expressed and identified as MDS proteins. Notably, we find that TS-MDS proteins and HK-MDS proteins have significantly different topological and functional properties. HK-MDS proteins are more central in protein interaction networks, associated with more functions, evolving more slowly and subjected to a greater number of post-translational modifications than TS-MDS proteins. Unlike TS-MDS proteins, HK-MDS proteins significantly correspond to essential genes, ageing genes, virus-targeted proteins, transcription factors and protein kinases. Moreover, we find that besides HK-MDS proteins, many TS-MDS proteins are also linked to disease related genes, suggesting the tissue specificity of human diseases. Furthermore, functional enrichment analysis reveals that HK-MDS proteins carry out universally necessary biological processes and TS-MDS proteins usually involve in tissue-dependent functions.

Conclusions

Our study uncovers key features of TS-MDS proteins and HK-MDS proteins, and is a step forward towards a better understanding of the controllability of human interactomes.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1233-0) contains supplementary material, which is available to authorized users.

Computational discovery of Epstein-Barr virus targeted human genes and signalling pathways

Epstein-Barr virus (EBV) plays important roles in the origin and the progression of human carcinomas, e.g. diffuse large B cell tumors, T cell lymphomas, etc. Discovering EBV targeted human genes and signaling pathways is vital to understand EBV tumorigenesis. In this study we propose a noise-tolerant homolog knowledge transfer method to reconstruct functional protein-protein interactions (PPI) networks between Epstein-Barr virus and Homo sapiens. The training set is augmented via homolog instances and the homolog noise is counteracted by support vector machine (SVM). Additionally we propose two methods to define subcellular co-localization (i.e. stringent and relaxed), based on which to further derive physical PPI networks. Computational results show that the proposed method achieves sound performance of cross validation and independent test. In the space of 648,672 EBV-human protein pairs, we obtain 51,485 functional interactions (7.94%), 869 stringent physical PPIs and 46,050 relaxed physical PPIs. Fifty-eight evidences are found from the latest database and recent literature to validate the model. This study reveals that Epstein-Barr virus interferes with normal human cell life, such as cholesterol homeostasis, blood coagulation, EGFR binding, p53 binding, Notch signaling, Hedgehog signaling, etc. The proteome-wide predictions are provided in the supplementary file for further biomedical research.

Systems medicine of inflammaging

Systems Medicine (SM) can be defined as an extension of Systems Biology (SB) to Clinical-Epidemiological disciplines through a shifting paradigm, starting from a cellular, toward a patient centered framework. According to this vision, the three pillars of SM are Biomedical hypotheses, experimental data, mainly achieved by Omics technologies and tailored computational, statistical and modeling tools. The three SM pillars are highly interconnected, and their balancing is crucial. Despite the great technological progresses producing huge amount of data (Big Data) and impressive computational facilities, the Bio-Medical hypotheses are still of primary importance. A paradigmatic example of unifying Bio-Medical theory is the concept of Inflammaging. This complex phenotype is involved in a large number of pathologies and patho-physiological processes such as aging, age-related diseases and cancer, all sharing a common inflammatory pathogenesis. This Biomedical hypothesis can be mapped into an ecological perspective capable to describe by quantitative and predictive models some experimentally observed features, such as microenvironment, niche partitioning and phenotype propagation. In this article we show how this idea can be supported by computational methods useful to successfully integrate, analyze and model large data sets, combining cross-sectional and longitudinal information on clinical, environmental and omics data of healthy subjects and patients to provide new multidimensional biomarkers capable of distinguishing between different pathological conditions, e.g. healthy versus unhealthy state, physiological versus pathological aging.