Getting connected: analysis and principles of biological networks

Profiling Protein-Protein Interactions in the Human Brain by Refined Cofractionation Mass Spectrometry

Proteins usually execute their biological functions through interactions with other proteins and by forming macromolecular complexes, but global profiling of protein complexes directly from human tissue samples has been limited. In this study, we utilized cofractionation mass spectrometry (CF-MS) to map protein complexes within the postmortem human brain with experimental replicates. First, we used concatenated anion and cation Ion Exchange Chromatography (IEX) to separate native protein complexes in 192 fractions and then proceeded with Data-Independent Acquisition (DIA) mass spectrometry to analyze the proteins in each fraction, quantifying a total of 4,804 proteins with 3,260 overlapping in both replicates. We improved the DIA's quantitative accuracy by implementing a constant amount of bovine serum albumin (BSA) in each fraction as an internal standard. Next, advanced computational pipelines, which integrate both a database-based complex analysis and an unbiased protein-protein interaction (PPI) search, were applied to identify protein complexes and construct protein-protein interaction networks in the human brain. Our study led to the identification of 486 protein complexes and 10054 binary protein-protein interactions, which represents the first global profiling of human brain PPIs using CF-MS. Overall, this study offers a resource and tool for a wide range of human brain research, including the identification of disease-specific protein complexes in the future.

Molecular-guided therapy for the treatment of patients with relapsed and refractory childhood cancers: a Beat Childhood Cancer Research Consortium trial

BACKGROUND

Children with relapsed central nervous system (CNS tumors), neuroblastoma, sarcomas, and other rare solid tumors face poor outcomes. This prospective clinical trial examined the feasibility of combining genomic and transcriptomic profiling of tumor samples with a molecular tumor board (MTB) approach to make real‑time treatment decisions for children with relapsed/refractory solid tumors.

METHODS

Subjects were divided into three strata: stratum 1-relapsed/refractory neuroblastoma; stratum 2-relapsed/refractory CNS tumors; and stratum 3-relapsed/refractory rare solid tumors. Tumor samples were sent for tumor/normal whole-exome (WES) and tumor whole-transcriptome (WTS) sequencing, and the genomic data were used in a multi-institutional MTB to make real‑time treatment decisions. The MTB recommended plan allowed for a combination of up to 4 agents. Feasibility was measured by time to completion of genomic sequencing, MTB review and initiation of treatment. Response was assessed after every two cycles using Response Evaluation Criteria in Solid Tumors (RECIST). Patient clinical benefit was calculated by the sum of the CR, PR, SD, and NED subjects divided by the sum of complete response (CR), partial response (PR), stable disease (SD), no evidence of disease (NED), and progressive disease (PD) subjects. Grade 3 and higher related and unexpected adverse events (AEs) were tabulated for safety evaluation.

RESULTS

A total of 186 eligible patients were enrolled with 144 evaluable for safety and 124 evaluable for response. The average number of days from biopsy to initiation of the MTB-recommended combination therapy was 38 days. Patient benefit was exhibited in 65% of all subjects, 67% of neuroblastoma subjects, 73% of CNS tumor subjects, and 60% of rare tumor subjects. There was little associated toxicity above that expected for the MGT drugs used during this trial, suggestive of the safety of utilizing this method of selecting combination targeted therapy.

CONCLUSIONS

This trial demonstrated the feasibility, safety, and efficacy of a comprehensive sequencing model to guide personalized therapy for patients with any relapsed/refractory solid malignancy. Personalized therapy was well tolerated, and the clinical benefit rate of 65% in these heavily pretreated populations suggests that this treatment strategy could be an effective option for relapsed and refractory pediatric cancers.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT02162732. Prospectively registered on June 11, 2014.

Review of Computational Methods and Database Sources for Predicting the Effects of Coding Frameshift Small Insertion and Deletion Variations

Genetic variations (including substitutions, insertions, and deletions) exert a profound influence on DNA sequences. These variations are systematically classified as synonymous, nonsynonymous, and nonsense, each manifesting distinct effects on proteins. The implementation of high-throughput sequencing has significantly augmented our comprehension of the intricate interplay between gene variations and protein structure and function, as well as their ramifications in the context of diseases. Frameshift variations, particularly small insertions and deletions (indels), disrupt protein coding and are instrumental in disease pathogenesis. This review presents a succinct review of computational methods, databases, current challenges, and future directions in predicting the consequences of coding frameshift small indels variations. We analyzed the predictive efficacy, reliability, and utilization of computational methods and variant account, reliability, and utilization of database. Besides, we also compared the prediction methodologies on GOF/LOF pathogenic variation data. Addressing the challenges pertaining to prediction accuracy and cross-species generalizability, nascent technologies such as AI and deep learning harbor immense potential to enhance predictive capabilities. The importance of interdisciplinary research and collaboration cannot be overstated for devising effective diagnosis, treatment, and prevention strategies concerning diseases associated with coding frameshift indels variations.

An in-silico approach to understand the potential role of Wnt inhibitory factor-1 (WIF-1) in the inhibition of the Wnt signalling pathway

WIF1 (Wnt inhibitory factor 1) is a potent tumour suppressor gene which is epigenetically silenced in numerous malignancies. The associations of WIF1 protein with the Wnt pathway molecules have not been fully explored, despite their involvement in the downregulation of several malignancies. In the present study, a computational approach encompassing the expression, gene ontology analysis and pathway analysis is employed to obtain an insight into the role of the WIF1 protein. Moreover, the interaction of the WIF1 domain with the Wnt pathway molecules was carried out to ascertain the tumour-suppressive role of the domain, along with the determination of their plausible interactions. Initially, the protein-protein interaction network analysis endowed us with the Wnt ligands (such as Wnt1, Wnt3a, Wnt4, Wnt5a, Wnt8a and Wnt9a), along with the Frizzled receptors (Fzd1 and Fzd2) and the low-density lipoprotein complex (Lrp5/6) as the foremost interactors of the protein. Further, the expression analysis of the aforementioned genes and proteins was determined using The Cancer Genome Atlas to comprehend the significance of the signalling molecules in the major cancer subtypes. Moreover, the associations of the aforementioned macromolecular entities with the WIF1 domain were explored using the molecular docking studies, whereas the dynamics and stability of the assemblage were investigated using 100 ns molecular dynamics simulations. Therefore, providing us insights into the plausible roles of WIF1 in inhibiting the Wnt pathways in various malignancies.Communicated by Ramaswamy H. Sarma.

The central regulatory circuit in the gene network controlling the morphogenesis of Drosophila mechanoreceptors: an in silico analysis

Identification of the mechanisms underlying the genetic control of spatial structure formation is among the relevant tasks of developmental biology. Both experimental and theoretical approaches and methods are used for this purpose, including gene network methodology, as well as mathematical and computer modeling. Reconstruction and analysis of the gene networks that provide the formation of traits allow us to integrate the existing experimental data and to identify the key links and intra-network connections that ensure the function of networks. Mathematical and computer modeling is used to obtain the dynamic characteristics of the studied systems and to predict their state and behavior. An example of the spatial morphological structure is the Drosophila bristle pattern with a strictly defined arrangement of its components - mechanoreceptors (external sensory organs) - on the head and body. The mechanoreceptor develops from a single sensory organ parental cell (SOPC), which is isolated from the ectoderm cells of the imaginal disk. It is distinguished from its surroundings by the highest content of proneural proteins (ASC), the products of the achaete-scute proneural gene complex (AS-C). The SOPC status is determined by the gene network we previously reconstructed and the AS-C is the key component of this network. AS-C activity is controlled by its subnetwork - the central regulatory circuit (CRC) comprising seven genes: AS-C, hairy, senseless (sens), charlatan (chn), scratch (scrt), phyllopod (phyl), and extramacrochaete (emc), as well as their respective proteins. In addition, the CRC includes the accessory proteins Daughterless (DA), Groucho (GRO), Ubiquitin (UB), and Seven-in-absentia (SINA). The paper describes the results of computer modeling of different CRC operation modes. As is shown, a cell is determined as an SOPC when the ASC content increases approximately 2.5-fold relative to the level in the surrounding cells. The hierarchy of the effects of mutations in the CRC genes on the dynamics of ASC protein accumulation is clarified. AS-C as the main CRC component is the most significant. The mutations that decrease the ASC content by more than 40 % lead to the prohibition of SOPC segregation.

Computational Pipeline for Analysis of Biomedical Networks with BioNAR

In a living cell, proteins interact to assemble both transient and constant molecular complexes, which transfer signals/information around internal pathways. Modern proteomic techniques can identify the constituent components of these complexes, but more detailed analysis demands a network approach linking the molecules together and analyzing the emergent architectural properties. The Bioconductor package BioNAR combines a selection of existing R protocols for network analysis with newly designed original methodological features to support step-by-step analysis of biological/biomedical . Critically, BioNAR supports a pipeline approach whereby many networks and iterative analyses can be performed. Here we present a network analysis pipeline that starts from initiating a network model from a list of components/proteins and their interactions through to identifying its functional components based solely on network topology. We demonstrate that BioNAR can help users achieve a number of network analysis goals that are difficult to achieve anywhere else. This includes how users can choose the optimal clustering algorithm from a range of options based on independent annotation enrichment, and predict a protein's influence within and across multiple subcomplexes in the network and estimate the co-occurrence or linkage between metadata at the network level (e.g., diseases and functions across the network, identifying the clusters whose components are likely to share common function and mechanisms). The package is freely available in Bioconductor release 3.17: https://bioconductor.org/packages/3.17/bioc/html/BioNAR.html. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Creating and annotating the network Support Protocol 1: Installing BioNAR from RStudio Support Protocol 2: Building the sample network from synaptome.db Basic Protocol 2: Network properties and centrality Basic Protocol 3: Network communities Basic protocol 4: Choosing the optimal clustering algorithm based on the enrichment with annotation terms Basic Protocol 5: Influencing network components and bridgeness Basic Protocol 6: Co-occurrence of the annotations.

BioNAR: an integrated biological network analysis package in bioconductor

Motivation

Biological function in protein complexes emerges from more than just the sum of their parts: molecules interact in a range of different sub-complexes and transfer signals/information around internal pathways. Modern proteomic techniques are excellent at producing a parts-list for such complexes, but more detailed analysis demands a network approach linking the molecules together and analysing the emergent architectural properties. Methods developed for the analysis of networks in social sciences have proven very useful for splitting biological networks into communities leading to the discovery of sub-complexes enriched with molecules associated with specific diseases or molecular functions that are not apparent from the constituent components alone.

Results

Here, we present the Bioconductor package BioNAR, which supports step-by-step analysis of biological/biomedical networks with the aim of quantifying and ranking each of the network's vertices based on network topology and clustering. Examples demonstrate that while BioNAR is not restricted to proteomic networks, it can predict a protein's impact within multiple complexes, and enables estimation of the co-occurrence of metadata, i.e. diseases and functions across the network, identifying the clusters whose components are likely to share common function and mechanisms.

Availability and implementation

The package is available from Bioconductor release 3.17: https://bioconductor.org/packages/release/bioc/html/BioNAR.html.

Network module analysis and molecular docking-based study on the mechanism of astragali radix against non-small cell lung cancer

BACKGROUND

Most lung cancer patients worldwide (stage IV non-small cell lung cancer, NSCLC) have a poor survival: 25%-30% patients die < 3 months. Yet, of those surviving > 3 months, 10%-15% patients survive (very) long. Astragali radix (AR) is an effective traditional Chinese medicine widely used for non-small cell lung cancer (NSCLC). However, the pharmacological mechanisms of AR on NSCLC remain to be elucidated.

METHODS

Ultra Performance Liquid Chromatography system coupled with Q-Orbitrap HRMS (UPLC-Q-Orbitrap HRMS) was performed for the qualitative analysis of AR components. Then, network module analysis and molecular docking-based approach was conducted to explore underlying mechanisms of AR on NSCLC. The target genes of AR were obtained from four databases including TCMSP (Traditional Chinese Medicine Systems Pharmacology) database, ETCM (The Encyclopedia of TCM) database, HERB (A high-throughput experiment- and reference-guided database of TCM) database and BATMAN-TCM (a Bioinformatics Analysis Tool for Molecular mechanism of TCM) database. NSCLC related genes were screened by GEO (Gene Expression Omnibus) database. The STRING database was used for protein interaction network construction (PIN) of AR-NSCLC shared target genes. The critical PIN were further constructed based on the topological properties of network nodes. Afterwards the hub genes and network modules were analyzed, and enrichment analysis were employed by the R package clusterProfiler. The Autodock Vina was utilized for molecular docking, and the Gromacs was utilized for molecular dynamics simulations Furthermore, the survival analysis was performed based on TCGA (The Cancer Genome Atlas) database.

RESULTS

Seventy-seven AR components absorbed in blood were obtained. The critical network was constructed with 1447 nodes and 28,890 edges. Based on topological analysis, 6 hub target genes and 7 functional modules were gained. were obtained including TP53, SRC, UBC, CTNNB1, EP300, and RELA. After module analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that AR may exert therapeutic effects on NSCLC by regulating JAK-STAT signaling pathway, PI3K-AKT signaling pathway, ErbB signaling pathway, as well as NFkB signaling pathway. After the intersection calculation of the hub targets and the proteins participated in the above pathways, TP53, SRC, EP300, and RELA were obtained. These proteins had good docking affinity with astragaloside IV. Furthermore, RELA was associated with poor prognosis of NSCLC patients.

CONCLUSIONS

This study could provide chemical component information references for further researches. The potential pharmacological mechanisms of AR on NSCLC were elucidated, promoting the clinical application of AR in treating NSCLC. RELA was selected as a promising candidate biomarker affecting the prognosis of NSCLC patients.

Next-generation bulked segregant analysis for Breeding 4.0

Functional cloning and manipulation of genes controlling various agronomic traits are important for boosting crop production. Although bulked segregant analysis (BSA) is an efficient method for functional cloning, its low throughput cannot satisfy the current need for crop breeding and food security. Here, we review the rationale and development of conventional BSA and discuss its strengths and drawbacks. We then propose next-generation BSA (NG-BSA) integrating multiple cutting-edge technologies, including high-throughput phenotyping, biological big data, and the use of machine learning. NG-BSA increases the resolution of genetic mapping and throughput for cloning quantitative trait genes (QTGs) and optimizes candidate gene selection while providing a means to elucidate the interaction network of QTGs. The ability of NG-BSA to efficiently batch-clone QTGs makes it an important tool for dissecting molecular mechanisms underlying various traits, as well as for the improvement of Breeding 4.0 strategy, especially in targeted improvement and population improvement of crops.

Biological Activities of Bismuth Compounds: An Overview of the New Findings and the Old Challenges Not Yet Overcome

Bismuth-based drugs have been used primarily to treat ulcers caused by Helicobacter pylori and other gastrointestinal ailments. Combined with antibiotics, these drugs also possess synergistic activity, making them ideal for multiple therapy regimens and overcoming bacterial resistance. Compounds based on bismuth have a low cost, are safe for human use, and some of them are also effective against tumoral cells, leishmaniasis, fungi, and viruses. However, these compounds have limited bioavailability in physiological environments. As a result, there is a growing interest in developing new bismuth compounds and approaches to overcome this challenge. Considering the beneficial properties of bismuth and the importance of discovering new drugs, this review focused on the last decade's updates involving bismuth compounds, especially those with potent activity and low toxicity, desirable characteristics for developing new drugs. In addition, bismuth-based compounds with dual activity were also highlighted, as well as their modes of action and structure-activity relationship, among other relevant discoveries. In this way, we hope this review provides a fertile ground for rationalizing new bismuth-based drugs.

Network analysis of osteoporosis provides a global view of associated comorbidities and their temporal relationships

We performed comorbidity-network analysis to obtain global view of comorbidity related with osteoporosis. We selected 10000-patients with osteoporosis registered in the National-Health-Insurance Service cohort-database. We found 45-significant disease-clusters. Of these, 14-disease-clusters were related to fra, while 10 were related to musculoskeletal diseases. Our findings will serve as basic data for further studies.

PURPOSE

Osteoporosis causes devastating fractures; however, its exact etiology remains unknown. Elucidating associated comorbidities and their temporal relationships could provide better insights into its pathogenesis. Comorbidity-network analysis was performed to obtain global view of these associations.

METHODS

We randomly selected 10000-patients with osteoporosis registered in the National-Health-Insurance Service cohort-database. These patients were identified using ICD-10 codes M81-M82, which represent osteoporosis without pathological fractures. Control group was created through propensity score matching. The comorbidities in each group were grouped into similar classifications to form "disease cluster"; 126 such clusters were identified. To create a comorbidity network, we selected disease clusters with high associations (i.e., odds ratios and relative risks ranked in the upper 50^th percentile). To identify the temporal relationships between these clusters and osteoporosis, trajectories of directions were identified.

RESULTS

Finally, we found 45 significant disease clusters. Of these, 14 disease clusters were related to fractures or injuries, while 10 were related to musculoskeletal diseases. Temporal analysis revealed that 15 disease clusters preceded osteoporosis; these included the following three with the strongest associations: "other fracture", "disorders of bone density and structure (M83-M85)", and "sequelae of injuries of neck and trunk (T91)". Thirty disease clusters followed osteoporosis; these included the following three with the strongest associations: "spine fracture," "spondylopathies (M45-M49)", and "pelvic region and thigh fracture,".

CONCLUSION

We obtained a global view of the osteoporosis comorbidity network, which is otherwise difficult to achieve through study of individual diseases. Our findings will serve as the basic data for further studies.

Benchmarking Gene Regulatory Network Inference Methods on Simulated and Experimental Data

Although the challenge of gene regulatory network inference has been studied for more than a decade, it is still unclear how well network inference methods work when applied to real data. Attempts to benchmark these methods on experimental data have yielded mixed results, in which sometimes even the best methods fail to outperform random guessing, and in other cases they perform reasonably well. So, one of the most valuable contributions one can currently make to the field of network inference is to benchmark methods on experimental data for which the true underlying network is already known, and report the results so that we can get a clearer picture of their efficacy. In this paper, we report results from the first, to our knowledge, benchmarking of network inference methods on single cell E. coli transcriptomic data. We report a moderate level of accuracy for the methods, better than random chance but still far from perfect. We also find that some methods that were quite strong and accurate on microarray and bulk RNA-seq data did not perform as well on the single cell data. Additionally, we benchmark a simple network inference method (Pearson correlation), on data generated through computer simulations in order to draw conclusions about general best practices in network inference studies. We predict that network inference would be more accurate using proteomic data rather than transcriptomic data, which could become relevant if high-throughput proteomic experimental methods are developed in the future. We also show through simulations that using a simplified model of gene expression that skips the mRNA step tends to substantially overestimate the accuracy of network inference methods, and advise against using this model for future in silico benchmarking studies.

Combining Mendelian randomization and network deconvolution for inference of causal networks with GWAS summary data

Mendelian randomization (MR) has been increasingly applied for causal inference with observational data by using genetic variants as instrumental variables (IVs). However, the current practice of MR has been largely restricted to investigating the total causal effect between two traits, while it would be useful to infer the direct causal effect between any two of many traits (by accounting for indirect or mediating effects through other traits). For this purpose we propose a two-step approach: we first apply an extended MR method to infer (i.e. both estimate and test) a causal network of total effects among multiple traits, then we modify a graph deconvolution algorithm to infer the corresponding network of direct effects. Simulation studies showed much better performance of our proposed method than existing ones. We applied the method to 17 large-scale GWAS summary datasets (with median N = 256879 and median #IVs = 48) to infer the causal networks of both total and direct effects among 11 common cardiometabolic risk factors, 4 cardiometabolic diseases (coronary artery disease, stroke, type 2 diabetes, atrial fibrillation), Alzheimer's disease and asthma, identifying some interesting causal pathways. We also provide an R Shiny app (https://zhaotongl.shinyapps.io/cMLgraph/) for users to explore any subset of the 17 traits of interest.

A Data-Mining Approach to Identify NF-kB-Responsive microRNAs in Tissues Involved in Inflammatory Processes: Potential Relevance in Age-Related Diseases

The nuclear factor NF-kB is the master transcription factor in the inflammatory process by modulating the expression of pro-inflammatory genes. However, an additional level of complexity is the ability to promote the transcriptional activation of post-transcriptional modulators of gene expression as non-coding RNA (i.e., miRNAs). While NF-kB’s role in inflammation-associated gene expression has been extensively investigated, the interplay between NF-kB and genes coding for miRNAs still deserves investigation. To identify miRNAs with potential NF-kB binding sites in their transcription start site, we predicted miRNA promoters by an in silico analysis using the PROmiRNA software, which allowed us to score the genomic region’s propensity to be miRNA cis-regulatory elements. A list of 722 human miRNAs was generated, of which 399 were expressed in at least one tissue involved in the inflammatory processes. The selection of “high-confidence” hairpins in miRbase identified 68 mature miRNAs, most of them previously identified as inflammamiRs. The identification of targeted pathways/diseases highlighted their involvement in the most common age-related diseases. Overall, our results reinforce the hypothesis that persistent activation of NF-kB could unbalance the transcription of specific inflammamiRNAs. The identification of such miRNAs could be of diagnostic/prognostic/therapeutic relevance for the most common inflammatory-related and age-related diseases.

Inference of monopartite networks from bipartite systems with different link types

Many of the real-world data sets can be portrayed as bipartite networks. Since connections between nodes of the same type are lacking, they need to be inferred. The standard way to do this is by converting the bipartite networks to their monopartite projection. However, this simple approach renders an incomplete representation of all the information in the original network. To this end, we propose a new statistical method to identify the most critical links in the bipartite network projection. Our method takes into account the heterogeneity of node connections. Moreover, it can handle situations where links of different types are present. We compare our method against the state-of-the-art and illustrate the findings with synthetic data and empirical examples of investor and political data.

The protein-protein interaction landscape of transcription factors during gynoecium development in Arabidopsis

Flowers are composed of organs whose identity is defined by the combinatorial activity of transcription factors (TFs). The interactions between MADS-box TFs and protein complex formation have been schematized in the floral quartet model of flower development. The gynoecium is the flower's female reproductive part, crucial for fruit and seed production and, hence, for reproductive success. After the establishment of carpel identity, many tissues arise to form a mature gynoecium. TFs have been described as regulators of gynoecium development, and some interactions and complexes have been identified. However, broad knowledge about the interactions among these TFs and their participation during development remains scarce. In this study, we used a systems biology approach to understand the formation of a complex reproductive unit-as the gynoecium-by mapping binary interactions between well-characterized TFs. We analyzed almost 4500 combinations and detected more than 250 protein-protein interactions (PPIs), resulting in a process-specific interaction map. Topological analyses suggest hidden functions and novel roles for many TFs. In addition, we observed a close relationship between TFs involved in auxin and cytokinin-signaling pathways and other TFs. Furthermore, we analyzed the network by combining PPI data, expression, and genetic data, which helped us to dissect it into several dynamic spatio-temporal subnetworks related to gynoecium development processes. Finally, we generated an extended PPI network that predicts new players in gynoecium development. Taken together, all these results serve as a valuable resource for the plant community.

MicroRNA Regulatory Network Analysis Using miRNet 2.0

MicroRNAs exert their effects in the context of gene regulatory networks. The recent development of high-throughput experimental approaches and the growing availability of gene expression data have permitted comprehensive functional studies of miRNAs. However, the data interpretation is often challenging due to the fact that miRNAs not only act cooperatively with other miRNAs but also participate in complex networks by interacting with other functional elements, including non-coding RNAs or transcription factors that often have extensive effects on cell biology. This chapter provides detailed practical procedures on how to use miRNet 2.0 ( https://www.mirnet.ca ) to perform miRNA regulatory network analytics to gain functional insights.

In-silico evidence of ADAM metalloproteinase pathology in cancer signaling networks

Lack of effective targeted therapies often contributes to poor clinical outcomes of aggressive malignancies associated with drug resistance, angiogenesis and metastasis. Literature mining portrays the major role of ADAM17 in cancer and inflammatory diseases. However, it is quite challenging to design a candidate drug for targeting ADAM17 due to its structural similarity with the catalytic domain of the matrix metalloproteases (MMPs). The present study reports the protein-protein interaction analysis of ADAM17, along with the molecular docking and MD simulation studies for the screened compounds. Our analysis confirms the association of ADAM17 with numerous oncogenes that facilitates cancer progression and inflammation, especially the members of the Notch, receptor tyrosine kinase (RTK) and TNFα pathways. The outcome provides evidence that the prevalent protease ADAM17 could attribute to cancer signaling regulation though the shedding of various inflammatory and oncogenic molecules. We have also exploited the analogues of the existing inhibitors, with an aim at discovering a potent molecule, which could be repurposed as a drug against ADAM17 inflicted cancer progression. Upon stringent screening, we delineated our choice into two specific compounds (I6 and I9; analogues of IK862, a type of y-lactam hydroxamates), possessing the lowest binding energy (-9.1 Kcal/mol), stable MD-simulation studies and superior pharmacodynamic properties. The current information illustrates the avenue to persuade further research on targeting ADAM17 with small molecular compounds (I6 and I9) in cancer therapeutics.Communicated by Ramaswamy H. Sarma.

Intersection of network medicine and machine learning towards investigating the key biomarkers and pathways underlying amyotrophic lateral sclerosis: a systematic review

BACKGROUND

Network medicine is an emerging area of research that focuses on delving into the molecular complexity of the disease, leading to the discovery of network biomarkers and therapeutic target discovery. Amyotrophic lateral sclerosis (ALS) is a complicated rare disease with unknown pathogenesis and no available treatment. In ALS, network properties appear to be potential biomarkers that can be beneficial in disease-related applications when explored independently or in tandem with machine learning (ML) techniques.

OBJECTIVE

This systematic literature review explores recent trends in network medicine and implementations of network-based ML algorithms in ALS. We aim to provide an overview of the identified primary studies and gather details on identifying the potential biomarkers and delineated pathways.

METHODS

The current study consists of searching for and investigating primary studies from PubMed and Dimensions.ai, published between 2018 and 2022 that reported network medicine perspectives and the coupling of ML techniques. Each abstract and full-text study was individually evaluated, and the relevant studies were finally included in the review for discussion once they met the inclusion and exclusion criteria.

RESULTS

We identified 109 eligible publications from primary studies representing this systematic review. The data coalesced into two themes: application of network science to identify disease modules and promising biomarkers in ALS, along with network-based ML approaches. Conclusion This systematic review gives an overview of the network medicine approaches and implementations of network-based ML algorithms in ALS to determine new disease genes, and identify critical pathways and therapeutic target discovery for personalized treatment.

Tetrabromobisphenol A effects on differentiating mouse embryonic stem cells reveals unexpected impact on immune system

Tetrabromobisphenol A (TBBPA) is a potent flame retardant used in numerous appliances and a major pollutant in households and ecosystems. In vertebrates, it was shown to affect neurodevelopment, the hypothalamic-pituitary-gonadal axis and thyroid signaling, but its toxicity and modes of actions are still a matter of debate. The molecular phenotype resulting from exposure to TBBPA is only poorly described, especially at the level of transcriptome reprogramming, which further limits our understanding of its molecular toxicity. In this work, we combined functional genomics and system biology to provide a system-wide description of the transcriptomic alterations induced by TBBPA acting on differentiating mESCs, and provide potential new toxicity markers. We found that TBBPA-induced transcriptome reprogramming affect a large collection of genes loosely connected within the network of biological pathways, indicating widespread interferences on biological processes. We also found two hotspots of action: at the level of neuronal differentiation markers, and surprisingly, at the level of immune system functions, which has been largely overlooked until now. This effect is particularly strong, as terminal differentiation markers of both myeloid and lymphoid lineages are strongly reduced: the membrane T cell receptor (Cd79a, Cd79b), interleukin seven receptor (Il7r), macrophages cytokine receptor (Csf1r), monocyte chemokine receptor (Ccr2). Also, the high affinity IgE receptor (Fcer1g), a key mediator of allergic reactions, is strongly induced. Thus, the molecular imbalance induce by TBBPA may be stronger than initially realized.

Molecular network strategy in multi-omics and mass spectrometry imaging

Human physiological activities and pathological changes arise from the coordinated interactions of multiple molecules. Mass spectrometry (MS)-based multi-omics and MS imaging (MSI)-based spatial omics are powerful methods used to investigate molecular information related to the phenotype of interest from homogenated or sliced samples, including the qualitative, relative quantitative and spatial distributions. Molecular network strategy provides efficient methods to help us understand and mine the biological patterns behind the phenotypic data. It illustrates and combines various relationships between molecules, and further performs the molecule identification and biological interpretation. Here, we describe the recent advances of network-based analysis and its applications for different biological processes, such as, obesity, central nervous system diseases, and environmental toxicology.

RIDAB: Electronic medical record-integrated real world data platform for predicting and summarizing interactions in biomedical research from heterogeneous data resources

BACKGROUND AND OBJECTIVE

With the advent of bioinformatics, biological databases have been constructed to computerize data. Biological systems can be described as interactions and relationships between elements constituting the systems, and they are organized in various biomedical open databases. These open databases have been used in approaches to predict functional interactions such as protein-protein interactions (PPI), drug-drug interactions (DDI) and disease-disease relationships (DDR). However, just combining interaction data has limited effectiveness in predicting the complex relationships occurring in a whole context. Each contributing source contains information on each element in a specific field of knowledge but there is a lack of inter-disciplinary insight in combining them.

METHODS

In this study, we propose the RWD Integrated platform for Discovering Associations in Biomedical research (RIDAB) to predict interactions between biomedical entities. RIDAB is established as a graph network to construct a platform that predicts the interactions of target entities. Biomedical open database is combined with EMRs each representing a biomedical network and a real-world data. To integrate databases from different domains to build the platform, mapping of the vocabularies was required. In addition, the appropriate structure of the network and the graph embedding method to be used were needed to be selected to fit the tasks.

RESULTS

The feasibility of the platform was evaluated using node similarity and link prediction for drug repositioning task, a commonly used task for biomedical network. In addition, we compared the US Food and Drug Administration (FDA)-approved repositioned drugs with the predicted result. By integrating EMR database with biomedical networks, the platform showed increased f1 score in predicting repositioned drugs, from 45.62% to 57.26%, compared to platforms based on biomedical networks alone.

CONCLUSIONS

This study demonstrates that the elements of biomedical research findings can be reflected by integrating EMR data with open-source biomedical networks. In addition, showed the feasibility of using the established platform to represent the integration of biomedical networks and reflected the relationship between real world networks.

Leaf trait network architecture shifts with species-richness and climate across forests at continental scale

Variation in the architecture of trait networks among ecosystems has been rarely quantified, but can provide high resolution of the contrasting adaptation of the whole phenotype. We constructed leaf trait networks (LTNs) from 35 structural, anatomical and compositional leaf traits for 394 tree species in nine forests from tropical to cold-temperate zones in China. Our analyses supported the hypothesis that LTNs would increase in modular complexity across forests in parallel with species-richness and climatic warmth and moisture, due to reduced phenotypic constraints and greater opportunities for niche differentiation. Additionally, we found that within LTNs, leaf economics traits including leaf thickness would have central importance, acting as hub traits with high connectivity due to their contributions to multiple functions. Across the continent, the greater species richness and trait diversity observed in forests under resource-rich climates enable greater complexity in whole phenotype structure and function as indicated by the trait network architecture.

Identification of dynamic driver sets controlling phenotypical landscapes

Controlling phenotypical landscapes is of vital interest to modern biology. This task becomes highly demanding because cellular decisions involve complex networks engaging in crosstalk interactions. Previous work on control theory indicates that small sets of compounds can control single phenotypes. However, a dynamic approach is missing to determine the drivers of the whole network dynamics. By analyzing 35 biologically motivated Boolean networks, we developed a method to identify small sets of compounds sufficient to decide on the entire phenotypical landscape. These compounds do not strictly prefer highly related compounds and show a smaller impact on the stability of the attractor landscape. The dynamic driver sets include many intervention targets and cellular reprogramming drivers in human networks. Finally, by using a new comprehensive model of colorectal cancer, we provide a complete workflow on how to implement our approach to shift from in silico to in vitro guided experiments.

Computational Network Inference for Bacterial Interactomics

Since the large-scale experimental characterization of protein–protein interactions (PPIs) is not possible for all species, several computational PPI prediction methods have been developed that harness existing data from other species. While PPI network prediction has been extensively used in eukaryotes, microbial network inference has lagged behind. However, bacterial interactomes can be built using the same principles and techniques; in fact, several methods are better suited to bacterial genomes. These predicted networks allow systems-level analyses in species that lack experimental interaction data. This review describes the current network inference and analysis techniques and summarizes the use of computationally-predicted microbial interactomes to date.

Computational analyses of mechanism of action (MoA): data, methods and integration

The elucidation of a compound's Mechanism of Action (MoA) is a challenging task in the drug discovery process, but it is important in order to rationalise phenotypic findings and to anticipate potential side-effects. Bioinformatic approaches, advances in machine learning techniques and the increasing deposition of high-throughput data in public databases have significantly contributed to recent advances in the field, but it is not straightforward to decide which data and methods are most suitable to use in a given case. In this review, we focus on these methods and data and their applications in generating MoA hypotheses for subsequent experimental validation. We discuss compound-specific data such as -omics, cell morphology and bioactivity data, as well as commonly used supplementary prior knowledge such as network and pathway data, and provide information on databases where this data can be accessed. In terms of methodologies, we discuss both well-established methods (connectivity mapping, pathway enrichment) as well as more developing methods (neural networks and multi-omics integration). Finally, we review case studies where the MoA of a compound was successfully suggested from computational analysis by incorporating multiple data modalities and/or methodologies. Our aim for this review is to provide researchers with insights into the benefits and drawbacks of both the data and methods in terms of level of understanding, biases and interpretation - and to highlight future avenues of investigation which we foresee will improve the field of MoA elucidation, including greater public access to -omics data and methodologies which are capable of data integration.

Construction and contextualization approaches for protein-protein interaction networks

Protein-protein interaction network (PPIN) analysis is a widely used method to study the contextual role of proteins of interest, to predict novel disease genes, disease or functional modules, and to identify novel drug targets. PPIN-based analysis uses both generic and context-specific networks. Multiple contextualization methodologies have been described, such as shortest-path algorithms, neighborhood-based methods, and diffusion/propagation algorithms. This review discusses these methods, provides intuitive representations of PPIN contextualization, and also examines how the quality of such context-specific networks could be improved by considering additional sources of evidence. As a heuristic, we observe that tasks such as identifying disease genes, drug targets, and protein complexes should consider local neighborhoods, while uncovering disease mechanisms and discovering disease-pathways would gain from diffusion-based construction.

Interpretation of network-based integration from multi-omics longitudinal data

Multi-omics integration is key to fully understand complex biological processes in an holistic manner. Furthermore, multi-omics combined with new longitudinal experimental design can unreveal dynamic relationships between omics layers and identify key players or interactions in system development or complex phenotypes. However, integration methods have to address various experimental designs and do not guarantee interpretable biological results. The new challenge of multi-omics integration is to solve interpretation and unlock the hidden knowledge within the multi-omics data. In this paper, we go beyond integration and propose a generic approach to face the interpretation problem. From multi-omics longitudinal data, this approach builds and explores hybrid multi-omics networks composed of both inferred and known relationships within and between omics layers. With smart node labelling and propagation analysis, this approach predicts regulation mechanisms and multi-omics functional modules. We applied the method on 3 case studies with various multi-omics designs and identified new multi-layer interactions involved in key biological functions that could not be revealed with single omics analysis. Moreover, we highlighted interplay in the kinetics that could help identify novel biological mechanisms. This method is available as an R package netOmics to readily suit any application.

Noncoding loci without epigenomic signals can be essential for maintaining global chromatin organization and cell viability

Most noncoding regions of the human genome do not harbor any annotated element and are even not marked with any epigenomic or protein binding signal. However, an overlooked aspect of their possible role in stabilizing 3D chromatin organization has not been extensively studied. To illuminate their structural importance, we started with the noncoding regions forming many 3D contacts (referred to as hubs) and performed a CRISPR library screening to identify dozens of hubs essential for cell viability. Hi-C and single-cell transcriptomic analyses showed that their deletion could significantly alter chromatin organization and affect the expressions of distal genes. This study revealed the 3D structural importance of noncoding loci that are not associated with any functional element, providing a previously unknown mechanistic understanding of disease-associated genetic variations (GVs). Furthermore, our analyses also suggest a possible approach to develop therapeutics targeting disease-specific noncoding regions that are critical for disease cell survival.

A Graph Feature Auto-Encoder for the prediction of unobserved node features on biological networks

BACKGROUND

Molecular interaction networks summarize complex biological processes as graphs, whose structure is informative of biological function at multiple scales. Simultaneously, omics technologies measure the variation or activity of genes, proteins, or metabolites across individuals or experimental conditions. Integrating the complementary viewpoints of biological networks and omics data is an important task in bioinformatics, but existing methods treat networks as discrete structures, which are intrinsically difficult to integrate with continuous node features or activity measures. Graph neural networks map graph nodes into a low-dimensional vector space representation, and can be trained to preserve both the local graph structure and the similarity between node features.

RESULTS

We studied the representation of transcriptional, protein-protein and genetic interaction networks in E. coli and mouse using graph neural networks. We found that such representations explain a large proportion of variation in gene expression data, and that using gene expression data as node features improves the reconstruction of the graph from the embedding. We further proposed a new end-to-end Graph Feature Auto-Encoder framework for the prediction of node features utilizing the structure of the gene networks, which is trained on the feature prediction task, and showed that it performs better at predicting unobserved node features than regular MultiLayer Perceptrons. When applied to the problem of imputing missing data in single-cell RNAseq data, the Graph Feature Auto-Encoder utilizing our new graph convolution layer called FeatGraphConv outperformed a state-of-the-art imputation method that does not use protein interaction information, showing the benefit of integrating biological networks and omics data with our proposed approach.

CONCLUSION

Our proposed Graph Feature Auto-Encoder framework is a powerful approach for integrating and exploiting the close relation between molecular interaction networks and functional genomics data.

Alveolar Regeneration in COVID-19 Patients: A Network Perspective

A viral infection involves entry and replication of viral nucleic acid in a host organism, subsequently leading to biochemical and structural alterations in the host cell. In the case of SARS-CoV-2 viral infection, over-activation of the host immune system may lead to lung damage. Albeit the regeneration and fibrotic repair processes being the two protective host responses, prolonged injury may lead to excessive fibrosis, a pathological state that can result in lung collapse. In this review, we discuss regeneration and fibrosis processes in response to SARS-CoV-2 and provide our viewpoint on the triggering of alveolar regeneration in coronavirus disease 2019 (COVID-19) patients.

Heterogeneous information network and its application to human health and disease

The molecular components with the functional interdependencies in human cell form complicated biological network. Diseases are mostly caused by the perturbations of the composite of the interaction multi-biomolecules, rather than an abnormality of a single biomolecule. Furthermore, new biological functions and processes could be revealed by discovering novel biological entity relationships. Hence, more and more biologists focus on studying the complex biological system instead of the individual biological components. The emergence of heterogeneous information network (HIN) offers a promising way to systematically explore complicated and heterogeneous relationships between various molecules for apparently distinct phenotypes. In this review, we first present the basic definition of HIN and the biological system considered as a complex HIN. Then, we discuss the topological properties of HIN and how these can be applied to detect network motif and functional module. Afterwards, methodologies of discovering relationships between disease and biomolecule are presented. Useful insights on how HIN aids in drug development and explores human interactome are provided. Finally, we analyze the challenges and opportunities for uncovering combinatorial patterns among pharmacogenomics and cell-type detection based on single-cell genomic data.

Transcriptome and Methylome Analysis Reveal Complex Cross-Talks between Thyroid Hormone and Glucocorticoid Signaling at Xenopus Metamorphosis

Background: Most work in endocrinology focus on the action of a single hormone, and very little on the cross-talks between two hormones. Here we characterize the nature of interactions between thyroid hormone and glucocorticoid signaling during Xenopus tropicalis metamorphosis. Methods: We used functional genomics to derive genome wide profiles of methylated DNA and measured changes of gene expression after hormonal treatments of a highly responsive tissue, tailfin. Clustering classified the data into four types of biological responses, and biological networks were modeled by system biology. Results: We found that gene expression is mostly regulated by either T₃ or CORT, or their additive effect when they both regulate the same genes. A small but non-negligible fraction of genes (12%) displayed non-trivial regulations indicative of complex interactions between the signaling pathways. Strikingly, DNA methylation changes display the opposite and are dominated by cross-talks. Conclusion: Cross-talks between thyroid hormones and glucocorticoids are more complex than initially envisioned and are not limited to the simple addition of their individual effects, a statement that can be summarized with the pseudo-equation: TH ∙ GC > TH + GC. DNA methylation changes are highly dynamic and buffered from genome expression.

Analysis of aging-related protein interactome and cross-network module comparisons across tissues provide new insights into aging

Delaying the human aging process and thus eliminating the risk factors for age-related diseases is one of the prime objectives. While various aging-associated genes and proteins have been characterized, which provide a significant understanding of the human aging process, a significant success in regulating aging is not achieved yet. Understanding how aging proteins interact with each other and also with other proteins could provide important insights into the underlying mechanisms governing the aging process. Therefore, in this work, information of gene expression was included to the static aging-related protein interactome to understand the network-based relationships among aging-related essential (AE) proteins, aging-related non-essential (ANE) proteins, and housekeeping-proteins that could regulate or influence aging. Comprehensive analyses provided various systems-level insights into the regulatory characteristics of aging; for example, (i) network-based correlation analysis predicted functional relationships among AE proteins and ANE proteins; (ii) network variability analysis predicted aging to affect different tissues in strikingly different ways by differentially regulating various regulatory interactions; (iii) cross-network comparisons identified two aging-related modules to be significantly conserved across most of the tissues. Overall, the findings obtained during this study could be helpful for researchers to delay, prevent, or even reverse various aspects of the aging.

Proteomic analysis reveals differential responses of Listeria monocytogenes to free and nanoencapsulated nisin

The ability of Listeria monocytogenes grow on ready-to-eat food is a major concern in food safety. Natural antimicrobials, such as nisin, can be used to control this pathogen, but the increasing reports of nisin tolerance and resistance make necessary novel approaches to increase its effectiveness, such as encapsulation. The goal of this study was to investigate how L. monocytogenes ATCC7644 regulates and shapes its proteome in response to sublethal doses of nisin and nisin-loaded phosphatidylcholine liposomes (lipo-nisin), compared to untreated cells growing under optimal conditions. Total proteins were extracted from L. monocytogenes cells treated for 1 h with free and lipo-nisin. As result, of 803 proteins that were initially identified, 64 and 53 proteins were differentially upregulated and downregulated respectively, in the treatments with nisin and lipo-nisin. Changes of Listeria proteome in response to treatments containing nisin were mainly related to ATP-binding cassette (ABC) transporter systems, transmembrane proteins, RNA-binding proteins and diverse stress response proteins. Some of the proteins uniquely detected in samples treated with free nisin were the membrane proteins SecD, Lmo1539 and the YfhO enzyme, which are related to translocation of L. monocytogenes virulence factors, activation of the LiaR-mediated stress defense and glycosylation of wall teichoic acid, respectively. The L. monocytogenes treated with liposome encapsulated nisin showed no expression of some stress response factors as compared with the free nisin, suggesting a reduction of stress mediated response and production of nisin-resistance factors by exposure to encapsulated nisin.

Immune gene expression networks in sepsis: A network biology approach

To study the dysregulated host immune response to infection in sepsis, gene expression profiles from the Gene Expression Omnibus (GEO) datasets GSE54514, GSE57065, GSE64456, GSE95233, GSE66099 and GSE72829 were selected. From the Kyoto Encyclopedia of Genes and Genomes (KEGG) immune system pathways, 998 unique genes were selected, and genes were classified as follows based on gene annotation from KEGG, Gene Ontology, and Reactome: adaptive immunity, antigen presentation, cytokines and chemokines, complement, hematopoiesis, innate immunity, leukocyte migration, NK cell activity, platelet activity, and signaling. After correlation matrix formation, correlation coefficient of 0.8 was selected for network generation and network analysis. Total transcriptome was analyzed for differentially expressed genes (DEG), followed by gene set enrichment analysis. The network topological structure revealed that adaptive immunity tended to form a prominent and isolated cluster in sepsis. Common genes within the cluster from the 6 datasets included CD247, CD8A, ITK, LAT, and LCK. The clustering coefficient and modularity parameters were increased in 5/6 and 4/6 datasets in the sepsis group that seemed to be associated with functional aspect of the network. GSE95233 revealed that the nonsurvivor group showed a prominent and isolated adaptive immunity cluster, whereas the survivor group had isolated complement-coagulation and platelet-related clusters. T cell receptor signaling (TCR) pathway and antigen processing and presentation pathway were down-regulated in 5/6 and 4/6 datasets, respectively. Complement and coagulation, Fc gamma, epsilon related signaling pathways were up-regulated in 5/6 datasets. Altogether, network and gene set enrichment analysis showed that adaptive-immunity-related genes along with TCR pathway were down-regulated and isolated from immune the network that seemed to be associated with unfavorable prognosis. Prominence of platelet and complement-coagulation-related genes in the immune network was associated with survival in sepsis. Complement-coagulation pathway was up-regulated in the sepsis group that was associated with favorable prognosis. Network and gene set enrichment analysis supported elucidation of sepsis pathogenesis.

ANCA: Alignment-Based Network Construction Algorithm

Dynamic biological networks model changes in the network topology over time. However, often the topologies of these networks are not available at specific time points. Existing algorithms for studying dynamic networks often ignore this problem and focus only on the time points at which experimental data is available. In this paper, we develop a novel alignment based network construction algorithm, ANCA, that constructs the dynamic networks at the missing time points by exploiting the information from a reference dynamic network. Our experiments on synthetic and real networks demonstrate that ANCA predicts the missing target networks accurately, and scales to large-scale biological networks in practical time. Our analysis of an E. coli protein-protein interaction network shows that ANCA successfully identifies key temporal changes in the biological networks. Our analysis also suggests that by focusing on the topological differences in the network, our method can be used to find important genes and temporal functional changes in the biological networks.

Early Drug Development and Evaluation of Putative Antitubercular Compounds in the -Omics Era

Tuberculosis (TB) is an infectious disease caused by the bacterium Mycobacterium tuberculosis. According to the WHO, the disease is one of the top 10 causes of death of people worldwide. Mycobacterium tuberculosis is an intracellular pathogen with an unusually thick, waxy cell wall and a complex life cycle. These factors, combined with M. tuberculosis ability to enter prolonged periods of latency, make the bacterium very difficult to eradicate. The standard treatment of TB requires 6-20months, depending on the drug susceptibility of the infecting strain. The need to take cocktails of antibiotics to treat tuberculosis effectively and the emergence of drug-resistant strains prompts the need to search for new antitubercular compounds. This review provides a perspective on how modern -omic technologies facilitate the drug discovery process for tuberculosis treatment. We discuss how methods of DNA and RNA sequencing, proteomics, and genetic manipulation of organisms increase our understanding of mechanisms of action of antibiotics and allow the evaluation of drugs. We explore the utility of mathematical modeling and modern computational analysis for the drug discovery process. Finally, we summarize how -omic technologies contribute to our understanding of the emergence of drug resistance.

SPONGEdb: a pan-cancer resource for competing endogenous RNA interactions

microRNAs (miRNAs) are post-transcriptional regulators involved in many biological processes and human diseases, including cancer. The majority of transcripts compete over a limited pool of miRNAs, giving rise to a complex network of competing endogenous RNA (ceRNA) interactions. Currently, gene-regulatory networks focus mostly on transcription factor-mediated regulation, and dedicated efforts for charting ceRNA regulatory networks are scarce. Recently, it became possible to infer ceRNA interactions genome-wide from matched gene and miRNA expression data. Here, we inferred ceRNA regulatory networks for 22 cancer types and a pan-cancer ceRNA network based on data from The Cancer Genome Atlas. To make these networks accessible to the biomedical community, we present SPONGEdb, a database offering a user-friendly web interface to browse and visualize ceRNA interactions and an application programming interface accessible by accompanying R and Python packages. SPONGEdb allows researchers to identify potent ceRNA regulators via network centrality measures and to assess their potential as cancer biomarkers through survival, cancer hallmark and gene set enrichment analysis. In summary, SPONGEdb is a feature-rich web resource supporting the community in studying ceRNA regulation within and across cancer types.

Using Interactome Big Data to Crack Genetic Mysteries and Enhance Future Crop Breeding

The functional genes underlying phenotypic variation and their interactions represent "genetic mysteries". Understanding and utilizing these genetic mysteries are key solutions for mitigating the current threats to agriculture posed by population growth and individual food preferences. Due to advances in high-throughput multi-omics technologies, we are stepping into an Interactome Big Data era that is certain to revolutionize genetic research. In this article, we provide a brief overview of current strategies to explore genetic mysteries. We then introduce the methods for constructing and analyzing the Interactome Big Data and summarize currently available interactome resources. Next, we discuss how Interactome Big Data can be used as a versatile tool to dissect genetic mysteries. We propose an integrated strategy that could revolutionize genetic research by combining Interactome Big Data with machine learning, which involves mining information hidden in Big Data to identify the genetic models or networks that control various traits, and also provide a detailed procedure for systematic dissection of genetic mysteries,. Finally, we discuss three promising future breeding strategies utilizing the Interactome Big Data to improve crop yields and quality.

System-Wide Pollution of Biomedical Data: Consequence of the Search for Hub Genes of Hepatocellular Carcinoma Without Spatiotemporal Consideration

Biomedical institutions rely on data evaluation and are turning into data factories. Big-data storage centers, supercomputing systems, and increased algorithmic efficiency allow us to analyze the ever-increasing amount of data generated every day in biomedical research centers. In network science, the principal intrinsic problem is how to integrate the data and information from different experiments on genes or proteins. Data curation is an essential process in annotating new functional data to known genes or proteins, undertaken by a biobank curator, which is then reflected in the calculated networks. We provide an example of how protein-protein networks today have space-time limits. The next step is the integration of data and information from different biobanks. Omics data and networks are essential parts of this step but also have flawed protocols and errors. Consider data from patients with cancer: from biopsy procedures to experimental tests, to archiving methods and computational algorithms, these are continuously handled so require critical and continuous "updates" to obtain reproducible, reliable, and correct results. We show, as a second example, how all this distorts studies in cellular hepatocellular carcinoma. It is not unlikely that these flawed data have been polluting biobanks for some time before stringent conditions for the veracity of data were implemented in Big data. Therefore, all this could contribute to errors in future medical decisions.

On Biases Of Attention In Scientific Discovery

How do nuances of scientists' attention influence what they discover? We pursue an understanding of the influences of patterns of attention on discovery with a case study about confirmations of protein-protein interactions over time. We find that modeling and accounting for attention can help us to recognize and interpret biases in large-scale and widely used databases of confirmed interactions and to better understand missing data and unknowns. Additionally, we present an analysis of how awareness of patterns of attention and use of debiasing techniques can foster earlier discoveries.

Dynamic organization of intracellular organelle networks

Intracellular organelles are membrane-bound and biochemically distinct compartments constructed to serve specialized functions in eukaryotic cells. Through extensive interactions, they form networks to coordinate and integrate their specialized functions for cell physiology. A fundamental property of these organelle networks is that they constantly undergo dynamic organization via membrane fusion and fission to remodel their internal connections and to mediate direct material exchange between compartments. The dynamic organization not only enables them to serve critical physiological functions adaptively but also differentiates them from many other biological networks such as gene regulatory networks and cell signaling networks. This review examines this fundamental property of the organelle networks from a systems point of view. The focus is exclusively on homotypic networks formed by mitochondria, lysosomes, endosomes, and the endoplasmic reticulum, respectively. First, key mechanisms that drive the dynamic organization of these networks are summarized. Then, several distinct organizational properties of these networks are highlighted. Next, spatial properties of the dynamic organization of these networks are emphasized, and their functional implications are examined. Finally, some representative molecular machineries that mediate the dynamic organization of these networks are surveyed. Overall, the dynamic organization of intracellular organelle networks is emerging as a fundamental and unifying paradigm in the internal organization of eukaryotic cells. This article is categorized under: Metabolic Diseases > Molecular and Cellular Physiology.

Effects of repetitive Iodine thyroid blocking on the foetal brain and thyroid in rats: a systems biology approach

A single administration of an iodine thyroid blocking agent is usually sufficient to protect thyroid from radioactive iodine and prevent thyroid cancer. Repeated administration of stable iodine (rKI) may be necessary during prolonged or repeated exposure to radioactive iodine. We previously showed that rKI for eight days offers protection without toxic effects in adult rats. However, the effect of rKI administration in the developing foetus is unknown, especially on brain development, although a correlation between impaired maternal thyroid status and a decrease in intelligence quotient of the progeny has been observed. This study revealed distinct gene expression profiles between the progeny of rats receiving either rKI or saline during pregnancy. To understand the implication of these differentially expressed (DE) genes, a systems biology approach was used to construct networks for each organ using three different techniques: Bayesian statistics, sPLS-DA and manual construction of a Process Descriptive (PD) network. The PD network showed DE genes from both organs participating in the same cellular processes that affect mitophagy and neuronal outgrowth. This work may help to evaluate the doctrine for using rKI in case of repetitive or prolonged exposure to radioactive particles upon nuclear accidents.

Stability Analysis of Biological Networks' Diffusion State

Computational knowledge acquired from noisy networks is not reliable and the network topology determines the reliability. Protein-protein interaction networks have uncertain topologies and noise that contain false positive and false negative edges at high rates. In this study, we analyze effects of the existing mutations in a network topology to the diffusion state of that network. To evaluate the sensitivity of the diffusion state, we derive the fitness measures based on the mathematically defined stability of a network. Searching for an influential set of edges in a network is a difficult problem. We handle the computational challenge by developing a novel metaheuristic optimization method and we find influential mutations time-efficiently. Our experiments, conducted on both synthetic and real networks from public databases, demonstrated that our method obtained better results than competitors for all types of network topologies. This is the first-time that the diffusion has been evaluated under topological mutations. Our analysis identifies significant biological results about the stability of biological - synthetic networks and diffusion state. In this manner, mutations in protein-protein interaction network topologies have a significant influence on the diffusion state of the network. Network stability is more affected by the network model than the network size.

Network Analyses of the Differential Expression of Heat Shock Proteins in Glioma

Heat shock protein (HSP) is a family of highly conserved protein, which exists widely in various organisms and has a variety of important physiological functions. Currently, there is no systematic analysis of HSPs in human glioma. The aim of this study was to investigate the characteristics of HSPs through constructing protein-protein interaction network (PPIN) considering the expression level of HSPs in glioma. After the identification of the differentially expressed HSPs in glioma tissues, a specific PPIN was constructed and found that there were many interactions between the differentially expressed HSPs in glioma. Subcellular localization analysis shows that HSPs and their interacting proteins distribute from the cell membrane to the nucleus in a multilayer structure. By functional enrichment analysis, gene ontology analysis, and Kyoto Encyclopedia of Genes and Genomes pathway analysis, the potential function of HSPs and two meaningful enrichment pathways was revealed. In addition, nine HSPs (DNAJA4, DNAJC6, DNAJC12, HSPA6, HSP90B1, DNAJB1, DNAJB6, DNAJC10, and SERPINH1) are prognostic markers for human brain glioma. These analyses provide a full view of HSPs about their expression, biological process, as well as clinical significance in glioma.