Capturing variation impact on molecular interactions in the IMEx Consortium mutations data set

Protein-protein interactions: Methods, databases, and applications in virus-host study

Almost all the cellular processes in a living system are controlled by proteins: They regulate gene expression, catalyze chemical reactions, transport small molecules across membranes, and transmit signal across membranes. Even, a viral infection is often initiated through virus-host protein interactions. Protein-protein interactions (PPIs) are the physical contacts between two or more proteins and they represent complex biological functions. Nowadays, PPIs have been used to construct PPI networks to study complex pathways for revealing the functions of unknown proteins. Scientists have used PPIs to find the molecular basis of certain diseases and also some potential drug targets. In this review, we will discuss how PPI networks are essential to understand the molecular basis of virus-host relationships and several databases which are dedicated to virus-host interaction studies. Here, we present a short but comprehensive review on PPIs, including the experimental and computational methods of finding PPIs, the databases dedicated to virus-host PPIs, and the associated various applications in protein interaction networks of some lethal viruses with their hosts.

Afterglow Implant for Arterial Embolization and Intraoperative Imaging

Transcatheter arterial embolization (TAE) is wildly used in clinical treatments. However, the online monitoring of the thrombosis formation is limited due to the challenges of the direct visualization of embolic agents and the real-time monitoring of dynamic blood flow. Thus, we developed a photochemical afterglow implant with strong afterglow intensity and a long lifetime for embolization and imaging. The liquid pre-implant injected into the abdominal aorta of mice was rapidly transformed into a hydrogel in situ to embolize the blood vessel. The vascular embolism position can be observed by the enhanced afterglow of the fixed implant, and the long lifetime of afterglow can also be used to monitor the effect of embolization. This provides an excellent candidate in bio-imaging to avoid the autofluorescence interference from continuous light excitation. The study suggests the potential usefulness of the implant as an embolic agent in TAE and artery imaging during a surgical procedure.

The IntAct database: efficient access to fine-grained molecular interaction data

The IntAct molecular interaction database (https://www.ebi.ac.uk/intact) is a curated resource of molecular interactions, derived from the scientific literature and from direct data depositions. As of August 2021, IntAct provides more than one million binary interactions, curated by twelve global partners of the International Molecular Exchange consortium, for which the IntAct database provides a shared curation and dissemination platform. The IMEx curation policy has always emphasised a fine-grained data and curation model, aiming to capture the relevant experimental detail essential for the interpretation of the provided molecular interaction data. Here, we present recent curation focus and progress, as well as a completely redeveloped website which presents IntAct data in a much more user-friendly and detailed way.

IID 2021: towards context-specific protein interaction analyses by increased coverage, enhanced annotation and enrichment analysis

Improved bioassays have significantly increased the rate of identifying new protein-protein interactions (PPIs), and the number of detected human PPIs has greatly exceeded early estimates of human interactome size. These new PPIs provide a more complete view of disease mechanisms but precise understanding of how PPIs affect phenotype remains a challenge. It requires knowledge of PPI context (e.g. tissues, subcellular localizations), and functional roles, especially within pathways and protein complexes. The previous IID release focused on PPI context, providing networks with comprehensive tissue, disease, cellular localization, and druggability annotations. The current update adds developmental stages to the available contexts, and provides a way of assigning context to PPIs that could not be previously annotated due to insufficient data or incompatibility with available context categories (e.g. interactions between membrane and cytoplasmic proteins). This update also annotates PPIs with conservation across species, directionality in pathways, membership in large complexes, interaction stability (i.e. stable or transient), and mutation effects. Enrichment analysis is now available for all annotations, and includes multiple options; for example, context annotations can be analyzed with respect to PPIs or network proteins. In addition to tabular view or download, IID provides online network visualization. This update is available at http://ophid.utoronto.ca/iid.

A protein interaction landscape of breast cancer

[Figure: see text].

DZIP1 Expression as a Prognostic Marker in Gastric Cancer: A Bioinformatics-Based Analysis

Purpose

Gastric cancer (GC) is a common type of cancer worldwide. It can relapse and metastasize even after standard treatment; therefore, it has a poor prognosis. Moreover, sensitive biomarkers for prognosis prediction in GC are lacking. In this study, using a bioinformatics approach, we aimed to examine the value of DAZ Interacting Protein 1 (DZIP1) as a prognostic predictor and therapeutic target in GC.

Methods

We explored the clinical relevance, function, and molecular role of DZIP1 in GC using MethSurv, cBioPortal, TIMER, Gene Expression Profiling Interactive Analysis, IMEx, ONCOMINE, MEXPRESS, and EWAS Atlas databases. The GSE118919 dataset was used to plot receiver operating characteristic curves. Using The Cancer Genome Atlas, we developed a Cox regression model and assessed the clinical significance of DZIPs. In addition, we used the "xCELL" algorithm to make reliable immune infiltration estimations. Western blot and immunohistochemistry were used to examine protein expression. The results were visualized with the 'ggplot2' and "circlize" packages.

Results

In GC patients, DZIP1 was over-expressed at both the mRNA and protein levels. High levels of DZIP1 were found to be associated with poor survival in patients with GC. Our results indicated that DZIP1 could be involved in multiple cancer-related pathways such as the PI3K-Akt signaling pathway, WNT signaling pathway, and RAS signaling pathway, and its expression was correlated with the infiltration of activated myeloid dendritic cells, naive CD4+ T cells, and naive CD8+ T cells. Furthermore, we found that mutations in DZIP1 were correlated with a good prognosis in GC patients. Finally, we demonstrated a correlation between hypomethylation of the DZIP1 gene promoter and a poor prognosis in GC.

Conclusion

This study is the first to demonstrate a significant correlation between high levels of DZIP1 and a poor prognosis in GC patients. Our results clarify multiple potential mechanisms that could contribute to this correlation and may thus provide novel insights into the clinical diagnosis and treatment of GC.

Detecting Rewiring Events in Protein-Protein Interaction Networks Based on Transcriptomic Data

Proteins rarely carry out their cellular functions in isolation. Instead, eukaryotic proteins engage in about six interactions with other proteins on average. The aggregated protein interactome of an organism forms a “hairy ball”-type protein-protein interaction (PPI) network. Yet, in a typical human cell, only about half of all proteins are expressed at a particular time. Hence, it has become common practice to prune the full PPI network to the subset of expressed proteins. If RNAseq data is available, one can further resolve the specific protein isoforms present in a cell or tissue. Here, we review various approaches, software tools and webservices that enable users to construct context-specific or tissue-specific PPI networks and how these are rewired between two cellular conditions. We illustrate their different functionalities on the example of the interactions involving the human TNR6 protein. In an outlook, we describe how PPI networks may be integrated with epigenetic data or with data on the activity of splicing factors.

Precise incorporation of transition metals into organolead oxyhalide crystalline materials for photocatalysis

Organolead halide crystalline materials are an emerging class of high-performance photocatalysts. However, limited studies have been performed to tune their photoactive properties by precise introduction of transition metals. Herein, we report the successful incorporation of four different transition metal centers (Mn²⁺, Co²⁺, Ni²⁺ and Zn²⁺) into a lead oxyhalide crystalline matrix via isoreticular synthesis. Importantly, the precise control of the incoming transition metal positions has been achieved by its octahedral coordination with three organic ligands. Among them, the Zn²⁺-incorporated material exhibits the highest catalytic activity and recyclable activity in benzylamine oxidation under UV light, which is probably ascribed to the long carrier lifetime and efficient carrier transfer.

The Sustainability of Energy Conversion Inhibition for Tumor Ferroptosis Therapy and Chemotherapy

The hyperactive energy metabolism mostly contributes the tumor cells growth and proliferation. Herein, the intelligent nanoparticles (P-B-D NPs) obtained by loading BAY-876 and doxorubicin (Dox)-Duplex into nanoparticles composed of disulfide bond (SS) containing polymer are reported, which provide an efficient resistance of tumor cells energy metabolism and tumor growth to conquer malignant tumor. In response to the reducing microenvironment of tumor tissue, the SS bond can be disintegrated by intracellular glutathione to block the synthesis of lipid repair enzyme-glutathione peroxidase 4 for ferroptosis therapy. More importantly, the released BAY-876 can inhibit the functionality of glucose transporter 1, restricting the glucose uptake of tumor cells to a low energy metabolism status. Meanwhile, Dox-Duplex can interact with ATP to reduce intracellular ATP content and release Dox to kill tumor cells. Collectively, this work offers a new idea for restricting tumor cells energy metabolism to inhibit their proliferation.

Hepatitis D virus in 2021: virology, immunology and new treatment approaches for a difficult-to-treat disease

Approximately 5% of individuals infected with hepatitis B virus (HBV) are coinfected with hepatitis D virus (HDV). Chronic HBV/HDV coinfection is associated with an unfavourable outcome, with many patients developing liver cirrhosis, liver failure and eventually hepatocellular carcinoma within 5-10 years. The identification of the HBV/HDV receptor and the development of novel in vitro and animal infection models allowed a more detailed study of the HDV life cycle in recent years, facilitating the development of specific antiviral drugs. The characterisation of HDV-specific CD4+ and CD8+T cell epitopes in untreated and treated patients also permitted a more precise understanding of HDV immunobiology and possibly paves the way for immunotherapeutic strategies to support upcoming specific therapies targeting viral or host factors. Pegylated interferon-α has been used for treating HDV patients for the last 30 years with only limited sustained responses. Here we describe novel treatment options with regard to their mode of action and their clinical effectiveness. Of those, the entry-inhibitor bulevirtide (formerly known as myrcludex B) received conditional marketing authorisation in the European Union (EU) in 2020 (Hepcludex). One additional drug, the prenylation inhibitor lonafarnib, is currently under investigation in phase III clinical trials. Other treatment strategies aim at targeting hepatitis B surface antigen, including the nucleic acid polymer REP2139Ca. These recent advances in HDV virology, immunology and treatment are important steps to make HDV a less difficult-to-treat virus and will be discussed.

High-Performance Ammonium Cobalt Phosphate Nanosheet Electrocatalyst for Alkaline Saline Water Oxidation

The development of highly efficient electrocatalysts toward the oxygen evolution reaction is imperative for advancing water splitting technology to generate clean hydrogen energy. Herein, a two dimensional (2D) nanosheet ammonium cobalt phosphate hydrate (NH4CoPO4·H2O) catalyst based on the earth-abundant non-noble metal is reported. When used for the challenging alkaline saline water electrolysis, the NH4CoPO4·H2O catalyst with the optimal thickness of 30 nm achieves current densities of 10 and 100 mA cm-2 at the record low overpotentials of 252 and 268 mV, respectively, while maintaining remarkable stability during the alkaline saline water oxidation at room temperature. X-ray absorption fine spectra reveal that the activation of Co (II) ions (in NH4CoPO4·H2O) to Co (III) species constructs the electrocatalytic active sites. The 2D nanosheet morphology of NH4CoPO4·H2O provides a larger active surface area and more surface-exposed active sites, which enable the nanosheet catalyst to facilitate the alkaline freshwater and simulated seawater oxidation with excellent activity. The facile and environmentally-benign H2O-mediated synthesis route under mild condition makes NH4CoPO4·H2O catalyst highly feasible for practical manufacturing. In comparison with noble metals, this novel electrocatalyst offers a cost-effective alternative for economic saline water oxidation to advance water electrolysis technology.

Analysing the yeast complexome-the Complex Portal rising to the challenge

The EMBL-EBI Complex Portal is a knowledgebase of macromolecular complexes providing persistent stable identifiers. Entries are linked to literature evidence and provide details of complex membership, function, structure and complex-specific Gene Ontology annotations. Data are freely available and downloadable in HUPO-PSI community standards and missing entries can be requested for curation. In collaboration with Saccharomyces Genome Database and UniProt, the yeast complexome, a compendium of all known heteromeric assemblies from the model organism Saccharomyces cerevisiae, was curated. This expansion of knowledge and scope has led to a 50% increase in curated complexes compared to the previously published dataset, CYC2008. The yeast complexome is used as a reference resource for the analysis of complexes from large-scale experiments. Our analysis showed that genes coding for proteins in complexes tend to have more genetic interactions, are co-expressed with more genes, are more multifunctional, localize more often in the nucleus, and are more often involved in nucleic acid-related metabolic processes and processes where large machineries are the predominant functional drivers. A comparison to genetic interactions showed that about 40% of expanded co-complex pairs also have genetic interactions, suggesting strong functional links between complex members.

Natural Products, the Fourth Industrial Revolution, and the Quintuple Helix

The profound interconnectedness of the sciences and technologies embodied in the Fourth Industrial Revolution is discussed in terms of the global role of natural products, and how that interplays with the development of sustainable and climate-conscious practices of cyberecoethnopharmacolomics within the Quintuple Helix for the promotion of a healthier planet and society.

m6A modification patterns and tumor immune landscape in clear cell renal carcinoma

Background

Recent studies have focused on the correlation between N6-methyladenosine (m⁶A) modification and specific tumor-infiltrating immune cells. However, the potential roles of m⁶A modification in the tumor immune landscape remain elusive.

Methods

We comprehensively evaluated the m⁶A modification patterns and tumor immune landscape of 513 clear cell renal cell carcinoma (ccRCC) patients, and correlated the m⁶A modification patterns with the immune landscape. The m6Ascore was established using principal component analysis. Multivariate Cox regression analysis was performed to evaluate the prognostic value of the m6Ascore.

Results

We identified three m6Aclusters—characterized by differences in Th17 signature, extent of intratumor heterogeneity, overall cell proliferation, aneuploidy, expression of immunomodulatory genes, overall somatic copy number alterations, and prognosis. The m6Ascore was established to quantify the m⁶A modification pattern of individual ccRCC patients. Further analyses revealed that the m6Ascore was an independent prognostic factor of ccRCC. Finally, we verified the prognostic value of the m6Ascore in the programmed cell death protein 1 (PD-1) blockade therapy of patients with advanced ccRCC.

Conclusions

This study demonstrated the correlation between m⁶A modification and the tumor immune landscape in ccRCC. The comprehensive evaluation of m⁶A modification patterns in individual ccRCC patients enhances our understanding of the tumor immune landscape and provides a new approach toward new and improved immunotherapeutic strategies for ccRCC patients.

Light-Triggered Transformable Ferrous Ion Delivery System for Photothermal Primed Chemodynamic Therapy

Chemodynamic therapy (CDT) involves the catalytic generation of highly toxic hydroxyl radicals (^. OH) from hydrogen peroxide (H₂ O₂ ) through metal-ion-mediated Fenton or Fenton-like reactions. Fe²⁺ is a classical catalyst ion, however, it suffers easy oxidation and systemic side-effects. Therefore, the development of a controllable Fe²⁺ delivery system is a challenge to maintain its valence state, reduce toxicity, and improve therapeutic efficacy. Reported here is a near-infrared (NIR) light-triggered Fe²⁺ delivery agent (LET-6) for fluorescence (FL) and photoacoustic (PA) dual-modality imaging guided, photothermal primed CDT. Thermal expansion caused by 808 nm laser irradiation triggers the transformation of LET-6 to expose Fe²⁺ from its hydrophobic layer, which primes the catalytic breakdown of endogenous H₂ O₂ within the tumor microenvironment, thus generating ^. OH for enhanced CDT. LET-6 shows remarkable therapeutic effects, both in vitro and in vivo, achieving 100 % tumor elimination after just one treatment. This high-performance Fe²⁺ delivery system provides a sound basis for future synergistic metal-ion-mediated cancer therapy.

Soft Robots for Ocean Exploration and Offshore Operations: A Perspective

The ocean and human activities related to the sea are under increasing pressure due to climate change, widespread pollution, and growth of the offshore energy sector. Data, in under-sampled regions of the ocean and in the offshore patches where the industrial expansion is taking place, are fundamental to manage successfully a sustainable development and to mitigate climate change. Existing technology cannot cope with the vast and harsh environments that need monitoring and sampling the most. The limiting factors are, among others, the spatial scales of the physical domain, the high pressure, and the strong hydrodynamic perturbations, which require vehicles with a combination of persistent autonomy, augmented efficiency, extreme robustness, and advanced control. In light of the most recent developments in soft robotics technologies, we propose that the use of soft robots may aid in addressing the challenges posed by abyssal and wave-dominated environments. Nevertheless, soft robots also allow for fast and low-cost manufacturing, presenting a new potential problem: marine pollution from ubiquitous soft sampling devices. In this study, the technological and scientific gaps are widely discussed, as they represent the driving factors for the development of soft robotics. Offshore industry supports increasing energy demand and the employment of robots on marine assets is growing. Such expansion needs to be sustained by the knowledge of the oceanic environment, where large remote areas are yet to be explored and adequately sampled. We offer our perspective on the development of sustainable soft systems, indicating the characteristics of the existing soft robots that promote underwater maneuverability, locomotion, and sampling. This perspective encourages an interdisciplinary approach to the design of aquatic soft robots and invites a discussion about the industrial and oceanographic needs that call for their application.

Metal-Organic Framework-Encapsulated CoCu Nanoparticles for the Selective Transfer Hydrogenation of Nitrobenzaldehydes: Engineering Active Armor by the Half-Way Injection Method

A novel armor-type composite of metal-organic framework (MOF)-encapsulated CoCu nanoparticles with a Fe₃ O₄ core (Fe₃ O₄ @SiO₂ -NH₂ -CoCu@UiO-66) has been designed and synthesized by the half-way injection method, which successfully serves as an efficient and recyclable catalyst for the selective transfer hydrogenation. In this half-way injection approach, the pre-synthetic Fe₃ O₄ @SiO₂ -NH₂ -CoCu was injected into the UiO-66 precursor solution halfway through the MOF budding period. The formed MOF armor could play a role of providing significant additional catalytic sites besides CoCu nanoparticles, protecting CoCu nanoparticles, and improving the catalyst stability, thus facilitating the selective transfer hydrogenation of nitrobenzaldehydes into corresponding nitrobenzyl alcohols in high selectivity (99 %) and conversion (99 %) rather than nitro group reduction products. Notably, this method achieves the precise assembly of a MOF-encapsulated composite, and the ingenious combination of MOF and nanoparticles exhibits excellent catalytic performance in the selective hydrogen transfer reaction, implementing a "1+1>2" strategy in catalysis.

A Network Biology Approach to Understanding the Tissue-Specific Roles of Non-Coding RNAs in Arthritis

Discovery of non-coding RNAs continues to provide new insights into some of the key molecular drivers of musculoskeletal diseases. Among these, microRNAs have received widespread attention for their roles in osteoarthritis and rheumatoid arthritis. With evidence to suggest that long non-coding RNAs and circular RNAs function as competing endogenous RNAs to sponge microRNAs, the net effect on gene expression in specific disease contexts can be elusive. Studies to date have focused on elucidating individual long non-coding-microRNA-gene target axes and circular RNA-microRNA-gene target axes, with a paucity of data integrating experimentally validated effects of non-coding RNAs. To address this gap, we curated recent studies reporting non-coding RNA axes in chondrocytes from human osteoarthritis and in fibroblast-like synoviocytes from human rheumatoid arthritis. Using an integrative computational biology approach, we then combined the findings into cell- and disease-specific networks for in-depth interpretation. We highlight some challenges to data integration, including non-existent naming conventions and out-of-date databases for non-coding RNAs, and some successes exemplified by the International Molecular Exchange Consortium for protein interactions. In this perspective article, we suggest that data integration is a useful in silico approach for creating non-coding RNA networks in arthritis and prioritizing interactions for further in vitro and in vivo experimentation in translational research.

A review of the hot spot analysis and the research status of single-atom catalysis based on the bibliometric analysis

The bibliometric method was used to analyze the development trend and research hotspots in past 10 years since the concept of single-atom catalysis was proposed in 2011. This article can provide some guidance for future research of SACs.

N6-methyladenosine mRNA methylation of PIK3CB regulates AKT signalling to promote PTEN-deficient pancreatic cancer progression

OBJECTIVE

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers worldwide. Thus far, most drugs have failed to significantly improve patient survival. N⁶-methyladenosine (m⁶A) plays an important role in the progression of PDAC, but its aberrant regulation driven by germline variants in human diseases remains unclear.

DESIGN

We first performed an exome-wide association analysis in 518 PDAC patients with overall survival and replicated in an independent population containing 552 PDAC patients. Then, a series of biochemical experiments in vitro and in vivo were conducted to investigate potential mechanisms of the candidate variant and its target gene PIK3CB underlying the PDAC progression. Moreover, the PIK3CB-selective inhibitor KIN-193 was used to block PDAC tumour growth.

RESULTS

We identified a missense variant rs142933486 in PIK3CB that is significantly associated with the overall survival of PDAC by reducing the PIK3CB m⁶A level, which facilitated its mRNA and protein expression levels mediated by the m⁶A 'writer' complex (METTL13/METTL14/WTAP) and the m⁶A 'reader' YTHDF2. The upregulation of PIK3CB is widely found in PDAC tumour tissues and significantly correlated with the poor prognosis of PDAC, especially in PTEN-deficient patients. We further demonstrated that PIK3CB overexpression substantially enhanced the proliferation and migration abilities of PTEN-deficient PDAC cells and activated AKT signalling pathway. Remarkably, KIN-193, a PIK3CB-selective inhibitor, is shown to serve as an effective anticancer agent for blocking PTEN-deficient PDAC.

CONCLUSIONS

These findings demonstrate aberrant m⁶A homoeostasis as an oncogenic mechanism in PDAC and highlight the potential of PIK3CB as a therapeutic target for this disease.

Towards a unified open access dataset of molecular interactions

The International Molecular Exchange (IMEx) Consortium provides scientists with a single body of experimentally verified protein interactions curated in rich contextual detail to an internationally agreed standard. In this update to the work of the IMEx Consortium, we discuss how this initiative has been working in practice, how it has ensured database sustainability, and how it is meeting emerging annotation challenges through the introduction of new interactor types and data formats. Additionally, we provide examples of how IMEx data are being used by biomedical researchers and integrated in other bioinformatic tools and resources.

Generalizable, Reproducible, and Neuroscientifically Interpretable Imaging Biomarkers for Alzheimer's Disease

Precision medicine for Alzheimer's disease (AD) necessitates the development of personalized, reproducible, and neuroscientifically interpretable biomarkers, yet despite remarkable advances, few such biomarkers are available. Also, a comprehensive evaluation of the neurobiological basis and generalizability of the end-to-end machine learning system should be given the highest priority. For this reason, a deep learning model (3D attention network, 3DAN) that can simultaneously capture candidate imaging biomarkers with an attention mechanism module and advance the diagnosis of AD based on structural magnetic resonance imaging is proposed. The generalizability and reproducibility are evaluated using cross-validation on in-house, multicenter (n = 716), and public (n = 1116) databases with an accuracy up to 92%. Significant associations between the classification output and clinical characteristics of AD and mild cognitive impairment (MCI, a middle stage of dementia) groups provide solid neurobiological support for the 3DAN model. The effectiveness of the 3DAN model is further validated by its good performance in predicting the MCI subjects who progress to AD with an accuracy of 72%. Collectively, the findings highlight the potential for structural brain imaging to provide a generalizable, and neuroscientifically interpretable imaging biomarker that can support clinicians in the early diagnosis of AD.

The IMEx Coronavirus interactome: an evolving map of Coronaviridae-Host molecular interactions

The current Coronavirus Disease 2019 (COVID-19) pandemic, caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), has spurred a wave of research of nearly unprecedented scale. Among the different strategies that are being used to understand the disease and develop effective treatments, the study of physical molecular interactions enables studying fine-grained resolution of the mechanisms behind the virus biology and the human organism response. Here we present a curated dataset of physical molecular interactions, manually extracted by IMEx Consortium curators focused on proteins from SARS-CoV-2, SARS-CoV-1 and other members of the Coronaviridae family. Currently, the dataset comprises over 2,200 binarized interactions extracted from 86 publications. The dataset can be accessed in the standard formats recommended by the Proteomics Standards Initiative (HUPO-PSI) at the IntAct database website ( www.ebi.ac.uk/intact ), and will be continuously updated as research on COVID-19 progresses.

Precision medicine - networks to the rescue

Genetic variants are often not predictive of the phenotypic outcome. Individuals carrying the same pathogenic variant, associated with Mendelian or complex disease, can manifest to different extents, from severe-to-mild to no disease. Improving the accuracy of predicted clinical manifestations of genetic variants has emerged as one of the biggest challenges in precision medicine, which can only be addressed by understanding the mechanisms underlying genotype-phenotype relationships. Efforts to understand the molecular basis of these relationships have identified complex systems of interacting biomolecules that underlie cellular function. Here, we review recent advances in how modeling cellular systems as networks of interacting proteins has fueled identification of disease-associated processes, delineation of underlying molecular mechanisms, and prediction of the pathogenicity of variants. This review is intended to be inspiring for clinicians, geneticists, and network biologists alike who aim to jointly advance our understanding of human disease and accelerate progress toward precision medicine.

DNA-Driven Two-Layer Core-Satellite Gold Nanostructures for Ultrasensitive MicroRNA Detection in Living Cells

It is a significant challenge to achieve controllable self-assembly of superstructures for biological applications in living cells. Here, a two-layer core-satellite assembly is driven by a Y-DNA, which is designed with three nucleotide chains that hybridized through complementary sequences. The two-layer core-satellite nanostructure (C₃₀ S₅ S₁₀ NS) is constructed using 30 nm gold nanoparticles (Au NPs) as the core, 5 nm Au NPs as the first satellite layer, and 10 nm Au NPs as the second satellite layer, resulting in a very strong circular dichroism (CD) and surface-enhanced Raman scattering. After optimization, the yield is up to 85%, and produces a g-factor of 0.16 × 10^-2 . The hybridization of the target microRNA (miRNA) with the molecular probe causes a significant drop in the CD and Raman signals, and this phenomenon is used to detect the miRNA in living cells. The CD signal has a good linear range of 0.011-20.94 amol ng_RNA^-1 and a limit of detection (LOD) of 0.0051 amol ng_RNA^-1 , while Raman signal with the range of 0.052-34.98 amol ng_RNA^-1 and an LOD of 2.81 × 10^-2 amol ng_RNA^-1 . This innovative dual-signal method can be used to quantify biomolecules in living cells, opening the way for ultrasensitive, highly accurate, and reliable diagnoses of clinical diseases.

Pseudo-atomic-scale metals well-dispersed on nano-carbons as ultra-low metal loading oxygen-evolving electrocatalysts

Solving challenges for the scaling-up, high metal loadings and low turnover frequency (TOF, defined as mol O2 per mol metal per second), of FeNi catalysts in water electrolysis, we report the first discovery of pH tunable tannic acid single molecular layer formed on nano-sized carbons (NCs), which promotes the gram-production of pseudo-atomic-scale FeNi oxyhydroxide nanoclusters well-dispersed on NCs. It results in ultra-low metal loading (0.42 μg cm-2) and remarkably large TOF of 14.03 s-1 for the oxygen evolution reaction, which is three orders of magnitude higher than that of state-of-the-art FeNi catalysts. A "volcano"-shaped activity trend in specific activity and TOF was found to depend on the Fe content in FeNi oxyhydroxide. The micro-morphologies from the atomic-level exposure of active sites and surface spectra analyses confirm the model of synergism between Ni and Fe centers.

Guilt-by-Association - Functional Insights Gained From Studying the LRRK2 Interactome

The Parkinson's disease-associated Leucine-rich repeat kinase 2 (LRRK2) is a complex multi-domain protein belonging to the Roco protein family, a unique group of G-proteins. Variants of this gene are associated with an increased risk of Parkinson's disease. Besides its well-characterized enzymatic activities, conferred by its GTPase and kinase domains, and a central dimerization domain, it contains four predicted repeat domains, which are, based on their structure, commonly involved in protein-protein interactions (PPIs). In the past decades, tremendous progress has been made in determining comprehensive interactome maps for the human proteome. Knowledge of PPIs has been instrumental in assigning functions to proteins involved in human disease and helped to understand the connectivity between different disease pathways and also significantly contributed to the functional understanding of LRRK2. In addition to an increased kinase activity observed for proteins containing PD-associated variants, various studies helped to establish LRRK2 as a large scaffold protein in the interface between cytoskeletal dynamics and the vesicular transport. This review first discusses a number of specific LRRK2-associated PPIs for which a functional consequence can at least be speculated upon, and then considers the representation of LRRK2 protein interactions in public repositories, providing an outlook on open research questions and challenges in this field.

A multifunctional metal-biopolymer coordinated double network hydrogel combined with multi-stimulus responsiveness, self-healing, shape memory and antibacterial properties

Outfitted with abundant hydrogen bonding and coordination active groups, carboxymethyl chitosan (CMC) possesses a class of naturally occurring ligands for coordination with metal ions, establishing its excellent potential for various fields. Herein, by incorporating the naturally derived CMC into a thermally reconfigurable agarose (Agar) gel medium, a novel type of metal-biopolymer coordinated double network hydrogel (DN gel) was successfully fabricated via the strong coordination interactions. The interpenetrated CMC was confirmed to retain its excellent chelating abilities within the bulk gel matrix, which resulted in a series of metal-coordinated DN gels through spontaneous self-associative complexation with metal ions such as Cu²⁺, Zn²⁺, Ni²⁺, Co²⁺, Fe³⁺, and Cr³⁺. Moreover, these two types of physical cross-links are functionally independent and reversible, which enables the programming of the hydrogel with multi-functionality, including pH-regulated shape memory behavior, multi-staged self-healing properties and durable antibacterial activities. Thus, we believe that the successful preparation of such a coordination-driven DN gel will lead to the development of biopolymer-based multifunctional hydrogels, as well as provide new insight into nanocomponent assembly and soft electronic biosensing systems for biomedical applications.

The functional landscape of the human phosphoproteome

Protein phosphorylation is a key post-translational modification regulating protein function in almost all cellular processes. Although tens of thousands of phosphorylation sites have been identified in human cells, approaches to determine the functional importance of each phosphosite are lacking. Here, we manually curated 112 datasets of phospho-enriched proteins, generated from 104 different human cell types or tissues. We re-analyzed the 6,801 proteomics experiments that passed our quality control criteria, creating a reference phosphoproteome containing 119,809 human phosphosites. To prioritize functional sites, we used machine learning to identify 59 features indicative of proteomic, structural, regulatory or evolutionary relevance and integrate them into a single functional score. Our approach identifies regulatory phosphosites across different molecular mechanisms, processes and diseases, and reveals genetic susceptibilities at a genomic scale. Several regulatory phosphosites were experimentally validated, including identifying a role in neuronal differentiation for phosphosites in SMARCC2, a member of the SWI/SNF chromatin-remodeling complex.

A Coordinated Approach by Public Domain Bioinformatics Resources to Aid the Fight Against Alzheimer's Disease Through Expert Curation of Key Protein Targets

BACKGROUND

The analysis and interpretation of data generated from patient-derived clinical samples relies on access to high-quality bioinformatics resources. These are maintained and updated by expert curators extracting knowledge from unstructured biological data described in free-text journal articles and converting this into more structured, computationally-accessible forms. This enables analyses such as functional enrichment of sets of genes/proteins using the Gene Ontology, and makes the searching of data more productive by managing issues such as gene/protein name synonyms, identifier mapping, and data quality.

OBJECTIVE

To undertake a coordinated annotation update of key public-domain resources to better support Alzheimer's disease research.

METHODS

We have systematically identified target proteins critical to disease process, in part by accessing informed input from the clinical research community.

RESULTS

Data from 954 papers have been added to the UniProtKB, Gene Ontology, and the International Molecular Exchange Consortium (IMEx) databases, with 299 human proteins and 279 orthologs updated in UniProtKB. 745 binary interactions were added to the IMEx human molecular interaction dataset.

CONCLUSION

This represents a significant enhancement in the expert curated data pertinent to Alzheimer's disease available in a number of biomedical databases. Relevant protein entries have been updated in UniProtKB and concomitantly in the Gene Ontology. Molecular interaction networks have been significantly extended in the IMEx Consortium dataset and a set of reference protein complexes created. All the resources described are open-source and freely available to the research community and we provide examples of how these data could be exploited by researchers.

The IMEx coronavirus interactome: an evolving map of Coronaviridae-host molecular interactions

The current coronavirus disease of 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus (SARS-CoV)-2, has spurred a wave of research of nearly unprecedented scale. Among the different strategies that are being used to understand the disease and develop effective treatments, the study of physical molecular interactions can provide fine-grained resolution of the mechanisms behind the virus biology and the human organism response. We present a curated dataset of physical molecular interactions focused on proteins from SARS-CoV-2, SARS-CoV-1 and other members of the Coronaviridae family that has been manually extracted by International Molecular Exchange (IMEx) Consortium curators. Currently, the dataset comprises over 4400 binarized interactions extracted from 151 publications. The dataset can be accessed in the standard formats recommended by the Proteomics Standards Initiative (HUPO-PSI) at the IntAct database website (https://www.ebi.ac.uk/intact) and will be continuously updated as research on COVID-19 progresses.

Complex Stability is Encoded in Binding Patch Softness: a Monomer-Based Approach to Predict Inter-Subunit Affinity of Protein Dimers

Knowledge about the structure and stability of protein-protein interactions is vital to decipher the behavior of protein systems. Prediction of protein complexes' stability is an interesting topic in the field of structural biology. There are some promising published computational approaches that predict the affinity between subunits of protein dimers using 3D structures of both subunits. In the current study, we classify protein complexes with experimentally measured affinities into distinct classes with different mean affinities. By predicting the mechanical stiffness of the protein binding patch (PBP) region on a single subunit, we successfully predict the assigned affinity class of the PBP in the classification step. Now to predict the experimentally measured affinity between protein monomers in solution, we just need the 3D structure of the suggested PBP on one subunit of the proposed dimer. We designed the SEPAS software and have made the software freely available for academic non-commercial research purposes at " http://biophysics.ir/affinity ". SEPAS predicts the stability of the intended dimer in a classwise manner by utilizing the computed mechanical stiffness of the introduced binding site on one subunit with the minimum accuracy of 0.72.

3D spatial organization and network-guided comparison of mutation profiles in Glioblastoma reveals similarities across patients

Glioblastoma multiforme (GBM) is the most aggressive type of brain tumor. Molecular heterogeneity is a hallmark of GBM tumors that is a barrier in developing treatment strategies. In this study, we used the nonsynonymous mutations of GBM tumors deposited in The Cancer Genome Atlas (TCGA) and applied a systems level approach based on biophysical characteristics of mutations and their organization in patient-specific subnetworks to reduce inter-patient heterogeneity and to gain potential clinically relevant insights. Approximately 10% of the mutations are located in "patches" which are defined as the set of residues spatially in close proximity that are mutated across multiple patients. Grouping mutations as 3D patches reduces the heterogeneity across patients. There are multiple patches that are relatively small in oncogenes, whereas there are a small number of very large patches in tumor suppressors. Additionally, different patches in the same protein are often located at different domains that can mediate different functions. We stratified the patients into five groups based on their potentially affected pathways that are revealed from the patient-specific subnetworks. These subnetworks were constructed by integrating mutation profiles of the patients with the interactome data. Network-guided clustering showed significant association between the groups and patient survival (P-value = 0.0408). Also, each group carries a set of signature 3D mutation patches that affect predominant pathways. We integrated drug sensitivity data of GBM cell lines with the mutation patches and the patient groups to analyze the possible therapeutic outcome of these patches. We found that Pazopanib might be effective in Group 3 by targeting CSF1R. Additionally, inhibiting ATM that is a mediator of PTEN phosphorylation may be ineffective in Group 2. We believe that from mutations to networks and eventually to clinical and therapeutic data, this study provides a novel perspective in the network-guided precision medicine.

Extensive disruption of protein interactions by genetic variants across the allele frequency spectrum in human populations

Each human genome carries tens of thousands of coding variants. The extent to which this variation is functional and the mechanisms by which they exert their influence remains largely unexplored. To address this gap, we leverage the ExAC database of 60,706 human exomes to investigate experimentally the impact of 2009 missense single nucleotide variants (SNVs) across 2185 protein-protein interactions, generating interaction profiles for 4797 SNV-interaction pairs, of which 421 SNVs segregate at > 1% allele frequency in human populations. We find that interaction-disruptive SNVs are prevalent at both rare and common allele frequencies. Furthermore, these results suggest that 10.5% of missense variants carried per individual are disruptive, a higher proportion than previously reported; this indicates that each individual's genetic makeup may be significantly more complex than expected. Finally, we demonstrate that candidate disease-associated mutations can be identified through shared interaction perturbations between variants of interest and known disease mutations.

Non-coding RNA regulatory networks

It is well established that the vast majority of human RNA transcripts do not encode for proteins and that non-coding RNAs regulate cell physiology and shape cellular functions. A subset of them is involved in gene regulation at different levels, from epigenetic gene silencing to post-transcriptional regulation of mRNA stability. Notably, the aberrant expression of many non-coding RNAs has been associated with aggressive pathologies. Rapid advances in network biology indicates that the robustness of cellular processes is the result of specific properties of biological networks such as scale-free degree distribution and hierarchical modularity, suggesting that regulatory network analyses could provide new insights on gene regulation and dysfunction mechanisms. In this study we present an overview of public repositories where non-coding RNA-regulatory interactions are collected and annotated, we discuss unresolved questions for data integration and we recall existing resources to build and analyse networks.

New advances in extracting and learning from protein-protein interactions within unstructured biomedical text data

Protein-protein interactions, or PPIs, constitute a basic unit of our understanding of protein function. Though substantial effort has been made to organize PPI knowledge into structured databases, maintenance of these resources requires careful manual curation. Even then, many PPIs remain uncurated within unstructured text data. Extracting PPIs from experimental research supports assembly of PPI networks and highlights relationships crucial to elucidating protein functions. Isolating specific protein-protein relationships from numerous documents is technically demanding by both manual and automated means. Recent advances in the design of these methods have leveraged emerging computational developments and have demonstrated impressive results on test datasets. In this review, we discuss recent developments in PPI extraction from unstructured biomedical text. We explore the historical context of these developments, recent strategies for integrating and comparing PPI data, and their application to advancing the understanding of protein function. Finally, we describe the challenges facing the application of PPI mining to the text concerning protein families, using the multifunctional 14-3-3 protein family as an example.

Emerging concepts in pseudoenzyme classification, evolution, and signaling

The 21st century is witnessing an explosive surge in our understanding of pseudoenzyme-driven regulatory mechanisms in biology. Pseudoenzymes are proteins that have sequence homology with enzyme families but that are proven or predicted to lack enzyme activity due to mutations in otherwise conserved catalytic amino acids. The best-studied pseudoenzymes are pseudokinases, although examples from other families are emerging at a rapid rate as experimental approaches catch up with an avalanche of freely available informatics data. Kingdom-wide analysis in prokaryotes, archaea and eukaryotes reveals that between 5 and 10% of proteins that make up enzyme families are pseudoenzymes, with notable expansions and contractions seemingly associated with specific signaling niches. Pseudoenzymes can allosterically activate canonical enzymes, act as scaffolds to control assembly of signaling complexes and their localization, serve as molecular switches, or regulate signaling networks through substrate or enzyme sequestration. Molecular analysis of pseudoenzymes is rapidly advancing knowledge of how they perform noncatalytic functions and is enabling the discovery of unexpected, and previously unappreciated, functions of their intensively studied enzyme counterparts. Notably, upon further examination, some pseudoenzymes have previously unknown enzymatic activities that could not have been predicted a priori. Pseudoenzymes can be targeted and manipulated by small molecules and therefore represent new therapeutic targets (or anti-targets, where intervention should be avoided) in various diseases. In this review, which brings together broad bioinformatics and cell signaling approaches in the field, we highlight a selection of findings relevant to a contemporary understanding of pseudoenzyme-based biology.

Pathway-specific protein domains are predictive for human diseases

Protein domains are basic functional units of proteins. Many protein domains are pervasive among diverse biological processes, yet some are associated with specific pathways. Human complex diseases are generally viewed as pathway-level disorders. Therefore, we hypothesized that pathway-specific domains could be highly informative for human diseases. To test the hypothesis, we developed a network-based scoring scheme to quantify specificity of domain-pathway associations. We first generated domain profiles for human proteins, then constructed a co-pathway protein network based on the associations between domain profiles. Based on the score, we classified human protein domains into pathway-specific domains (PSDs) and non-specific domains (NSDs). We found that PSDs contained more pathogenic variants than NSDs. PSDs were also enriched for disease-associated mutations that disrupt protein-protein interactions (PPIs) and tend to have a moderate number of domain interactions. These results suggest that mutations in PSDs are likely to disrupt within-pathway PPIs, resulting in functional failure of pathways. Finally, we demonstrated the prediction capacity of PSDs for disease-associated genes with experimental validations in zebrafish. Taken together, the network-based quantitative method of modeling domain-pathway associations presented herein suggested underlying mechanisms of how protein domains associated with specific pathways influence mutational impacts on diseases via perturbations in within-pathway PPIs, and provided a novel genomic feature for interpreting genetic variants to facilitate the discovery of human disease genes.

Protein domains are basic functional units of proteins, yet domain-based pathway annotations for proteins are challenging tasks because many domains are pervasive among diverse pathways. Therefore, we developed a network-based scoring scheme to measure pathway specificity of domains, and then used it to identify pathway-specific domains. Surprisingly, we observed substantially more disease mutations in pathway-specific domains than non-specific domains. We found evidences that mutations of pathway-specific domains tend to perturb pathway integrity via disrupting within-pathway protein-protein interactions. We also demonstrated prediction capacity of pathway-specific domains for complex diseases with experimental validations. Our study demonstrated the usefulness of pathway information for protein domains in interpreting non-random distribution of disease mutations among domains and identification of disease genes and variants.