A map of the interactome network of the metazoan C. elegans

Analysis of Haemonchus embryos at single cell resolution identifies two eukaryotic elongation factors as intervention target candidates

Advances in single cell technologies are allowing investigations of a wide range of biological processes and pathways in animals, such as the multicellular model organism Caenorhabditis elegans - a free-living nematode. However, there has been limited application of such technology to related parasitic nematodes which cause major diseases of humans and animals worldwide. With no vaccines against the vast majority of parasitic nematodes and treatment failures due to drug resistance or inefficacy, new intervention targets are urgently needed, preferably informed by a deep understanding of these nematodes' cellular and molecular biology - which is presently lacking for most worms. Here, we created the first single cell atlas for an early developmental stage of Haemonchus contortus - a highly pathogenic, C. elegans-related parasitic nematode. We obtained and curated RNA sequence (snRNA-seq) data from single nuclei from embryonating eggs of H. contortus (150,000 droplets), and selected high-quality transcriptomic data for > 14,000 single nuclei for analysis, and identified 19 distinct clusters of cells. Guided by comparative analyses with C. elegans, we were able to reproducibly assign seven cell clusters to body wall muscle, hypodermis, neuronal, intestinal or seam cells, and identified eight genes that were transcribed in all cell clusters/types, three of which were inferred to be essential in H. contortus. Two of these genes (i.e. Hc-eef-1A and Hc-eef1G), coding for eukaryotic elongation factors (called Hc-eEF1A and Hc-eEF1G), were also demonstrated to be transcribed and expressed in all key developmental stages of H. contortus. Together with these findings, sequence- and structure-based comparative analyses indicated the potential of Hc-eEF1A and/or Hc-eEF1G as intervention targets within the protein biosynthesis machinery of H. contortus. Future work will focus on single cell studies of all key developmental stages and tissues of H. contortus, and on evaluating the suitability of the two elongation factor proteins as drug targets in H. contortus and related nematodes, with a view to finding new nematocidal drug candidates.

Ancient eukaryotic protein interactions illuminate modern genetic traits and disorders

All eukaryotes share a common ancestor from roughly 1.5 - 1.8 billion years ago, a single-celled, swimming microbe known as LECA, the Last Eukaryotic Common Ancestor. Nearly half of the genes in modern eukaryotes were present in LECA, and many current genetic diseases and traits stem from these ancient molecular systems. To better understand these systems, we compared genes across modern organisms and identified a core set of 10,092 shared protein-coding gene families likely present in LECA, a quarter of which are uncharacterized. We then integrated >26,000 mass spectrometry proteomics analyses from 31 species to infer how these proteins interact in higher-order complexes. The resulting interactome describes the biochemical organization of LECA, revealing both known and new assemblies. We analyzed these ancient protein interactions to find new human gene-disease relationships for bone density and congenital birth defects, demonstrating the value of ancestral protein interactions for guiding functional genetics today.

MACSPI enables tissue-selective proteomic and interactomic analyses in multicellular organisms

Multicellular organisms are composed of many tissue types that have distinct morphologies and functions, which are largely driven by specialized proteomes and interactomes. To define the proteome and interactome of a specific type of tissue in an intact animal, we developed a localized proteomics approach called Methionine Analog-based Cell-Specific Proteomics and Interactomics (MACSPI). This method uses the tissue-specific expression of an engineered methionyl-tRNA synthetase to label proteins with a bifunctional amino acid 2-amino-5-diazirinylnonynoic acid in selected cells. We applied MACSPI in Caenorhabditis elegans, a model multicellular organism, to selectively label, capture, and profile the proteomes of the body wall muscle and the nervous system, which led to the identification of tissue-specific proteins. Using the photo-cross-linker, we successfully profiled HSP90 interactors in muscles and neurons and identified tissue-specific interactors and stress-related interactors. Our study demonstrates that MACSPI can be used to profile tissue-specific proteomes and interactomes in intact multicellular organisms.

PACS-1 variant protein is aberrantly localized in C. elegans model of PACS1/PACS2 syndromes

PACS (Phosphofurin Acidic Cluster Sorting Protein) proteins are known for their roles in sorting cargo proteins to organelles and can physically interact with WD40 repeat-containing protein WDR37. PACS1, PACS2, and WDR37 variants are associated with multisystemic syndromes and neurodevelopmental disorders characterized by intellectual disability, seizures, developmental delays, craniofacial abnormalities, and autism spectrum disorder. However, the effects of syndromic variants on function in vivo remains unknown. Here, we report the expression pattern of C. elegans orthologs of PACS and WDR37 and their interaction. We show that cePACS-1 and ceWDR-37 co-localize to somatic cytoplasm of many types of cells, and are mutually required for expression, supporting a conclusion that the intermolecular dependence of PACS1/PACS2/PACS-1 and WDR37/WDR-37 is evolutionarily conserved. We further show that editing in PACS1 and PACS2 variants in cePACS-1 changes protein localization in multiple cell types, including neurons. Moreover, expression of human PACS1 can functionally complement C. elegans PACS-1 in neurons, demonstrating conserved functions of the PACS-WDR37 axis in an invertebrate model system. Our findings reveal effects of human variants and suggest potential strategies to identify regulatory network components that may contribute to understanding molecular underpinnings of PACS/WDR37 syndromes.

Restrictor synergizes with Symplekin and PNUTS to terminate extragenic transcription

Transcription termination pathways mitigate the detrimental consequences of unscheduled promiscuous initiation occurring at hundreds of thousands of genomic cis-regulatory elements. The Restrictor complex, composed of the Pol II-interacting protein WDR82 and the RNA-binding protein ZC3H4, suppresses processive transcription at thousands of extragenic sites in mammalian genomes. Restrictor-driven termination does not involve nascent RNA cleavage, and its interplay with other termination machineries is unclear. Here we show that efficient termination at Restrictor-controlled extragenic transcription units involves the recruitment of the protein phosphatase 1 (PP1) regulatory subunit PNUTS, a negative regulator of the SPT5 elongation factor, and Symplekin, a protein associated with RNA cleavage complexes but also involved in cleavage-independent and phosphatase-dependent termination of noncoding RNAs in yeast. PNUTS and Symplekin act synergistically with, but independently from, Restrictor to dampen processive extragenic transcription. Moreover, the presence of limiting nuclear levels of Symplekin imposes a competition for its recruitment among multiple transcription termination machineries, resulting in mutual regulatory interactions. Hence, by synergizing with Restrictor, Symplekin and PNUTS enable efficient termination of processive, long-range extragenic transcription.

Mitochondrial p38 Mitogen-Activated Protein Kinase: Insights into Its Regulation of and Role in LONP1-Deficient Nematodes

p38 Mitogen-Activated Protein Kinase (MAPK) cascades are central regulators of numerous physiological cellular processes, including stress response signaling. In C. elegans, mitochondrial dysfunction activates a PMK-3/p38 MAPK signaling pathway (MAPKmt), but its functional role still remains elusive. Here, we demonstrate the induction of MAPKmt in worms deficient in the lonp-1 gene, which encodes the worm ortholog of mammalian mitochondrial LonP1. This induction is subjected to negative regulation by the ATFS-1 transcription factor through the CREB-binding protein (CBP) ortholog CBP-3, indicating an interplay between both activated MAPKmt and mitochondrial Unfolded Protein Response (UPRmt) surveillance pathways. Our results also reveal a genetic interaction in lonp-1 mutants between PMK-3 kinase and the ZIP-2 transcription factor. ZIP-2 has an established role in innate immunity but can also modulate the lifespan by maintaining mitochondrial homeostasis during ageing. We show that in lonp-1 animals, ZIP-2 is activated in a PMK-3-dependent manner but does not confer increased survival to pathogenic bacteria. However, deletion of zip-2 or pmk-3 shortens the lifespan of lonp-1 mutants, suggesting a possible crosstalk under conditions of mitochondrial perturbation that influences the ageing process. Furthermore, loss of pmk-3 specifically diminished the extreme heat tolerance of lonp-1 worms, highlighting the crucial role of PMK-3 in the heat shock response upon mitochondrial LONP-1 inactivation.

Dynamic compartmentalization of the pro-invasive transcription factor NHR-67 reveals a role for Groucho in regulating a proliferative-invasive cellular switch in C. elegans

A growing body of evidence suggests that cell division and basement membrane invasion are mutually exclusive cellular behaviors. How cells switch between proliferative and invasive states is not well understood. Here, we investigated this dichotomy in vivo by examining two cell types in the developing Caenorhabditis elegans somatic gonad that derive from equipotent progenitors, but exhibit distinct cell behaviors: the post-mitotic, invasive anchor cell and the neighboring proliferative, non-invasive ventral uterine (VU) cells. We show that the fates of these cells post-specification are more plastic than previously appreciated and that levels of NHR-67 are important for discriminating between invasive and proliferative behavior. Transcription of NHR-67 is downregulated following post-translational degradation of its direct upstream regulator, HLH-2 (E/Daughterless) in VU cells. In the nuclei of VU cells, residual NHR-67 protein is compartmentalized into discrete punctae that are dynamic over the cell cycle and exhibit liquid-like properties. By screening for proteins that colocalize with NHR-67 punctae, we identified new regulators of uterine cell fate maintenance: homologs of the transcriptional co-repressor Groucho (UNC-37 and LSY-22), as well as the TCF/LEF homolog POP-1. We propose a model in which the association of NHR-67 with the Groucho/TCF complex suppresses the default invasive state in non-invasive cells, which complements transcriptional regulation to add robustness to the proliferative-invasive cellular switch in vivo.

The protein interactome of the citrus Huanglongbing pathogen Candidatus Liberibacter asiaticus

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

A MEC-2/stomatin condensate liquid-to-solid phase transition controls neuronal mechanotransduction during touch sensing

A growing body of work suggests that the material properties of biomolecular condensates ensuing from liquid-liquid phase separation change with time. How this aging process is controlled and whether the condensates with distinct material properties can have different biological functions is currently unknown. Using Caenorhabditis elegans as a model, we show that MEC-2/stomatin undergoes a rigidity phase transition from fluid-like to solid-like condensates that facilitate transport and mechanotransduction, respectively. This switch is triggered by the interaction between the SH3 domain of UNC-89 (titin/obscurin) and MEC-2. We suggest that this rigidity phase transition has a physiological role in frequency-dependent force transmission in mechanosensitive neurons during body wall touch. Our data demonstrate a function for the liquid and solid phases of MEC-2/stomatin condensates in facilitating transport or mechanotransduction, and a previously unidentified role for titin homologues in neurons.

Identification of GUCA2A and COL3A1 as prognostic biomarkers in colorectal cancer by integrating analysis of RNA-Seq data and qRT-PCR validation

By 2030, it is anticipated that there will be 2.2 million new instances of colorectal cancer worldwide, along with 1.1 million yearly deaths. Therefore, it is critical to develop novel biomarkers that could help in CRC early detection. We performed an integrated analysis of four RNA-Seq data sets and TCGA datasets in this study to find novel biomarkers for diagnostic, prediction, and as potential therapeutic for this malignancy, as well as to determine the molecular mechanisms of CRC carcinogenesis. Four RNA-Seq datasets of colorectal cancer were downloaded from the Sequence Read Archive (SRA) database. The metaSeq package was used to integrate differentially expressed genes (DEGs). The protein-protein interaction (PPI) network of the DEGs was constructed using the string platform, and hub genes were identified using the cytoscape software. The gene ontology and KEGG pathway enrichment analysis were performed using enrichR package. Gene diagnostic sensitivity and its association to clinicopathological characteristics were demonstrated by statistical approaches. By using qRT-PCR, GUCA2A and COL3A1 were examined in colon cancer and rectal cancer. We identified 5037 differentially expressed genes, including (4752 upregulated, 285 downregulated) across the studies between CRC and normal tissues. Gene ontology and KEGG pathway analyses showed that the highest proportion of up-regulated DEGs was involved in RNA binding and RNA transport. Integral component of plasma membrane and mineral absorption pathways were identified as containing down-regulated DEGs. Similar expression patterns for GUCA2A and COL3A1 were seen in qRT-PCR and integrated RNA-Seq analysis. Additionally, this study demonstrated that GUCA2A and COL3A1 may play a significant role in the development of CRC.

TDP-43 Epigenetic Facets and Their Neurodegenerative Implications

Since its initial involvement in numerous neurodegenerative pathologies in 2006, either as a principal actor or as a cofactor, new pathologies implicating transactive response (TAR) DNA-binding protein 43 (TDP-43) are regularly emerging also beyond the neuronal system. This reflects the fact that TDP-43 functions are particularly complex and broad in a great variety of human cells. In neurodegenerative diseases, this protein is often pathologically delocalized to the cytoplasm, where it irreversibly aggregates and is subjected to various post-translational modifications such as phosphorylation, polyubiquitination, and cleavage. Until a few years ago, the research emphasis has been focused particularly on the impacts of this aggregation and/or on its widely described role in complex RNA splicing, whether related to loss- or gain-of-function mechanisms. Interestingly, recent studies have strengthened the knowledge of TDP-43 activity at the chromatin level and its implication in the regulation of DNA transcription and stability. These discoveries have highlighted new features regarding its own transcriptional regulation and suggested additional mechanistic and disease models for the effects of TPD-43. In this review, we aim to give a comprehensive view of the potential epigenetic (de)regulations driven by (and driving) this multitask DNA/RNA-binding protein.

Nuclear hormone receptor NHR-49 is an essential regulator of stress resilience and healthy aging in Caenorhabditis elegans

The genome of Caenorhabditis elegans encodes 284 nuclear hormone receptor, which perform diverse functions in development and physiology. One of the best characterized of these is NHR-49, related in sequence and function to mammalian hepatocyte nuclear factor 4α and peroxisome proliferator-activated receptor α. Initially identified as regulator of lipid metabolism, including fatty acid catabolism and desaturation, additional important roles for NHR-49 have since emerged. It is an essential contributor to longevity in several genetic and environmental contexts, and also plays vital roles in the resistance to several stresses and innate immune response to infection with various bacterial pathogens. Here, we review how NHR-49 is integrated into pertinent signaling circuits and how it achieves its diverse functions. We also highlight areas for future investigation including identification of regulatory inputs that drive NHR-49 activity and identification of tissue-specific gene regulatory outputs. We anticipate that future work on this protein will provide information that could be useful for developing strategies to age-associated declines in health and age-related human diseases.

Electrostatic complementarity at the interface drives transient protein-protein interactions

Understanding the mechanisms driving bio-molecules binding and determining the resulting complexes' stability is fundamental for the prediction of binding regions, which is the starting point for drug-ability and design. Characteristics like the preferentially hydrophobic composition of the binding interfaces, the role of van der Waals interactions, and the consequent shape complementarity between the interacting molecular surfaces are well established. However, no consensus has yet been reached on the role of electrostatic. Here, we perform extensive analyses on a large dataset of protein complexes for which both experimental binding affinity and pH data were available. Probing the amino acid composition, the disposition of the charges, and the electrostatic potential they generated on the protein molecular surfaces, we found that (i) although different classes of dimers do not present marked differences in the amino acid composition and charges disposition in the binding region, (ii) homodimers with identical binding region show higher electrostatic compatibility with respect to both homodimers with non-identical binding region and heterodimers. Interestingly, (iii) shape and electrostatic complementarity, for patches defined on short-range interactions, behave oppositely when one stratifies the complexes by their binding affinity: complexes with higher binding affinity present high values of shape complementarity (the role of the Lennard-Jones potential predominates) while electrostatic tends to be randomly distributed. Conversely, complexes with low values of binding affinity exploit Coulombic complementarity to acquire specificity, suggesting that electrostatic complementarity may play a greater role in transient (or less stable) complexes. In light of these results, (iv) we provide a novel, fast, and efficient method, based on the 2D Zernike polynomial formalism, to measure electrostatic complementarity without the need of knowing the complex structure. Expanding the electrostatic potential on a basis of 2D orthogonal polynomials, we can discriminate between transient and permanent protein complexes with an AUC of the ROC of [Formula: see text] 0.8. Ultimately, our work helps shedding light on the non-trivial relationship between the hydrophobic and electrostatic contributions in the binding interfaces, thus favoring the development of new predictive methods for binding affinity characterization.

Homo cerevisiae-Leveraging Yeast for Investigating Protein-Protein Interactions and Their Role in Human Disease

Understanding how genetic variation affects phenotypes represents a major challenge, particularly in the context of human disease. Although numerous disease-associated genes have been identified, the clinical significance of most human variants remains unknown. Despite unparalleled advances in genomics, functional assays often lack sufficient throughput, hindering efficient variant functionalization. There is a critical need for the development of more potent, high-throughput methods for characterizing human genetic variants. Here, we review how yeast helps tackle this challenge, both as a valuable model organism and as an experimental tool for investigating the molecular basis of phenotypic perturbation upon genetic variation. In systems biology, yeast has played a pivotal role as a highly scalable platform which has allowed us to gain extensive genetic and molecular knowledge, including the construction of comprehensive interactome maps at the proteome scale for various organisms. By leveraging interactome networks, one can view biology from a systems perspective, unravel the molecular mechanisms underlying genetic diseases, and identify therapeutic targets. The use of yeast to assess the molecular impacts of genetic variants, including those associated with viral interactions, cancer, and rare and complex diseases, has the potential to bridge the gap between genotype and phenotype, opening the door for precision medicine approaches and therapeutic development.

Determination of Key Components in the Bombyx mori p53 Apoptosis Regulation Network Using Y2H-Seq

The apoptosis pathway is highly conserved between invertebrates and mammals. Although genes encoding the classical apoptosis pathway can be found in the silkworm genome, the regulatory pathway and other apoptotic network genes have yet to be confirmed. Consequently, characterizing these genes and their underlying mechanisms could provide critical insights into the molecular basis of organ apoptosis and remodeling. A homolog of p53, a key apoptosis regulator in vertebrates, has been identified and cloned from Bombyx mori (Bmp53). This study confirmed via gene knockdown and overexpression that Bmp53 directly induces cell apoptosis and regulates the morphology and development of individuals during the metamorphosis stage. Furthermore, yeast two-hybrid sequencing (Y2H-Seq) identified several potential apoptotic regulatory interacting proteins, including the MDM2-like ubiquitination regulatory protein, which may represent an apoptosis factor unique to Bmp53 and which differs from that in other Lepidoptera. These results provide a theoretical basis for analyzing the various biological processes regulated by Bmp53 interaction groups and thus provide insight into the regulation of apoptosis in silkworms. The global interaction set identified in this study also provides a basic framework for future studies on apoptosis-dependent pupation in Lepidoptera.

Interactomics in plant defence: progress and opportunities

Interactomics is a branch of systems biology that deals with the study of protein-protein interactions and how these interactions influence phenotypes. Identifying the interactomes involved during host-pathogen interaction events may bring us a step closer to deciphering the molecular mechanisms underlying plant defence. Here, we conducted a systematic review of plant interactomics studies over the last two decades and found that while a substantial progress has been made in the field, plant-pathogen interactomics remains a less-travelled route. As an effort to facilitate the progress in this field, we provide here a comprehensive research pipeline for an in planta plant-pathogen interactomics study that encompasses the in silico prediction step to the validation step, unconfined to model plants. We also highlight four challenges in plant-pathogen interactomics with plausible solution(s) for each.

Altered blood gene expression in the obesity-related type 2 diabetes cluster may be causally involved in lipid metabolism: a Mendelian randomisation study

AIMS/HYPOTHESIS

The aim of this study was to identify differentially expressed long non-coding RNAs (lncRNAs) and mRNAs in whole blood of people with type 2 diabetes across five different clusters: severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes (MOD), mild diabetes (MD) and mild diabetes with high HDL-cholesterol (MDH). This was to increase our understanding of different molecular mechanisms underlying the five putative clusters of type 2 diabetes.

METHODS

Participants in the Hoorn Diabetes Care System (DCS) cohort were clustered based on age, BMI, HbA_1c, C-peptide and HDL-cholesterol. Whole blood RNA-seq was used to identify differentially expressed lncRNAs and mRNAs in a cluster compared with all others. Differentially expressed genes were validated in the Innovative Medicines Initiative DIabetes REsearCh on patient straTification (IMI DIRECT) study. Expression quantitative trait loci (eQTLs) for differentially expressed RNAs were obtained from a publicly available dataset. To estimate the causal effects of RNAs on traits, a two-sample Mendelian randomisation analysis was performed using public genome-wide association study (GWAS) data.

RESULTS

Eleven lncRNAs and 175 mRNAs were differentially expressed in the MOD cluster, the lncRNA AL354696.2 was upregulated in the SIDD cluster and GPR15 mRNA was downregulated in the MDH cluster. mRNAs and lncRNAs that were differentially expressed in the MOD cluster were correlated among each other. Six lncRNAs and 120 mRNAs validated in the IMI DIRECT study. Using two-sample Mendelian randomisation, we found 52 mRNAs to have a causal effect on anthropometric traits (n=23) and lipid metabolism traits (n=10). GPR146 showed a causal effect on plasma HDL-cholesterol levels (p = 2×10^-15), without evidence for reverse causality.

CONCLUSIONS/INTERPRETATION

Multiple lncRNAs and mRNAs were found to be differentially expressed among clusters and particularly in the MOD cluster. mRNAs in the MOD cluster showed a possible causal effect on anthropometric traits, lipid metabolism traits and blood cell fractions. Together, our results show that individuals in the MOD cluster show aberrant RNA expression of genes that have a suggested causal role on multiple diabetes-relevant traits.

Tanshinone IIA Regulates Keap1/Nrf2 Signal Pathway by Activating Sestrin2 to Restrain Pulmonary Fibrosis

Tanshinone IIA (Tan-IIA) is a major component extracted from the traditional herbal medicine Danshen, which has shown antipulmonary fibrosis by suppress reactive oxygen species-mediated activation of myofibroblast. However, the exact mechanism of Tan-IIA against pulmonary fibrosis (PF) remains unclear. This work aimed to explore the underlying mechanism of the protective effects of Tan-IIA on PF. By using high-throughput RNA-Seq analysis, we have compared the genome-wide gene expression profiles and pathway enrichment of Tan-IIA-treated NIH-3T3 cells with or without transforming growth factor beta 1 (TGF-[Formula: see text]1) induction. In normal NIH-3T3 cells, Tan-IIA treatment up-regulated 181 differential expression genes (DEGs) and down-regulated 137 DEGs. In TGF-[Formula: see text]1-induced NIH-3T3 cells, Tan-IIA treatment up-regulated 709 DEGs and down-regulated 1075 DEGs, and these DEGs were enriched in extracellular matrix organization, collagen fibril organization, cell adhesion, ECM-receptor interaction, PI3K-Akt signaling pathway and P53 signaling pathway. Moreover, there were 207 co-expressed DEGs between Tan-IIA treatment vs. the Control and TGF-[Formula: see text]1 plus Tan-IIA treatment vs. TGF-[Formula: see text]1 alone treatment, some of which were related to anti-oxidative stress. In both normal and TGF-[Formula: see text]1-induced NIH-3T3 cells, protein-protein interaction network analysis indicated that Tan-IIA can regulate the expression of several common anti-oxidant genes including Heme oxygenase 1 (Ho-1, also known as Homx1), Sestrin2 (Sesn2), GCL modifier subunit (Gclm), GCL catalytic subunit (Gclc) and Sequestosome-1 (Sqstm1). Quantitative Real-time polymerase chain reaction analysis confirmed some DEGs specifically expressing on Tan-IIA treated cells, which provided new candidates for further functional studies of Tan-IIA. In both in vitro and in vivo PF models, the protein expression of Sesn2 was significantly enhanced by Tan-IIA treatment. Overexpression and knockdown experiments showed that Sesn2 is required for Tan-IIA against TGF-[Formula: see text]1-induced myofibroblast activation by reinforcing nuclear factor-erythroid 2-related factor 2 (Nrf2)-mediated anti-oxidant response via downregulation of kelch-like ECH-associated protein 1 (Keap1). These results suggest Tan-IIA inhibits myofibroblast activation by activating Sesn2-Nrf2 signaling pathway, and provide a new insight into the essential role of Sesn2 in PF.

Small Heat Shock Proteins Collaborate with FAIM to Prevent Accumulation of Misfolded Protein Aggregates

Cells and tissues are continuously subject to environmental insults, such as heat shock and oxidative stress, which cause the accumulation of cytotoxic, aggregated proteins. We previously found that Fas Apoptosis Inhibitory Molecule (FAIM) protects cells from stress-induced cell death by preventing abnormal generation of protein aggregates similar to the effect of small heat shock proteins (HSPs). Protein aggregates are often associated with neurodegenerative diseases, including Alzheimer's disease (AD). In this study, we sought to determine how FAIM protein dynamics change during cellular stress and how FAIM prevents the formation of amyloid-β aggregates/fibrils, one of the pathological hallmarks of AD. Here, we found that the majority of FAIM protein shifts to the detergent-insoluble fraction in response to cellular stress. A similar shift to the insoluble fraction was also observed in small heat shock protein (sHSP) family molecules, such as HSP27, after stress. We further demonstrate that FAIM is recruited to sHSP-containing complexes after cellular stress induction. These data suggest that FAIM might prevent protein aggregation in concert with sHSPs. In fact, we observed the additional effect of FAIM and HSP27 on the prevention of protein aggregates using an in vitro amyloid-β aggregation model system. Our work provides new insights into the interrelationships among FAIM, sHSPs, and amyloid-β aggregation.

Molecular architecture of the C. elegans centriole

Uncovering organizing principles of organelle assembly is a fundamental pursuit in the life sciences. Caenorhabditis elegans was key in identifying evolutionary conserved components governing assembly of the centriole organelle. However, localizing these components with high precision has been hampered by the minute size of the worm centriole, thus impeding understanding of underlying assembly mechanisms. Here, we used Ultrastructure Expansion coupled with STimulated Emission Depletion (U-Ex-STED) microscopy, as well as electron microscopy (EM) and electron tomography (ET), to decipher the molecular architecture of the worm centriole. Achieving an effective lateral resolution of approximately 14 nm, we localize centriolar and PeriCentriolar Material (PCM) components in a comprehensive manner with utmost spatial precision. We found that all 12 components analysed exhibit a ring-like distribution with distinct diameters and often with a 9-fold radial symmetry. Moreover, we uncovered that the procentriole assembles at a location on the centriole margin where SPD-2 and ZYG-1 also accumulate. Moreover, SAS-6 and SAS-5 were found to be present in the nascent procentriole, with SAS-4 and microtubules recruited thereafter. We registered U-Ex-STED and EM data using the radial array of microtubules, thus allowing us to map each centriolar and PCM protein to a specific ultrastructural compartment. Importantly, we discovered that SAS-6 and SAS-4 exhibit a radial symmetry that is offset relative to microtubules, leading to a chiral centriole ensemble. Furthermore, we established that the centriole is surrounded by a region from which ribosomes are excluded and to which SAS-7 localizes. Overall, our work uncovers the molecular architecture of the C. elegans centriole in unprecedented detail and establishes a comprehensive framework for understanding mechanisms of organelle biogenesis and function.

Respiratory Syncytial virus NS1 protein targets the transactivator binding domain of MED25

Human RSV is the leading cause of infantile bronchiolitis in the world and one of the major causes of childhood deaths in resource-poor settings. It is a major unmet target for vaccines and anti-viral drugs. Respiratory syncytial virus has evolved a unique strategy to evade host immune response by coding for two non-structural proteins NS1 and NS2. Recently it was shown that in infected cells, nuclear NS1 could be involved in transcription regulation of host genes linked to innate immune response, via interactions with chromatin and the Mediator complex. Here we identified the MED25 Mediator subunit as an NS1 interactor in a yeast two-hybrid screen. We demonstrate that NS1 directly interacts with MED25 in vitro and in cellula, and that this interaction involves the MED25 transactivator binding ACID domain on the one hand, and the C-terminal α3 helix of NS1, with an additional contribution of the globular domain of NS1, on the other hand. By NMR we show that the NS1 α3 sequence primarily binds to the MED25 ACID H2 face, similarly to the α-helical transactivation domains (TADs) of transcription regulators such as Herpex simplex VP16 and ATF6α, a master regulator of ER stress response activated upon viral infection. Moreover, we found out that the NS1 could compete with ATF6α TAD for binding to MED25. These findings point to a mechanism of NS1 interfering with innate immune response by impairing recruitment by cellular TADs of the Mediator via MED25 and hence transcription of specific genes by RNA polymerase II.

SIPGCN: A Novel Deep Learning Model for Predicting Self-Interacting Proteins from Sequence Information Using Graph Convolutional Networks

Protein is the basic organic substance that constitutes the cell and is the material condition for the life activity and the guarantee of the biological function activity. Elucidating the interactions and functions of proteins is a central task in exploring the mysteries of life. As an important protein interaction, self-interacting protein (SIP) has a critical role. The fast growth of high-throughput experimental techniques among biomolecules has led to a massive influx of available SIP data. How to conduct scientific research using the massive amount of SIP data has become a new challenge that is being faced in related research fields such as biology and medicine. In this work, we design an SIP prediction method SIPGCN using a deep learning graph convolutional network (GCN) based on protein sequences. First, protein sequences are characterized using a position-specific scoring matrix, which is able to describe the biological evolutionary message, then their hidden features are extracted by the deep learning method GCN, and, finally, the random forest is utilized to predict whether there are interrelationships between proteins. In the cross-validation experiment, SIPGCN achieved 93.65% accuracy and 99.64% specificity in the human data set. SIPGCN achieved 90.69% and 99.08% of these two indicators in the yeast data set, respectively. Compared with other feature models and previous methods, SIPGCN showed excellent results. These outcomes suggest that SIPGCN may be a suitable instrument for predicting SIP and may be a reliable candidate for future wet experiments.

OLA-1, an Obg-like ATPase, integrates hunger with temperature information in sensory neurons in C. elegans

Animals detect changes in both their environment and their internal state and modify their behavior accordingly. Yet, it remains largely to be clarified how information of environment and internal state is integrated and how such integrated information modifies behavior. Well-fed C. elegans migrates to past cultivation temperature on a thermal gradient, which is disrupted when animals are starved. We recently reported that the neuronal activities synchronize between a thermosensory neuron AFD and an interneuron AIY, which is directly downstream of AFD, in well-fed animals, while this synchrony is disrupted in starved animals. However, it remained to be determined whether the disruption of the synchrony is derived from modulation of the transmitter release from AFD or from the modification of reception or signal transduction in AIY. By performing forward genetics on a transition of thermotaxis behavior along starvation, we revealed that OLA-1, an Obg-like ATPase, functions in AFD to promote disruption of AFD-AIY synchrony and behavioral transition. Our results suggest that the information of hunger is delivered to the AFD thermosensory neuron and gates transmitter release from AFD to disrupt thermotaxis, thereby shedding light onto a mechanism for the integration of environmental and internal state to modulate behavior.

A role for the mitotic proteins Bub3 and BuGZ in transcriptional regulation of catalase-3 expression

The spindle assembly checkpoint factors Bub3 and BuGZ play critical roles in mitotic process, but little is known about their roles in other cellular processes in eukaryotes. In aerobic organisms, transcriptional regulation of catalase genes in response to developmental or environmental stimuli is necessary for redox homeostasis. Here, we demonstrate that Bub3 and BuGZ negatively regulate cat-3 transcription in the model filamentous fungus Neurospora crassa. The absence of Bub3 caused a significant decrease in BuGZ protein levels. Our data indicate that BuGZ and Bub3 interact directly via the GLEBS domain of BuGZ. Despite loss of the interaction, the amount of BuGZ mutant protein negatively correlated with the cat-3 expression level, indicating that BuGZ amount rather than Bub3-BuGZ interaction determines cat-3 transcription level. Further experiments demonstrated that BuGZ binds directly to the cat-3 gene and responses to cat-3 overexpression induced by oxidative stresses. However, the zinc finger domains of BuGZ have no effects on DNA binding, although mutations of these highly conserved domains lead to loss of cat-3 repression. The deposition of BuGZ along cat-3 chromatin hindered the recruitment of transcription activators GCN4/CPC1 and NC2 complex, thereby preventing the assembly of the transcriptional machinery. Taken together, our results establish a mechanism for how mitotic proteins Bub3 and BuGZ functions in transcriptional regulation in a eukaryotic organism.

Systematic analysis of olfactory protein-protein interactions network of fruitfly, Drosophila melanogaster

Olfaction is one of the physiological traits of insect behavior. Insects have evolved a sophisticated olfactory system and use a combined coding strategy to process general odor. Drosophila melanogaster is a powerful model to reveal the molecular and cellular mechanisms of odor detection. Identifying new olfactory targets through complex interactions will contribute to a better understanding of the functions, interactions, and signaling pathways of olfactory proteins. However, the mechanism of D. melanogaster olfaction is still unclear, and more olfactory proteins are required to be discovered. In this study, we tried to explore essential proteins in the olfactory system of D. melanogaster and conduct protein-protein interactions (PPIs) analysis. We constructed the PPIs network of the olfactory system of D. melanogaster, consisting of 863 proteins and 18,959 interactions. Various methods were used to perform functional enrichment analysis, topological analysis and cluster analysis. Our results confirmed that Class B scavenger receptors (SR-Bs), glutathione S-transferases (GSTs), and UDP-glycosyltransferases (UGTs) play an essential role in olfaction of D. melanogaster. The proteins obtained in this study can be used for subsequent functional identification in D. melanogaster olfactory.

Interaction network among factors involved in heterocyst-patterning in cyanobacteria

The genetically regulated pattern of heterocyst formation in multicellular cyanobacteria represents the simplest model to address how patterns emerge and are established, the signals that control them, and the regulatory pathways that act downstream. Although numerous factors involved in this process have been identified, the mechanisms of action of many of them remain largely unknown. The aim of this study was to identify specific relationships between 14 factors required for cell differentiation and pattern formation by exploring their putative physical interactions in the cyanobacterium model Nostoc sp. PCC 7120 and by probing their evolutionary conservation and distribution across the cyanobacterial phylum. A bacterial two-hybrid assay indicated that 10 of the 14 factors studied here are engaged in more than one protein-protein interaction. The transcriptional regulator PatB was central in this network as it showed the highest number of binary interactions. A phylum-wide genomic survey of the distribution of these factors in cyanobacteria showed that they are all highly conserved in the genomes of heterocyst-forming strains, with the PatN protein being almost restricted to this clade. Interestingly, eight of the factors that were shown to be capable of protein interactions were identified as key elements in the evolutionary genomics analysis. These data suggest that a network of 12 proteins may play a crucial role in heterocyst development and patterning. Unraveling the physical and functional interactions between these factors during heterocyst development will certainly shed light on the mechanisms underlying pattern establishment in cyanobacteria.

A modified TurboID approach identifies tissue-specific centriolar components in C. elegans

Proximity-dependent labeling approaches such as BioID have been a great boon to studies of protein-protein interactions in the context of cytoskeletal structures such as centrosomes which are poorly amenable to traditional biochemical approaches like immunoprecipitation and tandem affinity purification. Yet, these methods have so far not been applied extensively to invertebrate experimental models such as C. elegans given the long labeling times required for the original promiscuous biotin ligase variant BirA*. Here, we show that the recently developed variant TurboID successfully probes the interactomes of both stably associated (SPD-5) and dynamically localized (PLK-1) centrosomal components. We further develop an indirect proximity labeling method employing a GFP nanobody-TurboID fusion, which allows the identification of protein interactors in a tissue-specific manner in the context of the whole animal. Critically, this approach utilizes available endogenous GFP fusions, avoiding the need to generate multiple additional strains for each target protein and the potential complications associated with overexpressing the protein from transgenes. Using this method, we identify homologs of two highly conserved centriolar components, Cep97 and BLD10/Cep135, which are present in various somatic tissues of the worm. Surprisingly, neither protein is expressed in early embryos, likely explaining why these proteins have escaped attention until now. Our work expands the experimental repertoire for C. elegans and opens the door for further studies of tissue-specific variation in centrosome architecture.

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.

Neuropeptide signaling and SKN-1 orchestrate differential responses of the proteostasis network to dissimilar proteotoxic insults

The protein homeostasis (proteostasis) network (PN) encompasses mechanisms that maintain proteome integrity by controlling various biological functions. Loss of proteostasis leads to toxic protein aggregation (proteotoxicity), which underlies the manifestation of neurodegeneration. How the PN responds to dissimilar proteotoxic challenges and how these responses are regulated at the organismal level are largely unknown. Here, we report that, while torsin chaperones protect from the toxicity of neurodegeneration-causing polyglutamine stretches, they exacerbate the toxicity of the Alzheimer's disease-causing Aβ peptide in neurons and muscles. These opposing effects are accompanied by differential modulations of gene expression, including that of three neuropeptides that are involved in tailoring the organismal response to dissimilar proteotoxic insults. This mechanism is regulated by insulin/IGF signaling and the transcription factor SKN-1/NRF. Our work delineates a mechanism by which the PN orchestrates differential responses to dissimilar proteotoxic challenges and points at potential targets for therapeutic interventions.

Comparative Virus-Host Protein Interactions of the Bluetongue Virus NS4 Virulence Factor

Bluetongue virus (BTV) is the etiologic agent of a non-contagious arthropod-borne disease transmitted to wild and domestic ruminants. BTV induces a large panel of clinical manifestations ranging from asymptomatic infection to lethal hemorrhagic fever. Despite the fact that BTV has been studied extensively, we still have little understanding of the molecular determinants of BTV virulence. In our report, we have performed a comparative yeast two-hybrid (Y2H) screening approach to search direct cellular targets of the NS4 virulence factor encoded by two different serotypes of BTV: BTV8 and BTV27. This led to identifying Wilms’ tumor 1-associated protein (WTAP) as a new interactor of the BTV-NS4. In contrast to BTV8, 1, 4 and 25, NS4 proteins from BTV27 and BTV30 are unable to interact with WTAP. This interaction with WTAP is carried by a peptide of 34 amino acids (NS422−55) within its putative coil-coiled structure. Most importantly, we showed that binding to WTAP is restored with a chimeric protein where BTV27-NS4 is substituted by BTV8-NS4 in the region encompassing residue 22 to 55. We also demonstrated that WTAP silencing reduces viral titers and the expression of viral proteins, suggesting that BTV-NS4 targets a cellular function of WTAP to increase its viral replication.

OUP accepted manuscript

Barcode fusion genetics (BFG) utilizes deep sequencing to improve the throughput of protein–protein interaction (PPI) screening in pools. BFG has been implemented in Yeast two-hybrid (Y2H) screens (BFG-Y2H). While Y2H requires test protein pairs to localize in the nucleus for reporter reconstruction, dihydrofolate reductase protein-fragment complementation assay (DHFR-PCA) allows proteins to localize in broader subcellular contexts and proves to be largely orthogonal to Y2H. Here, we implemented BFG to DHFR-PCA (BFG-PCA). This plasmid-based system can leverage ORF collections across model organisms to perform comparative analysis, unlike the original DHFR-PCA that requires yeast genomic integration. The scalability and quality of BFG-PCA were demonstrated by screening human and yeast interactions for >11 000 bait-prey pairs. BFG-PCA showed high-sensitivity and high-specificity for capturing known interactions for both species. BFG-Y2H and BFG-PCA capture distinct sets of PPIs, which can partially be explained based on the domain orientation of the reporter tags. BFG-PCA is a high-throughput protein interaction technology to interrogate binary PPIs that exploits clone collections from any species of interest, expanding the scope of PPI assays.

Scaling-up a fragment-based protein-protein interaction method using a human reference interaction set

Protein–protein interactions (PPIs) are essential in understanding numerous aspects of protein function. Here, we significantly scaled and modified analyses of the recently developed all‐vs‐all sequencing (AVA‐Seq) approach using a gold‐standard human protein interaction set (hsPRS‐v2) containing 98 proteins. Binary interaction analyses recovered 20 of 47 (43%) binary PPIs from this positive reference set (PRS), comparing favorably with other methods. However, the increase of 20× in the interaction search space for AVA‐Seq analysis in this manuscript resulted in numerous changes to the method required for future use in genome‐wide interaction studies. We show that standard sequencing analysis methods must be modified to consider the possible recovery of thousands of positives among millions of tested interactions in a single sequencing run. The PRS data were used to optimize data scaling, auto‐activator removal, rank interaction features (such as orientation and unique fragment pairs), and statistical cutoffs. Using these modifications to the method, AVA‐Seq recovered >500 known and novel PPIs, including interactions between wild‐type fragments of tumor protein p53 and minichromosome maintenance complex proteins 2 and 5 (MCM2 and MCM5) that could be of interest in human disease.

PCMD-1 bridges the centrioles and the pericentriolar material scaffold in C. elegans

Correct cell division relies on the formation of a bipolar spindle. In animal cells, microtubule nucleation at the spindle poles is facilitated by the pericentriolar material (PCM), which assembles around a pair of centrioles. Although centrioles are essential for PCM assembly, the proteins that anchor the PCM to the centrioles are less known. Here, we investigate the molecular function of PCMD-1 in bridging the PCM and the centrioles in Caenorhabditis elegans. We demonstrate that the centrosomal recruitment of PCMD-1 is dependent on the outer centriolar protein SAS-7. The most C-terminal part of PCMD-1 is sufficient to target it to the centrosome, and the coiled-coil domain promotes its accumulation by facilitating self-interaction. We reveal that PCMD-1 interacts with the PCM scaffold protein SPD-5, the mitotic kinase PLK-1 and the centriolar protein SAS-4. Using an ectopic translocation assay, we show that PCMD-1 can selectively recruit downstream PCM scaffold components to an ectopic location in the cell, indicating that PCMD-1 is able to anchor the PCM scaffold proteins at the centrioles. Our work suggests that PCMD-1 is an essential functional bridge between the centrioles and the PCM.

Reduction of Derlin activity suppresses Notch-dependent tumours in the C. elegans germ line

Regulating the balance between self-renewal (proliferation) and differentiation is key to the long-term functioning of all stem cell pools. In the Caenorhabditis elegans germline, the primary signal controlling this balance is the conserved Notch signaling pathway. Gain-of-function mutations in the GLP-1/Notch receptor cause increased stem cell self-renewal, resulting in a tumour of proliferating germline stem cells. Notch gain-of-function mutations activate the receptor, even in the presence of little or no ligand, and have been associated with many human diseases, including cancers. We demonstrate that reduction in CUP-2 and DER-2 function, which are Derlin family proteins that function in endoplasmic reticulum-associated degradation (ERAD), suppresses the C. elegans germline over-proliferation phenotype associated with glp-1(gain-of-function) mutations. We further demonstrate that their reduction does not suppress other mutations that cause over-proliferation, suggesting that over-proliferation suppression due to loss of Derlin activity is specific to glp-1/Notch (gain-of-function) mutations. Reduction of CUP-2 Derlin activity reduces the expression of a read-out of GLP-1/Notch signaling, suggesting that the suppression of over-proliferation in Derlin loss-of-function mutants is due to a reduction in the activity of the mutated GLP-1/Notch(GF) receptor. Over-proliferation suppression in cup-2 mutants is only seen when the Unfolded Protein Response (UPR) is functioning properly, suggesting that the suppression, and reduction in GLP-1/Notch signaling levels, observed in Derlin mutants may be the result of activation of the UPR. Chemically inducing ER stress also suppress glp-1(gf) over-proliferation but not other mutations that cause over-proliferation. Therefore, ER stress and activation of the UPR may help correct for increased GLP-1/Notch signaling levels, and associated over-proliferation, in the C. elegans germline.

Notch signaling is a highly conserved signaling pathway that is utilized in many cell fate decisions in many organisms. In the C. elegans germline, Notch signaling is the primary signal that regulates the balance between stem cell proliferation and differentiation. Notch gain-of-function mutations cause the receptor to be active, even when a signal that is normally needed to activate the receptor is absent. In the germline of C. elegans, gain-of-function mutations in GLP-1, a Notch receptor, results in over-proliferation of the stem cells and tumour formation. Here we demonstrate that a reduction or loss of Derlin activity, which is a conserved family of proteins involved in endoplasmic reticulum-associated degradation (ERAD), suppresses over-proliferation due to GLP-1/Notch gain-of-function mutations. Furthermore, we demonstrate that a surveillance mechanism utilized in cells to monitor and react to proteins that are not folded properly (Unfolded Protein Response-UPR) must be functioning well in order for the loss of Derlin activity to supress over-proliferation caused by glp-1/Notch gain-of-function mutations. This suggests that activation of the UPR may be the mechanism at work for suppressing this type of over-proliferation, when Derlin activity is reduced. Therefore, decreasing Derlin activity may be a means of reducing the impact of phenotypes and diseases due to certain Notch gain-of-function mutations.

RNA methyltransferase METTL16: Targets and function

The N6-methyladenosine (m6A) RNA methyltransferase METTL16 is an emerging player in the RNA modification landscape of the human cell. Originally thought to be a ribosomal RNA methyltransferase, it has now been shown to bind and methylate the MAT2A messenger RNA (mRNA) and U6 small nuclear RNA (snRNA). It has also been shown to bind the MALAT1 long noncoding RNA and several other RNAs. METTL16's methyltransferase domain contains the Rossmann-like fold of class I methyltransferases and uses S-adenosylmethionine (SAM) as the methyl donor. It has an RNA methylation consensus sequence of UACAGARAA (modified A underlined), and structural requirements for its known RNA interactors. In addition to the methyltransferase domain, METTL16 protein has two other RNA binding domains, one of which resides in a vertebrate conserved region, and a putative nuclear localization signal. The role of METTL16 in the cell is still being explored, however evidence suggests it is essential for most cells. This is currently hypothesized to be due to its role in regulating the splicing of MAT2A mRNA in response to cellular SAM levels. However, one of the more pressing questions remaining is what role METTL16's methylation of U6 snRNA plays in splicing and potentially cellular survival. METTL16 also has several other putative coding and noncoding RNA interactors but the definitive methylation status of those RNAs and the role METTL16 plays in their life cycle is yet to be determined. Overall, METTL16 is an intriguing RNA binding protein and methyltransferase whose important functions in the cell are just beginning to be understood. This article is categorized under: RNA Processing > RNA Editing and Modification RNA Interactions with Proteins and Other Molecules > RNA-Protein Complexes.

Network-based analysis of virulence factors for uncovering Aeromonas veronii pathogenesis

BACKGROUND

Aeromonas veronii is a bacterial pathogen in aquaculture, which produces virulence factors to enable it colonize and evade host immune defense. Given that experimental verification of virulence factors is time-consuming and laborious, few virulence factors have been characterized. Moreover, most studies have only focused on single virulence factors, resulting in biased interpretation of the pathogenesis of A. veronii.

RESULTS

In this study, a PPI network at genome-wide scale for A. veronii was first constructed followed by prediction and mapping of virulence factors on the network. When topological characteristics were analyzed, the virulence factors had higher degree and betweenness centrality than other proteins in the network. In particular, the virulence factors tended to interact with each other and were enriched in two network modules. One of the modules mainly consisted of histidine kinases, response regulators, diguanylate cyclases and phosphodiesterases, which play important roles in two-component regulatory systems and the synthesis and degradation of cyclic-diGMP. Construction of the interspecies PPI network between A. veronii and its host Oreochromis niloticus revealed that the virulence factors interacted with homologous proteins in the host. Finally, the structures and interacting sites of the virulence factors during interaction with host proteins were predicted.

CONCLUSIONS

The findings here indicate that the virulence factors probably regulate the virulence of A. veronii by involving in signal transduction pathway and manipulate host biological processes by mimicking and binding competitively to host proteins. Our results give more insight into the pathogenesis of A. veronii and provides important information for designing targeted antibacterial drugs.

PredHPI: an integrated web server platform for the detection and visualization of host-pathogen interactions using sequence-based methods

MOTIVATION

Understanding the mechanisms underlying infectious diseases is fundamental to develop prevention strategies. Host-pathogen interactions (HPIs) are actively studied worldwide to find potential genomic targets for the development of novel drugs, vaccines and other therapeutics. Determining which proteins are involved in the interaction system behind an infectious process is the first step to develop an efficient disease control strategy. Very few computational methods have been implemented as web services to infer novel HPIs, and there is not a single framework which combines several of those approaches to produce and visualize a comprehensive analysis of HPIs.

RESULTS

Here, we introduce PredHPI, a powerful framework that integrates both the detection and visualization of interaction networks in a single web service, facilitating the apprehension of model and non-model host-pathogen systems to aid the biologists in building hypotheses and designing appropriate experiments. PredHPI is built on high-performance computing resources on the backend capable of handling proteome-scale sequence data from both the host as well as pathogen. Data are displayed in an information-rich and interactive visualization, which can be further customized with user-defined layouts. We believe PredHPI will serve as an invaluable resource to diverse experimental biologists and will help advance the research in the understanding of complex infectious diseases.

AVAILABILITY AND IMPLEMENTATION

PredHPI tool is freely available at http://bioinfo.usu.edu/PredHPI/.

SUPPLEMENTARY INFORMATION

Sup plementary data are available at Bioinformatics online.

Artificial intelligence in drug design: algorithms, applications, challenges and ethics

The discovery paradigm of drugs is rapidly growing due to advances in machine learning (ML) and artificial intelligence (AI). This review covers myriad faces of AI and ML in drug design. There is a plethora of AI algorithms, the most common of which are summarized in this review. In addition, AI is fraught with challenges that are highlighted along with plausible solutions to them. Examples are provided to illustrate the use of AI and ML in drug discovery and in predicting drug properties such as binding affinities and interactions, solubility, toxicology, blood–brain barrier permeability and chemical properties. The review also includes examples depicting the implementation of AI and ML in tackling intractable diseases such as COVID-19, cancer and Alzheimer’s disease. Ethical considerations and future perspectives of AI are also covered in this review.

Perspectives on Complexity, Chaos and Thermodynamics in Environmental Pathology

Though complexity science and chaos theory have become a common scientific divulgation theme, medical disciplines, and pathology in particular, still rely on a deterministic, reductionistic approach and still hesitate to fully appreciate the intrinsic complexity of living beings. Herein, complexity, chaos and thermodynamics are introduced with specific regard to biomedical sciences, then their interconnections and implications in environmental pathology are discussed, with particular regard to a morphopathological, image analysis-based approach to biological interfaces. Biomedical disciplines traditionally approach living organisms by dissecting them ideally down to the molecular level in order to gain information about possible molecule to molecule interactions, to derive their macroscopic behaviour. Given the complex and chaotic behaviour of living systems, this approach is extremely limited in terms of obtainable information and may lead to misinterpretation. Environmental pathology, as a multidisciplinary discipline, should grant privilege to an integrated, possibly systemic approach, prone to manage the complex and chaotic aspects characterizing living organisms. Ultimately, environmental pathology should be interested in improving the well-being of individuals and the population, and ideally the health of the entire ecosystem/biosphere and should not focus merely on single diseases, diseased organs/tissues, cells and/or molecules.

Modulation of the ubiquitin-proteasome system by marine natural products

The ubiquitin-proteasome system (UPS) is a key player in the maintenance of cellular protein homeostasis (proteostasis). Since proteasome function declines upon aging leading to the acceleration of its progression and the manifestation of age-related pathologies, many attempts have been performed towards proteasome activation as a strategy to promote healthspan and longevity. The marine environment hosts a plethora of organisms that produce a vast array of primary and secondary metabolites, the majority of which are unique, exhibiting a wide spectrum of biological activities. The fact that these biologically important compounds are also present in edible marine organisms has sparked the interest for elucidating their potential health-related applications. In this review, we focus on the antioxidant, anti-aging, anti-aggregation and anti-photoaging properties of various marine constituents. We further discuss representatives of marine compounds classes with regard to their potential (direct or indirect) action on UPS components that could serve as UPS modulators and exert beneficial effects on conditions such as oxidative stress, aging and age-related diseases.

Identification of host factors binding to dengue and Zika virus subgenomic RNA by efficient yeast three-hybrid screens of the human ORFeome

Flaviviruses such as the dengue (DENV) and the Zika virus (ZIKV) are important human pathogens causing around 100 million symptomatic infections each year. During infection, small subgenomic flavivirus RNAs (sfRNAs) are formed inside the infected host cell as a result of incomplete degradation of the viral RNA genome by cellular exoribonuclease XRN1. Although the full extent of sfRNA functions is to be revealed, these non-coding RNAs are key virulence factors and their detrimental effects on multiple cellular processes seem to consistently involve molecular interactions with RNA-binding proteins (RBPs). Discovery of such sfRNA-binding host-factors has followed established biochemical pull-down approaches skewed towards highly abundant proteins hampering proteome-wide coverage. Yeast three-hybrid (Y3H) systems represent an attractive alternative approach. To facilitate proteome-wide screens for RBP, we revisited and improved existing RNA-Y3H methodology by (1) implementing full-length ORF libraries in combination with (2) efficient yeast mating to increase screening depth and sensitivity, and (3) stringent negative controls to eliminate over-representation of non-specific RNA-binders. These improvements were validated employing the well-characterized interaction between DDX6 (DEAD-box helicase 6) and sfRNA of DENV as paradigm. Our advanced Y3H system was used to screen for human proteins binding to DENV and ZIKV sfRNA, resulting in a list of 69 putative sfRNA-binders, including several previously reported as well as numerous novel RBP host factors. Our methodology requiring no sophisticated infrastructure or analytic pipeline may be employed for the discovery of meaningful RNA-protein interactions at large scale in other fields.

Applying model approaches in non-model systems: A review and case study on coral cell culture

Model systems approaches search for commonality in patterns underlying biological diversity and complexity led by common evolutionary paths. The success of the approach does not rest on the species chosen but on the scalability of the model and methods used to develop the model and engage research. Fine-tuning approaches to improve coral cell cultures will provide a robust platform for studying symbiosis breakdown, the calcification mechanism and its disruption, protein interactions, micronutrient transport/exchange, and the toxicity of nanoparticles, among other key biological aspects, with the added advantage of minimizing the ethical conundrum of repeated testing on ecologically threatened organisms. The work presented here aimed to lay the foundation towards development of effective methods to sort and culture reef-building coral cells with the ultimate goal of obtaining immortal cell lines for the study of bleaching, disease and toxicity at the cellular and polyp levels. To achieve this objective, the team conducted a thorough review and tested the available methods (i.e. cell dissociation, isolation, sorting, attachment and proliferation). The most effective and reproducible techniques were combined to consolidate culture methods and generate uncontaminated coral cell cultures for ~7 days (10 days maximum). The tests were conducted on scleractinian corals Pocillopora acuta of the same genotype to harmonize results and reduce variation linked to genetic diversity. The development of cell separation and identification methods in conjunction with further investigations into coral cell-type specific metabolic requirements will allow us to tailor growth media for optimized monocultures as a tool for studying essential reef-building coral traits such as symbiosis, wound healing and calcification at multiple scales.

Scarcity of scale-free topology is universal across biochemical networks

Biochemical reactions underlie the functioning of all life. Like many examples of biology or technology, the complex set of interactions among molecules within cells and ecosystems poses a challenge for quantification within simple mathematical objects. A large body of research has indicated many real-world biological and technological systems, including biochemistry, can be described by power-law relationships between the numbers of nodes and edges, often described as "scale-free". Recently, new statistical analyses have revealed true scale-free networks are rare. We provide a first application of these methods to data sampled from across two distinct levels of biological organization: individuals and ecosystems. We analyze a large ensemble of biochemical networks including networks generated from data of 785 metagenomes and 1082 genomes (sampled from the three domains of life). The results confirm no more than a few biochemical networks are any more than super-weakly scale-free. Additionally, we test the distinguishability of individual and ecosystem-level biochemical networks and show there is no sharp transition in the structure of biochemical networks across these levels of organization moving from individuals to ecosystems. This result holds across different network projections. Our results indicate that while biochemical networks are not scale-free, they nonetheless exhibit common structure across different levels of organization, independent of the projection chosen, suggestive of shared organizing principles across all biochemical networks.

Exploration of binary protein-protein interactions between tick-borne flaviviruses and Ixodes ricinus

Background

Louping ill virus (LIV) and tick-borne encephalitis virus (TBEV) are tick-borne flaviviruses that are both transmitted by the major European tick, Ixodes ricinus. Despite the importance of I. ricinus as an arthropod vector, its capacity to acquire and subsequently transmit viruses, known as vector competence, is poorly understood. At the molecular scale, vector competence is governed in part by binary interactions established between viral and cellular proteins within infected tick cells.

Methods

To investigate virus-vector protein–protein interactions (PPIs), the entire set of open reading frames for LIV and TBEV was screened against an I. ricinus cDNA library established from three embryonic tick cell lines using yeast two-hybrid methodology (Y2H). PPIs revealed for each viral bait were retested in yeast by applying a gap repair (GR) strategy, and notably against the cognate protein of both viruses, to determine whether the PPIs were specific for a single virus or common to both. The interacting tick proteins were identified by automatic BLASTX, and in silico analyses were performed to expose the biological processes targeted by LIV and TBEV.

Results

For each virus, we identified 24 different PPIs involving six viral proteins and 22 unique tick proteins, with all PPIs being common to both viruses. According to our data, several viral proteins (pM, M, NS2A, NS4A, 2K and NS5) target multiple tick protein modules implicated in critical biological pathways. Of note, the NS5 and pM viral proteins establish PPI with several tumor necrosis factor (TNF) receptor-associated factor (TRAF) proteins, which are essential adaptor proteins at the nexus of multiple signal transduction pathways.

Conclusion

We provide the first description of the TBEV/LIV-I. ricinus PPI network, and indeed of any PPI network involving a tick-borne virus and its tick vector. While further investigation will be needed to elucidate the role of each tick protein in the replication cycle of tick-borne flaviviruses, our study provides a foundation for understanding the vector competence of I. ricinus at the molecular level. Indeed, certain PPIs may represent molecular determinants of vector competence of I. ricinus for TBEV and LIV, and potentially for other tick-borne flaviviruses.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13071-021-04651-3.

Exploring the conservation of Alzheimer-related pathways between H. sapiens and C. elegans: a network alignment approach

Alzheimer disease (AD) is a neurodegenerative disorder with an –as of yet– unclear etiology and pathogenesis. Research to unveil disease processes underlying AD often relies on the use of neurodegenerative disease model organisms, such as Caenorhabditis elegans. This study sought to identify biological pathways implicated in AD that are conserved in Homo sapiens and C. elegans. Protein–protein interaction networks were assembled for amyloid precursor protein (APP) and Tau in H. sapiens—two proteins whose aggregation is a hallmark in AD—and their orthologs APL-1 and PTL-1 for C. elegans. Global network alignment was used to compare these networks and determine similar, likely conserved, network regions. This comparison revealed that two prominent pathways, the APP-processing and the Tau-phosphorylation pathways, are highly conserved in both organisms. While the majority of interactions between proteins in those pathways are known to be associated with AD in human, they remain unexamined in C. elegans, signifying the need for their further investigation. In this work, we have highlighted conserved interactions related to AD in humans and have identified specific proteins that can act as targets for experimental studies in C. elegans, aiming to uncover the underlying mechanisms of AD.

Integrative Analysis Identified Key Schizophrenia Risk Factors from an Abnormal Behavior Mouse Gene Set

Schizophrenia (SCZ) is a severe chronic psychiatric illness with heterogeneous symptoms. However, the pathogenesis of SCZ is unclear, and the number of well-defined SCZ risk factors is limited. We hypothesized that an abnormal behavior (AB) gene set verified by mouse model experiments can be used to better understand SCZ risks. In this work, we carried out an integrative bioinformatics analysis to study two types of risk genes that are either differentially expressed (DEGs) in the case-control study data or carry reported SCZ genetic variants (MUTs). Next, we used RNA-Seq expression data from the hippocampus (HIPPO) and dorsolateral prefrontal cortex (DLPFC) to define the key genes affected by different types (DEGs and MUTs) in different brain regions (DLPFC and HIPPO): DLPFC-kDEG, DLPFC-kMUT, HIPPO-kDEG, and HIPPO-kMUT. The four hub genes (SHANK1, SHANK2, DLG4, and NLGN3) of the biological functionally enriched terms were strongly linked to SCZ via gene co-expression network analysis. Then, we observed that specific spatial expressions of DLPFC-kMUT and HIPPO-kMUT were convergent in the early stages and divergent in the later stages of development. In addition, all four types of key genes showed significantly larger average protein-protein interaction degrees than the background. Comparing the different cell types, the expression of four types of key genes showed specificity in different dimensions. Together, our results offer new insights into potential risk factors and help us understand the complexity and regional heterogeneity of SCZ.

Classification and prediction of protein-protein interaction interface using machine learning algorithm

Structural insight of the protein-protein interaction (PPI) interface can provide knowledge about the kinetics, thermodynamics and molecular functions of the complex while elucidating its role in diseases and further enabling it as a potential therapeutic target. However, owing to experimental lag in solving protein-protein complex structures, three-dimensional (3D) knowledge of the PPI interfaces can be gained via computational approaches like molecular docking and post-docking analyses. Despite development of numerous docking tools and techniques, success in identification of native like interfaces based on docking score functions is limited. Hence, we employed an in-depth investigation of the structural features of the interface that might successfully delineate native complexes from non-native ones. We identify interface properties, which show statistically significant difference between native and non-native interfaces belonging to homo and hetero, protein-protein complexes. Utilizing these properties, a support vector machine (SVM) based classification scheme has been implemented to differentiate native and non-native like complexes generated using docking decoys. Benchmarking and comparative analyses suggest very good performance of our SVM classifiers. Further, protein interactions, which are proven via experimental findings but not resolved structurally, were subjected to this approach where 3D-models of the complexes were generated and most likely interfaces were predicted. A web server called Protein Complex Prediction by Interface Properties (PCPIP) is developed to predict whether interface of a given protein-protein dimer complex resembles known protein interfaces. The server is freely available at http://www.hpppi.iicb.res.in/pcpip/ .