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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.

Differentially Expressed Genes, miRNAs and Network Models: A Strategy to Shed Light on Molecular Interactions Driving HNSCC Tumorigenesis

Head and neck squamous cell carcinoma (HNSCC) is among the most common cancer worldwide, accounting for hundreds thousands deaths annually. Unfortunately, most patients are diagnosed in an advanced stage and only a percentage respond favorably to therapies. To help fill this gap, we hereby propose a retrospective in silico study to shed light on gene-miRNA interactions driving the development of HNSCC. Moreover, to identify topological biomarkers as a source for designing new drugs. To achieve this, gene and miRNA profiles from patients and controls are holistically reevaluated using protein-protein interaction (PPI) and bipartite miRNA-target networks. Cytoskeletal remodeling, extracellular matrix (ECM), immune system, proteolysis, and energy metabolism have emerged as major functional modules involved in the pathogenesis of HNSCC. Of note, the landscape of our findings depicts a concerted molecular action in activating genes promoting cell cycle and proliferation, and inactivating those suppressive. In this scenario, genes, including VEGFA, EMP1, PPL, KRAS, MET, TP53, MMPs and HOXs, and miRNAs, including mir-6728 and mir-99a, emerge as key players in the molecular interactions driving HNSCC tumorigenesis. Despite the heterogeneity characterizing these HNSCC subtypes, and the limitations of a study pointing to relationships that could be context dependent, the overlap with previously published studies is encouraging. Hence, it supports further investigation for key molecules, both those already and not correlated to HNSCC.

The Protein Network in Subcutaneous Fat Biopsies from Patients with AL Amyloidosis: More Than Diagnosis?

AL amyloidosis is caused by the misfolding of immunoglobulin light chains leading to an impaired function of tissues and organs in which they accumulate. Due to the paucity of -omics profiles from undissected samples, few studies have addressed amyloid-related damage system wide. To fill this gap, we evaluated proteome changes in the abdominal subcutaneous adipose tissue of patients affected by the AL isotypes κ and λ. Through our retrospective analysis based on graph theory, we have herein deduced new insights representing a step forward from the pioneering proteomic investigations previously published by our group. ECM/cytoskeleton, oxidative stress and proteostasis were confirmed as leading processes. In this scenario, some proteins, including glutathione peroxidase 1 (GPX1), tubulins and the TRiC complex, were classified as biologically and topologically relevant. These and other results overlap with those already reported for other amyloidoses, supporting the hypothesis that amyloidogenic proteins could induce similar mechanisms independently of the main fibril precursor and of the target tissues/organs. Of course, further studies based on larger patient cohorts and different tissues/organs will be essential, which would be a key point that would allow for a more robust selection of the main molecular players and a more accurate correlation with clinical aspects.

Trojan-horse silk fibroin nanocarriers loaded with a re-call antigen to redirect immunity against cancer

BACKGROUND

The current challenge for immunotherapies is to generate effective antitumor immunity. Since tumor immune escape mechanisms do not impact pre-existing and consolidated immune responses, we tested the hypothesis of redirecting a pregenerated immunity to cancer: to recall a non-tumor antigen response against the tumor, silk fibroin nanoparticles (SFNs) have been selected as 'Trojan-horse' carriers, promoting the antigen uptake by the tumor cells.

METHODS

SFNs have been loaded with either ovalbumin (OVA) or CpG oligonucleotide (CpG) as antigen or adjuvant, respectively. In vitro uptake of SFNs by tumor (B16/F10 melanoma and MB49 bladder cancer) or dendritic cells, as well as the presence of OVA-specific T cells in splenic and tumor-infiltrating lymphocytes, were assessed by cytometric analyses. Proof-of-concept of in vivo efficacy was achieved in an OVA-hyperimmune B16/F10 murine melanoma model: SFNs-OVA or SFNs-CpG were injected, separately or in association, into the subcutaneous peritumoral area. Cancer dimensions/survival time were monitored, while, at the molecular level, system biology approaches based on graph theory and experimental proteomic data were performed.

RESULTS

SFNs were efficiently in vitro uptaken by cancer and dendritic cells. In vivo peritumor administration of SFNs-OVA redirected OVA-specific cytotoxic T cells intratumorally. Proteomics and systems biology showed that peritumoral treatment with either SFNs-OVA or SFNs-CpG dramatically modified tumor microenvironment with respect to the control (CTR), mainly involving functional modules and hubs related to angiogenesis, inflammatory mediators, immune function, T complex and serpins expression, redox homeostasis, and energetic metabolism. Both SFNs-OVA and SFNs-CpG significantly delayed melanoma growth/survival time, and their effect was additive.

CONCLUSIONS

Both SFNs-OVA and SFNs-CpG induce effective anticancer response through complementary mechanisms and show the efficacy of an innovative active immunotherapy approach based on the redirection of pre-existing immunity against cancer cells. This approach could be universally applied for solid cancer treatments if translated into the clinic using re-call antigens of childhood vaccination.

Rhizobacteria communities reshaped by red mud based passivators is vital for reducing soil Cd accumulation in edible amaranth

Red mud (RM) was constantly reported to immobilize soil cadmium (Cd) and reduce Cd uptake by crops, but few studies investigated whether and how RM influenced rhizobacteria communities, which was a vital factor determining Cd bioavailability and plant growth. To address this concern, high-throughput sequencing and bioinformatics were used to analyze microbiological mechanisms underlying RM application reducing Cd accumulation in edible amaranth. Based on multiple statistical models (Detrended correspondence analysis, Bray-Curtis, weighted UniFrac, and Phylogenetic tree), this study found that RM reduced Cd content in plants not only through increasing rhizosphere soil pH, but by reshaping rhizobacteria communities. Special taxa (Alphaproteobacteria, Gammaproteobacteria, Actinobacteriota, and Gemmatimonadota) associated with growth promotion, anti-disease ability, and Cd resistance of plants preferentially colonized in the rhizosphere. Moreover, RM distinctly facilitated soil microbes' proliferation and microbial biofilm formation by up-regulating intracellular organic metabolism pathways and down-regulating cell motility metabolic pathways, and these microbial metabolites/microbial biofilm (e.g., organic acid, carbohydrates, proteins, S^2-, and PO₄^3-) and microbial cells immobilized rhizosphere soil Cd via the biosorption and chemical chelation. This study revealed an important role of reshaped rhizobacteria communities acting in reducing Cd content in plants after RM application.

Rhizosphere analysis of field-grown Panax ginseng with different degrees of red skin provides the basis for preventing red skin syndrome

Background

Ginseng red skin root syndrome (GRS) is one of the most common ginseng (Panax ginseng Meyer) diseases. It leads to a severe decline in P. ginseng quality and seriously affects the P. ginseng industry in China. However, as a root disease, the characteristics of the GRS rhizosphere microbiome are still unclear.

Methods

The amplicon bacterial 16 S rRNA genes and fungal ITS (Internal Transcribed Spacer) regions Illumina sequencing technology, combined with microbial diversity and composition analysis based on R software, was used to explore the relationship between soil ecological environment and GRS.

Results

There were significant differences in the diversity and richness of soil microorganisms between the rhizosphere with different degrees of disease, especially between healthy P. ginseng (HG) and heavily diseased groups. The variation characteristics of microbial abundance in different taxa levels were analyzed. The interaction network of rhizosphere microorganisms of P. ginseng under GRS background was established. We also found that different P. ginseng rhizosphere microbial communities have multiple changes in stability and complexity through the established interaction network. Microbes closely related to potential pathogenic fungi were also identified according to the interaction network, which provided clues for looking for biological control agents. Finally, the Distance-based redundancy analysis (dbRDA) results indicated that total phosphorus (TP), available potassium (AK), available phosphorus (AP), catalase (CAT), invertase (INV) are the key factors that influence the microbial communities. Moreover, the content of these key factors in the rhizosphere was negatively correlated with disease degrees.

Conclusions

In this study, we comprehensively analyzed the rhizosphere characteristics of P. ginseng with different levels of disease, and explored the interaction relationship among microorganisms. These results provide a basis for soil improvement and biological control of field-grown in the future.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12866-021-02430-9.

Ecological network analysis reveals distinctive microbial modules associated with heavy metal contamination of abandoned mine soils in Korea

Heavy metal pollution in soil around abandoned mine sites is one of the most critical environmental issues worldwide. Soil microbes form complex communities and perform ecological functions individually or in cooperation with other organisms to adapt to harsh environments. In this study, we investigated the distribution patterns of bacterial and fungal communities in non-contaminated and heavy metal-contaminated soil of the abandoned Samkwang mine in Korea to explore microbial interaction mechanisms and their modular structures. As expected, the bacterial and fungal community structures showed large differences depending on the degree of heavy metal contamination. The microbial network was divided into three modules based on the levels of heavy metal pollution: heavy metal-tolerant (HM-Tol), heavy metal-mid-tolerant (HM-mTol), and heavy metal-sensitive (HM-Sens) modules. Taxonomically, microbes assigned to Vicinamibacterales, Pedosphaeraceae, Nitrosomonadaceae, and Gemmatimonadales were the major groups constituting the HM-Tol module. Among the detected heavy metals (As, Pb, Cd, Cu, and Zn), copper concentrations played a key role in the formation of the HM-Tol module. In addition, filamentous fungi (Fusarium and Mortierella) showed potential interactions with bacteria (Nitrosomonadaceae) that could contribute to module stability in heavy metal-contaminated areas. Overall, heavy metal contamination was accompanied by distinct microbial communities, which could participate in the bioremediation of heavy metals. Analysis of the microbial interactions among bacteria and fungi in the presence of heavy metals could provide fundamental information for developing bioremediation mechanisms for the recovery of heavy metal-contaminated soil.

Delineating infection strategies of Leishmania donovani secretory proteins in Human through host-pathogen protein Interactome prediction

Interactions of Leishmania donovani secretory virulence factors with the host proteins and their interplay during the infection process in humans is poorly studied in Visceral Leishmaniasis. Lack of a holistic study of pathway level de-regulations caused due to these virulence factors leads to a poor understanding of the parasite strategies to subvert the host immune responses, secure its survival inside the host and further the spread of infection to the visceral organs. In this study, we propose a computational workflow to predict host-pathogen protein interactome of L.donovani secretory virulence factors with human proteins combining sequence-based Interolog mapping and structure-based Domain Interaction mapping techniques. We further employ graph theoretical approaches and shortest path methods to analyze the interactome. Our study deciphers the infection paths involving some unique and understudied disease-associated signaling pathways influencing the cellular phenotypic responses in the host. Our statistical analysis based in silico knockout study unveils for the first time UBC, 1433Z and HS90A mediator proteins as potential immunomodulatory candidates through which the virulence factors employ the infection paths. These identified pathways and novel mediator proteins can be effectively used as possible targets to control and modulate the infection process further aiding in the treatment of Visceral Leishmaniasis.

Topological clustering of regulatory genes confers pathogenic tolerance to cassava brown streak virus (CBSV) in cassava

Robustness, a naïve property of biological systems, enables organisms to maintain functions during perturbation and is crucial for improving the resilience of crops to prevailing stress conditions and diseases, guaranteeing food security. Most studies of robustness in crops have focused on genetic superiority based upon individual genes, overlooking the collaborative actions of multiple responsive genes and the regulatory network topology. This research aims to uncover patterns of gene cooperation leading to organismal robustness by studying the topology of gene co-expression networks (GCNs) of both CBSV virus resistant and susceptible cassava cultivars. The resulting GCNs show higher topological clustering of cooperative genes in the resistant cultivar, suggesting that the network architecture is central to attaining robustness. Despite a reduction in the number of hub genes in the resistant cultivar following the perturbation, essential biological functions contained in the network were maintained through neighboring genes that withstood the shock. The susceptible cultivar seemingly coped by inducing more gene actions in the network but could not maintain the functions required for plant growth. These findings underscore the importance of regulatory network architecture in ensuring phenotypic robustness and deepen our understanding of transcriptional regulation.

WIPER: Weighted in-Path Edge Ranking for biomolecular association networks

Background

In network biology researchers generate biomolecular networks with candidate genes or proteins experimentally-derived from high-throughput data and known biomolecular associations. Current bioinformatics research focuses on characterizing candidate genes/proteins, or nodes, with network characteristics, e.g., betweenness centrality. However, there have been few research reports to characterize and prioritize biomolecular associations ("edges"), which can represent gene regulatory events essential to biological processes.

Method

We developed Weighted In-Path Edge Ranking (WIPER), a new computational algorithm which can help evaluate all biomolecular interactions/associations ("edges") in a network model and generate a rank order of every edge based on their in-path traversal scores and statistical significance test result. To validate whether WIPER worked as we designed, we tested the algorithm on synthetic network models.

Results

Our results showed WIPER can reliably discover both critical "well traversed in-path edges", which are statistically more traversed than normal edges, and "peripheral in-path edges", which are less traversed than normal edges. Compared with other simple measures such as betweenness centrality, WIPER provides better biological interpretations. In the case study of analyzing postanal pig hearts gene expression, WIPER highlighted new signaling pathways suggestive of cardiomyocyte regeneration and proliferation. In the case study of Alzheimer's disease genetic disorder association, WIPER reports SRC:APP, AR:APP, APP:FYN, and APP:NES edges (gene-gene associations) both statistically and biologically important from PubMed co-citation.

Conclusion

We believe that WIPER will become an essential software tool to help biologists discover and validate essential signaling/regulatory events from high-throughput biology data in the context of biological networks.

Availability

The free WIPER API is described at discovery.informatics.uab.edu/wiper/.

Habitat Heterogeneity and Connectivity: Effects on the Planktonic Protist Community Structure at Two Adjacent Coastal Sites (the Lagoon and the Gulf of Venice, Northern Adriatic Sea, Italy) Revealed by Metabarcoding

The Lagoon of Venice (LoV) and the Gulf of Venice (GoV), two adjacent coastal Long Term Ecological Research (LTER) sites in the northern Adriatic Sea, represent a transitional/marine coupled ecosystem under the influence of regional and local factors. In this study, these sites were sampled on four dates from April 2016 to February 2017 for environmental DNA and relevant abiotic variables, aiming to assess the relative importance of habitat heterogeneity and connectivity in structuring the protist community. High Throughput Sequencing of V4-18S rRNA gene from 56 samples collected at seven stations produced ca 6 million reads, grouped into 7,336 Operational Taxonomic Units (OTUs) at 97% similarity, which were affiliated to protists belonging to 34 taxonomic groups. The whole community was dominated by Bacillariophyta, especially in spring-summer in the LoV, and by Dinophyta, mainly in the GoV. Ciliophora, Syndiniales, and Cryptophyceae were the next more abundant groups. The community structure varied across the seasons and was different in the two ecosystems, which shared 96% of the reads but showed a high proportion of OTUs distributed preferentially in one of the two sites (specialists) and a different partitioning of trophic categories. GoV specialists were mainly Dinophyceae (>56%), followed by Syndiniales and Bacillariophyta, while the LoV specialists were distributed among several groups, including Bacillariophyta, Syndiniales, Ciliophora, Cryptophyceae, and Trebouxiophyceae. The main abiotic drivers of the differences between protist communities were salinity and temperature, which however explained a minor part of the variance (17%), pointing at a higher relevance of biotic factors and inter-taxa relationships. This was more evident in the LoV, where the network analysis highlighted a higher number of OTUs' connections than in the GoV. Overall, the metabarcoding approach allowed to depict the composition of the whole protist community in the lagoon and adjacent coastal waters with high resolution, revealing many taxa so far not reported in the area. In addition, despite no clear barrier to dispersal processes, differences in the relative abundance and temporal variability of local protist communities indicate that environmental heterogeneity, in these adjacent and connected ecosystems, can be strong enough to allow for ecological segregation.

Metagenomic Characterization Reveals Pronounced Seasonality in the Diversity and Structure of the Phyllosphere Bacterial Community in a Mediterranean Ecosystem

We explore how the phyllosphere microbial community responds to a very seasonal environment such as the Mediterranean. For this, we studied the epiphytic bacterial community of a Mediterranean ecosystem in summer and winter, expecting to detect seasonal differences at their maximum. With high-throughput sequencing (HTS), we detected the operational taxonomic units (OTUs) present in the phyllosphere and also in the surrounding air. The epiphytic community is approximately five orders of magnitude denser than the airborne one and is made almost exclusively by habitat specialists. The two communities differ considerably but Proteobacteria and Actinobacteria are dominant in both. Of the five most abundant phyllosphere OTUs, two were closely related to Sphingomonas strains, one to Methylobacterium and the other two to Rhizobiales and Burkholderiales. We found the epiphytic community to become much richer, more distinct, even and diverse, denser and more connected in summer. In contrast, there was no difference in the level of bacterial colonization of the phyllosphere between the two seasons, although there were seasonal differences for individual taxonomic groups: Firmicutes, Gemmatimonadetes and Chlroroflexi had a higher participation in summer, whereas the major Proteobacteria classes presented reverse patterns, with Betaproteobacteria increasing in summer at the expense of the prominent Alphaproteobacteria.

Mapping biological process relationships and disease perturbations within a pathway network

Molecular interaction networks are routinely used to map the organization of cellular function. Edges represent interactions between genes, proteins, or metabolites. However, in living cells, molecular interactions are dynamic, necessitating context-dependent models. Contextual information can be integrated into molecular interaction networks through the inclusion of additional molecular data, but there are concerns about completeness and relevance of this data. We developed an approach for representing the organization of human cellular processes using pathways as the nodes in a network. Pathways represent spatial and temporal sets of context-dependent interactions, generating a high-level network when linked together, which incorporates contextual information without the need for molecular interaction data. Analysis of the pathway network revealed linked communities representing functional relationships, comparable to those found in molecular networks, including metabolism, signaling, immunity, and the cell cycle. We mapped a range of diseases onto this network and find that pathways associated with diseases tend to be functionally connected, highlighting the perturbed functions that result in disease phenotypes. We demonstrated that disease pathways cluster within the network. We then examined the distribution of cancer pathways and showed that cancer pathways tend to localize within the signaling, DNA processes and immune modules, although some cancer-associated nodes are found in other network regions. Altogether, we generated a high-confidence functional network, which avoids some of the shortcomings faced by conventional molecular models. Our representation provides an intuitive functional interpretation of cellular organization, which relies only on high-quality pathway and Gene Ontology data. The network is available at https://data.mendeley.com/datasets/3pbwkxjxg9/1.

Building protein-protein interaction networks for Leishmania species through protein structural information

Background

Systematic analysis of a parasite interactome is a key approach to understand different biological processes. It makes possible to elucidate disease mechanisms, to predict protein functions and to select promising targets for drug development. Currently, several approaches for protein interaction prediction for non-model species incorporate only small fractions of the entire proteomes and their interactions. Based on this perspective, this study presents an integration of computational methodologies, protein network predictions and comparative analysis of the protozoan species Leishmania braziliensis and Leishmania infantum. These parasites cause Leishmaniasis, a worldwide distributed and neglected disease, with limited treatment options using currently available drugs.

Results

The predicted interactions were obtained from a meta-approach, applying rigid body docking tests and template-based docking on protein structures predicted by different comparative modeling techniques. In addition, we trained a machine-learning algorithm (Gradient Boosting) using docking information performed on a curated set of positive and negative protein interaction data. Our final model obtained an AUC = 0.88, with recall = 0.69, specificity = 0.88 and precision = 0.83. Using this approach, it was possible to confidently predict 681 protein structures and 6198 protein interactions for L. braziliensis, and 708 protein structures and 7391 protein interactions for L. infantum. The predicted networks were integrated to protein interaction data already available, analyzed using several topological features and used to classify proteins as essential for network stability.

Conclusions

The present study allowed to demonstrate the importance of integrating different methodologies of interaction prediction to increase the coverage of the protein interaction of the studied protocols, besides it made available protein structures and interactions not previously reported.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2105-6) contains supplementary material, which is available to authorized users.

Interactome Analysis of Microtubule-targeting Agents Reveals Cytotoxicity Bases in Normal Cells

Cancer causes millions of deaths annually and microtubule-targeting agents (MTAs) are the most commonly-used anti-cancer drugs. However, the high toxicity of MTAs on normal cells raises great concern. Due to the non-selectivity of MTA targets, we analyzed the interaction network in a non-cancerous human cell. Subnetworks of fourteen MTAs were reconstructed and the merged network was compared against a randomized network to evaluate the functional richness. We found that 71.4% of the MTA interactome nodes are shared, which affects cellular processes such as apoptosis, cell differentiation, cell cycle control, stress response, and regulation of energy metabolism. Additionally, possible secondary targets were identified as client proteins of interphase microtubules. MTAs affect apoptosis signaling pathways by interacting with client proteins of interphase microtubules, suggesting that their primary targets are non-tumor cells. The paclitaxel and doxorubicin networks share essential topological axes, suggesting synergistic effects. This may explain the exacerbated toxicity observed when paclitaxel and doxorubicin are used in combination for cancer treatment.

The pangenome of the genus Clostridium

The pangenome for the genus Clostridium sensu stricto, which was obtained using highly curated and annotated genomes from 16 species is presented; some of these cause disease, while others are used for the production of added-value chemicals. Multilocus sequencing analysis revealed that species of this genus group into at least two clades that include non-pathogenic and pathogenic strains, suggesting that pathogenicity is dispersed across the phylogenetic tree. The core genome of the genus includes 546 protein families, which mainly comprise those involved in protein translation and DNA repair. The GS-GOGAT may represent the central pathway for generating organic nitrogen from inorganic nitrogen sources. Glycerol and glucose metabolism genes are well represented in the core genome together with a set of energy conservation systems. A metabolic network comprising proteins/enzymes, RNAs and metabolites, whose topological structure is a non-random and scale-free network with hierarchically structured modules was built. These modules shed light on the interactions between RNAs, proteins and metabolites, revealing biological features of transcription and translation, cell wall biosynthesis, C1 metabolism and N metabolism. Network analysis identified four nodes that function as hubs and bottlenecks, namely, coenzyme A, HPr kinases, S-adenosylmethionine and the ribonuclease P-protein, suggesting pivotal roles for them in Clostridium.