Host-microbe interaction systems biology: lifecycle transcriptomics and comparative genomics

In silico characterization, docking, and simulations to understand host-pathogen interactions in an effort to enhance crop production in date palms

Food safety remains a significant challenge despite the growth and development in agricultural research and the advent of modern biotechnological and agricultural tools. Though the agriculturist struggles to aid the growing population's needs, many pathogen-based plant diseases by their direct impact on cell division and tissue development have led to the loss of tons of food crops every year. Though there are many conventional and traditional methods to overcome this issue, the amount and time spend are huge. Scientists have developed systems biology tools to study the root cause of the problem and rectify it. Host-pathogen protein interactions (HPIs) have a promising role in identifying the pathogens' strategy to conquer the host organism. In this paper, the interactions between the host Rhynchophorus ferrugineus (an invasive wood-boring pest that destroys palm) and the pathogens Proteus mirabilis, Serratia marcescens, and Klebsiella pneumoniae are comprehensively studied using protein-protein interactions, molecular docking, and followed by 200 ns molecular dynamic simulations. This study elucidates the structural and functional basis of these proteins leading towards better plant health, production, and reliability.

NinimHMDA: Neural integration of neighborhood information on a multiplex heterogeneous network for multiple types of human Microbe-Disease association

MOTIVATION

Many computational methods have been recently proposed to identify differentially abundant microbes related to a single disease; however, few studies have focused on large-scale microbe-disease association prediction using existing experimentally verified associations. This area has critical meanings. For example, it can help to rank and select potential candidate microbes for different diseases at-scale for downstream lab validation experiments and it utilizes existing evidence instead of the microbiome abundance data which usually costs money and time to generate.

RESULTS

We construct a multiplex heterogeneous network (MHEN) using human microbe-disease association database, Disbiome, and other prior biological databases, and define the large-scale human microbe-disease association prediction as link prediction problems on MHEN. We develop an end-to-end graph convolutional neural network-based mining model NinimHMDA which can not only integrate different prior biological knowledge but also predict different types of microbe-disease associations (e.g. a microbe may be reduced or elevated under the impact of a disease) using one-time model training. To the best of our knowledge, this is the first method that targets on predicting different association types between microbes and diseases. Results from large-scale cross validation and case studies show that our model is highly competitive compared to other commonly used approaches.

AVAILABILITY

The codes are available at Github https://github.com/yuanjing-ma/NinimHMDA.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Rediscovering a Forgotten System of Symbiosis: Historical Perspective and Future Potential

While the majority of symbiosis research is focused on bacteria, microbial eukaryotes play important roles in the microbiota and as pathogens, especially the incredibly diverse Fungi kingdom. The recent emergence of widespread pathogens in wildlife (bats, amphibians, snakes) and multidrug-resistant opportunists in human populations (Candida auris) has highlighted the importance of better understanding animal-fungus interactions. Regardless of their prominence there are few animal-fungus symbiosis models, but modern technological advances are allowing researchers to utilize novel organisms and systems. Here, I review a forgotten system of animal-fungus interactions: the beetle-fungus symbioses of Drugstore and Cigarette beetles with their symbiont Symbiotaphrina. As pioneering systems for the study of mutualistic symbioses, they were heavily researched between 1920 and 1970, but have received only sporadic attention in the past 40 years. Several features make them unique research organisms, including (1) the symbiont is both extracellular and intracellular during the life cycle of the host, and (2) both beetle and fungus can be cultured in isolation. Specifically, fungal symbionts intracellularly infect cells in the larval and adult beetle gut, while accessory glands in adult females harbor extracellular fungi. In this way, research on the microbiota, pathogenesis/infection, and mutualism can be performed. Furthermore, these beetles are economically important stored-product pests found worldwide. In addition to providing a historical perspective of the research undertaken and an overview of beetle biology and their symbiosis with Symbiotaphrina, I performed two analyses on publicly available genomic data. First, in a preliminary comparative genomic analysis of the fungal symbionts, I found striking differences in the pathways for the biosynthesis of two B vitamins important for the host beetle, thiamine and biotin. Second, I estimated the most recent common ancestor for Drugstore and Cigarette beetles at 8.8-13.5 Mya using sequence divergence (CO1 gene). Together, these analyses demonstrate that modern methods and data (genomics, transcriptomes, etc.) have great potential to transform these beetle-fungus systems into model systems again.

Study on the differential gene expression of elm leaves fed on by Tetraneura akinire Sasaki

BACKGROUND

To study the essential molecular mechanism of gall formation is very important.

OBJECTIVE

To investigate the differential gene expression in leaves fed on by Tetraneura akinire Sasaki and to provide a basis for the better understanding of the essential molecular mechanism of gall formation.

METHODS

The infected leaves of the elm were divided into three periods: initial formation period (T2), growth and differentiation period (T3), and cracking period (T4). The untouched leaves were used as the control (T1). RNA-Seq was performed, and the high-quality sequences were mapped to the reference genome and the elm gene database to obtain the gene expression profiles. The expression level of each gene was calculated by the RPKM method. A combination of FDR ≤ 0.01 and the absolute value of |log2 ratio (T/CK)| ≥ 2 was used as the threshold to determine the significance of gene expression. Finally, GO and pathway enrichment analyses were used to identify the significantly enriched functional classification and metabolic pathways in DEGs.

RESULTS

The results revealed that approximately 244 mRNAs were detected between T1 and T2, including 192 up-regulated and 52 down-regulated mRNAs; approximately 175 mRNAs were detected between T1 and T3, including 145 up-regulated and 30 down-regulated mRNAs; and approximately 372 mRNAs were detected between T1 and T4, including 360 up-regulated and 12 down-regulated mRNAs. Approximately 34 differentially expressed genes were identified by Venn analysis. Comparing the three infection periods to the control, there were 28 up-regulated and six down-regulated mRNAs. Additionally, 562 genes were used for cluster analysis, which revealed that the gene expression in T2 and T3 changed greatly. Genes related to cell proliferation and respiration, such as microtubulin and 6-phosphoric acid fructose kinase were mainly up-regulated during the T2 period. Genes encoding lipoxygenase, glutathione-S-transferase, superoxide dismutase and protease inhibitor were up-regulated during T2 and T3. Genes encoding lignocellulose synthase were up-regulated during T4, which suggests the reinforcement of the cell wall to improve the resistance to the damage of the Tetraneura akinire Sasaki.

CONCLUSIONS

The results showed that the feeding of Tetraneura akinire Sasaki caused the differential expression of elm genes and influenced cellular energy metabolism. These changes in physiological response and gene expression of the elm compose the physiological and molecular basis of the gall formation and may improve the resistance of elm to Tetraneura akinire Sasaki.

Prediction of microbe-disease association from the integration of neighbor and graph with collaborative recommendation model

BACKGROUND

Accumulating clinical researches have shown that specific microbes with abnormal levels are closely associated with the development of various human diseases. Knowledge of microbe-disease associations can provide valuable insights for complex disease mechanism understanding as well as the prevention, diagnosis and treatment of various diseases. However, little effort has been made to predict microbial candidates for human complex diseases on a large scale.

METHODS

In this work, we developed a new computational model for predicting microbe-disease associations by combining two single recommendation methods. Based on the assumption that functionally similar microbes tend to get involved in the mechanism of similar disease, we adopted neighbor-based collaborative filtering and a graph-based scoring method to compute association possibility of microbe-disease pairs. The promising prediction performance could be attributed to the use of hybrid approach based on two single recommendation methods as well as the introduction of Gaussian kernel-based similarity and symptom-based disease similarity.

RESULTS

To evaluate the performance of the proposed model, we implemented leave-one-out and fivefold cross validations on the HMDAD database, which is recently built as the first database collecting experimentally-confirmed microbe-disease associations. As a result, NGRHMDA achieved reliable results with AUCs of 0.9023 ± 0.0031 and 0.9111 in the validation frameworks of fivefold CV and LOOCV. In addition, 78.2% microbe samples and 66.7% disease samples are found to be consistent with the basic assumption of our work that microbes tend to get involved in the similar disease clusters, and vice versa.

CONCLUSIONS

Compared with other methods, the prediction results yielded by NGRHMDA demonstrate its effective prediction performance for microbe-disease associations. It is anticipated that NGRHMDA can be used as a useful tool to search the most potential microbial candidates for various diseases, and therefore boosts the medical knowledge and drug development. The codes and dataset of our work can be downloaded from https://github.com/yahuang1991/NGRHMDA .

In vivo expression technology and 5' end mapping of the Borrelia burgdorferi transcriptome identify novel RNAs expressed during mammalian infection

Borrelia burgdorferi, the bacterial pathogen responsible for Lyme disease, modulates its gene expression profile in response to the environments encountered throughout its tick-mammal infectious cycle. To begin to characterize the B. burgdorferi transcriptome during murine infection, we previously employed an in vivo expression technology-based approach (BbIVET). This identified 233 putative promoters, many of which mapped to un-annotated regions of the complex, segmented genome. Herein, we globally identify the 5' end transcriptome of B. burgdorferi grown in culture as a means to validate non-ORF associated promoters discovered through BbIVET. We demonstrate that 119 BbIVET promoters are associated with transcription start sites (TSSs) and validate novel RNA transcripts using Northern blots and luciferase promoter fusions. Strikingly, 49% of BbIVET promoters were not found to associate with TSSs. This finding suggests that these sequences may be primarily active in the mammalian host. Furthermore, characterization of the 6042 B. burgdorferi TSSs reveals a variety of RNAs including numerous antisense and intragenic transcripts, leaderless RNAs, long untranslated regions and a unique nucleotide frequency for initiating intragenic transcription. Collectively, this is the first comprehensive map of TSSs in B. burgdorferi and characterization of previously un-annotated RNA transcripts expressed by the spirochete during murine infection.

Trachoma and Ocular Chlamydial Infection in the Era of Genomics

Trachoma is a blinding disease usually caused by infection with Chlamydia trachomatis (Ct) serovars A, B, and C in the upper tarsal conjunctiva. Individuals in endemic regions are repeatedly infected with Ct throughout childhood. A proportion of individuals experience prolonged or severe inflammatory episodes that are known to be significant risk factors for ocular scarring in later life. Continued scarring often leads to trichiasis and in-turning of the eyelashes, which causes pain and can eventually cause blindness. The mechanisms driving the chronic immunopathology in the conjunctiva, which largely progresses in the absence of detectable Ct infection in adults, are likely to be multifactorial. Socioeconomic status, education, and behavior have been identified as contributing to the risk of scarring and inflammation. We focus on the contribution of host and pathogen genetic variation, bacterial ecology of the conjunctiva, and host epigenetic imprinting including small RNA regulation by both host and pathogen in the development of ocular pathology. Each of these factors or processes contributes to pathogenic outcomes in other inflammatory diseases and we outline their potential role in trachoma.

Multi-omic network signatures of disease

To better understand dynamic disease processes, integrated multi-omic methods are needed, yet comparing different types of omic data remains difficult. Integrative solutions benefit experimenters by eliminating potential biases that come with single omic analysis. We have developed the methods needed to explore whether a relationship exists between co-expression network models built from transcriptomic and proteomic data types, and whether this relationship can be used to improve the disease signature discovery process. A naïve, correlation based method is utilized for comparison. Using publicly available infectious disease time series data, we analyzed the related co-expression structure of the transcriptome and proteome in response to SARS-CoV infection in mice. Transcript and peptide expression data was filtered using quality scores and subset by taking the intersection on mapped Entrez IDs. Using this data set, independent co-expression networks were built. The networks were integrated by constructing a bipartite module graph based on module member overlap, module summary correlation, and correlation to phenotypes of interest. Compared to the module level results, the naïve approach is hindered by a lack of correlation across data types, less significant enrichment results, and little functional overlap across data types. Our module graph approach avoids these problems, resulting in an integrated omic signature of disease progression, which allows prioritization across data types for down-stream experiment planning. Integrated modules exhibited related functional enrichments and could suggest novel interactions in response to infection. These disease and platform-independent methods can be used to realize the full potential of multi-omic network signatures. The data (experiment SM001) are publically available through the NIAID Systems Virology (https://www.systemsvirology.org) and PNNL (http://omics.pnl.gov) web portals. Phenotype data is found in the supplementary information. The ProCoNA package is available as part of Bioconductor 2.13.

Interspecies protein-protein interaction network construction for characterization of host-pathogen interactions: a Candida albicans-zebrafish interaction study

Background

Despite clinical research and development in the last decades, infectious diseases remain a top global problem in public health today, being responsible for millions of morbidities and mortalities each year. Therefore, many studies have sought to investigate host-pathogen interactions from various viewpoints in attempts to understand pathogenic and defensive mechanisms, which could help control pathogenic infections. However, most of these efforts have focused predominately on the host or the pathogen individually rather than on a simultaneous analysis of both interaction partners.

Results

In this study, with the help of simultaneously quantified time-course Candida albicans-zebrafish interaction transcriptomics and other omics data, a computational framework was developed to construct the interspecies protein-protein interaction (PPI) network for C. albicans-zebrafish interactions based on the inference of ortholog-based PPIs and the dynamic modeling of regulatory responses. The identified C. albicans-zebrafish interspecies PPI network highlights the association between C. albicans pathogenesis and the zebrafish redox process, indicating that redox status is critical in the battle between the host and pathogen.

Conclusions

Advancing from the single-species network construction method, the interspecies network construction approach allows further characterization and elucidation of the host-pathogen interactions. With continued accumulation of interspecies transcriptomics data, the proposed method could be used to explore progressive network rewiring over time, which could benefit the development of network medicine for infectious diseases.

Cell extract-containing medium for culture of intracellular fastidious bacteria

The culture of fastidious microorganisms is a critical step in infectious disease studies. As a proof-of-concept experiment, we evaluated an empirical medium containing eukaryotic cell extracts for its ability to support the growth of Coxiella burnetii. Here, we demonstrate the exponential growth of several bacterial strains, including the C. burnetii Nine Mile phase I and phase II strains, and C. burnetii isolates from humans and animals. Low-oxygen-tension conditions and the presence of small hydrophilic molecules and short peptides were critical for facilitating growth. Moreover, bacterial antigenicity was conserved, revealing the potential for this culture medium to be used in diagnostic tests and in the elaboration of vaccines against C. burnetii. We were also able to grow the majority of previously tested intracellular and fastidious bacterial species, including Tropheryma whipplei, Mycobacterium bovis, Leptospira spp., Borrelia spp., and most putative bioterrorism agents. However, we were unable to culture Rickettsia africae and Legionella spp. in this medium. The versatility of this medium should encourage its use as a replacement for the cell-based culture systems currently used for growing several facultative and putative intracellular bacterial species.

21st century natural product research and drug development and traditional medicines

Natural products and related structures are essential sources of new pharmaceuticals, because of the immense variety of functionally relevant secondary metabolites of microbial and plant species. Furthermore, the development of powerful analytical tools based upon genomics, proteomics, metabolomics, bioinformatics and other 21st century technologies are greatly expediting identification and characterization of these natural products. Here we discuss the synergistic and reciprocal benefits of linking these 'omics technologies with robust ethnobotanical and ethnomedical studies of traditional medicines, to provide critically needed improved medicines and treatments that are inexpensive, accessible, safe and reliable. However, careless application of modern technologies can challenge traditional knowledge and biodiversity that are the foundation of traditional medicines. To address such challenges while fulfilling the need for improved (and new) medicines, we encourage the development of Regional Centres of 'omics Technologies functionally linked with Regional Centres of Genetic Resources, especially in regions of the world where use of traditional medicines is prevalent and essential for health.

Systems biology of host-fungus interactions: turning complexity into simplicity

Modeling interactions between fungi and their hosts at the systems level requires a molecular understanding both of how the host orchestrates immune surveillance and tolerance, and how this activation, in turn, affects fungal adaptation and survival. The transition from the commensal to pathogenic state, and the co-evolution of fungal strains within their hosts, necessitates the molecular dissection of fungal traits responsible for these interactions. There has been a dramatic increase in publically available genome-wide resources addressing fungal pathophysiology and host–fungal immunology. The integration of these existing data and emerging large-scale technologies addressing host–pathogen interactions requires novel tools to connect genome-wide data sets and theoretical approaches with experimental validation so as to identify inherent and emerging properties of host–pathogen relationships and to obtain a holistic view of infectious processes. If successful, a better understanding of the immune response in health and microbial diseases will eventually emerge and pave the way for improved therapies.

Characterization and identification of productivity-associated rhizobacteria in wheat

The rhizosphere is populated by a numerous and diverse array of rhizobacteria, and many impact productivity in largely unknown ways. Here we characterize the rhizobacterial community in a wheat variety categorized according to shoot biomass using 16S rRNA pyrosequencing abundance data. Plants were grown in homogenized field soil under greenhouse conditions, and DNA was extracted and pyrosequenced, resulting in 29,007 quality sequences. Operational taxonomic units (OTUs) that were significantly associated with biomass productivity were identified using an exact test adjusted for the false-discovery rate. The productivity deviation expressed as a percentage of the total mean square for regression (PMSR) was determined for each OTU. Out of 719 OTUs, 42 showed significant positive associations and 39 showed significant negative associations (q value, ≤0.05). OTUs with the greatest net positive associations, by genus, were as follows: Duganella, OTU 43 and OTU 3; Janthinobacterium, OTU 278; Pseudomonas, OTU 588; and Cellvibrio, OTU 1847. Those with negative associations were as follows: Bacteria, OTU 273; Chryseobacterium, OTU 508; Proteobacteria, OTU 249; and Enterobacter, OTU 357. Shoot biomass productivity was strongly correlated with the balance between the overall abundances of positive- and negative-productivity-associated OTUs. High-productivity rhizospheres contained 9.2 significant positives for every negatively associated rhizobacterium, while low-productivity rhizospheres showed 2.3 significant negatives for every positively associated rhizobacterium. Overall rhizobacterial community diversity as measured by the Chao1, Shannon, and Simpson indexes was nonlinearly related to productivity, closely fitting a wavelike cubic equation. We conclude that shoot biomass productivity is strongly related to the ratio of positive- to negative-productivity-associated rhizobacteria in the rhizosphere. This study identifies significant OTUs composing the productive and unproductive rhizobacterial communities.

RNA-seq based transcriptional map of bovine respiratory disease pathogen "Histophilus somni 2336"

Genome structural annotation, i.e., identification and demarcation of the boundaries for all the functional elements in a genome (e.g., genes, non-coding RNAs, proteins and regulatory elements), is a prerequisite for systems level analysis. Current genome annotation programs do not identify all of the functional elements of the genome, especially small non-coding RNAs (sRNAs). Whole genome transcriptome analysis is a complementary method to identify “novel” genes, small RNAs, regulatory regions, and operon structures, thus improving the structural annotation in bacteria. In particular, the identification of non-coding RNAs has revealed their widespread occurrence and functional importance in gene regulation, stress and virulence. However, very little is known about non-coding transcripts in Histophilus somni, one of the causative agents of Bovine Respiratory Disease (BRD) as well as bovine infertility, abortion, septicemia, arthritis, myocarditis, and thrombotic meningoencephalitis. In this study, we report a single nucleotide resolution transcriptome map of H. somni strain 2336 using RNA-Seq method.

The RNA-Seq based transcriptome map identified 94 sRNAs in the H. somni genome of which 82 sRNAs were never predicted or reported in earlier studies. We also identified 38 novel potential protein coding open reading frames that were absent in the current genome annotation. The transcriptome map allowed the identification of 278 operon (total 730 genes) structures in the genome. When compared with the genome sequence of a non-virulent strain 129Pt, a disproportionate number of sRNAs (∼30%) were located in genomic region unique to strain 2336 (∼18% of the total genome). This observation suggests that a number of the newly identified sRNAs in strain 2336 may be involved in strain-specific adaptations.

From transcriptional landscapes to the identification of biomarkers for robustness

The ability of microorganisms to adapt to changing environments and gain cell robustness, challenges the prediction of their history-dependent behaviour. Using our model organism Bacillus cereus, a notorious Gram-positive food spoilage and pathogenic spore-forming bacterium, a strategy will be described that allows for identification of biomarkers for robustness. First an overview will be presented of its two-component systems that generally include a transmembrane sensor histidine kinase and its cognate response regulator, allowing rapid and robust responses to fluctuations in the environment. The role of the multisensor hybrid kinase RsbK and the PP2C-type phosphatase RsbY system in activation of the general stress sigma factor σB is highlighted. An extensive comparative analysis of transcriptional landscapes derived from B. cereus exposed to mild stress conditions such as heat, acid, salt and oxidative stress, revealed that, amongst others σB regulated genes were induced in most conditions tested. The information derived from the transcriptome data was subsequently implemented in a framework for identifying and selecting cellular biomarkers at their mRNA, protein and/or activity level, for mild stressinduced microbial robustness towards lethal stresses. Exposure of unstressed and mild stress-adapted cells to subsequent lethal stress conditions (heat, acid and oxidative stress) allowed for quantification of the robustness advantage provided by mild stress pretreatment using the plate-count method. The induction levels of the selected candidate-biomarkers, σB protein, catalase activity and transcripts of certain proteases upon mild stress treatment, were significantly correlated to mild stress-induced enhanced robustness towards lethal thermal, oxidative and acid stresses, and were therefore suitable to predict these adaptive traits. Cellular biomarkers that are quantitatively correlated to adaptive behavior will facilitate our ability to predict the impact of adaptive behavior on cell robustness and will allow to control and/or exploit these adaptive traits. Extrapolation to other species and genera is discussed such as avenues towards mechanism-based design of microbial fitness and robustness.

Bacterial pathogen evolution: breaking news

The immense social and economic impact of bacterial pathogens, from drug-resistant infections in hospitals to the devastation of agricultural resources, has resulted in major investment to understand the causes and consequences of pathogen evolution. Recent genome sequencing projects have provided insight into the evolution of bacterial genome structures; revealing the impact of mobile DNA on genome restructuring and pathogenicity. Sequencing of multiple genomes of related strains has enabled the delineation of pathogen evolution and facilitated the tracking of bacterial pathogens globally. Other recent theoretical and empirical studies have shown that pathogen evolution is significantly influenced by ecological factors, such as the distribution of hosts within the environment and the effects of co-infection. We suggest that the time is ripe for experimentalists to use genomics in conjunction with evolutionary ecology experiments to further understanding of how bacterial pathogens evolve.

Systems biology approaches to understanding mycobacterial survival mechanisms

The advent of high-throughput platforms for the interrogation of biological systems at the cellular and molecular level have allowed living cells to be observed and understood at a hitherto unprecedented level of detail and have enabled the construction of comprehensive, predictive in silico models. Here, we review the application of such high-throughput, systems-biological techniques to mycobacteria-specifically to the pernicious human pathogen Mycobacterium tuberculosis (MTb) and its ability to survive in human hosts. We discuss the development and application of transcriptomic, proteomic, regulomic, and metabolomic techniques for MTb as well as the development and application of genome-scale in silico models. Thus far, systems-biological approaches have largely focused on in vitro models of MTb growth; reliably extending these approaches to in vivo conditions relevant to infection is a significant challenge for the future that holds the ultimate promise of novel chemotherapeutic interventions.

Integration of ‘omics’ data: does it lead to new insights into host–microbe interactions?

The interaction between both beneficial and pathogenic microbes and their host has been the subject of many studies. Although the field of systems biology is rapidly evolving, the use of a systems biology approach by means of high-throughput techniques to study host–microbe interactions is just beginning to be explored. In this review, we discuss some of the most recently developed high-throughput ‘omics’ techniques and their use in the context of host–microbe interaction. Moreover, we highlight studies combining several techniques that are pioneering the integration of ‘omics’ data related to host–microbe interactions. Finally, we list the major challenges ahead for successful systems biology research on host–microbe interactions.