An OMICs-based meta-analysis to support infection state stratification.

MOTIVATION: A fundamental problem for disease treatment is that while antibiotics are a powerful counter to bacteria, they are ineffective against viruses. Often, bacterial and viral infections are confused due to their similar symptoms and lack of rapid diagnostics. With many clinicians relying primarily on symptoms for diagnosis, overuse and misuse of modern antibiotics are rife, contributing to the growing pool of antibiotic resistance. To ensure an individual receives optimal treatment given their disease state and to reduce over-prescription of antibiotics, the host response can in theory be measured quickly to distinguish between the two states. To establish a predictive biomarker panel of disease state (viral/bacterial/no-infection), we conducted a meta-analysis of human blood infection studies using machine learning. RESULTS: We focused on publicly available gene expression data from two widely used platforms, Affymetrix and Illumina microarrays as they represented a significant proportion of the available data. We were able to develop multi-class models with high accuracies with our best model predicting 93% of bacterial and 89% viral samples correctly. To compare the selected features in each of the different technologies, we reverse-engineered the underlying molecular regulatory network and explored the neighbourhood of the selected features. The networks highlighted that although on the gene-level the models differed, they contained genes from the same areas of the network. Specifically, this convergence was to pathways including the Type I interferon Signalling Pathway, Chemotaxis, Apoptotic Processes and Inflammatory/Innate Response. AVAILABILITY: Data and code are available on the Gene Expression Omnibus and github. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Computationally predicted gene regulatory networks in molluscan biomineralization identify extracellular matrix production and ion transportation pathways.

MOTIVATION: The molecular processes regulating molluscan shell production remain relatively uncharacterized, despite the clear evolutionary and societal importance of biomineralization. RESULTS: Here we built the first computationally predicted gene regulatory network (GRN) for molluscan biomineralization using Antarctic clam (Laternula elliptica) mantle gene expression data produced over an age-categorized shell damage-repair time-course. We used previously published in vivo in situ hybridization expression data to ground truth gene interactions predicted by the GRN and show that candidate biomineralization genes from different shell layers, and hence microstructures, were connected in unique modules. We characterized two biomineralization modules of the GRN and hypothesize that one module is responsible for translating the extracellular proteins required for growing, repairing or remodelling the nacreous shell layer, whereas the second module orchestrates the transport of both ions and proteins to the shell secretion site, which are required during normal shell growth, and repair. Our findings demonstrate that unbiased computational methods are particularly valuable for studying fundamental biological processes and gene interactions in non-model species where rich sources of gene expression data exist, but annotation rates are poor and the ability to carry out true functional tests are still lacking. AVAILABILITY AND IMPLEMENTATION: The raw RNA-Seq data is freely available for download from NCBI SRA (Accession: PRJNA398984), the assembled and annotated transcriptome can be viewed and downloaded from molluscDB (ensembl.molluscdb.org) and in addition, the assembled transcripts, reconstructed GRN, modules and detailed annotations are all available as Supplementary Files. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Dying for change: A roadmap to refine the fish acute toxicity test after 40 years of applying a lethal endpoint.

The fish acute toxicity test (TG203; OECD, 2019) is frequently used and highly embedded in hazard and risk assessment globally. The test estimates the concentration of a chemical that kills 50% of the fish (LC50) over a 96 h exposure and is considered one of the most severe scientific procedures undertaken. Over the years, discussions at the Organisation for Economic Co-operation and Development (OECD) have resulted in changes to the test which reduce the number of fish used, as well as the development of a (potential) replacement test (TG236, OECD, 2013). However, refinement of the mortality endpoint with an earlier (moribundity) endpoint was not considered feasible during the Test Guideline's (TG) last update in 2019. Several stakeholders met at a UK-based workshop to discuss how TG203 can be refined, and identified two key opportunities to reduce fish suffering: (1) application of clinical signs that predict mortality and (2) shortening the test duration. However, several aspects need to be addressed before these refinements can be adopted. TG203 has required recording of major categories of sublethal clinical signs since its conception, with the option to record more detailed signs introduced in the 2019 update. However, in the absence of guidance, differences in identification, recording and reporting of clinical signs between technicians and laboratories is likely to have generated piecemeal data of varying quality. Harmonisation of reporting templates, and training in clinical sign recognition and recording are needed to standardise clinical sign data. This is critical to enable robust data-driven detection of clinical signs that predict mortality. Discussions suggested that the 96 h duration of TG203 cannot stand up to scientific scrutiny. Feedback and data from UK contract research organisations (CROs) conducting the test were that a substantial proportion of mortalities occur in the first 24 h. Refinement of TG203 by shortening the test duration would reduce suffering (and test failure rate) but requires a mechanism to correct new results to previous 96 h LC50 data. The actions needed to implement both refinement opportunities are summarised here within a roadmap. A shift in regulatory assessment, where the 96 h LC50 is a familiar base for decisions, will also be critical.

Acute changes in the colonic microbiota are associated with large intestinal forms of surgical colic.

BACKGROUND: Horses that undergo surgery for treatment of primary large colon disease have been reported to be at increased risk of developing recurrent colic episodes postoperatively. The reasons for this are currently unknown. The aim of the current study was to characterise the faecal microbiota of horses with colic signs associated with primary large colon lesions treated surgically and to compare the composition of their faecal microbiota to that of a control group of horses undergoing emergency orthopaedic treatment. Faecal samples were collected from horses in both groups on admission to hospital, during hospitalisation and following discharge from hospital for a total duration of 12 weeks. Additionally, colonic content samples were collected from surgical colic patients if pelvic flexure enterotomy was performed during laparotomy. A total of 12 samples were collected per horse. DNA was extracted from samples using a commercial kit. Amplicon mixtures were created by PCR amplification of the V1 - V2 regions of the bacterial 16S rRNA genes and submitted for sequencing using the Ion Torrent PGM next-generation sequencing system. Multivariate data analysis was used to characterise the faecal microbiota and to investigate differences between groups. RESULTS: Reduced species richness was evident in the colonic samples of the colic group compared to concurrent sampling of the faeces. Alpha and beta diversity differed significantly between the faecal and colonic microbiota with 304 significantly differentially abundant OTUs identified. Only 46 OTUs varied significantly between the colic and control group. There were no significant differences in alpha and beta diversity of faecal microbiota between colic and control horses at admission. However, this lack of significant differences between groups should be interpreted with caution due to a small sample size. CONCLUSIONS: The results of the current study suggest that faecal samples collected at hospital admission in colic cases may not accurately represent changes in upper gut microbiota in horses with colic due to large colon disease.

In Silico Computational Transcriptomics Reveals Novel Endocrine Disruptors in Largemouth Bass ( Micropterus salmoides).

In recent years, decreases in fish populations have been attributed, in part, to the effect of environmental chemicals on ovarian development. To understand the underlying molecular events we developed a dynamic model of ovary development linking gene transcription to key physiological end points, such as gonadosomatic index (GSI), plasma levels of estradiol (E2) and vitellogenin (VTG), in largemouth bass ( Micropterus salmoides). We were able to identify specific clusters of genes, which are affected at different stages of ovarian development. A subnetwork was identified that closely linked gene expression and physiological end points and by interrogating the Comparative Toxicogenomic Database (CTD), quercetin and tretinoin (ATRA) were identified as two potential candidates that may perturb this system. Predictions were validated by investigation of reproductive associated transcripts using qPCR in ovary and in the liver of both male and female largemouth bass treated after a single injection of quercetin and tretinoin (10 and 100 µg/kg). Both compounds were found to significantly alter the expression of some of these genes. Our findings support the use of omics and online repositories for identification of novel, yet untested, compounds. This is the first study of a dynamic model that links gene expression patterns across stages of ovarian development.

A Network Biology Approach Identifies Molecular Cross-Talk between Normal Prostate Epithelial and Prostate Carcinoma Cells.

The advent of functional genomics has enabled the genome-wide characterization of the molecular state of cells and tissues, virtually at every level of biological organization. The difficulty in organizing and mining this unprecedented amount of information has stimulated the development of computational methods designed to infer the underlying structure of regulatory networks from observational data. These important developments had a profound impact in biological sciences since they triggered the development of a novel data-driven investigative approach. In cancer research, this strategy has been particularly successful. It has contributed to the identification of novel biomarkers, to a better characterization of disease heterogeneity and to a more in depth understanding of cancer pathophysiology. However, so far these approaches have not explicitly addressed the challenge of identifying networks representing the interaction of different cell types in a complex tissue. Since these interactions represent an essential part of the biology of both diseased and healthy tissues, it is of paramount importance that this challenge is addressed. Here we report the definition of a network reverse engineering strategy designed to infer directional signals linking adjacent cell types within a complex tissue. The application of this inference strategy to prostate cancer genome-wide expression profiling data validated the approach and revealed that normal epithelial cells exert an anti-tumour activity on prostate carcinoma cells. Moreover, by using a Bayesian hierarchical model integrating genetics and gene expression data and combining this with survival analysis, we show that the expression of putative cell communication genes related to focal adhesion and secretion is affected by epistatic gene copy number variation and it is predictive of patient survival. Ultimately, this study represents a generalizable approach to the challenge of deciphering cell communication networks in a wide spectrum of biological systems.

A Bayesian model for the identification of differentially expressed genes in Daphnia magna exposed to munition pollutants.

In this article we propose a Bayesian hierarchical model for the identification of differentially expressed genes in Daphnia magna organisms exposed to chemical compounds, specifically munition pollutants in water. The model we propose constitutes one of the very first attempts at a rigorous modeling of the biological effects of water purification. We have data acquired from a purification system that comprises four consecutive purification stages, which we refer to as "ponds," of progressively more contaminated water. We model the expected expression of a gene in a pond as the sum of the mean of the same gene in the previous pond plus a gene-pond specific difference. We incorporate a variable selection mechanism for the identification of the differential expressions, with a prior distribution on the probability of a change that accounts for the available information on the concentration of chemical compounds present in the water. We carry out posterior inference via MCMC stochastic search techniques. In the application, we reduce the complexity of the data by grouping genes according to their functional characteristics, based on the KEGG pathway database. This also increases the biological interpretability of the results. Our model successfully identifies a number of pathways that show differential expression between consecutive purification stages. We also find that changes in the transcriptional response are more strongly associated to the presence of certain compounds, with the remaining contributing to a lesser extent. We discuss the sensitivity of these results to the model parameters that measure the influence of the prior information on the posterior inference.

Systems Biology Approach Reveals a Calcium-Dependent Mechanism for Basal Toxicity in Daphnia magna.

The expanding diversity and ever increasing amounts of man-made chemicals discharged to the environment pose largely unknown hazards to ecosystem and human health. The concept of adverse outcome pathways (AOPs) emerged as a comprehensive framework for risk assessment. However, the limited mechanistic information available for most chemicals and a lack of biological pathway annotation in many species represent significant challenges to effective implementation of this approach. Here, a systems level, multistep modeling strategy demonstrates how to integrate information on chemical structure with mechanistic insight from genomic studies, and phenotypic effects to define a putative adverse outcome pathway. Results indicated that transcriptional changes indicative of intracellular calcium mobilization were significantly overrepresented in Daphnia magna (DM) exposed to sublethal doses of presumed narcotic chemicals with log Kow ≥ 1.8. Treatment of DM with a calcium ATPase pump inhibitor substantially recapitulated the common transcriptional changes. We hypothesize that calcium mobilization is a potential key molecular initiating event in DM basal (narcosis) toxicity. Heart beat rate analysis and metabolome analysis indicated sublethal effects consistent with perturbations of calcium preceding overt acute toxicity. Together, the results indicate that altered calcium homeostasis may be a key early event in basal toxicity or narcosis induced by lipophilic compounds.

Gene transcription, metabolite and lipid profiling in eco-indicator daphnia magna indicate diverse mechanisms of toxicity by legacy and emerging flame-retardants.

The use of chemical flame-retardants (FR) in consumer products has steadily increased over the last 30 years. Toxicity data exist for legacy FRs such as pentabromodiphenyl ether (pentaBDE), but less is known about effects of new formulations. To address this issue, the toxicity of seven FR chemicals and formulations was assessed on the freshwater crustacean Daphnia magna. Acute 48-h nominal LC50 values for penta- and octabromodiphenyl ether (pentaBDE, octaBDE), Firemaster 550 (FM550), Firemaster BZ-54 (BZ54), bis(2-ethylhexyl) tetrabromophthalate (BEH-TEBP), triphenyl phosphate (TPhP), and nonbrominated BEH-TEBP analog bis(2-ethylhexyl) phthalate (BEHP) ranged from 0.058 mg/L (pentaBDE) to 3.96 mg/L (octaBDE). mRNA expression, (1)H NMR-based metabolomic and lipidomic profiling at 1/10 LC50 revealed distinct patterns of molecular response for each exposure, suggesting pentaPBDE affects transcription and translation, octaBDE and BEH-TEBP affect glycosphingolipid biosynthesis and BZ54 affects Wnt and Hedgehog signal pathways as well as glycosaminoglycan degradation. Brominated components of FM550 (i.e., BZ54) were significantly higher in Daphnia after 48 h following 1/10 LC50 exposure. FM550 elicited significant mRNA changes at five concentrations across a range from 1/10(6) LC50 to 1/2 LC50. Analyses suggest FM550 impairs nutrient utilization or uptake in Daphnia.

The COPD Knowledge Base: enabling data analysis and computational simulation in translational COPD research.

BACKGROUND: Previously we generated a chronic obstructive pulmonary disease (COPD) specific knowledge base (http://www.copdknowledgebase.eu) from clinical and experimental data, text-mining results and public databases. This knowledge base allowed the retrieval of specific molecular networks together with integrated clinical and experimental data. RESULTS: The COPDKB has now been extended to integrate over 40 public data sources on functional interaction (e.g. signal transduction, transcriptional regulation, protein-protein interaction, gene-disease association). In addition we integrated COPD-specific expression and co-morbidity networks connecting over 6 000 genes/proteins with physiological parameters and disease states. Three mathematical models describing different aspects of systemic effects of COPD were connected to clinical and experimental data. We have completely redesigned the technical architecture of the user interface and now provide html and web browser-based access and form-based searches. A network search enables the use of interconnecting information and the generation of disease-specific sub-networks from general knowledge. Integration with the Synergy-COPD Simulation Environment enables multi-scale integrated simulation of individual computational models while integration with a Clinical Decision Support System allows delivery into clinical practice. CONCLUSIONS: The COPD Knowledge Base is the only publicly available knowledge resource dedicated to COPD and combining genetic information with molecular, physiological and clinical data as well as mathematical modelling. Its integrated analysis functions provide overviews about clinical trends and connections while its semantically mapped content enables complex analysis approaches. We plan to further extend the COPDKB by offering it as a repository to publish and semantically integrate data from relevant clinical trials. The COPDKB is freely available after registration at http://www.copdknowledgebase.eu.

Data-Driven Characterization of Genetic Variability in Disease Pathways and Pesticide-Induced Nervous System Disease in the United States Population.

BACKGROUND: Genetic susceptibility to chemicals is incompletely characterized. However, nervous system disease development following pesticide exposure can vary in a population, implying some individuals may have higher genetic susceptibility to pesticide-induced nervous system disease. OBJECTIVES: We aimed to build a computational approach to characterize single-nucleotide polymorphisms (SNPs) implicated in chemically induced adverse outcomes and used this framework to assess the link between differential population susceptibility to pesticides and human nervous system disease. METHODS: We integrated publicly available datasets of Chemical-Gene, Gene-Pathway, and SNP-Disease associations to build Chemical-Pathway-Gene-SNP-Disease linkages for humans. As a case study, we integrated these linkages with spatialized pesticide application data for the US from 1992 to 2018 and spatialized nervous system disease rates for 2018. Through this, we characterized SNPs that may be important in states with high disease occurrence based on the pesticides used there. RESULTS: We found that the number of SNP hits per pesticide in US states positively correlated with disease incidence and prevalence for Alzheimer's disease, Parkinson disease, and multiple sclerosis. We performed frequent itemset mining to differentiate pesticides used over time in states with high and low disease occurrence and found that only 19% of pesticide sets overlapped between 10 states with high disease occurrence and 10 states with low disease occurrence rates, and more SNPs were implicated in pathways in high disease occurrence states. Through a cross-validation of subsets of five high and low disease occurrence states, we characterized SNPs, genes, pathways, and pesticides more frequently implicated in high disease occurrence states. DISCUSSION: Our findings support that pesticides contribute to nervous system disease, and we developed priority lists of SNPs, pesticides, and pathways for further study. This data-driven approach can be adapted to other chemicals, diseases, and locations to characterize differential population susceptibility to chemical exposures. https://doi.org/10.1289/EHP14108.

Spatial proteomics of skeletal muscle using thin cryosections reveals metabolic adaptation at the muscle-tendon transition zone.

Morphological studies of skeletal muscle tissue provide insights into the architecture of muscle fibers, the surrounding cells, and the extracellular matrix (ECM). However, a spatial proteomics analysis of the skeletal muscle including the muscle-tendon transition zone is lacking. Here, we prepare cryotome muscle sections of the mouse soleus muscle and measure each slice using short liquid chromatography-mass spectrometry (LC-MS) gradients. We generate 3,000 high-resolution protein profiles that serve as the basis for a network analysis to reveal the complex architecture of the muscle-tendon junction. Among the protein profiles that increase from muscle to tendon, we find proteins related to neuronal activity, fatty acid biosynthesis, and the renin-angiotensin system (RAS). Blocking the RAS in cultured mouse tenocytes using losartan reduces the ECM synthesis. Overall, our analysis of thin cryotome sections provides a spatial proteome of skeletal muscle and reveals that the RAS acts as an additional regulator of the matrix within muscle-tendon junctions.

DKK3 as a potential novel biomarker in patients with autosomal polycystic kidney disease.

Backgound: Autosomal dominant polycystic kidney disease (ADPKD) is the most common inherited kidney disease, and leads to a steady loss of kidney function in adulthood. The variable course of the disease makes it necessary to identify the patients with rapid disease progression who will benefit the most from targeted therapies and interventions. Currently, magnetic resonance imaging-based volumetry of the kidney is the most commonly used tool for this purpose. Biomarkers that can be easily and quantitatively determined, which allow a prediction of the loss of kidney function, have not yet been established in clinical practice. The glycoprotein Dickkopf 3 (DKK3) which is secreted in the renal tubular epithelium upon stress and contributes to tubulointerstitial fibrosis via the Wnt signaling pathway, was recently described as a biomarker for estimating risk of kidney function loss, but has not been investigated for ADPKD. This study aimed to obtain a first insight into whether DKK3 may indeed improve outcome prediction in ADPKD in the future. Methods: In 184 ADPKD patients from the AD(H)PKD registry and 47 healthy controls, the urinary DKK3 (uDKK3) levels were determined using ELISA. Multiple linear regression was used to examine the potential of these values in outcome prediction. Results: ADPKD patients showed significantly higher uDKK3 values compared with the controls (mean 1970 ± 5287 vs 112 ± 134.7 pg/mg creatinine). Furthermore, there was a steady increase in uDKK3 with an increase in the Mayo class (A/B 1262 ± 2315 vs class D/E 3104 ± 7627 pg/mg creatinine), the best-established biomarker of progression in ADPKD. uDKK3 also correlated with estimated glomerular filtration rate (eGFR). Patients with PKD1 mutations show higher uDKK3 levels compared with PKD2 patients (PKD1: 2304 ± 5119; PKD2: 506.6 ± 526.8 pg/mg creatinine). Univariate linear regression showed uDKK3 as a significant predictor of future eGFR slope estimation. In multiple linear regression this effect was not significant in models also containing height-adjusted total kidney volume and/or eGFR. However, adding both copeptin levels and the interaction term between copeptin and uDKK3 to the model resulted in a significant predictive value of all these three variables and the highest R2 of all models examined (∼0.5). Conclusion: uDKK3 shows a clear correlation with the Mayo classification in patients with ADPKD. uDKK3 levels correlated with kidney function, which could indicate that uDKK3 also predicts a disproportionate loss of renal function in this collective. Interestingly, we found an interaction between copeptin and uDKK3 in our prediction models and the best model containing both variables and their interaction term resulted in a fairly good explanation of variance in eGFR slope compared with previous models. Considering the limited number of patients in these analyses, future studies will be required to confirm the results. Nonetheless, uDKK3 appears to be an attractive candidate to improve outcome prediction of ADPKD in the future.

Long-Term Human Immune Reconstitution, T-Cell Development, and Immune Reactivity in Mice Lacking the Murine Major Histocompatibility Complex: Validation with Cellular and Gene Expression Profiles.

BACKGROUND: Humanized mice transplanted with CD34+ hematopoietic cells (HPCs) are broadly used to study human immune responses and infections in vivo and for testing therapies pre-clinically. However, until now, it was not clear whether interactions between the mouse major histocompatibility complexes (MHCs) and/or the human leukocyte antigens (HLAs) were necessary for human T-cell development and immune reactivity. METHODS: We evaluated the long-term (20-week) human hematopoiesis and human T-cell development in NOD Scid Gamma (NSG) mice lacking the expression of MHC class I and II (NSG-DKO). Triplicate experiments were performed with HPCs obtained from three donors, and humanization was confirmed in the reference strain NOD Rag Gamma (NRG). Further, we tested whether humanized NSG-DKO mice would respond to a lentiviral vector (LV) systemic delivery of HLA-A*02:01, HLA-DRB1*04:01, human GM-CSF/IFN-α, and the human cytomegalovirus gB antigen. RESULTS: Human immune reconstitution was detectable in peripheral blood from 8 to 20 weeks after the transplantation of NSG-DKO. Human single positive CD4+ and CD8+ T-cells were detectable in lymphatic tissues (thymus, bone marrow, and spleen). LV delivery harnessed the detection of lymphocyte subsets in bone marrow (αß and γδ T-cells and NK cells) and the expression of HLA-DR. Furthermore, RNA sequencing showed that LV delivery increased the expression of different human reactome pathways, such as defense responses to other organisms and viruses. CONCLUSIONS: Human T-cell development and reactivity are independent of the expression of murine MHCs in humanized mice. Therefore, humanized NSG-DKO is a promising new model for studying human immune responses, as it abrogates the xenograft mouse MHC interference.

Comprehensive mapping of the AOP-Wiki database: identifying biological and disease gaps.

Introduction: The Adverse Outcome Pathway (AOP) concept facilitates rapid hazard assessment for human health risks. AOPs are constantly evolving, their number is growing, and they are referenced in the AOP-Wiki database, which is supported by the OECD. Here, we present a study that aims at identifying well-defined biological areas, as well as gaps within the AOP-Wiki for future research needs. It does not intend to provide a systematic and comprehensive summary of the available literature on AOPs but summarizes and maps biological knowledge and diseases represented by the already developed AOPs (with OECD endorsed status or under validation). Methods: Knowledge from the AOP-Wiki database were extracted and prepared for analysis using a multi-step procedure. An automatic mapping of the existing information on AOPs (i.e., genes/proteins and diseases) was performed using bioinformatics tools (i.e., overrepresentation analysis using Gene Ontology and DisGeNET), allowing both the classification of AOPs and the development of AOP networks (AOPN). Results: AOPs related to diseases of the genitourinary system, neoplasms and developmental anomalies are the most frequently investigated on the AOP-Wiki. An evaluation of the three priority cases (i.e., immunotoxicity and non-genotoxic carcinogenesis, endocrine and metabolic disruption, and developmental and adult neurotoxicity) of the EU-funded PARC project (Partnership for the Risk Assessment of Chemicals) are presented. These were used to highlight under- and over-represented adverse outcomes and to identify and prioritize gaps for further research. Discussion: These results contribute to a more comprehensive understanding of the adverse effects associated with the molecular events in AOPs, and aid in refining risk assessment for stressors and mitigation strategies. Moreover, the FAIRness (i.e., data which meets principles of findability, accessibility, interoperability, and reusability (FAIR)) of the AOPs appears to be an important consideration for further development.

The experimental renal cell carcinoma model in the chick embryo.

The clear cell subtype of renal carcinoma (CCRCC) is highly vascularized and despite a slow progression rate, it is potentially a highly aggressive tumor. Although a doubling of median progression-free survival in CCRCC patients treated by targeted therapies has been observed, the fact that tumors escape after anti-VEGF treatment suggests alternative pathways. The chick chorioallantoic membrane (CAM) is a well-established model, which allows in vivo studies of tumor angiogenesis and the testing of anti-angiogenic molecules. However, only a few data exist on CCRCC grafted onto CAM. We aimed to validate herein the CAM as a suitable model for studying the development of CCRCC and the interactions with the surrounding stroma. Our study uses both CCRCC cell lines and fresh tumor samples after surgical resection. We demonstrate that in both cases CCRCC can be grafted onto the CAM, to survive and to induce an angiogenic process. We further provide insights into the transcriptional regulation of the model by performing a differential analysis of tumor-derived and stroma-derived transcripts.

Molecular toxicity identification evaluation (mTIE) approach predicts chemical exposure in Daphnia magna.

Daphnia magna is a bioindicator organism accepted by several international water quality regulatory agencies. Current approaches for assessment of water quality rely on acute and chronic toxicity that provide no insight into the cause of toxicity. Recently, molecular approaches, such as genome wide gene expression responses, are enabling an alternative mechanism based approach to toxicity assessment. While these genomic methods are providing important mechanistic insight into toxicity, statistically robust prediction systems that allow the identification of chemical contaminants from the molecular response to exposure are needed. Here we apply advanced machine learning approaches to develop predictive models of contaminant exposure using a D. magna gene expression data set for 36 chemical exposures. We demonstrate here that we can discriminate between chemicals belonging to different chemical classes including endocrine disruptors and inorganic and organic chemicals based on gene expression. We also show that predictive models based on indices of whole pathway transcriptional activity can achieve comparable results while facilitating biological interpretability.

A systems biology approach sheds new light on Escherichia coli acid resistance.

In order to develop an infection, diarrhogenic Escherichia coli has to pass through the stomach, where the pH can be as low as 1. Mechanisms that enable E. coli to survive in low pH are thus potentially relevant for pathogenicity. Four acid response systems involved in reducing the concentration of intracellular protons have been identified so far. However, it is still unclear to what extent the regulation of other important cellular functions may be required for survival in acid conditions. Here, we have combined molecular and phenotypic analysis of wild-type and mutant strains with computational network inference to identify molecular pathways underlying E. coli response to mild and strong acid conditions. The interpretative model we have developed led to the hypothesis that a complex transcriptional programme, dependent on the two-component system regulator OmpR and involving a switch between aerobic and anaerobic metabolism, may be key for survival. Experimental validation has shown that the OmpR is responsible for controlling a sizeable component of the transcriptional programme to acid exposure. Moreover, we found that a ΔompR strain was unable to mount any transcriptional response to acid exposure and had one of the strongest acid sensitive phenotype observed.

Present and future challenges for the investigation of transgenerational epigenetic inheritance.

Epigenetic pathways are essential in different biological processes and in phenotype-environment interactions in response to different stressors and they can induce phenotypic plasticity. They encompass several processes that are mitotically and, in some cases, meiotically heritable, so they can be transferred to subsequent generations via the germline. Transgenerational Epigenetic Inheritance (TEI) describes the phenomenon that phenotypic traits, such as changes in fertility, metabolic function, or behavior, induced by environmental factors (e.g., parental care, pathogens, pollutants, climate change), can be transferred to offspring generations via epigenetic mechanisms. Investigations on TEI contribute to deciphering the role of epigenetic mechanisms in adaptation, adversity, and evolution. However, molecular mechanisms underlying the transmission of epigenetic changes between generations, and the downstream chain of events leading to persistent phenotypic changes, remain unclear. Therefore, inter-, (transmission of information between parental and offspring generation via direct exposure) and transgenerational (transmission of information through several generations with disappearance of the triggering factor) consequences of epigenetic modifications remain major issues in the field of modern biology. In this article, we review and describe the major gaps and issues still encountered in the TEI field: the general challenges faced in epigenetic research; deciphering the key epigenetic mechanisms in inheritance processes; identifying the relevant drivers for TEI and implement a collaborative and multi-disciplinary approach to study TEI. Finally, we provide suggestions on how to overcome these challenges and ultimately be able to identify the specific contribution of epigenetics in transgenerational inheritance and use the correct tools for environmental science investigation and biomarkers identification.