Understanding the limits of animal models as predictors of human biology: lessons learned from the sbv IMPROVER Species Translation Challenge

Vascular Endothelial Growth Factor-B Blockade with CSL346 in Diabetic Kidney Disease: A Phase 2A Randomized Controlled Trial

Key Points

The vascular endothelial growth factor B inhibitor CSL346 (8 or 16 mg/kg q4w) did not reduce urinary albumin-creatinine ratio at week 16 versus placebo in patients with type 2 diabetes mellitus and diabetic kidney disease. CSL346 was generally well tolerated at both doses; however, CSL346 (16 mg/kg) significantly increased diastolic BP versus placebo.

Background

Increased vascular endothelial growth factor B (VEGF-B) expression in patients with diabetic kidney disease (DKD) is associated with increased lipid deposition in glomerular podocytes. Reducing VEGF-B activity in animal models of DKD using an anti–VEGF-B antibody improved histological evidence of glomerular injury and reduced albuminuria, effects attributed to prevention of ectopic lipid deposition in the kidney. CSL346 is a novel humanized monoclonal antibody that binds VEGF-B with high affinity. Targeting VEGF-B in patients with type 2 diabetes mellitus may improve DKD progression markers.

Methods

An international, randomized, double-blind, placebo-controlled, phase 2a study (NCT04419467 ) assessed CSL346 (8 or 16 mg/kg subcutaneously every 4 weeks for 12 weeks) in participants with type 2 diabetes mellitus and a urinary albumin-creatinine ratio (UACR) ≥150 mg/g (17.0 mg/mmol), and eGFR >20 ml/min per 1.73 m2. Efficacy, safety/tolerability, pharmacokinetics, and pharmacodynamics of CSL346 were evaluated. The primary analysis compared the change from baseline in log-transformed UACR between the two CSL346 dose groups combined versus placebo at week 16.

Results

In total, 114 participants were randomized. CSL346 did not significantly reduce UACR compared with placebo at week 16 (combined CSL346 group difference from placebo [95% confidence interval], 4.0% [−14.7 to 26.8]). Furthermore, no effect was seen in participant subgroups (degree of kidney impairment or sodium-glucose cotransporter 2 inhibitor use) or on urinary biomarkers reflecting proximal tubular injury. CSL346 was generally well tolerated; however, diastolic BP was significantly higher with CSL346 16 mg/kg versus placebo from week 2 onward, with differences ranging from +3.8 to +5.3 mm Hg (P = 0.002 at week 16).

Conclusions

CSL346 did not reduce UACR compared with placebo at 16 weeks in participants with type 2 diabetes mellitus and DKD and was associated with an increase in diastolic BP.

Clinical Trial registry name and registration number:

VEGF-B Blockade with the Monoclonal Antibody CSL346 in Subjects with DKD, NCT04419467 .

Challenges and opportunities of translating animal research into human trials in Ethiopia

BACKGROUND AND OBJECTIVES

Although the goal of translational research is to bring biomedical knowledge from the laboratory to clinical trial and therapeutic products for improving health, this goal has not been well achieved as often as desired because of many barriers documented in different countries. Therefore, the aim of this study was to investigate the challenges and opportunities of translating animal research into human trials in Ethiopia.

METHODS

A descriptive qualitative study, using in-depth interviews, was conducted in which preclinical and clinical trial researchers who have been involved in animal research or clinical trials as principal investigator were involved. Data were analyzed using inductive thematic process.

RESULTS

Six themes were emerged for challenges: lack of financial and human capacity, inadequate infrastructure, operational obstacles and poor research governance, lack of collaboration, lack of reproducibility of results and prolonged ethical and regulatory approval processes. Furthermore, three themes were synthesized for opportunities: growing infrastructure and resources, improved human capacity and better administrative processes and initiatives for collaboration.

CONCLUSION AND RECOMMENDATIONS

The study found that the identified characteristics/features are of high importance either to hurdle or enable the practice of translating animal research into human trials. The study suggests that there should be adequate infrastructure and finance, human capacity building, good research governance, improved ethical and regulatory approval process, multidisciplinary collaboration, and incentives and recognition for researchers to overcome the identified challenges and allow translating of animal research into human trials to proceed more efficiently.

Computational strategies for cross-species knowledge transfer and translational biomedicine

Research organisms provide invaluable insights into human biology and diseases, serving as essential tools for functional experiments, disease modeling, and drug testing. However, evolutionary divergence between humans and research organisms hinders effective knowledge transfer across species. Here, we review state-of-the-art methods for computationally transferring knowledge across species, primarily focusing on methods that utilize transcriptome data and/or molecular networks. We introduce the term "agnology" to describe the functional equivalence of molecular components regardless of evolutionary origin, as this concept is becoming pervasive in integrative data-driven models where the role of evolutionary origin can become unclear. Our review addresses four key areas of information and knowledge transfer across species: (1) transferring disease and gene annotation knowledge, (2) identifying agnologous molecular components, (3) inferring equivalent perturbed genes or gene sets, and (4) identifying agnologous cell types. We conclude with an outlook on future directions and several key challenges that remain in cross-species knowledge transfer.

AutoTransOP: translating omics signatures without orthologue requirements using deep learning

The development of therapeutics and vaccines for human diseases requires a systematic understanding of human biology. Although animal and in vitro culture models can elucidate some disease mechanisms, they typically fail to adequately recapitulate human biology as evidenced by the predominant likelihood of clinical trial failure. To address this problem, we developed AutoTransOP, a neural network autoencoder framework, to map omics profiles from designated species or cellular contexts into a global latent space, from which germane information for different contexts can be identified without the typically imposed requirement of matched orthologues. This approach was found in general to perform at least as well as current alternative methods in identifying animal/culture-specific molecular features predictive of other contexts-most importantly without requiring homology matching. For an especially challenging test case, we successfully applied our framework to a set of inter-species vaccine serology studies, where 1-to-1 mapping between human and non-human primate features does not exist.

Artificial intelligence for neurodegenerative experimental models

INTRODUCTION

Experimental models are essential tools in neurodegenerative disease research. However, the translation of insights and drugs discovered in model systems has proven immensely challenging, marred by high failure rates in human clinical trials.

METHODS

Here we review the application of artificial intelligence (AI) and machine learning (ML) in experimental medicine for dementia research.

RESULTS

Considering the specific challenges of reproducibility and translation between other species or model systems and human biology in preclinical dementia research, we highlight best practices and resources that can be leveraged to quantify and evaluate translatability. We then evaluate how AI and ML approaches could be applied to enhance both cross-model reproducibility and translation to human biology, while sustaining biological interpretability.

DISCUSSION

AI and ML approaches in experimental medicine remain in their infancy. However, they have great potential to strengthen preclinical research and translation if based upon adequate, robust, and reproducible experimental data.

HIGHLIGHTS

There are increasing applications of AI in experimental medicine. We identified issues in reproducibility, cross-species translation, and data curation in the field. Our review highlights data resources and AI approaches as solutions. Multi-omics analysis with AI offers exciting future possibilities in drug discovery.

Application of transfer learning to predict drug-induced human in vivo gene expression changes using rat in vitro and in vivo data

The liver is the primary site for the metabolism and detoxification of many compounds, including pharmaceuticals. Consequently, it is also the primary location for many adverse reactions. As the liver is not readily accessible for sampling in humans; rodent or cell line models are often used to evaluate potential toxic effects of a novel compound or candidate drug. However, relating the results of animal and in vitro studies to relevant clinical outcomes for the human in vivo situation still proves challenging. In this study, we incorporate principles of transfer learning within a deep artificial neural network allowing us to leverage the relative abundance of rat in vitro and in vivo exposure data from the Open TG-GATEs data set to train a model to predict the expected pattern of human in vivo gene expression following an exposure given measured human in vitro gene expression. We show that domain adaptation has been successfully achieved, with the rat and human in vitro data no longer being separable in the common latent space generated by the network. The network produces physiologically plausible predictions of human in vivo gene expression pattern following an exposure to a previously unseen compound. Moreover, we show the integration of the human in vitro data in the training of the domain adaptation network significantly improves the temporal accuracy of the predicted rat in vivo gene expression pattern following an exposure to a previously unseen compound. In this way, we demonstrate the improvements in prediction accuracy that can be achieved by combining data from distinct domains.

Systems analyses of the Fabry kidney transcriptome and its response to enzyme replacement therapy identified and cross-validated enzyme replacement therapy-resistant targets amenable to drug repurposing

Fabry disease is a rare disorder caused by variations in the alpha-galactosidase gene. To a degree, Fabry disease is manageable via enzyme replacement therapy (ERT). By understanding the molecular basis of Fabry nephropathy (FN) and ERT's long-term impact, here we aimed to provide a framework for selection of potential disease biomarkers and drug targets. We obtained biopsies from eight control individuals and two independent FN cohorts comprising 16 individuals taken prior to and after up to ten years of ERT, and performed RNAseq analysis. Combining pathway-centered analyses with network-science allowed computation of transcriptional landscapes from four nephron compartments and their integration with existing proteome and drug-target interactome data. Comparing these transcriptional landscapes revealed high inter-cohort heterogeneity. Kidney compartment transcriptional landscapes comprehensively reflected differences in FN cohort characteristics. With exception of a few aspects, in particular arteries, early ERT in patients with classical Fabry could lastingly revert FN gene expression patterns to closely match that of control individuals. Pathways nonetheless consistently altered in both FN cohorts pre-ERT were mostly in glomeruli and arteries and related to the same biological themes. While keratinization-related processes in glomeruli were sensitive to ERT, a majority of alterations, such as transporter activity and responses to stimuli, remained dysregulated or reemerged despite ERT. Inferring an ERT-resistant genetic module of expressed genes identified 69 drugs for potential repurposing matching the proteins encoded by 12 genes. Thus, we identified and cross-validated ERT-resistant gene product modules that, when leveraged with external data, allowed estimating their suitability as biomarkers to potentially track disease course or treatment efficacy and potential targets for adjunct pharmaceutical treatment.

Harnessing the potential of machine learning and artificial intelligence for dementia research

Progress in dementia research has been limited, with substantial gaps in our knowledge of targets for prevention, mechanisms for disease progression, and disease-modifying treatments. The growing availability of multimodal data sets opens possibilities for the application of machine learning and artificial intelligence (AI) to help answer key questions in the field. We provide an overview of the state of the science, highlighting current challenges and opportunities for utilisation of AI approaches to move the field forward in the areas of genetics, experimental medicine, drug discovery and trials optimisation, imaging, and prevention. Machine learning methods can enhance results of genetic studies, help determine biological effects and facilitate the identification of drug targets based on genetic and transcriptomic information. The use of unsupervised learning for understanding disease mechanisms for drug discovery is promising, while analysis of multimodal data sets to characterise and quantify disease severity and subtype are also beginning to contribute to optimisation of clinical trial recruitment. Data-driven experimental medicine is needed to analyse data across modalities and develop novel algorithms to translate insights from animal models to human disease biology. AI methods in neuroimaging outperform traditional approaches for diagnostic classification, and although challenges around validation and translation remain, there is optimism for their meaningful integration to clinical practice in the near future. AI-based models can also clarify our understanding of the causality and commonality of dementia risk factors, informing and improving risk prediction models along with the development of preventative interventions. The complexity and heterogeneity of dementia requires an alternative approach beyond traditional design and analytical approaches. Although not yet widely used in dementia research, machine learning and AI have the potential to unlock current challenges and advance precision dementia medicine.

Transcriptomic congruence analysis for evaluating model organisms

Model organisms are instrumental substitutes for human studies to expedite basic, translational, and clinical research. Despite their indispensable role in mechanistic investigation and drug development, molecular congruence of animal models to humans has long been questioned and debated. Little effort has been made for an objective quantification and mechanistic exploration of a model organism's resemblance to humans in terms of molecular response under disease or drug treatment. We hereby propose a framework, namely Congruence Analysis for Model Organisms (CAMO), for transcriptomic response analysis by developing threshold-free differential expression analysis, quantitative concordance/discordance scores incorporating data variabilities, pathway-centric downstream investigation, knowledge retrieval by text mining, and topological gene module detection for hypothesis generation. Instead of a genome-wide vague and dichotomous answer of "poorly" or "greatly" mimicking humans, CAMO assists researchers to numerically quantify congruence, to dissect true cross-species differences from unwanted biological or cohort variabilities, and to visually identify molecular mechanisms and pathway subnetworks that are best or least mimicked by model organisms, which altogether provides foundations for hypothesis generation and subsequent translational decisions.

Crowdsourced benchmarking of taxonomic metagenome profilers: lessons learned from the sbv IMPROVER Microbiomics challenge

Background

Selection of optimal computational strategies for analyzing metagenomics data is a decisive step in determining the microbial composition of a sample, and this procedure is complex because of the numerous tools currently available. The aim of this research was to summarize the results of crowdsourced sbv IMPROVER Microbiomics Challenge designed to evaluate the performance of off-the-shelf metagenomics software as well as to investigate the robustness of these results by the extended post-challenge analysis. In total 21 off-the-shelf taxonomic metagenome profiling pipelines were benchmarked for their capacity to identify the microbiome composition at various taxon levels across 104 shotgun metagenomics datasets of bacterial genomes (representative of various microbiome samples) from public databases. Performance was determined by comparing predicted taxonomy profiles with the gold standard.

Results

Most taxonomic profilers performed homogeneously well at the phylum level but generated intermediate and heterogeneous scores at the genus and species levels, respectively. kmer-based pipelines using Kraken with and without Bracken or using CLARK-S performed best overall, but they exhibited lower precision than the two marker-gene-based methods MetaPhlAn and mOTU. Filtering out the 1% least abundance species—which were not reliably predicted—helped increase the performance of most profilers by increasing precision but at the cost of recall. However, the use of adaptive filtering thresholds determined from the sample’s Shannon index increased the performance of most kmer-based profilers while mitigating the tradeoff between precision and recall.

Conclusions

kmer-based metagenomic pipelines using Kraken/Bracken or CLARK-S performed most robustly across a large variety of microbiome datasets. Removing non-reliably predicted low-abundance species by using diversity-dependent adaptive filtering thresholds further enhanced the performance of these tools. This work demonstrates the applicability of computational pipelines for accurately determining taxonomic profiles in clinical and environmental contexts and exemplifies the power of crowdsourcing for unbiased evaluation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08803-2.

Mimicking the Natural Basement Membrane for Advanced Tissue Engineering

Advancements in the field of tissue engineering have led to the elucidation of physical and chemical characteristics of physiological basement membranes (BM) as specialized forms of the extracellular matrix. Efforts to recapitulate the intricate structure and biological composition of the BM have encountered various advancements due to its impact on cell fate, function, and regulation. More attention has been paid to synthesizing biocompatible and biofunctional fibrillar scaffolds that closely mimic the natural BM. Specific modifications in biomimetic BM have paved the way for the development of in vitro models like alveolar-capillary barrier, airway models, skin, blood-brain barrier, kidney barrier, and metastatic models, which can be used for personalized drug screening, understanding physiological and pathological pathways, and tissue implants. In this Review, we focus on the structure, composition, and functions of in vivo BM and the ongoing efforts to mimic it synthetically. Light has been shed on the advantages and limitations of various forms of biomimetic BM scaffolds including porous polymeric membranes, hydrogels, and electrospun membranes This Review further elaborates and justifies the significance of BM mimics in tissue engineering, in particular in the development of in vitro organ model systems.

In Vitro and In Vivo Modeling of Normal and Leukemic Bone Marrow Niches: Cellular Senescence Contribution to Leukemia Induction and Progression

Cellular senescence is recognized as a dynamic process in which cells evolve and adapt in a context dependent manner; consequently, senescent cells can exert both beneficial and deleterious effects on their surroundings. Specifically, senescent mesenchymal stromal cells (MSC) in the bone marrow (BM) have been linked to the generation of a supporting microenvironment that enhances malignant cell survival. However, the study of MSC's senescence role in leukemia development has been straitened not only by the availability of suitable models that faithfully reflect the structural complexity and biological diversity of the events triggered in the BM, but also by the lack of a universal, standardized method to measure senescence. Despite these constraints, two- and three dimensional in vitro models have been continuously improved in terms of cell culture techniques, support materials and analysis methods; in addition, research on animal models tends to focus on the development of techniques that allow tracking leukemic and senescent cells in the living organism, as well as to modify the available mice strains to generate individuals that mimic human BM characteristics. Here, we present the main advances in leukemic niche modeling, discussing advantages and limitations of the different systems, focusing on the contribution of senescent MSC to leukemia progression.

Integrated Micro-Devices for a Lab-in-Organoid Technology Platform: Current Status and Future Perspectives

Advancements in stem cell technology together with an improved understanding of in vitro organogenesis have enabled new routes that exploit cell-autonomous self-organization responses of adult stem cells (ASCs) and homogenous pluripotent stem cells (PSCs) to grow complex, three-dimensional (3D), mini-organ like structures on demand, the so-called organoids. Conventional optical and electrical neurophysiological techniques to acquire functional data from brain organoids, however, are not adequate for chronic recordings of neural activity from these model systems, and are not ideal approaches for throughput screenings applied to drug discovery. To overcome these issues, new emerging approaches aim at fusing sensing mechanisms and/or actuating artificial devices within organoids. Here we introduce and develop the concept of the Lab-in-Organoid (LIO) technology for in-tissue sensing and actuation within 3D cell aggregates. This challenging technology grounds on the self-aggregation of brain cells and on integrated bioelectronic micro-scale devices to provide an advanced tool for generating 3D biological brain models with in-tissue artificial functionalities adapted for routine, label-free functional measurements and for assay's development. We complete previously reported results on the implementation of the integrated self-standing wireless silicon micro-devices with experiments aiming at investigating the impact on neuronal spheroids of sinusoidal electro-magnetic fields as those required for wireless power and data transmission. Finally, we discuss the technology headway and future perspectives.

Selected transgenic murine models of human autoimmune liver diseases

Murine models of human diseases are of outmost importance for both studying molecular mechanisms driving their development and testing new treatment strategies. In this review, we first discuss the etiology and risk factors for autoimmune liver disease, including primary biliary cholangitis, autoimmune hepatitis and primary sclerosing cholangitis. Second, we highlight important features of murine transgenic models that make them useful for basic scientists, drug developers and clinical researchers. Next, a brief description of each disease is followed by the characterization of selected animal models.

Translatable pathways classification (TransPath-C) for inferring processes germane to human biology from animal studies data: example application in neurobiology

How to translate insights gained from studies in one organismal species for what is most likely to be germane in another species, such as from mice to humans, is a ubiquitous challenge in basic biology as well as biomedicine. This is an especially difficult problem when there are few molecular features that are obviously important in both species for a given phenotype of interest. Neuropathologies are a prominent realm of this complication. Schizophrenia is complex psychiatric disorder that affects 1% of the population. Many genetic factors have been proposed to drive the development of schizophrenia, and the 22q11 microdeletion (MD) syndrome has been shown to dramatically increase this risk. Due to heterogeneity of presentation of symptoms, diagnosis and formulation of treatment options for patients can often be delayed, and there is an urgent need for novel therapeutics directed toward the treatment of schizophrenia. Here, we present a novel computational approach, Translational Pathways Classification (TransPath-C), that can be used to identify shared pathway dysregulation between mouse models and human schizophrenia cohorts. This method uses variation of pathway activation in the mouse model to predict both mouse and human disease phenotype. Analysis of shared dysregulated pathways called out by both the mouse and human classifiers of TransPath-C can identify pathways that can be targeted in both preclinical and human cohorts of schizophrenia. In application to the 22q11 MD mouse model, our findings suggest that PAR1 pathway activation found upregulated in this mouse phenotype is germane for the corresponding human schizophrenia cohort such that inhibition of PAR1 may offer a novel therapeutic target.

Machine learning applications for therapeutic tasks with genomics data

Thanks to the increasing availability of genomics and other biomedical data, many machine learning algorithms have been proposed for a wide range of therapeutic discovery and development tasks. In this survey, we review the literature on machine learning applications for genomics through the lens of therapeutic development. We investigate the interplay among genomics, compounds, proteins, electronic health records, cellular images, and clinical texts. We identify 22 machine learning in genomics applications that span the whole therapeutics pipeline, from discovering novel targets, personalizing medicine, developing gene-editing tools, all the way to facilitating clinical trials and post-market studies. We also pinpoint seven key challenges in this field with potentials for expansion and impact. This survey examines recent research at the intersection of machine learning, genomics, and therapeutic development.

Recapitulating the Cancer Microenvironment Using Bioprinting Technology for Precision Medicine

The complex and heterogenous nature of cancer contributes to the development of cancer cell drug resistance. The construction of the cancer microenvironment, including the cell-cell interactions and extracellular matrix (ECM), plays a significant role in the development of drug resistance. Traditional animal models used in drug discovery studies have been associated with feasibility issues that limit the recapitulation of human functions; thus, in vitro models have been developed to reconstruct the human cancer system. However, conventional two-dimensional and three-dimensional (3D) in vitro cancer models are limited in their ability to emulate complex cancer microenvironments. Advances in technologies, including bioprinting and cancer microenvironment reconstruction, have demonstrated the potential to overcome some of the limitations of conventional models. This study reviews some representative bioprinted in vitro models used in cancer research, particularly fabrication strategies for modeling and consideration of essential factors needed for the reconstruction of the cancer microenvironment. In addition, we highlight recent studies that applied such models, including application in precision medicine using advanced bioprinting technologies to fabricate biomimetic cancer models. Furthermore, we discuss current challenges in 3D bioprinting and suggest possible strategies to construct in vitro models that better mimic the pathophysiology of the cancer microenvironment for application in clinical settings.

Rescuing monopronucleated-derived human blastocysts: a model to study chromosomal topography and fingerprinting

OBJECTIVE

To quantify the percentage of monopronuclear-derived blastocysts (MNBs) that are potentially useful for reproductive purposes using classic and state-of-the-art chromosome analysis approaches, and to study chromosomal distribution in the inner cell mass (ICM) and trophectoderm (TE) for intertissue/intratissue concordance comparison.

DESIGN

Prospective experimental study.

SETTING

Single-center in vitro fertilization clinic and reproductive genetics laboratory.

PATIENT(S)

A total of 1,128 monopronuclear zygotes were obtained between June 2016 and December 2018.

INTERVENTION(S)

MNBs were whole-fixed or biopsied to obtain a portion of ICM and 2 TE portions (TE1 and TE2) and were subsequently analyzed by fluorescence in situ hybridization, new whole-genome sequencing, and fingerprinting by single-nucleotide polymorphism array-based techniques (a-SNP).

MAIN OUTCOME MEASURE(S)

We assessed MNB rate, ploidy rate, and chromosomal constitution by new whole-genome sequencing, and parental composition by comparative a-SNP, performed in a "trio"-format (embryo/parents). The 24-chromosome distribution was compared between the TE and the ICM and within the TE.

RESULT(S)

A total of 18.4% of monopronuclear zygotes progressed to blastocysts; 77.6% of MNBs were diploid; 20% of MNBs were male and euploid, which might be reproductively useful. Seventy-five percent of MNBs were biparental and half of them were euploid, indicating that 40% might be reproductively useful. Intratissue concordance (TE1/TE2) was established for 93.3% and 73.3% for chromosome matching. Intertissue concordance (TE/ICM) was established for 78.8%, but 57.6% for chromosome matching. When segmental aneuploidy was not considered, intratissue concordance and chromosome matching increased to 100% and 80%, respectively, and intertissue concordance and chromosome matching increased to 84.8% and 75.8%, respectively.

CONCLUSION(S)

The a-SNP-trio strategy provides information about ploidy, euploidy, and parental origin in a single biopsy. This approach enabled us to identify 40% of MNBs with reproductive potential, which can have a significant effect in the clinical setting. Additionally, segmental aneuploidy is relevant for mismatched preimplantation genetic testing of aneuploidies, both within and between MNB tissues. Repeat biopsy might clarify whether segmental aneuploidy is a prone genetic character.

A New Era of Neuro-Oncology Research Pioneered by Multi-Omics Analysis and Machine Learning

Although the incidence of central nervous system (CNS) cancers is not high, it significantly reduces a patient’s quality of life and results in high mortality rates. A low incidence also means a low number of cases, which in turn means a low amount of information. To compensate, researchers have tried to increase the amount of information available from a single test using high-throughput technologies. This approach, referred to as single-omics analysis, has only been partially successful as one type of data may not be able to appropriately describe all the characteristics of a tumor. It is presently unclear what type of data can describe a particular clinical situation. One way to solve this problem is to use multi-omics data. When using many types of data, a selected data type or a combination of them may effectively resolve a clinical question. Hence, we conducted a comprehensive survey of papers in the field of neuro-oncology that used multi-omics data for analysis and found that most of the papers utilized machine learning techniques. This fact shows that it is useful to utilize machine learning techniques in multi-omics analysis. In this review, we discuss the current status of multi-omics analysis in the field of neuro-oncology and the importance of using machine learning techniques.

Manoeuvrability and biocompatibility of endodontic tricalcium silicate-based putties

OBJECTIVES

The present study evaluated the indentation depth, storage modulus and biocompatibility of an experimental endodontic putty designed for endodontic perforation repair and direct pulp-capping (NeoPutty). The results were compared with the properties associated with the commercially available EndoSequence BC RRM Putty (ES Putty).

METHODS

Indentation depth was measured by a profilometer following indentation with the 1/4 lb Gilmore needle. Elastic modulus was evaluated using a strain-controlled rheometer. The effects of eluents derived from these two putties were examined on the viability and proliferation of human dental pulp stem cells (hDPSCs) and human periodontal ligament fibroblasts (hPDLFs), before (1 st testing cycle) and after complete setting (2nd testing cycle).

RESULTS

The ES Putty became more difficult to ident and acquired a larger storage modulus after exposure to atmospheric moisture. Biocompatibility results indicated that both putties were relatively more cytotoxic than the bioinert Teflon negative control, but much less cytotoxic than the zinc oxide-eugenol cement negative control. NeoPutty was less cytotoxic than ES putty in the 1st testing cycle, particularly with hDPSCs. Both putties exhibited more favourable cytotoxicity profiles after complete setting.

CONCLUSIONS

NeoPutty has a better window of maneuverability after exposure to atmospheric moisture. From an in vitro cytotoxicity perspective, the NeoPutty may be considered more biocompatible than ES putty.

CLINICAL SIGNIFICANCE

The experimental NeoPutty is biocompatible and is capable of reducing the frustration of shortened shelf life when jar-stored endodontic putties are exposed to atmospheric moisture during repeated opening of the lid for clinical retrieval.

Airborne PAHs inhibit gap junctional intercellular communication and activate MAPKs in human bronchial epithelial cell line

Inhalation exposures to polycyclic aromatic hydrocarbons (PAHs) have been associated with various adverse health effects, including chronic lung diseases and cancer. Using human bronchial epithelial cell line HBE1, we investigated the effects of structurally different PAHs on tissue homeostatic processes, namely gap junctional intercellular communication (GJIC) and MAPKs activity. Rapid (<1 h) and sustained (up to 24 h) inhibition of GJIC was induced by low/middle molecular weight (MW) PAHs, particularly by those with a bay- or bay-like region (1- and 9-methylanthracene, fluoranthene), but also by fluorene and pyrene. In contrast, linear low MW (anthracene, 2-methylanthracene) or higher MW (chrysene) PAHs did not affect GJIC. Fluoranthene, 1- and 9-methylanthracene induced strong and sustained activation of MAPK ERK1/2, whereas MAPK p38 was activated rather nonspecifically by all tested PAHs. Low/middle MW PAHs can disrupt tissue homeostasis in human airway epithelium via structure-dependent nongenotoxic mechanisms, which can contribute to their human health hazards.

Cancer systems immunology

Tumor immunology is undergoing a renaissance due to the recent profound clinical successes of tumor immunotherapy. These advances have coincided with an exponential growth in the development of -omics technologies. Armed with these technologies and their associated computational and modeling toolsets, systems biologists have turned their attention to tumor immunology in an effort to understand the precise nature and consequences of interactions between tumors and the immune system. Such interactions are inherently multivariate, spanning multiple time and size scales, cell types, and organ systems, rendering systems biology approaches particularly amenable to their interrogation. While in its infancy, the field of 'Cancer Systems Immunology' has already influenced our understanding of tumor immunology and immunotherapy. As the field matures, studies will move beyond descriptive characterizations toward functional investigations of the emergent behavior that govern tumor-immune responses. Thus, Cancer Systems Immunology holds incredible promise to advance our ability to fight this disease.

Mechanisms of action, chemical characteristics, and model systems of obesogens

There is increasing evidence for the role of environmental endocrine disrupting contaminants, coined obesogens, in exacerbating the rising obesity epidemic. Obesogens can be found in everyday items ranging from pesticides to food packaging. Although research shows that obesogens can have effects on adipocyte size, phenotype, metabolic activity, and hormone levels, much remains unknown about these chemicals. This review will discuss what is currently known about the mechanisms of obesogens, including expression of the PPARs, hormone interference, and inflammation. Strategies for identifying obesogenic chemicals and their mechanisms through chemical characteristics and model systems will also be discussed. Ultimately, research should focus on improving models to discern precise mechanisms of obesogenic action and to test therapeutics targeting these mechanisms.

Translating preclinical models to humans

Computational models for cross-species translation could improve drug development

Metabolomics for Animal Models of Rare Human Diseases: An Expert Review and Lessons Learned

Rare diseases occur with a frequency ≤1:1500-1:2500 depending on the location and applicable definitions across countries. Although individually rare, they collectively affect as much as 4-8% of population imposing a large burden on public health. Rarity in prevalence means prolonged path to accurate diagnosis, lack of treatment options, and also limited chances for preclinical studies of pathogenesis. I discuss in this expert review (1) what metabolomics, as a high throughput systems sciences technology, offers for rare disease studies, (2) why animal models are important for the study of rare human diseases and what should be kept in mind while using animal models, and finally, (3) provide examples of recent research to highlight how metabolomics on animal models of rare diseases perform, and how these results can lead to the knowhow, which raises genome, metabolome, and phenotype integration to a whole new level. In sum, metabolomics has been for years in clinical use for diagnosis of certain types of rare diseases. Determination of pathogenesis of more complex diseases and testing of treatment strategies is where animal models and systems biology analytical approaches are necessary. From gathered data, it is possible to go back to diagnostic and prognostic markers for rare diseases, which so far lack reliable and robust diagnosis and therapeutic options. In the future, a major challenge is to reveal the links between genotype, metabolism, and phenotype. Rare diseases could be the key in that process.

In silico identification and wet lab validation of novel cryptic B cell epitopes in ZnT8 zinc transporter autoantigen

Zinc transporter 8 (ZnT8) is a novel immunodominant autoantigen, associated with Type-1 diabetes. A non-synonymous polymorphism (R325W) in its gene is associated with Type-2 diabetes. In this study, we performed an in silico B cell epitope prediction followed by wetlab validation of ZnT8. Apart from the previously characterized polymorphic epitope (BE-5 TAASR*DS), two novel epitopes BE-2 (N-terminus) and BE-6 (C-terminus) were identified. Wet lab validation of these epitopes was carried out by measuring ZnT8 specific isotypes (IgG, IgM and IgA) in the sera of Normal Glucose Tolerant (NGT), Type-1 diabetic (T1DM) and Type-2 diabetic (T2DM) patients by indirect ELISA. Unexpectedly, compared to NGT, significantly decreased levels of IgG and IgA isotypes was seen in T1DM subjects without complications. IgM levels were reduced in T1DM subjects with retinopathy. Newly diagnosed T1DM subjects initiated on insulin therapy showed an increase in IgA and decrease in IgM titre. Like T1DM, significantly reduced level of IgG, IgM and IgA was seen in T2DM subjects. For the first time, we have identified novel cryptic B cell epitopes in ZnT8 autoantigen against which the naturally occurring autoantibody levels were found to be reduced in diabetes.

Stratification of amyotrophic lateral sclerosis patients: a crowdsourcing approach

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease where substantial heterogeneity in clinical presentation urgently requires a better stratification of patients for the development of drug trials and clinical care. In this study we explored stratification through a crowdsourcing approach, the DREAM Prize4Life ALS Stratification Challenge. Using data from >10,000 patients from ALS clinical trials and 1479 patients from community-based patient registers, more than 30 teams developed new approaches for machine learning and clustering, outperforming the best current predictions of disease outcome. We propose a new method to integrate and analyze patient clusters across methods, showing a clear pattern of consistent and clinically relevant sub-groups of patients that also enabled the reliable classification of new patients. Our analyses reveal novel insights in ALS and describe for the first time the potential of a crowdsourcing to uncover hidden patient sub-populations, and to accelerate disease understanding and therapeutic development.

Computational translation of genomic responses from experimental model systems to humans

The high failure rate of therapeutics showing promise in mouse models to translate to patients is a pressing challenge in biomedical science. Though retrospective studies have examined the fidelity of mouse models to their respective human conditions, approaches for prospective translation of insights from mouse models to patients remain relatively unexplored. Here, we develop a semi-supervised learning approach for inference of disease-associated human differentially expressed genes and pathways from mouse model experiments. We examined 36 transcriptomic case studies where comparable phenotypes were available for mouse and human inflammatory diseases and assessed multiple computational approaches for inferring human biology from mouse datasets. We found that semi-supervised training of a neural network identified significantly more true human biological associations than interpreting mouse experiments directly. Evaluating the experimental design of mouse experiments where our model was most successful revealed principles of experimental design that may improve translational performance. Our study shows that when prospectively evaluating biological associations in mouse studies, semi-supervised learning approaches, combining mouse and human data for biological inference, provide the most accurate assessment of human in vivo disease processes. Finally, we proffer a delineation of four categories of model system-to-human “Translation Problems” defined by the resolution and coverage of the datasets available for molecular insight translation and suggest that the task of translating insights from model systems to human disease contexts may be better accomplished by a combination of translation-minded experimental design and computational approaches.

Empirical comparison of genomic responses in mouse models and human disease contexts is not sufficient for addressing the challenge of prospective translation from mouse models to human disease contexts. We address this challenge by developing a semi-supervised machine learning approach that combines supervised modeling of mouse datasets with unsupervised modeling of human disease-context datasets to predict human in vivo differentially expressed genes and enriched pathways. Semi-supervised training of a feed forward neural network was the most efficacious model for translating experimentally derived mouse biological associations to the human in vivo disease context. We find that computational generalization of signaling insights substantially improves upon direct generalization of mouse experimental insights and argue that such approaches can facilitate more clinically impactful translation of insights from preclinical studies in model systems to patients.

Human ex vivo 3D bone model recapitulates osteocyte response to metastatic prostate cancer

Prostate cancer (PCa) is the second leading cause of cancer deaths among American men. Unfortunately, there is no cure once the tumor is established within the bone niche. Although osteocytes are master regulators of bone homeostasis and remodeling, their role in supporting PCa metastases remains poorly defined. This is largely due to a lack of suitable ex vivo models capable of recapitulating the physiological behavior of primary osteocytes. To address this need, we integrated an engineered bone tissue model formed by 3D-networked primary human osteocytes, with conditionally reprogrammed (CR) primary human PCa cells. CR PCa cells induced a significant increase in the expression of fibroblast growth factor 23 (FGF23) by osteocytes. The expression of the Wnt inhibitors sclerostin and dickkopf-1 (Dkk-1), exhibited contrasting trends, where sclerostin decreased while Dkk-1 increased. Furthermore, alkaline phosphatase (ALP) was induced with a concomitant increase in mineralization, consistent with the predominantly osteoblastic PCa-bone metastasis niche seen in patients. Lastly, we confirmed that traditional 2D culture failed to reproduce these key responses, making the use of our ex vivo engineered human 3D bone tissue an ideal platform for modeling PCa-bone interactions.

Pulmonary arterial hypertension and the potential roles of metallothioneins: A focused review

The pathophysiology of pulmonary arterial hypertension (PAH) is underlined by cell proliferation and vasoconstriction of pulmonary arterioles this involves multiple molecular factors or proteins, but it is not clear what the exact roles of these factors/proteins are. In addition, there may be other factors/proteins that have not been identified that contribute to PAH pathophysiology. Therefore, research has focused on investigating novel role players, in order to facilitate a better understanding of how PAH develop. Evidence suggest that mitochondrial regulators are key role players in PAH pathophysiology, but regulators that have not received sufficient attention in PAH are metallothioneins (MTs). In PAH patients, MT expression is elevated compared to healthy individuals, suggesting that MTs may be possible biomarkers. In other disease-models, MTs have been shown to regulate cell proliferation and vasoconstriction, processes that are instrumental in PAH pathophysiology. Due to the involvement of these processes in PAH pathophysiology and the ability of MTs to modulate them, this paper propose that cellular MTs may also play a role in PAH development. This paper suggests that PAH-research should perhaps begin to investigate the involvement of cellular MTs in the development of PAH.

Embracing Transparency Through Data Sharing

Low rates of reproducibility and translatability of data from nonclinical research have been reported. Major causes of irreproducibility include oversights in study design, failure to characterize reagents and protocols, a lack of access to detailed methods and data, and an absence of universally accepted and applied standards and guidelines. Specific areas of concern include uncharacterized antibodies and cell lines, the use of inappropriate sampling and testing protocols, a lack of transparency and access to raw data, and deficiencies in the translatability of findings to the clinic from studies using animal models of disease. All stakeholders—academia, industry, funding agencies, regulators, nonprofit entities, and publishers—are encouraged to play active roles in addressing these challenges by formulating and promoting access to best practices and standard operating procedures and validating data collaboratively at each step of the biomedical research life cycle.

Ex vivo construction of human primary 3D-networked osteocytes

A human bone tissue model was developed by constructing ex vivo the 3D network of osteocytes via the biomimetic assembly of primary human osteoblastic cells with 20-25μm microbeads and subsequent microfluidic perfusion culture. The biomimetic assembly: (1) enabled 3D-constructed cells to form cellular network via processes with an average cell-to-cell distance of 20-25μm, and (2) inhibited cell proliferation within the interstitial confine between the microbeads while the confined cells produced extracellular matrix (ECM) to form a mechanically integrated structure. The mature osteocytic expressions of SOST and FGF23 genes became significantly higher, especially for SOST by 250 folds during 3D culture. The results validate that the bone tissue model: (1) consists of 3D cellular network of primary human osteocytes, (2) mitigates the osteoblastic differentiation and proliferation of primary osteoblast-like cells encountered in 2D culture, and (3) therefore reproduces ex vivo the phenotype of human 3D-networked osteocytes. The 3D tissue construction approach is expected to provide a clinically relevant and high-throughput means for evaluating drugs and treatments that target bone diseases with in vitro convenience.

Systems toxicology meta-analysis of in vitro assessment studies: biological impact of a candidate modified-risk tobacco product aerosol compared with cigarette smoke on human organotypic cultures of the aerodigestive tract

Systems biology combines comprehensive molecular analyses with quantitative modeling to understand the characteristics of a biological system as a whole. Leveraging a similar approach, systems toxicology aims to decipher complex biological responses following exposures. This work reports a systems toxicology meta-analysis in the context of in vitro assessment of a candidate modified-risk tobacco product (MRTP) using three human organotypic cultures of the aerodigestive tract (buccal, bronchial, and nasal epithelia). Complementing a series of functional measures, a causal network enrichment analysis of transcriptomic data was used to compare quantitatively the biological impact of aerosol from the Tobacco Heating System (THS) 2.2, a candidate MRTP, with 3R4F cigarette smoke (CS) at similar nicotine concentrations. Lower toxicity was observed in all cultures following exposure to THS2.2 aerosol compared with 3R4F CS. Because of their morphological differences, a smaller exposure impact was observed in the buccal (stratified epithelium) compared with the bronchial and nasal (pseudostratified epithelium). However, the causal network enrichment approach supported a similar mechanistic impact of CS across the three cultures, including the impact on xenobiotic, oxidative stress, and inflammatory responses. At comparable nicotine concentrations, THS2.2 aerosol elicited reduced and more transient effects on these processes. To demonstrate the benefits of additional data modalities, we employed a newly established targeted mass-spectrometry marker panel to further confirm the reduced cellular stress responses elicited by THS2.2 aerosol compared with 3R4F CS in the nasal culture. Overall, this work demonstrates the applicability and robustness of the systems toxicology approach for in vitro inhalation toxicity assessment.

Hypoxic Three-Dimensional Cellular Network Construction Replicates Ex Vivo the Phenotype of Primary Human Osteocytes

Osteocytes are deeply embedded in the mineralized matrix of bone and are nonproliferative, making them a challenge to isolate and maintain using traditional in vitro culture methods without sacrificing their inimitable phenotype. We studied the synergistic effects of two microenvironmental factors that are vital in retaining, ex vivo, the phenotype of primary human osteocytes: hypoxia and three-dimensional (3D) cellular network. To recapitulate the lacunocanalicular structure of bone tissue, we assembled and cultured primary human osteocytic cells with biphasic calcium phosphate microbeads in a microfluidic perfusion culture device. The 3D cellular network was constructed by the following: (1) the inhibited proliferation of cells entrapped by microbeads, biomimetically resembling lacunae, and (2) the connection of neighboring cells by dendrites through the mineralized, canaliculi-like interstitial spaces between the microbeads. We found that hypoxia synergistically and remarkably upregulated the mature osteocytic gene expressions of the 3D-networked cells, SOST (encoding sclerostin) and FGF23 (encoding fibroblast growth factor 23), by several orders of magnitude in comparison to those observed from two-dimensional and normoxic culture controls. Intriguingly, hypoxia facilitated the self-assembly of a nonproliferating, osteoblastic monolayer on the surface of the 3D-networked cells, replicating the osteoblastic endosteal cell layer found at the interface between native bone and bone marrow tissues. Our ability to replicate, with hypoxia, the strong expressions of these mature osteocytic markers, SOST and FGF23, is important since these (1) could not be significantly produced in vitro and (2) are new important targets for treating bone diseases. Our findings are therefore expected to facilitate ex vivo studies of human bone diseases using primary human bone cells and enable high-throughput evaluation of potential bone-targeting therapies with clinical relevance.

Human blood gene signature as a marker for smoking exposure: computational approaches of the top ranked teams in the sbv IMPROVER Systems Toxicology challenge

Crowdsourcing has emerged as a framework to address methodological challenges in omics data analysis and assess the extent to which omics data are predictive of phenotypes of interest. The sbv IMPROVER Systems Toxicology Challenge was designed to leverage crowdsourcing to determine whether human blood gene expression levels are informative of current and past smoking. Participating teams were invited to use a training gene expression dataset to derive parsimonious models (up to 40 genes) that can accurately classify subjects into exposure groups: smokers, former smokers that quit for at least one year, and never-smokers. Teams were ranked based on two classification performance metrics evaluated on a blinded test dataset. The analytical approaches of the first- and third-ranked teams, that are presented in detail in this article, involved feature selection by moderated t-test or LASSO regression and linear discriminant analysis (LDA) and logistic regression classifiers, respectively. While the 12-gene signature of the top team allowed the classification of current smokers with 100% sensitivity at 93% specificity, discriminating former smokers from never-smokers was much more challenging (65% sensitivity at 57% specificity). Gene ontology molecular functions and KEGG pathways associated with current smoking included G protein-coupled receptor activity, signaling receptor activity, calcium ion binding, and the Neuroactive ligand-receptor interaction pathway. Selection of marker genes by either moderated t-test or multivariate LASSO regression followed by LDA or logistic regression, are robust approaches to classification with omics data, confirming in part findings of previous sbv IMPROVER challenges. While current smoking is accurately identified based on blood mRNA levels, smoking cessation for more than one year is accompanied by a "normalization" of the expression of certain mRNAs, making it difficult to distinguish former smokers from never-smokers.

The sbv IMPROVER Systems Toxicology Computational Challenge: Identification of Human and Species-Independent Blood Response Markers as Predictors of Smoking Exposure and Cessation Status

Cigarette smoking entails chronic exposure to a mixture of harmful chemicals that trigger molecular changes over time, and is known to increase the risk of developing diseases. Risk assessment in the context of 21^st century toxicology relies on the elucidation of mechanisms of toxicity and the identification of exposure response markers, usually from high-throughput data, using advanced computational methodologies. The sbv IMPROVER Systems Toxicology computational challenge (Fall 2015-Spring 2016) aimed to evaluate whether robust and sparse (≤40 genes) human (sub-challenge 1, SC1) and species-independent (sub-challenge 2, SC2) exposure response markers (so called gene signatures) could be extracted from human and mouse blood transcriptomics data of current (S), former (FS) and never (NS) smoke-exposed subjects as predictors of smoking and cessation status. Best-performing computational methods were identified by scoring anonymized participants' predictions. Worldwide participation resulted in 12 (SC1) and six (SC2) final submissions qualified for scoring. The results showed that blood gene expression data were informative to predict smoking exposure (i.e. discriminating smoker versus never or former smokers) status in human and across species with a high level of accuracy. By contrast, the prediction of cessation status (i.e. distinguishing FS from NS) remained challenging, as reflected by lower classification performances. Participants successfully developed inductive predictive models and extracted human and species-independent gene signatures, including genes with high consensus across teams. Post-challenge analyses highlighted "feature selection" as a key step in the process of building a classifier and confirmed the importance of testing a gene signature in independent cohorts to ensure the generalized applicability of a predictive model at a population-based level. In conclusion, the Systems Toxicology challenge demonstrated the feasibility of extracting a consistent blood-based smoke exposure response gene signature and further stressed the importance of independent and unbiased data and method evaluations to provide confidence in systems toxicology-based scientific conclusions.

Species translatable blood gene signature as a marker of exposure to smoking: computational approaches of the top ranked teams in the sbv IMPROVER Systems Toxicology challenge

Crowdsourcing has been used to address computational challenges in systems biology and assess translation of findings across species. Sub-challenge 2 of the sbv IMPROVER Systems Toxicology Challenge was designed to determine whether a common set of genes can be used to identify exposure to cigarette smoke in both human and mouse. Participating teams used a training set of human and mouse blood gene expression data to derive parsimonious models (up to 40 genes) that classify subjects into exposure groups: smokers, former smokers, and never-smokers. Teams were ranked based on two classification performance metrics evaluated on a blinded test dataset. Prediction of current exposure to cigarette smoke in human and mouse by a common prediction model was achieved by the top ranked team (Team 219) with 89% balanced accuracy (BAC), while past exposure was predicted with only 57% BAC. The prediction model of the top ranked team was a random forest classifier trained on sets of genes that appeared best for each species separately with no overlap between species. By contrast, Team 264, ranked second (tied with Team 250), selected genes that were simultaneously predictive in both species and achieved 80% and 59% BAC when predicting current and past exposure, respectively. These performance values were lower than the 96.5% and 61% BAC estimates for current and past exposure, respectively, obtained by Team 264 (top ranked in sub-challenge 1) when using only human data. Unlike past exposure, current exposure to cigarette smoke can be accurately assessed in both human and mouse with a common prediction model based on blood mRNAs. However, requiring a common gene signature to be predictive in both species resulted in a substantial decrease in balanced accuracy for prediction of current exposure to cigarette smoke (from 96.5% to 80%), suggesting species-specific responses exist.

Crowd-Sourced Verification of Computational Methods and Data in Systems Toxicology: A Case Study with a Heat-Not-Burn Candidate Modified Risk Tobacco Product

Systems toxicology intends to quantify the effect of toxic molecules in biological systems and unravel their mechanisms of toxicity. The development of advanced computational methods is required for analyzing and integrating high throughput data generated for this purpose as well as for extrapolating predictive toxicological outcomes and risk estimates. To ensure the performance and reliability of the methods and verify conclusions from systems toxicology data analysis, it is important to conduct unbiased evaluations by independent third parties. As a case study, we report here the results of an independent verification of methods and data in systems toxicology by crowdsourcing. The sbv IMPROVER systems toxicology computational challenge aimed to evaluate computational methods for the development of blood-based gene expression signature classification models with the ability to predict smoking exposure status. Participants created/trained models on blood gene expression data sets including smokers/mice exposed to 3R4F (a reference cigarette) or noncurrent smokers/Sham (mice exposed to air). Participants applied their models on unseen data to predict whether subjects classify closer to smoke-exposed or nonsmoke exposed groups. The data sets also included data from subjects that had been exposed to potential modified risk tobacco products (MRTPs) or that had switched to a MRTP after exposure to conventional cigarette smoke. The scoring of anonymized participants' predictions was done using predefined metrics. The top 3 performers' methods predicted class labels with area under the precision recall scores above 0.9. Furthermore, although various computational approaches were used, the crowd's results confirmed our own data analysis outcomes with regards to the classification of MRTP-related samples. Mice exposed directly to a MRTP were classified closer to the Sham group. After switching to a MRTP, the confidence that subjects belonged to the smoke-exposed group decreased significantly. Smoking exposure gene signatures that contributed to the group separation included a core set of genes highly consistent across teams such as AHRR, LRRN3, SASH1, and P2RY6. In conclusion, crowdsourcing constitutes a pertinent approach, in complement to the classical peer review process, to independently and unbiasedly verify computational methods and data for risk assessment using systems toxicology.

Reconciled rat and human metabolic networks for comparative toxicogenomics and biomarker predictions

The laboratory rat has been used as a surrogate to study human biology for more than a century. Here we present the first genome-scale network reconstruction of Rattus norvegicus metabolism, iRno, and a significantly improved reconstruction of human metabolism, iHsa. These curated models comprehensively capture metabolic features known to distinguish rats from humans including vitamin C and bile acid synthesis pathways. After reconciling network differences between iRno and iHsa, we integrate toxicogenomics data from rat and human hepatocytes, to generate biomarker predictions in response to 76 drugs. We validate comparative predictions for xanthine derivatives with new experimental data and literature-based evidence delineating metabolite biomarkers unique to humans. Our results provide mechanistic insights into species-specific metabolism and facilitate the selection of biomarkers consistent with rat and human biology. These models can serve as powerful computational platforms for contextualizing experimental data and making functional predictions for clinical and basic science applications.

The rat is a widely-used model for human biology, but we must be aware of metabolic differences. Here, the authors reconstruct the genome-scale metabolic network of the rat, and after reconciling it with an improved human metabolic model, demonstrate the power of the models to integrate toxicogenomics data, providing species-specific biomarker predictions in response to a panel of drugs.

How Adverse Outcome Pathways Can Aid the Development and Use of Computational Prediction Models for Regulatory Toxicology

Efforts are underway to transform regulatory toxicology and chemical safety assessment from a largely empirical science based on direct observation of apical toxicity outcomes in whole organism toxicity tests to a predictive one in which outcomes and risk are inferred from accumulated mechanistic understanding. The adverse outcome pathway (AOP) framework provides a systematic approach for organizing knowledge that may support such inference. Likewise, computational models of biological systems at various scales provide another means and platform to integrate current biological understanding to facilitate inference and extrapolation. We argue that the systematic organization of knowledge into AOP frameworks can inform and help direct the design and development of computational prediction models that can further enhance the utility of mechanistic and in silico data for chemical safety assessment. This concept was explored as part of a workshop on AOP-Informed Predictive Modeling Approaches for Regulatory Toxicology held September 24-25, 2015. Examples of AOP-informed model development and its application to the assessment of chemicals for skin sensitization and multiple modes of endocrine disruption are provided. The role of problem formulation, not only as a critical phase of risk assessment, but also as guide for both AOP and complementary model development is described. Finally, a proposal for actively engaging the modeling community in AOP-informed computational model development is made. The contents serve as a vision for how AOPs can be leveraged to facilitate development of computational prediction models needed to support the next generation of chemical safety assessment.

Development of a Three-Dimensional Adipose Tissue Model for Studying Embryonic Exposures to Obesogenic Chemicals

Obesity is a rising issue especially in the United States that can lead to heart problems, type II diabetes, and respiratory problems. Since the 1970s, obesity rates in the United States have more than doubled in adults and children. Recent evidence suggests that exposure to certain chemicals, termed "obesogens," in utero may alter metabolic processes, predisposing individuals to weight gain. There is a need to develop a three-dimensional human tissue system that is able to model the effects of obesogens in vitro in order to better understand the impact of obesogens on early development. Human embryonic-derived stem cells in three-dimensional collagen embedded silk scaffolds were exposed to three different obesogens: Bisphenol A (BPA), Bisphenol S (BPS), and Tributyltin (TBT). The exposed tissues accumulated triglycerides and increased expression of adipogenic genes (Perilipin (PLIN1), peroxisome proliferator-activated receptor gamma (PPARy), fatty acid binding protein 4 (FABP4)) compared to equivalent control cultures with no obesogen exposure. These cultures were also compared to human adult stem cell cultures, which did not respond the same upon addition of obesogens. These results demonstrate the successful development of a representative tissue model of in utero obesogen exposures. This tissue system could be used to determine mechanisms of action of current obesogens and to screen other potential obesogens.

Trans-species learning of cellular signaling systems with bimodal deep belief networks

MOTIVATION

Model organisms play critical roles in biomedical research of human diseases and drug development. An imperative task is to translate information/knowledge acquired from model organisms to humans. In this study, we address a trans-species learning problem: predicting human cell responses to diverse stimuli, based on the responses of rat cells treated with the same stimuli.

RESULTS

We hypothesized that rat and human cells share a common signal-encoding mechanism but employ different proteins to transmit signals, and we developed a bimodal deep belief network and a semi-restricted bimodal deep belief network to represent the common encoding mechanism and perform trans-species learning. These 'deep learning' models include hierarchically organized latent variables capable of capturing the statistical structures in the observed proteomic data in a distributed fashion. The results show that the models significantly outperform two current state-of-the-art classification algorithms. Our study demonstrated the potential of using deep hierarchical models to simulate cellular signaling systems.

AVAILABILITY AND IMPLEMENTATION

The software is available at the following URL: http://pubreview.dbmi.pitt.edu/TransSpeciesDeepLearning/. The data are available through SBV IMPROVER website, https://www.sbvimprover.com/challenge-2/overview, upon publication of the report by the organizers.

CONTACT

xinghua@pitt.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Modeling signal transduction from protein phosphorylation to gene expression

BACKGROUND

Signaling networks are of great importance for us to understand the cell’s regulatory mechanism. The rise of large-scale genomic and proteomic data, and prior biological knowledge has paved the way for the reconstruction and discovery of novel signaling pathways in a data-driven manner. In this study, we investigate computational methods that integrate proteomics and transcriptomic data to identify signaling pathways transmitting signals in response to specific stimuli. Such methods can be applied to cancer genomic data to infer perturbed signaling pathways.

METHOD

We proposed a novel Bayesian Network (BN) framework to integrate transcriptomic data with proteomic data reflecting protein phosphorylation states for the purpose of identifying the pathways transmitting the signal of diverse stimuli in rat and human cells. We represented the proteins and genes as nodes in a BN in which edges reflect the regulatory relationship between signaling proteins. We designed an efficient inference algorithm that incorporated the prior knowledge of pathways and searched for a network structure in a data-driven manner.

RESULTS

We applied our method to infer rat and human specific networks given gene expression and proteomic datasets. We were able to effectively identify sparse signaling networks that modeled the observed transcriptomic and proteomic data. Our methods were able to identify distinct signaling pathways for rat and human cells in a data-driven manner, based on the facts that rat and human cells exhibited distinct transcriptomic and proteomics responses to a common set of stimuli. Our model performed well in the SBV IMPROVER challenge in comparison to other models addressing the same task. The capability of inferring signaling pathways in a data-driven fashion may contribute to cancer research by identifying distinct aberrations in signaling pathways underlying heterogeneous cancers subtypes.

A crowd-sourcing approach for the construction of species-specific cell signaling networks

Motivation: Animal models are important tools in drug discovery and for understanding human biology in general. However, many drugs that initially show promising results in rodents fail in later stages of clinical trials. Understanding the commonalities and differences between human and rat cell signaling networks can lead to better experimental designs, improved allocation of resources and ultimately better drugs.

Results: The sbv IMPROVER Species-Specific Network Inference challenge was designed to use the power of the crowds to build two species-specific cell signaling networks given phosphoproteomics, transcriptomics and cytokine data generated from NHBE and NRBE cells exposed to various stimuli. A common literature-inspired reference network with 220 nodes and 501 edges was also provided as prior knowledge from which challenge participants could add or remove edges but not nodes. Such a large network inference challenge not based on synthetic simulations but on real data presented unique difficulties in scoring and interpreting the results. Because any prior knowledge about the networks was already provided to the participants for reference, novel ways for scoring and aggregating the results were developed. Two human and rat consensus networks were obtained by combining all the inferred networks. Further analysis showed that major signaling pathways were conserved between the two species with only isolated components diverging, as in the case of ribosomal S6 kinase RPS6KA1. Overall, the consensus between inferred edges was relatively high with the exception of the downstream targets of transcription factors, which seemed more difficult to predict.

Contact:ebilal@us.ibm.com or gustavo@us.ibm.com.

Supplementary information:Supplementary data are available at Bioinformatics online.

Inter-species inference of gene set enrichment in lung epithelial cells from proteomic and large transcriptomic datasets

Motivation: Translating findings in rodent models to human models has been a cornerstone of modern biology and drug development. However, in many cases, a naive ‘extrapolation’ between the two species has not succeeded. As a result, clinical trials of new drugs sometimes fail even after considerable success in the mouse or rat stage of development. In addition to in vitro studies, inter-species translation requires analytical tools that can predict the enriched gene sets in human cells under various stimuli from corresponding measurements in animals. Such tools can improve our understanding of the underlying biology and optimize the allocation of resources for drug development.

Results: We developed an algorithm to predict differential gene set enrichment as part of the sbv IMPROVER (systems biology verification in Industrial Methodology for Process Verification in Research) Species Translation Challenge, which focused on phosphoproteomic and transcriptomic measurements of normal human bronchial epithelial (NHBE) primary cells under various stimuli and corresponding measurements in rat (NRBE) primary cells. We find that gene sets exhibit a higher inter-species correlation compared with individual genes, and are potentially more suited for direct prediction. Furthermore, in contrast to a similar cross-species response in protein phosphorylation states 5 and 25 min after exposure to stimuli, gene set enrichment 6 h after exposure is significantly different in NHBE cells compared with NRBE cells. In spite of this difference, we were able to develop a robust algorithm to predict gene set activation in NHBE with high accuracy using simple analytical methods.

Availability and implementation: Implementation of all algorithms is available as source code (in Matlab) at http://bhanot.biomaps.rutgers.edu/wiki/codes_SC3_Predicting_GeneSets.zip, along with the relevant data used in the analysis. Gene sets, gene expression and protein phosphorylation data are available on request.

Contact:hormoz@kitp.ucsb.edu

Inter-species pathway perturbation prediction via data-driven detection of functional homology

MOTIVATION

Experiments in animal models are often conducted to infer how humans will respond to stimuli by assuming that the same biological pathways will be affected in both organisms. The limitations of this assumption were tested in the IMPROVER Species Translation Challenge, where 52 stimuli were applied to both human and rat cells and perturbed pathways were identified. In the Inter-species Pathway Perturbation Prediction sub-challenge, multiple teams proposed methods to use rat transcription data from 26 stimuli to predict human gene set and pathway activity under the same perturbations. Submissions were evaluated using three performance metrics on data from the remaining 26 stimuli.

RESULTS

We present two approaches, ranked second in this challenge, that do not rely on sequence-based orthology between rat and human genes to translate pathway perturbation state but instead identify transcriptional response orthologs across a set of training conditions. The translation from rat to human accomplished by these so-called direct methods is not dependent on the particular analysis method used to identify perturbed gene sets. In contrast, machine learning-based methods require performing a pathway analysis initially and then mapping the pathway activity between organisms. Unlike most machine learning approaches, direct methods can be used to predict the activation of a human pathway for a new (test) stimuli, even when that pathway was never activated by a training stimuli.

AVAILABILITY

Gene expression data are available from ArrayExpress (accession E-MTAB-2091), while software implementations are available from http://bioinformaticsprb.med.wayne.edu?p=50 and http://goo.gl/hJny3h.

CONTACT

christoph.hafemeister@nyu.edu or atarca@med.wayne.edu.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Predicting protein phosphorylation from gene expression: top methods from the IMPROVER Species Translation Challenge

MOTIVATION

Using gene expression to infer changes in protein phosphorylation levels induced in cells by various stimuli is an outstanding problem. The intra-species protein phosphorylation challenge organized by the IMPROVER consortium provided the framework to identify the best approaches to address this issue.

RESULTS

Rat lung epithelial cells were treated with 52 stimuli, and gene expression and phosphorylation levels were measured. Competing teams used gene expression data from 26 stimuli to develop protein phosphorylation prediction models and were ranked based on prediction performance for the remaining 26 stimuli. Three teams were tied in first place in this challenge achieving a balanced accuracy of about 70%, indicating that gene expression is only moderately predictive of protein phosphorylation. In spite of the similar performance, the approaches used by these three teams, described in detail in this article, were different, with the average number of predictor genes per phosphoprotein used by the teams ranging from 3 to 124. However, a significant overlap of gene signatures between teams was observed for the majority of the proteins considered, while Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were enriched in the union of the predictor genes of the three teams for multiple proteins.

AVAILABILITY AND IMPLEMENTATION

Gene expression and protein phosphorylation data are available from ArrayExpress (E-MTAB-2091). Software implementation of the approach of Teams 49 and 75 are available at http://bioinformaticsprb.med.wayne.edu and http://people.cs.clemson.edu/∼luofeng/sbv.rar, respectively.

CONTACT

gyanbhanot@gmail.com or luofeng@clemson.edu or atarca@med.wayne.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Inter-species prediction of protein phosphorylation in the sbv IMPROVER species translation challenge

MOTIVATION

Animal models are widely used in biomedical research for reasons ranging from practical to ethical. An important issue is whether rodent models are predictive of human biology. This has been addressed recently in the framework of a series of challenges designed by the systems biology verification for Industrial Methodology for Process Verification in Research (sbv IMPROVER) initiative. In particular, one of the sub-challenges was devoted to the prediction of protein phosphorylation responses in human bronchial epithelial cells, exposed to a number of different chemical stimuli, given the responses in rat bronchial epithelial cells. Participating teams were asked to make inter-species predictions on the basis of available training examples, comprising transcriptomics and phosphoproteomics data.

RESULTS

Here, the two best performing teams present their data-driven approaches and computational methods. In addition, post hoc analyses of the datasets and challenge results were performed by the participants and challenge organizers. The challenge outcome indicates that successful prediction of protein phosphorylation status in human based on rat phosphorylation levels is feasible. However, within the limitations of the computational tools used, the inclusion of gene expression data does not improve the prediction quality. The post hoc analysis of time-specific measurements sheds light on the signaling pathways in both species.

AVAILABILITY AND IMPLEMENTATION

A detailed description of the dataset, challenge design and outcome is available at www.sbvimprover.com. The code used by team IGB is provided under http://github.com/uci-igb/improver2013. Implementations of the algorithms applied by team AMG are available at http://bhanot.biomaps.rutgers.edu/wiki/AMG-sc2-code.zip.

CONTACT

meikelbiehl@gmail.com.