Integrative omics for health and disease

Potential relevance of pig gut content transplantation for production and research

It is becoming increasingly evident that the gastrointestinal microbiota has a significant impact on the overall health and production of the pig. This has led to intensified research on the composition of the gastrointestinal microbiota, factors affecting it, and the impact of the microbiota on health, growth performance, and more recently, behavior of the host. Swine production research has been heavily focused on assessing the effects of feed additives and dietary modifications to alter or take advantage of select characteristics of gastrointestinal microbes to improve health and feed conversion efficiency. Research on faecal microbiota transplantation (FMT) as a possible tool to improve outcomes in pigs through manipulation of the gastrointestinal microbiome is very recent and limited data is available. Results on FMT in humans demonstrating the transfer of phenotypic traits from donors to recipients and the high efficacy of FMT to treat Clostridium difficile infections in humans, together with data from pigs relating GI-tract microbiota composition with growth performance has likely played an important role in the interest towards this strategy in pig production. However, several factors can influence the impact of FMT on the recipient, and these need to be identified and optimized before this tool can be applied to pig production. There are obvious inherent biosecurity and regulatory issues in this strategy, since the donor's microbiome can never be completely screened for all possible non-desirable microorganisms. However, considering the success observed in humans, it seems worth investigating this strategy for certain applications in pig production. Further, FMT research may lead to the identification of specific bacterial group(s) essential for a particular outcome, resulting in the development of banks of clones which can be used as targeted therapeutics, rather than the broader approach applied in FMT. This review examines the factors associated with the use of FMT, and its potential application to swine production, and includes research on using the pig as model for human medical purposes.

Molecular and epigenetic markers as promising tools to quantify the effect of occupational exposures and the risk of developing non-communicable diseases

Non-communicable diseases (NCDs) are chronic diseases that are by far the leading cause of death in the world. Many occupational hazards, together with social, economic and demographic factors, have been associated to NCDs development. Genetic susceptibility or environmental exposures alone are not usually sufficient to explain the pathogenesis of NCDs, but can be integrated in a more complex scenario that can result in pathological phenotypes. Epigenetics is a crucial component of this scenario, as its changes are related to specific exposures, therefore potentially able to display the effects of environment on the genome, filling the gap between genetic asset and environment in explaining disease development. To date, the most promising biomarkers have been assessed in occupational cohorts as well as in case/control studies and include DNA methylation, histone modifications, microRNA expression, extracellular vesicles, telomere length, and mitochondrial alterations.

Proteomic investigations into hypertension: what's new and how might it affect clinical practice?

Introduction: Hypertension is a multifactorial disease that has, thus far, proven to be a difficult target for pharmacological intervention. The application of proteomic strategies may help to identify new biomarkers for the early diagnosis and prompt treatment of hypertension, in order to control blood pressure and prevent organ damage. Areas covered: Advances in proteomics have led to the discovery of new biomarkers to help track the pathophysiological processes implicated in hypertension. These findings not only help to better understand the nature of the disease, but will also contribute to the clinical needs for a timely diagnosis and more precise treatment. In this review, we provide an overview of new biomarkers identified in hypertension through the application of proteomic techniques, and we also discuss the difficulties and challenges in identifying biomarkers in this clinical setting. We performed a literature search in PubMed with the key words 'hypertension' and 'proteomics', and focused specifically on the most recent literature on the utility of proteomics in hypertension research. Expert opinion: There have been several promising biomarkers of hypertension identified by proteomics, but too few have been introduced to the clinic. Thus, further investigations in larger cohorts are necessary to test the feasibility of this strategy for patients. Also, this emerging field would profit from more collaboration between clinicians and researchers.

Integration of Metabolomic and Other Omics Data in Population-Based Study Designs: An Epidemiological Perspective

It is not controversial that study design considerations and challenges must be addressed when investigating the linkage between single omic measurements and human phenotypes. It follows that such considerations are just as critical, if not more so, in the context of multi-omic studies. In this review, we discuss (1) epidemiologic principles of study design, including selection of biospecimen source(s) and the implications of the timing of sample collection, in the context of a multi-omic investigation, and (2) the strengths and limitations of various techniques of data integration across multi-omic data types that may arise in population-based studies utilizing metabolomic data.

The Redox architecture of physiological function

The ability of organisms to accommodate variations in metabolic need and environmental conditions is essential for their survival. However, an explanation is lacking as to how the necessary accommodations in response to these challenges are organized and coordinated from (sub)cellular to higher-level physiological functions, especially in mammals. We propose that the chemistry that enables coordination and synchronization of these processes dates to the origins of Life. We offer a conceptual framework based upon the nature of electron exchange (Redox) processes that co-evolved with biological complexification, giving rise to a multi-layered system in which intra/intercellular and inter-organ exchange processes essential to sensing and adaptation stay fully synchronized. Our analysis explains why Redox is both the lingua franca and the mechanism that enable integration by connecting the various elements of regulatory processes. We here define these interactions across levels of organization as the 'Redox Interactome'. This framework provides novel insight into the chemical and biological basis of Redox signalling and may explain the recent convergence of metabolism, bioenergetics, and inflammation as well as the relationship between Redox stress and human disease.

A NOTCH added to metabolomics

Deregulated metabolism is a hallmark of cancer. In the accompanying study by Sellers et al. published in the British Journal of Cancer, metabolism-related transcriptomics data from in silico data sets are analysed, with the findings being further investigated in the experiments on tumour tissue slices and finally validated in patients. The study adds to our growing understanding of therapeutically accessible metabolic reprogramming in malignancies.

An ATAC-seq atlas of chromatin accessibility in mouse tissues

The Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) is a fundamental epigenomics approach and has been widely used in profiling the chromatin accessibility dynamics in multiple species. A comprehensive reference of ATAC-seq datasets for mammalian tissues is important for the understanding of regulatory specificity and developmental abnormality caused by genetic or environmental alterations. Here, we report an adult mouse ATAC-seq atlas by producing a total of 66 ATAC-seq profiles from 20 primary tissues of both male and female mice. The ATAC-seq read enrichment, fragment size distribution, and reproducibility between replicates demonstrated the high quality of the full dataset. We identified a total of 296,574 accessible elements, of which 26,916 showed tissue-specific accessibility. Further, we identified key transcription factors specific to distinct tissues and found that the enrichment of each motif reflects the developmental similarities across tissues. In summary, our study provides an important resource on the mouse epigenome and will be of great importance to various scientific disciplines such as development, cell reprogramming, and genetic disease.

The next generation of precision medicine: observational studies, electronic health records, biobanks and continuous monitoring

Precision medicine can utilize new techniques in order to more effectively translate research findings into clinical practice. In this article, we first explore the limitations of traditional study designs, which stem from (to name a few): massive cost for the assembly of large patient cohorts; non-representative patient data; and the astounding complexity of human biology. Second, we propose that harnessing electronic health records and mobile device biometrics coupled to longitudinal data may prove to be a solution to many of these problems by capturing a 'real world' phenotype. We envision that future biomedical research utilizing more precise approaches to patient care will utilize continuous and longitudinal data sources.

Personalized medicine-concepts, technologies, and applications in inflammatory skin diseases

The current state, tools, and applications of personalized medicine with special emphasis on inflammatory skin diseases like psoriasis and atopic dermatitis are discussed. Inflammatory pathways are outlined as well as potential targets for monoclonal antibodies and small-molecule inhibitors.

A multi-omics data simulator for complex disease studies and its application to evaluate multi-omics data analysis methods for disease classification

BACKGROUND

An integrative multi-omics analysis approach that combines multiple types of omics data including genomics, epigenomics, transcriptomics, proteomics, metabolomics, and microbiomics has become increasing popular for understanding the pathophysiology of complex diseases. Although many multi-omics analysis methods have been developed for complex disease studies, only a few simulation tools that simulate multiple types of omics data and model their relationships with disease status are available, and these tools have their limitations in simulating the multi-omics data.

RESULTS

We developed the multi-omics data simulator OmicsSIMLA, which simulates genomics (i.e., single-nucleotide polymorphisms [SNPs] and copy number variations), epigenomics (i.e., bisulphite sequencing), transcriptomics (i.e., RNA sequencing), and proteomics (i.e., normalized reverse phase protein array) data at the whole-genome level. Furthermore, the relationships between different types of omics data, such as methylation quantitative trait loci (SNPs influencing methylation), expression quantitative trait loci (SNPs influencing gene expression), and expression quantitative trait methylations (methylations influencing gene expression), were modeled. More importantly, the relationships between these multi-omics data and the disease status were modeled as well. We used OmicsSIMLA to simulate a multi-omics dataset for breast cancer under a hypothetical disease model and used the data to compare the performance among existing multi-omics analysis methods in terms of disease classification accuracy and runtime. We also used OmicsSIMLA to simulate a multi-omics dataset with a scale similar to an ovarian cancer multi-omics dataset. The neural network-based multi-omics analysis method ATHENA was applied to both the real and simulated data and the results were compared. Our results demonstrated that complex disease mechanisms can be simulated by OmicsSIMLA, and ATHENA showed the highest prediction accuracy when the effects of multi-omics features (e.g., SNPs, copy number variations, and gene expression levels) on the disease were strong. Furthermore, similar results can be obtained from ATHENA when analyzing the simulated and real ovarian multi-omics data.

CONCLUSIONS

OmicsSIMLA will be useful to evaluate the performace of different multi-omics analysis methods. Sample sizes and power can also be calculated by OmicsSIMLA when planning a new multi-omics disease study.

Metabolomics and Cytokine Analysis for Identification of Severe Drug-Induced Liver Injury

AIM

To evaluate the levels of metabolites and cytokines in the serum of patients with severe and non-severe idiosyncratic drug-induced liver injury (DILI) and to identify biomarkers of DILI severity.

METHODS

Gas chromatography-mass spectrometry (GC-MS) and ultraperformance liquid chromatography-mass spectrometry (UPLC-MS) based metabolomic approaches were used to evaluate the metabolome of serum samples from 29 DILI patients of severity grade 3 (non-severe), 27 of severity grade 4 (severe), and 36 healthy control (HC). The levels of total keratin-18 (K18), fragment K18, and 27 cytokines were determined by enzyme-linked immunosorbent assay.

RESULTS

The alkaline phosphatase activity ( p = 0.021) and international normalized ratio (INR) ( p < 0.001) differed significantly between the severe and non-severe groups. The severe group had a higher serum fragment K18 level than the non-severe group. A multivariate analysis showed good separation between all pairs of the HC, non-severe, and severe groups. According to the orthogonal partial least-squares-discriminant analysis (OPLS-DA) model, 14 metabolites were selected by GC-MS and 17 by UPLC-MS. Among these metabolites, the levels of 16 were increased and of 15 were decreased in the severe group. A pathway analysis revealed major changes in the primary bile acid biosynthesis and alpha-linolenic acid metabolic pathways. The levels of PDGF-bb, IP-10, IL-1Rα, MIP-1β, and TNF-α differed significantly between the severe and non-severe groups, and the levels of most of the metabolites were negatively correlated with those of these cytokines. An OPLS-DA model that included the detected metabolites and cytokines revealed clear separation of the severe and non-severe groups.

CONCLUSION

We identified 31 metabolites and 5 cytokines related to the severity of idiosyncratic DILI. The primary bile acid biosynthesis and alpha-linolenic acid metabolism pathways were also related to the severity of DILI. A model that incorporated the metabolites and cytokines showed clear separation between patients with severe and non-severe DILI, suggesting that these biomarkers have potential as indicators of DILI severity.

Tissue Engineering: Understanding the Role of Biomaterials and Biophysical Forces on Cell Functionality Through Computational and Structural Biotechnology Analytical Methods

Within the past 25 years, tissue engineering (TE) has grown enormously as a science and as an industry. Although classically concerned with the recapitulation of tissue and organ formation in our body for regenerative medicine, the evolution of TE research is intertwined with progress in other fields through the examination of cell function and behaviour in isolated biomimetic microenvironments. As such, TE applications now extend beyond the field of tissue regeneration research, operating as a platform for modifiable, physiologically-representative in vitro models with the potential to improve the translation of novel therapeutics into the clinic through a more informed understanding of the relevant molecular biology, structural biology, anatomy, and physiology. By virtue of their biomimicry, TE constructs incorporate features of extracellular macrostructure, molecular adhesive moieties, and biomechanical properties, converging with computational and structural biotechnology advances. Accordingly, this mini-review serves to contextualise TE for the computational and structural biotechnology reader and provides an outlook on how the disciplines overlap with respect to relevant advanced analytical applications.

Integrating Microfabrication into Biological Investigations: the Benefits of Interdisciplinarity

The advent of micro and nanotechnologies, such as microfabrication, have impacted scientific research and contributed to meaningful real-world applications, to a degree seen during historic technological revolutions. Some key areas benefitting from the invention and advancement of microfabrication platforms are those of biological and biomedical sciences. Modern therapeutic approaches, involving point-of-care, precision or personalized medicine, are transitioning from the experimental phase to becoming the standard of care. At the same time, biological research benefits from the contribution of microfluidics at every level from single cell to tissue engineering and organoids studies. The aim of this commentary is to describe, through proven examples, the interdisciplinary process used to develop novel biological technologies and to emphasize the role of technical knowledge in empowering researchers who are specialized in a niche area to look beyond and innovate.

In-depth serum proteomics reveals biomarkers of psoriasis severity and response to traditional Chinese medicine

Serum and plasma contain abundant biological information that reflect the body's physiological and pathological conditions and are therefore a valuable sample type for disease biomarkers. However, comprehensive profiling of the serological proteome is challenging due to the wide range of protein concentrations in serum. Methods: To address this challenge, we developed a novel in-depth serum proteomics platform capable of analyzing the serum proteome across ~10 orders or magnitude by combining data obtained from Data Independent Acquisition Mass Spectrometry (DIA-MS) and customizable antibody microarrays. Results: Using psoriasis as a proof-of-concept disease model, we screened 50 serum proteomes from healthy controls and psoriasis patients before and after treatment with traditional Chinese medicine (YinXieLing) on our in-depth serum proteomics platform. We identified 106 differentially-expressed proteins in psoriasis patients involved in psoriasis-relevant biological processes, such as blood coagulation, inflammation, apoptosis and angiogenesis signaling pathways. In addition, unbiased clustering and principle component analysis revealed 58 proteins discriminating healthy volunteers from psoriasis patients and 12 proteins distinguishing responders from non-responders to YinXieLing. To further demonstrate the clinical utility of our platform, we performed correlation analyses between serum proteomes and psoriasis activity and found a positive association between the psoriasis area and severity index (PASI) score with three serum proteins (PI3, CCL22, IL-12B). Conclusion: Taken together, these results demonstrate the clinical utility of our in-depth serum proteomics platform to identify specific diagnostic and predictive biomarkers of psoriasis and other immune-mediated diseases.

Multi-Omics Strategies Uncover Host-Pathogen Interactions

With the success of the Human Genome Project, large-scale systemic projects became a reality that enabled rapid development of the systems biology field. Systems biology approaches to host-pathogen interactions have been instrumental in the discovery of some specifics of Gram-negative bacterial recognition, host signal transduction, and immune tolerance. However, further research, particularly using multi-omics approaches, is essential to untangle the genetic, immunologic, (post)transcriptional, (post)translational, and metabolic mechanisms underlying progression from infection to clearance of microbes. The key to understanding host-pathogen interactions lies in acquiring, analyzing, and modeling multimodal data obtained through integrative multi-omics experiments. In this article, we will discuss how multi-omics analyses are adding to our understanding of the molecular basis of host-pathogen interactions and systemic maladaptive immune response of the host to microbes and microbial products.

100 Years of evolving gene-disease complexities and scientific debutants

It's been over 100 years since the word `gene' is around and progressively evolving in several scientific directions. Time-to-time technological advancements have heavily revolutionized the field of genomics, especially when it's about, e.g. triple code development, gene number proposition, genetic mapping, data banks, gene-disease maps, catalogs of human genes and genetic disorders, CRISPR/Cas9, big data and next generation sequencing, etc. In this manuscript, we present the progress of genomics from pea plant genetics to the human genome project and highlight the molecular, technical and computational developments. Studying genome and epigenome led to the fundamentals of development and progression of human diseases, which includes chromosomal, monogenic, multifactorial and mitochondrial diseases. World Health Organization has classified, standardized and maintained all human diseases, when many academic and commercial online systems are sharing information about genes and linking to associated diseases. To efficiently fathom the wealth of this biological data, there is a crucial need to generate appropriate gene annotation repositories and resources. Our focus has been how many gene-disease databases are available worldwide and which sources are authentic, timely updated and recommended for research and clinical purposes. In this manuscript, we have discussed and compared 43 such databases and bioinformatics applications, which enable users to connect, explore and, if possible, download gene-disease data.

Redox Systems Biology of Nutrition and Oxidative Stress

Diet and nutrition contribute to both beneficial and harmful aspects of oxidative processes. The harmful processes, termed oxidative stress, occur with many human diseases. Major advances in understanding oxidative stress and nutrition have occurred with broad characterization of dietary oxidants and antioxidants, and with mechanistic studies showing antioxidant efficacy. However, randomized controlled trials in humans with free-radical-scavenging antioxidants and the glutathione precursor N-acetylcysteine have provided limited or inconsistent evidence for health benefits. This, combined with emerging redox theory, indicates that holistic models are needed to understand the interplay of nutrition and oxidative stress. The purpose of this article is to highlight how recent advances in redox theory and the development of new omics tools and data-driven approaches provide a framework for future nutrition and oxidative stress research. Here we describe why a holistic approach is needed to understand the impact of nutrition on oxidative stress and how recent advances in omics and data analysis methods are viable tools for systems nutrition approaches. Based on the extensive research on glutathione and related thiol antioxidant systems, we summarize the advancing framework for diet and oxidative stress in which antioxidant systems are a component of a larger redox network that serves as a responsive interface between the environment and an individual. The feasibility for redox network analysis has been established by experimental models in which dietary factors are systematically varied and oxidative stress markers are linked through integrated omics (metabolome, transcriptome, proteome). With this framework, integrated redox network models will support optimization of diet to protect against oxidative stress and disease.

A genome-wide association study on photic sneeze reflex in the Chinese population

Photic sneeze reflex (PSR) is an interesting but yet mysterious phenotype featured by individuals’ response of sneezing in exposure to bright light. To uncover the underlying genetic markers (single nucleotide polymorphisms, SNPs), a genome-wide association study (GWAS) was conducted exclusively in a Chinese population of 3417 individuals (PSR prevalence at 25.6%), and reproducibly identified both a replicative rs10427255 on 2q22.3 and a novel locus of rs1032507 on 3p12.1 in various effect models (additive, as well as dominant and recessive). Minor alleles respectively contributed to increased or reduced risk for PSR with odds ratio (95% confidence interval) at 1.68 ([1.50, 1.88]) for rs10427255 and 0.65 ([0.58, 0.72]) for rs1032507. The two independent SNPs were intergenic, and collectively enhanced PSR classification by lifting the area-under-curve value in ROC curve to 0.657. Together with previous GWAS in other populations, the result substantiated the polygenic and non-ethnicity-specific nature behind the PSR phenotype.

Unsupervised classification of multi-omics data during cardiac remodeling using deep learning

Integration of multi-omics in cardiovascular diseases (CVDs) presents high potentials for translational discoveries. By analyzing abundance levels of heterogeneous molecules over time, we may uncover biological interactions and networks that were previously unidentifiable. However, to effectively perform integrative analysis of temporal multi-omics, computational methods must account for the heterogeneity and complexity in the data. To this end, we performed unsupervised classification of proteins and metabolites in mice during cardiac remodeling using two innovative deep learning (DL) approaches. First, long short-term memory (LSTM)-based variational autoencoder (LSTM-VAE) was trained on time-series numeric data. The low-dimensional embeddings extracted from LSTM-VAE were then used for clustering. Second, deep convolutional embedded clustering (DCEC) was applied on images of temporal trends. Instead of a two-step procedure, DCEC performes a joint optimization for image reconstruction and cluster assignment. Additionally, we performed K-means clustering, partitioning around medoids (PAM), and hierarchical clustering. Pathway enrichment analysis using the Reactome knowledgebase demonstrated that DL methods yielded higher numbers of significant biological pathways than conventional clustering algorithms. In particular, DCEC resulted in the highest number of enriched pathways, suggesting the strength of its unified framework based on visual similarities. Overall, unsupervised DL is shown to be a promising analytical approach for integrative analysis of temporal multi-omics.

Big science and big data in nephrology

There have been tremendous advances during the last decade in methods for large-scale, high-throughput data generation and in novel computational approaches to analyze these datasets. These advances have had a profound impact on biomedical research and clinical medicine. The field of genomics is rapidly developing toward single-cell analysis, and major advances in proteomics and metabolomics have been made in recent years. The developments on wearables and electronic health records are poised to change clinical trial design. This rise of 'big data' holds the promise to transform not only research progress, but also clinical decision making towards precision medicine. To have a true impact, it requires integrative and multi-disciplinary approaches that blend experimental, clinical and computational expertise across multiple institutions. Cancer research has been at the forefront of the progress in such large-scale initiatives, so-called 'big science,' with an emphasis on precision medicine, and various other areas are quickly catching up. Nephrology is arguably lagging behind, and hence these are exciting times to start (or redirect) a research career to leverage these developments in nephrology. In this review, we summarize advances in big data generation, computational analysis, and big science initiatives, with a special focus on applications to nephrology.

Iron and iron-dependent reactive oxygen species in the regulation of macrophages and fibroblasts in non-healing chronic wounds

Chronic wounds pose a stern challenge to health care systems with growing incidence especially in the aged population. In the presence of increased iron concentrations, recruitment of monocytes from the circulation and activation towards ROS and RNS releasing M1 macrophages together with the persistence of senescent fibroblasts at the wound site are significantly enhanced. This unrestrained activation of pro-inflammatory macrophages and senescent fibroblasts has increasingly been acknowledged as main driver causing non-healing wounds. In a metaphor, macrophages act like stage directors of wound healing, resident fibroblasts constitute main actors and increased iron concentrations are decisive parts of the libretto, and - if dysregulated - are responsible for the development of non-healing wounds. This review will focus on recent cellular and molecular findings from chronic venous leg ulcers and diabetic non-healing wounds both constituting the most common pathologies often resulting in limb amputations of patients. This not only causes tremendous suffering and loss of life quality, but is also associated with an increase in mortality and a major socio-economic burden. Despite recent advances, the underlying molecular mechanisms are not completely understood. Overwhelming evidence shows that reactive oxygen species and the transition metal and trace element iron at pathological concentrations are crucially involved in a complex interplay between cells of different histogenetic origin and their extracellular niche environment. This interplay depends on a variety of cellular, non-cellular biochemical and cell biological mechanisms. Here, we will highlight recent progress in the field of iron-dependent regulation of macrophages and fibroblasts and related pathologies linked to non-healing chronic wounds.

Collection of biochemical samples with brain-wide electrophysiological recordings from a freely moving rodent

Bridging accumulating insights from microscopic and macroscopic studies in neuroscience research requires monitoring of neuronal population dynamics and quantifying specific molecules or genes from the brain of identical animals. To this end, by minimizing the size and weight of an electrode array, we developed a method that records local field potential signals of multiple brain regions from one side of the hemisphere in a freely moving rodent. At the same time, extracellular cerebrospinal fluid for biochemical assays or a small part of brain tissue samples for gene expression assays are collected from the other side of the hemisphere. This method allows ongoing stable recordings and sample collections for at least two months. The methodological concept is applicable to a wide range of biological reactions at various spatiotemporal scales, allowing us to integrate an idea of physiolomics into existing omics analyses, leading to a new combination of multi-omics approaches.

Artificial intelligence for precision oncology: beyond patient stratification

The data-driven identification of disease states and treatment options is a crucial challenge for precision oncology. Artificial intelligence (AI) offers unique opportunities for enhancing such predictive capabilities in the lab and the clinic. AI, including its best-known branch of research, machine learning, has significant potential to enable precision oncology well beyond relatively well-known pattern recognition applications, such as the supervised classification of single-source omics or imaging datasets. This perspective highlights key advances and challenges in that direction. Furthermore, it argues that AI's scope and depth of research need to be expanded to achieve ground-breaking progress in precision oncology.

Genetics and molecular mechanisms of frontotemporal lobar degeneration: an update and future avenues

Frontotemporal lobar degeneration (FTLD) is the second most common form of dementia after Alzheimer's disease. The study and the dissection of FTLD is complex due to its clinical, pathological, and genetic heterogeneity. In this review, we survey the state-of-the-art genetics of familial FTLD and recapitulate our current understanding of the genetic architecture of sporadic FTLD by summarizing results of genome-wide association studies performed in FTLD to date. We then discuss the challenges of translating these heterogeneous genetic features into the understanding of the molecular underpinnings of FTLD pathogenesis. We particularly highlight a number of susceptibility processes that appear to be conserved across familial and sporadic cases (e.g., and the cellular waste disposal pathways, and immune system signaling) and finally describe cutting-edge approaches, based on mathematical prediction tools, highlighting novel intriguing risk pathways such as DNA damage response as an emerging theme in FTLD.

Leukemia multiclass assessment and classification from Microarray and RNA-seq technologies integration at gene expression level

In more recent years, a significant increase in the number of available biological experiments has taken place due to the widespread use of massive sequencing data. Furthermore, the continuous developments in the machine learning and in the high performance computing areas, are allowing a faster and more efficient analysis and processing of this type of data. However, biological information about a certain disease is normally widespread due to the use of different sequencing technologies and different manufacturers, in different experiments along the years around the world. Thus, nowadays it is of paramount importance to attain a correct integration of biologically-related data in order to achieve genuine benefits from them. For this purpose, this work presents an integration of multiple Microarray and RNA-seq platforms, which has led to the design of a multiclass study by collecting samples from the main four types of leukemia, quantified at gene expression. Subsequently, in order to find a set of differentially expressed genes with the highest discernment capability among different types of leukemia, an innovative parameter referred to as coverage is presented here. This parameter allows assessing the number of different pathologies that a certain gen is able to discern. It has been evaluated together with other widely known parameters under assessment of an ANOVA statistical test which corroborated its filtering power when the identified genes are subjected to a machine learning process at multiclass level. The optimal tuning of gene extraction evaluated parameters by means of this statistical test led to the selection of 42 highly relevant expressed genes. By the use of minimum-Redundancy Maximum-Relevance (mRMR) feature selection algorithm, these genes were reordered and assessed under the operation of four different classification techniques. Outstanding results were achieved by taking exclusively the first ten genes of the ranking into consideration. Finally, specific literature was consulted on this last subset of genes, revealing the occurrence of practically all of them with biological processes related to leukemia. At sight of these results, this study underlines the relevance of considering a new parameter which facilitates the identification of highly valid expressed genes for simultaneously discerning multiple types of leukemia.

Low-dose cadmium disrupts mitochondrial citric acid cycle and lipid metabolism in mouse lung

Cadmium (Cd) causes acute and chronic lung toxicities at occupational exposure levels, yet the impacts of Cd exposure at low levels through dietary intake remain largely uncharacterized. Health concerns arise because humans do not have an effective Cd elimination mechanism, resulting in a long (10- to 35-y) biological half-life. Previous studies showed increased mitochondrial oxidative stress and cell death by Cd yet the details of mitochondrial alterations by low levels of Cd remain unexplored. In the current study, we examined the impacts of Cd burden at a low environmental level on lung metabolome, redox proteome, and inflammation in mice given Cd at low levels by drinking water (0, 0.2, 0.6 and 2.0 mg Cd/L) for 16 weeks. The results showed that mice accumulated lung Cd comparable to non-smoking humans and showed inflammation in lung by histopathology at 2 mg Cd/L. The results of high resolution metabolomics combined with bioinformatics showed that mice treated with 2 mg Cd/L increased levels of lipids in the lung, accompanied by disruption in mitochondrial energy metabolism. In addition, targeted metabolomic analysis showed that these mice had increased accumulation of mitochondrial carnitine and citric acid cycle intermediates. The results of redox proteomics showed that Cd at 2 mg/L stimulated oxidation of isocitrate dehydrogenase, malate dehydrogenase and ATP synthase. Taken together, the results showed impaired mitochondrial function and accumulation of lipids in the lung with a Cd dose response relevant to non-smokers without occupational exposures. These findings suggest that dietary Cd intake could be an important variable contributing to human pulmonary disorders.

Engineering Precision Medicine

Advances in genomic sequencing and bioinformatics have led to the prospect of precision medicine where therapeutics can be advised by the genetic background of individuals. For example, mapping cancer genomics has revealed numerous genes that affect the therapeutic outcome of a drug. Through materials and cell engineering, many opportunities exist for engineers to contribute to precision medicine, such as engineering biosensors for diagnosis and health status monitoring, developing smart formulations for the controlled release of drugs, programming immune cells for targeted cancer therapy, differentiating pluripotent stem cells into desired lineages, fabricating bioscaffolds that support cell growth, or constructing "organs-on-chips" that can screen the effects of drugs. Collective engineering efforts will help transform precision medicine into a more personalized and effective healthcare approach. As continuous progress is made in engineering techniques, more tools will be available to fully realize precision medicine's potential.

Growth Performance and Meat Quality Evaluations in Three-Way Cross Cattle Developed for the Tibetan Plateau and their Molecular Understanding by Integrative Omics Analysis

Despite of favorable characteristics of high protein, low fat, and free-pollution, yak meat has intrinsically poor performance in tenderness and color, which is ever challenging yak sector. To this end, a three-way cross system was first developed for high quality beef of the Tibetan Plateau using Angus cattle ( Bos taurus) as terminal sire to mate with 1/2 yak (F1) generated from♂Qaidam cattle ( Bos taurus) × ♀yak ( Bos grunniens). The withers height, chest girth, and body weight of 1/4 yak (F2) were all great higher than that of yak and 1/2 yak ( P < 0.01), especially at later period, suggesting the faster growth rate of 1/4 yak. Also the dressing percentage was much better in 1/4 yak ( P < 0.01). Tenderness and meat color were both significantly improved in 1/4 yak with some unpleasant sacrifice of PUFAs, such as EPA and DHA, and meat protein, given the significantly lower shear force and higher L* ( P < 0.01). A total of 769 genes, including SREBF1, GHR, and FASN, the widely recognized causal genes of meat quality, were identified from 11947 differently expressed genes by the data integration of transcriptome, GWAS and QTL. These genes were significantly enriched for important pathway and GO terms, such as insulin signaling pathway, fatty acid biosynthesis, calcium signaling pathway, metabolic pathway, and cellular response to stress ( P < 0.01). And 12 promising candidates were exemplified with annotation of H3K4me3 data from divergent meat quality, such as OSTF1, NRAS1, and KCNJ11. Interestingly, 75 high-altitude adaptive candidate genes were also detected in the list. This study is a first step toward high quality beef of the Tibetan Plateau and provides useful information for their molecular understanding.

Database Resources of the BIG Data Center in 2019

The BIG Data Center at Beijing Institute of Genomics (BIG) of the Chinese Academy of Sciences provides a suite of database resources in support of worldwide research activities in both academia and industry. With the vast amounts of multi-omics data generated at unprecedented scales and rates, the BIG Data Center is continually expanding, updating and enriching its core database resources through big data integration and value-added curation. Resources with significant updates in the past year include BioProject (a biological project library), BioSample (a biological sample library), Genome Sequence Archive (GSA, a data repository for archiving raw sequence reads), Genome Warehouse (GWH, a centralized resource housing genome-scale data), Genome Variation Map (GVM, a public repository of genome variations), Science Wikis (a catalog of biological knowledge wikis for community annotations) and IC4R (Information Commons for Rice). Newly released resources include EWAS Atlas (a knowledgebase of epigenome-wide association studies), iDog (an integrated omics data resource for dog) and RNA editing resources (for editome-disease associations and plant RNA editosome, respectively). To promote biodiversity and health big data sharing around the world, the Open Biodiversity and Health Big Data (BHBD) initiative is introduced. All of these resources are publicly accessible at http://bigd.big.ac.cn.

New frontiers in precision medicine for sepsis-induced immunoparalysis

INTRODUCTION

In the last decade, the sepsis research field has shifted focus from targeting hyperinflammation to reversing sepsis-induced immunoparalysis. Sepsis-induced immunoparalysis is very heterogeneous: the magnitude and the nature of the underlying immune defects differ considerably between patients, but also within individuals over time. Therefore, a 'one-treatment-fits-all' strategy for sepsis-induced immunoparalysis is bound to fail, and an individualized 'precision medicine' approach is required. Such a strategy is nevertheless hampered by the unsuitability of the currently available markers to identify the many immune defects that can manifest in individual patients. Areas covered: We describe the currently available markers for sepsis-induced immunoparalysis and limitations pertaining to their use. Furthermore, future prospects and caveats are discussed, focusing on 'omics' approaches: genomics, transcriptomics, epigenomics, and metabolomics. Finally, we present a contemporary overview of adjuvant immunostimulatory therapies. Expert opinion: The integration of multiple omics techniques offers a systems biology approach which can yield biomarker profiles that accurately and comprehensively gauge the extent and nature of sepsis-induced immunoparalysis. We expect this development to be instrumental in facilitating precision medicine for sepsis-induced immunoparalysis, consisting of the application of targeted immunostimulatory therapies and follow-up measurements to monitor the response to treatment and to titrate or adjust medication.

Towards precision medicine for pain: diagnostic biomarkers and repurposed drugs

We endeavored to identify objective blood biomarkers for pain, a subjective sensation with a biological basis, using a stepwise discovery, prioritization, validation, and testing in independent cohorts design. We studied psychiatric patients, a high risk group for co-morbid pain disorders and increased perception of pain. For discovery, we used a powerful within-subject longitudinal design. We were successful in identifying blood gene expression biomarkers that were predictive of pain state, and of future emergency department (ED) visits for pain, more so when personalized by gender and diagnosis. MFAP3, which had no prior evidence in the literature for involvement in pain, had the most robust empirical evidence from our discovery and validation steps, and was a strong predictor for pain in the independent cohorts, particularly in females and males with PTSD. Other biomarkers with best overall convergent functional evidence for involvement in pain were GNG7, CNTN1, LY9, CCDC144B, and GBP1. Some of the individual biomarkers identified are targets of existing drugs. Moreover, the biomarker gene expression signatures were used for bioinformatic drug repurposing analyses, yielding leads for possible new drug candidates such as SC-560 (an NSAID), and amoxapine (an antidepressant), as well as natural compounds such as pyridoxine (vitamin B6), cyanocobalamin (vitamin B12), and apigenin (a plant flavonoid). Our work may help mitigate the diagnostic and treatment dilemmas that have contributed to the current opioid epidemic.

Effects of high fat medium conditions on cellular gene expression profile: a network analysis approach

AIM

This study aimed to evaluate high fat medium (HFM) effect on the gene expression profile of human Sk-hep1 cells and to determine critical differential proteins.

BACKGROUND

There is a correlation between high fat diet (HFD), obesity, and non-alcoholic fatty liver disease. Despite wide range of investigations, understanding molecular mechanism of HFD effect on onset and progression of NAFLD warrants further examination. In this study, network analysis is applied to obtain a clear perspective about HFD effects and NAFLD.

METHODS

Gene expression profiles of human Sk-hep1 cells treated with HFM versus controls were extracted from GEO. Data were analyzed by GEO2R where the significant and characterized DEGs were included in the PPI network. The top 10 nodes of query DEGs based on four centrality parameters were selected to determine central nodes. The common hub nodes with at least other one central group were identified as central nodes. Action map was provided for the introduced central nodes.

RESULTS

Heterogeneous nuclear ribonucleoprotein family including A1, A2/B1, D, R, and D-like, and five proteins (PRPF40A, SRSF1, PCF11, LSM8, and HSP90AA1) were introduced as differential proteins.

CONCLUSION

mRNA processing and several biological terms including hypoxia and oxidative stress, apoptosis, regulation of cell morphology and cytoskeletal organization, and differentiation of micro tubes were introduced as dysregulated terms under HFM condition.

Harnessing Human Microphysiology Systems as Key Experimental Models for Quantitative Systems Pharmacology

Two technologies that have emerged in the last decade offer a new paradigm for modern pharmacology, as well as drug discovery and development. Quantitative systems pharmacology (QSP) is a complementary approach to traditional, target-centric pharmacology and drug discovery and is based on an iterative application of computational and systems biology methods with multiscale experimental methods, both of which include models of ADME-Tox and disease. QSP has emerged as a new approach due to the low efficiency of success in developing therapeutics based on the existing target-centric paradigm. Likewise, human microphysiology systems (MPS) are experimental models complementary to existing animal models and are based on the use of human primary cells, adult stem cells, and/or induced pluripotent stem cells (iPSCs) to mimic human tissues and organ functions/structures involved in disease and ADME-Tox. Human MPS experimental models have been developed to address the relatively low concordance of human disease and ADME-Tox with engineered, experimental animal models of disease. The integration of the QSP paradigm with the use of human MPS has the potential to enhance the process of drug discovery and development.

Workflow Development for the Functional Characterization of ncRNAs

During the last decade, ncRNAs have been investigated intensively and revealed their regulatory role in various biological processes. Worldwide research efforts have identified numerous ncRNAs and multiple RNA subtypes, which are attributed to diverse functionalities known to interact with different functional layers, from DNA and RNA to proteins. This makes the prediction of functions for newly identified ncRNAs challenging. Current bioinformatics and systems biology approaches show promising results to facilitate an identification of these diverse ncRNA functionalities. Here, we review (a) current experimental protocols, i.e., for Next Generation Sequencing, for a successful identification of ncRNAs; (b) sequencing data analysis workflows as well as available computational environments; and (c) state-of-the-art approaches to functionally characterize ncRNAs, e.g., by means of transcriptome-wide association studies, molecular network analyses, or artificial intelligence guided prediction. In addition, we present a strategy to cover the identification and functional characterization of unknown transcripts by using connective workflows.

Personalized Metabolomics

The human metabolome is the cumulative product of ingested metabolites and those produced by the body and its microbiota. Together these metabolites can dynamically report on the health and disease state of an individual, as well as their response to drug treatments and other external perturbations. Profiling metabolites in human body fluids provides an opportunity to identify biomarkers and stratify patients for personalized treatments but requires the development of high-throughput approaches compatible with large cohort and longitudinal studies. Here we review in detail sample preparation and analytical liquid chromatography-mass spectrometry (LC-MS) methods to measure the broad chemical diversity of metabolites found in human plasma and urine.

Cancer biomarker discovery and translation: proteomics and beyond

INTRODUCTION

Cancer is often diagnosed at late stages when the chance of cure is relatively low and although research initiatives in oncology discover many potential cancer biomarkers, few transition to clinical applications. This review addresses the current landscape of cancer biomarker discovery and translation with a focus on proteomics and beyond. Areas covered: The review examines proteomic and genomic techniques for cancer biomarker detection and outlines advantages and challenges of integrating multiple omics approaches to achieve optimal sensitivity and address tumor heterogeneity. This discussion is based on a systematic literature review and direct participation in translational studies. Expert commentary: Identifying aggressive cancers early on requires improved sensitivity and implementation of biomarkers representative of tumor heterogeneity. During the last decade of genomic and proteomic research, significant advancements have been made in next generation sequencing and mass spectrometry techniques. This in turn has led to a dramatic increase in identification of potential genomic and proteomic cancer biomarkers. However, limited successes have been shown with translation of these discoveries into clinical practice. We believe that the integration of these omics approaches is the most promising molecular tool for comprehensive cancer evaluation, early detection and transition to Precision Medicine in oncology.

An integrative approach for building personalized gene regulatory networks for precision medicine

Only a small fraction of patients respond to the drug prescribed to treat their disease, which means that most are at risk of unnecessary exposure to side effects through ineffective drugs. This inter-individual variation in drug response is driven by differences in gene interactions caused by each patient's genetic background, environmental exposures, and the proportions of specific cell types involved in disease. These gene interactions can now be captured by building gene regulatory networks, by taking advantage of RNA velocity (the time derivative of the gene expression state), the ability to study hundreds of thousands of cells simultaneously, and the falling price of single-cell sequencing. Here, we propose an integrative approach that leverages these recent advances in single-cell data with the sensitivity of bulk data to enable the reconstruction of personalized, cell-type- and context-specific gene regulatory networks. We expect this approach will allow the prioritization of key driver genes for specific diseases and will provide knowledge that opens new avenues towards improved personalized healthcare.

Metabolic Signature of Dietary Iron Overload in a Mouse Model

Iron is an essential co-factor for several metabolic processes, including the Krebs cycle and mitochondrial oxidative phosphorylation. Therefore, maintaining an appropriate iron balance is essential to ensure sufficient energy production and to avoid excessive reactive oxygen species formation. Iron overload impairs mitochondrial fitness; however, little is known about the associated metabolic changes. Here we aimed to characterize the metabolic signature triggered by dietary iron overload over time in a mouse model, where mice received either a standard or a high-iron diet. Metabolic profiling was assessed in blood, plasma and liver tissue. Peripheral blood was collected by means of volumetric absorptive microsampling (VAMS). Extracted blood and tissue metabolites were analyzed by liquid chromatography combined to high resolution mass spectrometry. Upon dietary iron loading we found increased glucose, aspartic acid and 2-/3-hydroxybutyric acid levels but low lactate and malate levels in peripheral blood and plasma, pointing to a re-programming of glucose homeostasis and the Krebs cycle. Further, iron loading resulted in the stimulation of the urea cycle in the liver. In addition, oxidative stress was enhanced in circulation and coincided with increased liver glutathione and systemic cysteine synthesis. Overall, iron supplementation affected several central metabolic circuits over time. Hence, in vivo investigation of metabolic signatures represents a novel and useful tool for getting deeper insights into iron-dependent regulatory circuits and for monitoring of patients with primary and secondary iron overload, and those ones receiving iron supplementation therapy.

Genetic and non-genetic determinants of clinical phenotypes in cardiomyopathy

Cardiomyopathy, a leading cause of death worldwide, is etiologically and phenotypically heterogeneous and is caused by a combination of genetic and non-genetic factors. Major genomic determinants of dilated cardiomyopathy (DCM) are titin truncating mutations and lamin A/C mutations. Patients with these two genotypes show critically different phenotypes, including penetrance, coexistence with a conduction system abnormality, cardiac prognosis, and treatment response. The transcriptomic and epigenomic characteristics of DCM include activation of the DNA damage response, metabolic reprogramming, and dedifferentiation. The proteomic and metabolomic signatures of the DCM heart include a rigorous dependency for free fatty acids, activation of the stress response, and metabolic reprogramming. Proteomic and metabolomic analyses of blood show a distinct immune response and an unexpected link with pathology-specific microbiota in DCM. The direct integration of multi-omics data will not only elucidate inter-omics associations but also enable omics-based patient stratification, which will lead to a deeper understanding of cardiomyopathy and the development of precision medicine in cardiology.

Advances in multiple omics of natural-killer/T cell lymphoma

Natural-killer/T cell lymphoma (NKTCL) represents the most common subtype of extranodal lymphoma with aggressive clinical behavior. Prevalent in Asians and South Americans, the pathogenesis of NKTCL remains to be fully elucidated. Using system biology techniques including genomics, transcriptomics, epigenomics, and metabolomics, novel biomarkers and therapeutic targets have been revealed in NKTCL. Whole-exome sequencing studies identify recurrent somatic gene mutations, involving RNA helicases, tumor suppressors, JAK-STAT pathway molecules, and epigenetic modifiers. Another genome-wide association study reports that single nucleotide polymorphisms mapping to the class II MHC region on chromosome 6 contribute to lymphomagenesis. Alterations of oncogenic signaling pathways janus kinase-signal transducer and activator of transcription (JAK-STAT), nuclear factor-κB (NF-κB), mitogen-activated protein kinase (MAPK), WNT, and NOTCH, as well as epigenetic dysregulation of microRNA and long non-coding RNAs, are also frequently observed in NKTCL. As for metabolomic profiling, abnormal amino acids metabolism plays an important role on disease progression of NKTCL. Of note, through targeting multiple omics aberrations, clinical outcome of NKTCL patients has been significantly improved by asparaginase-based regimens, immune checkpoints inhibitors, and histone deacetylation inhibitors. Future investigations will be emphasized on molecular classification of NKTCL using integrated analysis of system biology, so as to optimize targeted therapeutic strategies of NKTCL in the era of precision medicine.

integRATE: a desirability-based data integration framework for the prioritization of candidate genes across heterogeneous omics and its application to preterm birth

BACKGROUND

The integration of high-quality, genome-wide analyses offers a robust approach to elucidating genetic factors involved in complex human diseases. Even though several methods exist to integrate heterogeneous omics data, most biologists still manually select candidate genes by examining the intersection of lists of candidates stemming from analyses of different types of omics data that have been generated by imposing hard (strict) thresholds on quantitative variables, such as P-values and fold changes, increasing the chance of missing potentially important candidates.

METHODS

To better facilitate the unbiased integration of heterogeneous omics data collected from diverse platforms and samples, we propose a desirability function framework for identifying candidate genes with strong evidence across data types as targets for follow-up functional analysis. Our approach is targeted towards disease systems with sparse, heterogeneous omics data, so we tested it on one such pathology: spontaneous preterm birth (sPTB).

RESULTS

We developed the software integRATE, which uses desirability functions to rank genes both within and across studies, identifying well-supported candidate genes according to the cumulative weight of biological evidence rather than based on imposition of hard thresholds of key variables. Integrating 10 sPTB omics studies identified both genes in pathways previously suspected to be involved in sPTB as well as novel genes never before linked to this syndrome. integRATE is available as an R package on GitHub ( https://github.com/haleyeidem/integRATE ).

CONCLUSIONS

Desirability-based data integration is a solution most applicable in biological research areas where omics data is especially heterogeneous and sparse, allowing for the prioritization of candidate genes that can be used to inform more targeted downstream functional analyses.

The Utilization of Autoantibodies in Approaches to Precision Health

Precision health (PH) applied to autoimmune disease will need paradigm shifts in the use and application of autoantibodies and other biomarkers. For example, autoantibodies combined with other multi-analyte “omic” profiles will form the basis of disease prediction allowing for earlier intervention linked to disease prevention strategies, as well as earlier, effective and personalized interventions for established disease. As medical intervention moves to disease prediction and a model of “intent to PREVENT,” diagnostics will include an early symptom/risk-based, as opposed to a disease-based approach. Newer diagnostic platforms that utilize emerging megatrends such as deep learning and artificial intelligence and close the gaps in autoantibody diagnostics will benefit from paradigm shifts thereby facilitating the PH agenda.

Application of metabolomics part II: Focus on fatty acids and their metabolites in healthy adults

Fatty acids (FAs) play critical roles in health and disease. The detection of FA imbalances through metabolomics can provide an overview of an individual’s health status, particularly as regards chronic inflammatory disorders. In this study, we aimed to establish sensitive reference value ranges for targeted plasma FAs in a well-defined population of healthy adults. Plasma samples were collected from 159 participants admitted as outpatients. A total of 24 FAs were analyzed using gas chromatography-mass spectrometry, and physiological values and 95% reference intervals were calculated using an approximate method of analysis. The differences among the age groups for the relative levels of stearic acid (P=0.005), the omega-6/omega-3 ratio (P=0.027), the arachidonic acid/eicosapentaenoic acid ratio (P<0.001) and the linoleic acid-produced dihomo-gamma-linolenic acid (P=0.046) were statistically significant. The majority of relative FA levels were higher in males than in females. The levels of myristic acid (P=0.0170) and docosahexaenoic acid (P=0.033) were signifi-cantly different between the sexes. The reference values for the FAs examined in this study represent a baseline for further studies examining the reproducibility of this methodology and sensitivities for nutrient deficiency detection and investigating the biochemical background of pathological conditions. The application of these values to clinical practice will allow for the discrimination between health and disease and contribute to early prevention and treatment.

From Single Level Analysis to Multi-Omics Integrative Approaches: A Powerful Strategy towards the Precision Oncology

Integration of multi-omics data from different molecular levels with clinical data, as well as epidemiologic risk factors, represents an accurate and promising methodology to understand the complexity of biological systems of human diseases, including cancer. By the extensive use of novel technologic platforms, a large number of multidimensional data can be derived from analysis of health and disease systems. Comprehensive analysis of multi-omics data in an integrated framework, which includes cumulative effects in the context of biological pathways, is therefore eagerly awaited. This strategy could allow the identification of pathway-addiction of cancer cells that may be amenable to therapeutic intervention. However, translation into clinical settings requires an optimized integration of omics data with clinical vision to fully exploit precision cancer medicine. We will discuss the available technical approach and more recent developments in the specific field.

Integrative workflows for network analysis

Due to genetic heterogeneity across patients, the identification of effective disease signatures and therapeutic targets is challenging. Addressing this challenge, we have previously developed a network-based approach, which integrates heterogeneous sources of biological information to identify disease specific core-regulatory networks. In particular, our workflow uses a multi-objective optimization function to calculate a ranking score for network components (e.g. feedback/feedforward loops) based on network properties, biomedical and high-throughput expression data. High ranked network components are merged to identify the core-regulatory network(s) that is then subjected to dynamical analysis using stimulus-response and in silico perturbation experiments for the identification of disease gene signatures and therapeutic targets. In a case study, we implemented our workflow to identify bladder and breast cancer specific core-regulatory networks underlying epithelial-mesenchymal transition from the E2F1 molecular interaction map.In this study, we review our workflow and described how it has developed over time to understand the mechanisms underlying disease progression and prediction of signatures for clinical decision making.

Network and Pathway Analysis of Toxicogenomics Data

Toxicogenomics is the study of the molecular effects of chemical, biological and physical agents in biological systems, with the aim of elucidating toxicological mechanisms, building predictive models and improving diagnostics. The vast majority of toxicogenomics data has been generated at the transcriptome level, including RNA-seq and microarrays, and large quantities of drug-treatment data have been made publicly available through databases and repositories. Besides the identification of differentially expressed genes (DEGs) from case-control studies or drug treatment time series studies, bioinformatics methods have emerged that infer gene expression data at the molecular network and pathway level in order to reveal mechanistic information. In this work we describe different resources and tools that have been developed by us and others that relate gene expression measurements with known pathway information such as over-representation and gene set enrichment analyses. Furthermore, we highlight approaches that integrate gene expression data with molecular interaction networks in order to derive network modules related to drug toxicity. We describe the two main parts of the approach, i.e., the construction of a suitable molecular interaction network as well as the conduction of network propagation of the experimental data through the interaction network. In all cases we apply methods and tools to publicly available rat in vivo data on anthracyclines, an important class of anti-cancer drugs that are known to induce severe cardiotoxicity in patients. We report the results and functional implications achieved for four anthracyclines (doxorubicin, epirubicin, idarubicin, and daunorubicin) and compare the information content inherent in the different computational approaches.

Integrative analysis of transcriptomics and clinical data uncovers the tumor-suppressive activity of MITF in prostate cancer

The dysregulation of gene expression is an enabling hallmark of cancer. Computational analysis of transcriptomics data from human cancer specimens, complemented with exhaustive clinical annotation, provides an opportunity to identify core regulators of the tumorigenic process. Here we exploit well-annotated clinical datasets of prostate cancer for the discovery of transcriptional regulators relevant to prostate cancer. Following this rationale, we identify Microphthalmia-associated transcription factor (MITF) as a prostate tumor suppressor among a subset of transcription factors. Importantly, we further interrogate transcriptomics and clinical data to refine MITF perturbation-based empirical assays and unveil Crystallin Alpha B (CRYAB) as an unprecedented direct target of the transcription factor that is, at least in part, responsible for its tumor-suppressive activity in prostate cancer. This evidence was supported by the enhanced prognostic potential of a signature based on the concomitant alteration of MITF and CRYAB in prostate cancer patients. In sum, our study provides proof-of-concept evidence of the potential of the bioinformatics screen of publicly available cancer patient databases as discovery platforms, and demonstrates that the MITF-CRYAB axis controls prostate cancer biology.