The transition from genomics to phenomics in personalized population health.

Modern health care faces several serious challenges, including an ageing population and its inherent burden of chronic diseases, rising costs and marginal quality metrics. By assessing and optimizing the health trajectory of each individual using a data-driven personalized approach that reflects their genetics, behaviour and environment, we can start to address these challenges. This assessment includes longitudinal phenome measures, such as the blood proteome and metabolome, gut microbiome composition and function, and lifestyle and behaviour through wearables and questionnaires. Here, we review ongoing large-scale genomics and longitudinal phenomics efforts and the powerful insights they provide into wellness. We describe our vision for the transformation of the current health care from disease-oriented to data-driven, wellness-oriented and personalized population health.

Every life has equal value.

The 2013 Lasker∼Bloomberg Public Service Award will be given to Bill and Melinda Gates "for leading an historic transformation in the way we view the globe's most pressing health concerns and improving the lives of millions of the world's most vulnerable."

Taking Systems Medicine to Heart.

Systems medicine is a holistic approach to deciphering the complexity of human physiology in health and disease. In essence, a living body is constituted of networks of dynamically interacting units (molecules, cells, organs, etc) that underlie its collective functions. Declining resilience because of aging and other chronic environmental exposures drives the system to transition from a health state to a disease state; these transitions, triggered by acute perturbations or chronic disturbance, manifest as qualitative shifts in the interactions and dynamics of the disease-perturbed networks. Understanding health-to-disease transitions poses a high-dimensional nonlinear reconstruction problem that requires deep understanding of biology and innovation in study design, technology, and data analysis. With a focus on the principles of systems medicine, this Review discusses approaches for deciphering this biological complexity from a novel perspective, namely, understanding how disease-perturbed networks function; their study provides insights into fundamental disease mechanisms. The immediate goals for systems medicine are to identify early transitions to cardiovascular (and other chronic) diseases and to accelerate the translation of new preventive, diagnostic, or therapeutic targets into clinical practice, a critical step in the development of personalized, predictive, preventive, and participatory (P4) medicine.

High resolution time-course mapping of early transcriptomic, molecular and cellular phenotypes in Huntington's disease CAG knock-in mice across multiple genetic backgrounds.

Huntington's disease is a dominantly inherited neurodegenerative disease caused by the expansion of a CAG repeat in the HTT gene. In addition to the length of the CAG expansion, factors such as genetic background have been shown to contribute to the age at onset of neurological symptoms. A central challenge in understanding the disease progression that leads from the HD mutation to massive cell death in the striatum is the ability to characterize the subtle and early functional consequences of the CAG expansion longitudinally. We used dense time course sampling between 4 and 20 postnatal weeks to characterize early transcriptomic, molecular and cellular phenotypes in the striatum of six distinct knock-in mouse models of the HD mutation. We studied the effects of the HttQ111 allele on the C57BL/6J, CD-1, FVB/NCr1, and 129S2/SvPasCrl genetic backgrounds, and of two additional alleles, HttQ92 and HttQ50, on the C57BL/6J background. We describe the emergence of a transcriptomic signature in HttQ111/+ mice involving hundreds of differentially expressed genes and changes in diverse molecular pathways. We also show that this time course spanned the onset of mutant huntingtin nuclear localization phenotypes and somatic CAG-length instability in the striatum. Genetic background strongly influenced the magnitude and age at onset of these effects. This work provides a foundation for understanding the earliest transcriptional and molecular changes contributing to HD pathogenesis.

Transcriptional regulatory networks underlying gene expression changes in Huntington's disease.

Transcriptional changes occur presymptomatically and throughout Huntington's disease (HD), motivating the study of transcriptional regulatory networks (TRNs) in HD We reconstructed a genome-scale model for the target genes of 718 transcription factors (TFs) in the mouse striatum by integrating a model of genomic binding sites with transcriptome profiling of striatal tissue from HD mouse models. We identified 48 differentially expressed TF-target gene modules associated with age- and CAG repeat length-dependent gene expression changes in Htt CAG knock-in mouse striatum and replicated many of these associations in independent transcriptomic and proteomic datasets. Thirteen of 48 of these predicted TF-target gene modules were also differentially expressed in striatal tissue from human disease. We experimentally validated a specific model prediction that SMAD3 regulates HD-related gene expression changes using chromatin immunoprecipitation and deep sequencing (ChIP-seq) of mouse striatum. We found CAG repeat length-dependent changes in the genomic occupancy of SMAD3 and confirmed our model's prediction that many SMAD3 target genes are downregulated early in HD.

Disrupted SOX10 function causes spongiform neurodegeneration in gray tremor mice.

Mice homozygous for the gray tremor (gt) mutation have a pleiotropic phenotype that includes pigmentation defects, megacolon, whole body tremors, sporadic seizures, hypo- and dys-myelination of the central nervous system (CNS) and peripheral nervous system, vacuolation of the CNS, and early death. Vacuolation similar to that caused by prions was originally reported to be transmissible, but subsequent studies showed the inherited disease was not infectious. The gt mutation mapped to distal mouse chromosome 15, to the same region as Sox10, which encodes a transcription factor with essential roles in neural crest survival and differentiation. As dominant mutations in mouse or human SOX10 cause white spotting and intestinal aganglionosis, we screened the Sox10 coding region for mutations in gt/gt DNA. An adenosine to guanine transversion was identified in exon 2 that changes a highly conserved glutamic acid residue in the SOX10 DNA binding domain to glycine. This mutant allele was not seen in wildtype mice, including the related GT/Le strain, and failed to complement a Sox10 null allele. Gene expression analysis revealed significant down-regulation of genes involved in myelin lipid biosynthesis pathways in gt/gt brains. Knockout mice for some of these genes develop CNS vacuolation and/or myelination defects, suggesting that their down-regulation may contribute to these phenotypes in gt mutants and could underlie the neurological phenotypes associated with peripheral demyelinating neuropathy-central dysmyelinating leukodystrophy-Waardenburg syndrome-Hirschsprung disease, caused by mutations in human SOX10.

RNASEQR--a streamlined and accurate RNA-seq sequence analysis program.

Next-generation sequencing (NGS) technologies-based transcriptomic profiling method often called RNA-seq has been widely used to study global gene expression, alternative exon usage, new exon discovery, novel transcriptional isoforms and genomic sequence variations. However, this technique also poses many biological and informatics challenges to extracting meaningful biological information. The RNA-seq data analysis is built on the foundation of high quality initial genome localization and alignment information for RNA-seq sequences. Toward this goal, we have developed RNASEQR to accurately and effectively map millions of RNA-seq sequences. We have systematically compared RNASEQR with four of the most widely used tools using a simulated data set created from the Consensus CDS project and two experimental RNA-seq data sets generated from a human glioblastoma patient. Our results showed that RNASEQR yields more accurate estimates for gene expression, complete gene structures and new transcript isoforms, as well as more accurate detection of single nucleotide variants (SNVs). RNASEQR analyzes raw data from RNA-seq experiments effectively and outputs results in a manner that is compatible with a wide variety of specialized downstream analyses on desktop computers.

A CD133-related gene expression signature identifies an aggressive glioblastoma subtype with excessive mutations.

Cancer cells are heterogeneous and, it has been proposed, fall into at least two classes: the tumor-initiating cancer stem cells (CSC) and the more differentiated tumor cells. The transmembrane protein CD133 has been widely used to isolate putative CSC populations in several cancer types, but its validity as a CSC marker and hence its clinical ramifications remain controversial. Here, we conducted transcriptomic profiling of sorted CD133(+) and CD133(-) cells from human glioblastoma multiforme (GBM) and, by subtractive analysis, established a CD133 gene expression signature composed of 214 differentially expressed genes. Extensive computational comparisons with a compendium of published gene expression profiles reveal that the CD133 gene signature transcriptionally resembles human ES cells and in vitro cultured GBM stem cells, and this signature successfully distinguishes GBM from lower-grade gliomas. More importantly, the CD133 gene signature identifies an aggressive subtype of GBM seen in younger patients with shorter survival who bear excessive genomic mutations as surveyed through the Cancer Genome Atlas Network GBM mutation spectrum. Furthermore, the CD133 gene signature distinguishes higher-grade breast and bladder cancers from their lower-grade counterparts. Our systematic analysis provides molecular and genetic support for the stem cell-like nature of CD133(+) cells and an objective means for evaluating cancer aggressiveness.

MicroRNA as biomarkers and regulators in B-cell chronic lymphocytic leukemia.

Early cancer detection and disease stratification or classification are critical to successful treatment. Accessible, reliable, and informative cancer biomarkers can be medically valuable and can provide some relevant insights into cancer biology. Recent studies have suggested improvements in detecting malignancies by the use of specific extracellular microRNAs (miRNAs) in plasma. In chronic lymphocytic leukemia (CLL), an incurable hematologic disorder, sensitive, early, and noninvasive diagnosis and better disease classification would be very useful for more effective therapies. We show here that circulating miRNAs can be sensitive biomarkers for CLL, because certain extracellular miRNAs are present in CLL patient plasma at levels significantly different from healthy controls and from patients affected by other hematologic malignancies. The levels of several of these circulating miRNAs also displayed significant differences between zeta-associated protein 70 (ZAP-70)(+) and ZAP-70(-) CLL. We also determined that the level of circulating miR-20a correlates reliably with diagnosis-to-treatment time. Network analysis of our data, suggests a regulatory network associated with BCL2 and ZAP-70 expression in CLL. This hypothesis suggests the possibility of using the levels of specific miRNAs in plasma to detect CLL and to determine the ZAP-70 status.

New and improved proteomics technologies for understanding complex biological systems: addressing a grand challenge in the life sciences.

This White Paper sets out a Life Sciences Grand Challenge for Proteomics Technologies to enhance our understanding of complex biological systems, link genomes with phenotypes, and bring broad benefits to the biosciences and the US economy. The paper is based on a workshop hosted by the National Institute of Standards and Technology (NIST) in Gaithersburg, MD, 14-15 February 2011, with participants from many federal R&D agencies and research communities, under the aegis of the US National Science and Technology Council (NSTC). Opportunities are identified for a coordinated R&D effort to achieve major technology-based goals and address societal challenges in health, agriculture, nutrition, energy, environment, national security, and economic development.

Circulating microRNAs, potential biomarkers for drug-induced liver injury.

Drug-induced liver injury is a frequent side effect of many drugs, constitutes a significant threat to patient health and has an enormous economic impact on health care expenditures. Numerous efforts have been made to identify reliable and predictive markers to detect the early signs of drug-induced injury to the liver, one of the most vulnerable organs in the body. These studies have, however, not delivered any more informative candidates than the serum aminotransferase markers that have been available for approximately 30 years. Using acetaminophen overdose-induced liver injury in the mouse as a model system, we have observed highly significant differences in the spectrum and levels of microRNAs in both liver tissues and in plasma between control and overdosed animals. Based on our survey of microRNA expression among normal tissues, some of the microRNAs, like messenger RNAs, display restricted tissue distributions. A number of elevated circulating microRNAs in plasma collected from acetaminophen-overdosed animals are highly expressed in the liver. We have demonstrated that specific microRNA species, such as mir-122 and mir-192, both are enriched in the liver tissue and exhibit dose- and exposure duration-dependent changes in the plasma that parallel serum aminotransferase levels and the histopathology of liver degeneration, but their changes can be detected significantly earlier. These findings suggest the potential of using specific circulating microRNAs as sensitive and informative biomarkers for drug-induced liver injury.

Gene expression dynamics in the macrophage exhibit criticality.

Cells are dynamical systems of biomolecular interactions that process information from their environment to mount diverse yet specific responses. A key property of many self-organized systems is that of criticality: a state of a system in which, on average, perturbations are neither dampened nor amplified, but are propagated over long temporal or spatial scales. Criticality enables the coordination of complex macroscopic behaviors that strike an optimal balance between stability and adaptability. It has long been hypothesized that biological systems are critical. Here, we address this hypothesis experimentally for system-wide gene expression dynamics in the macrophage. To this end, we have developed a method, based on algorithmic information theory, to assess macrophage criticality, and we have validated the method on networks with known properties. Using global gene expression data from macrophages stimulated with a variety of Toll-like receptor agonists, we found that macrophage dynamics are indeed critical, providing the most compelling evidence to date for this general principle of dynamics in biological systems.

A systems approach to prion disease.

Prions cause transmissible neurodegenerative diseases and replicate by conformational conversion of normal benign forms of prion protein (PrP(C)) to disease-causing PrP(Sc) isoforms. A systems approach to disease postulates that disease arises from perturbation of biological networks in the relevant organ. We tracked global gene expression in the brains of eight distinct mouse strain-prion strain combinations throughout the progression of the disease to capture the effects of prion strain, host genetics, and PrP concentration on disease incubation time. Subtractive analyses exploiting various aspects of prion biology and infection identified a core of 333 differentially expressed genes (DEGs) that appeared central to prion disease. DEGs were mapped into functional pathways and networks reflecting defined neuropathological events and PrP(Sc) replication and accumulation, enabling the identification of novel modules and modules that may be involved in genetic effects on incubation time and in prion strain specificity. Our systems analysis provides a comprehensive basis for developing models for prion replication and disease, and suggests some possible therapeutic approaches.

Modulation of Immune Checkpoints by Chemotherapy in Human Colorectal Liver Metastases.

Metastatic colorectal cancer (CRC) is a major cause of cancer-related death, and incidence is rising in younger populations (younger than 50 years). Current chemotherapies can achieve response rates above 50%, but immunotherapies have limited value for patients with microsatellite-stable (MSS) cancers. The present study investigates the impact of chemotherapy on the tumor immune microenvironment. We treat human liver metastases slices with 5-fluorouracil (5-FU) plus either irinotecan or oxaliplatin, then perform single-cell transcriptome analyses. Results from eight cases reveal two cellular subtypes with divergent responses to chemotherapy. Susceptible tumors are characterized by a stemness signature, an activated interferon pathway, and suppression of PD-1 ligands in response to 5-FU+irinotecan. Conversely, immune checkpoint TIM-3 ligands are maintained or upregulated by chemotherapy in CRC with an enterocyte-like signature, and combining chemotherapy with TIM-3 blockade leads to synergistic tumor killing. Our analyses highlight chemomodulation of the immune microenvironment and provide a framework for combined chemo-immunotherapies.

Gene expression relationship between prostate cancer cells of Gleason 3, 4 and normal epithelial cells as revealed by cell type-specific transcriptomes.

BACKGROUND: Prostate cancer cells in primary tumors have been typed CD10-/CD13-/CD24hi/CD26+/CD38lo/CD44-/CD104-. This CD phenotype suggests a lineage relationship between cancer cells and luminal cells. The Gleason grade of tumors is a descriptive of tumor glandular differentiation. Higher Gleason scores are associated with treatment failure. METHODS: CD26+ cancer cells were isolated from Gleason 3+3 (G3) and Gleason 4+4 (G4) tumors by cell sorting, and their gene expression or transcriptome was determined by Affymetrix DNA array analysis. Dataset analysis was used to determine gene expression similarities and differences between G3 and G4 as well as to prostate cancer cell lines and histologically normal prostate luminal cells. RESULTS: The G3 and G4 transcriptomes were compared to those of prostatic cell types of non-cancer, which included luminal, basal, stromal fibromuscular, and endothelial. A principal components analysis of the various transcriptome datasets indicated a closer relationship between luminal and G3 than luminal and G4. Dataset comparison also showed that the cancer transcriptomes differed substantially from those of prostate cancer cell lines. CONCLUSIONS: Genes differentially expressed in cancer are potential biomarkers for cancer detection, and those differentially expressed between G3 and G4 are potential biomarkers for disease stratification given that G4 cancer is associated with poor outcomes. Differentially expressed genes likely contribute to the prostate cancer phenotype and constitute the signatures of these particular cancer cell types.

Human Gut Microbiota and Gastrointestinal Cancer.

Human gut microbiota play an essential role in both healthy and diseased states of humans. In the past decade, the interactions between microorganisms and tumors have attracted much attention in the efforts to understand various features of the complex microbial communities, as well as the possible mechanisms through which the microbiota are involved in cancer prevention, carcinogenesis, and anti-cancer therapy. A large number of studies have indicated that microbial dysbiosis contributes to cancer susceptibility via multiple pathways. Further studies have suggested that the microbiota and their associated metabolites are not only closely related to carcinogenesis by inducing inflammation and immune dysregulation, which lead to genetic instability, but also interfere with the pharmacodynamics of anticancer agents. In this article, we mainly reviewed the influence of gut microbiota on cancers in the gastrointestinal (GI) tract (including esophageal, gastric, colorectal, liver, and pancreatic cancers) and the regulation of microbiota by diet, prebiotics, probiotics, synbiotics, antibiotics, or the Traditional Chinese Medicine. We also proposed some new strategies in the prevention and treatment of GI cancers that could be explored in the future. We hope that this review could provide a comprehensive overview of the studies on the interactions between the gut microbiota and GI cancers, which are likely to yield translational opportunities to reduce cancer morbidity and mortality by improving prevention, diagnosis, and treatment.

Ultrafast Comparison of Personal Genomes via Precomputed Genome Fingerprints.

We present an ultrafast method for comparing personal genomes. We transform the standard genome representation (lists of variants relative to a reference) into "genome fingerprints" via locality sensitive hashing. The resulting genome fingerprints can be meaningfully compared even when the input data were obtained using different sequencing technologies, processed using different pipelines, represented in different data formats and relative to different reference versions. Furthermore, genome fingerprints are robust to up to 30% missing data. Because of their reduced size, computation on the genome fingerprints is fast and requires little memory. For example, we could compute all-against-all pairwise comparisons among the 2504 genomes in the 1000 Genomes data set in 67 s at high quality (21 µs per comparison, on a single processor), and achieved a lower quality approximation in just 11 s. Efficient computation enables scaling up a variety of important genome analyses, including quantifying relatedness, recognizing duplicative sequenced genomes in a set, population reconstruction, and many others. The original genome representation cannot be reconstructed from its fingerprint, effectively decoupling genome comparison from genome interpretation; the method thus has significant implications for privacy-preserving genome analytics.

The rs16906252:C>T SNP is not associated with increased overall survival or temozolomide response in a Han-Chinese glioma cohort.

The methylation status of O-6-methylguanine-DNA methyltransferase (MGMT) is associated with the prognosis in gliomas and in other cancers. Recent studies showed that rs16906252, an SNP in the MGMT promoter, is associated with promoter methylation and is a predictor of the overall survival time (OST) and the response to temozolomide (TMZ) treatment. However, these findings haven't been systematically investigated in the Han-Chinese population. We analyzed the relevance between rs16906252 polymorphisms, the MGMT methylation status, and the OST in 72 Han-Chinese gliomas patients. The MGMT promoter methylation was measured by bisulfite conversion followed by pyro-sequencing, while rs16906252 was measured by restriction endonuclease digestion. Contrary to the previous findings, we found no association between rs16906252 genotypes and promoter methylation on MGMT. The lower-grade glioma (LGGs) patients carrying the C allele with rs16906252 showed a surprisingly better OST (P = 0.04). Furthermore, the LGG patients carrying hypo-methylated MGMT promoter and rs16906252 T allele showed significantly poorer prognosis. The prognostic benefit of MGMT promoter methylation and genotypes on gliomas patients is marginal. A new molecular stratified patient grouping of LGGs is potentially associated with poorer OST. Active MGMT might have a protective role in LGG tumors, enabling evolution to severe malignancy.