Shizhen Qin

A wellness study of 108 individuals using personal, dense, dynamic data clouds

Personal data for 108 individuals were collected during a 9-month period, including whole genome sequences; clinical tests, metabolomes, proteomes, and microbiomes at three time points; and daily activity tracking. Using all of these data, we generated a correlation network that revealed communities of related analytes associated with physiology and disease. Connectivity within analyte communities enabled the identification of known and candidate biomarkers (e.g., gamma-glutamyltyrosine was densely interconnected with clinical analytes for cardiometabolic disease). We calculated polygenic scores from genome-wide association studies (GWAS) for 127 traits and diseases, and used these to discover molecular correlates of polygenic risk (e.g., genetic risk for inflammatory bowel disease was negatively correlated with plasma cystine). Finally, behavioral coaching informed by personal data helped participants to improve clinical biomarkers. Our results show that measurement of personal data clouds over time can improve our understanding of health and disease, including early transitions to disease states.

A Third Approach to Gene Prediction Suggests Thousands of Additional Human Transcribed Regions

The identification and characterization of the complete ensemble of genes is a main goal of deciphering the digital information stored in the human genome. Many algorithms for computational gene prediction have been described, ultimately derived from two basic concepts: (1) modeling gene structure and (2) recognizing sequence similarity. Successful hybrid methods combining these two concepts have also been developed. We present a third orthogonal approach to gene prediction, based on detecting the genomic signatures of transcription, accumulated over evolutionary time. We discuss four algorithms based on this third concept: Greens and CHOWDER, which quantify mutational strand biases caused by transcription-coupled DNA repair, and ROAST and PASTA, which are based on strand-specific selection against polyadenylation signals. We combined these algorithms into an integrated method called FEAST, which we used to predict the location and orientation of thousands of putative transcription units not overlapping known genes. Many of the newly predicted transcriptional units do not appear to code for proteins. The new algorithms are particularly apt at detecting genes with long introns and lacking sequence conservation. They therefore complement existing gene prediction methods and will help identify functional transcripts within many apparent “genomic deserts.”

Quod erat faciendum: sequence analysis of the H2-D and H2-Q regions of 129/SvJ mice

Abstract. The H2-D and -Q regions of the mouse major histocompatibility complex (Mhc or H2) have been sequenced from strain 129/SvJ (haplotype bc), revealing a D/Q region different from all other investigated haplotypes, including the closely related b haplotype. The 300-kb class I-rich region consists of the classical class I, H2-D, and 11 non-classical class I genes. The Q region was formed by two series of tandem duplications. Comparison of the segment between the D and Q1 genes with the H2-K region provides evidence that class I genes were translocated from the K region to the D region, and gives a new explanation for the weak locus specificity of the H-K and H2-D alleles.

Systems Approach to Neurodegenerative Disease Biomarker Discovery

Biomarkers are essential for performing early diagnosis, monitoring neurodegenerative disease progression, gauging responses to therapies, and stratifying neurodegenerative diseases into their different subtypes. A wide range of molecular markers are under investigation in tissues and biofluids as well as through imaging; moreover, many are prominent proteins present in cerebrospinal fluid. However, in more frequently and easily collected fluids such as plasma, these proteins show only a modest correlation with disease and thus lack the necessary sensitivity or specificity for clinical use. High-throughput and quantitative proteomic technologies and systems-driven approaches to biofluid analysis are now being utilized in the search for better biomarkers. Biomarker discovery involves many critical steps including study design, sample preparation, protein and peptide separation and identification, and bioinformatics and data integration issues that must be carefully controlled before independent confirmation and validation. In this review, we summarize current proteomic and nucleic acid technologies involved in the discovery of biomarkers of neurodegenerative diseases, particularly Alzheimers, Parkinsons, Huntingtons, and prion diseases.

SRM targeted proteomics in search for biomarkers of HCV‐induced progression of fibrosis to cirrhosis in HALT‐C patients

The current gold standard for diagnosis of hepatic fibrosis and cirrhosis is the traditional invasive liver biopsy. It is desirable to assess hepatic fibrosis with noninvasive means. Targeted proteomic techniques allow an unbiased assessment of proteins and might be useful to identify proteins related to hepatic fibrosis. We utilized selected reaction monitoring (SRM) targeted proteomics combined with an organ‐specific blood protein strategy to identify and quantify 38 liver‐specific proteins. A combination of protein C and retinol‐binding protein 4 in serum gave promising preliminary results as candidate biomarkers to distinguish patients at different stages of hepatic fibrosis due to chronic infection with hepatitis C virus (HCV). Also, alpha‐1‐B glycoprotein, complement factor H and insulin‐like growth factor binding protein acid labile subunit performed well in distinguishing patients from healthy controls.

The Spectrum of Circulating RNA: A Window Into Systems Toxicology

Adverse effects caused by therapeutic drugs are a serious and costly health concern. Despite the bodys systemic responses to therapeutics, the liver is often the focus of damage and is usually the focus of studies of toxic effects due to its active roles in the metabolism of xenobiotics. It is extremely difficult, however, to assess systemic responses with currently available methods. Comprehensive cataloging of cell-free circulating RNAs using next-generation sequencing technology may open a window to assess drug-associated adverse effects at the systems level. To explore this potential, we conducted an RNA profiling study using the well-characterized acetaminophen overdose mouse model on liver and plasma with microarray and next-generation sequencing platforms, respectively. After drug treatment, the levels of a number of transcripts, both endogenous and exogenous RNAs, showed significant changes in plasma, reflecting not only the classical liver injury induced by acetaminophen overdose but also damage in tissues other than the liver. The changes in exogenous RNAs also reflect alteration on dieting behavior after acetaminophen overdose. Besides reporting an extensive list of circulating RNA-based biomarker candidates, this study illustrates the possibility of using circulating RNAs to assess global effects of therapeutics. This could also lead to a new approach for a more comprehensive assessment of the efficacy and safety of therapeutics.

Glycocapture-Assisted Global Quantitative Proteomics (gagQP) Reveals Multiorgan Responses in Serum Toxicoproteome

Blood is an ideal window for viewing our health and disease status. Because blood circulates throughout the entire body and carries secreted, shed, and excreted signature proteins from every organ and tissue type, it is thus possible to use the blood proteome to achieve a comprehensive assessment of multiple-organ physiology and pathology. To date, the blood proteome has been frequently examined for diseases of individual organs; studies on compound insults impacting multiple organs are, however, elusive. We believe that a characterization of peripheral blood for organ-specific proteins affords a powerful strategy to allow early detection, staging, and monitoring of diseases and their treatments at a whole-body level. In this paper we test this hypothesis by examining a mouse model of acetaminophen (APAP)-induced hepatic and extra-hepatic toxicity. We used a glycocapture-assisted global quantitative proteomics (gagQP) approach to study serum proteins and validated our results using Western blot. We discovered in mouse sera both hepatic and extra-hepatic organ-specific proteins. From our validation, it was determined that selected organ-specific proteins had changed their blood concentration during the course of toxicity development and recovery. Interestingly, the peak responding time of proteins specific to different organs varied in a time-course study. The collected molecular information shed light on a complex, dynamic, yet interweaving, multiorgan-enrolled APAP toxicity. The developed technique as well as the identified protein markers is translational to human studies. We hope our work can broaden the utility of blood proteomics in diagnosis and research of the whole-body response to pathogenic cues.

Biomarkers of drug-induced liver injury

Correspondence: Kai Wang Institute for Systems Biology 401 Terry Avenue North Seattle, Washington 98109, USA Tel +1 206 732 1336 Fax +1 206 732 1299 Email kwang@systemsbiology.org Abstract: The liver plays a central role in metabolizing xenobiotics; therefore, it is highly susceptible to toxicity from these chemicals. Certain drugs, when taken in overdose and sometimes even when used within therapeutic range, may cause injury to the organ. Druginduced liver injury is now not only a leading cause of acute liver failure in the US, but is also a leading reason for discontinuation of drugs in development and for regulatory actions against previously approved drugs. The current clinical biomarkers to detect and monitor drug-induced liver injury are inadequate in terms of sensitivity and/or specificity, prompting the need for more informative biomarkers. The development of high throughput proteomics, genomics, and metabolomics technologies has the potential to fulfill such demand. The discipline of systems toxicology may add to our understanding of perturbed xenobiotic networks, which may lead to network-specific surrogate markers and therapeutic means to stop or reverse xenobiotic-induced liver injury.