A comprehensive rat transcriptome built from large scale RNA-seq-based annotation.

The rat is an important model organism in biomedical research for studying human disease mechanisms and treatments, but its annotated transcriptome is far from complete. We constructed a Rat Transcriptome Re-annotation named RTR using RNA-seq data from 320 samples in 11 different organs generated by the SEQC consortium. Totally, there are 52 807 genes and 114 152 transcripts in RTR. Transcribed regions and exons in RTR account for ∼42% and ∼6.5% of the genome, respectively. Of all 73 074 newly annotated transcripts in RTR, 34 213 were annotated as high confident coding transcripts and 24 728 as high confident long noncoding transcripts. Different tissues rather than different stages have a significant influence on the expression patterns of transcripts. We also found that 11 715 genes and 15 852 transcripts were expressed in all 11 tissues and that 849 house-keeping genes expressed different isoforms among tissues. This comprehensive transcriptome is freely available at http://www.unimd.org/rtr/. Our new rat transcriptome provides essential reference for genetics and gene expression studies in rat disease and toxicity models.

FastMM: an efficient toolbox for personalized constraint-based metabolic modeling.

BACKGROUND: Constraint-based metabolic modeling has been applied to understand metabolism related disease mechanisms, to predict potential new drug targets and anti-metabolites, and to identify biomarkers of complex diseases. Although the state-of-art modeling toolbox, COBRA 3.0, is powerful, it requires substantial computing time conducting flux balance analysis, knockout analysis, and Markov Chain Monte Carlo (MCMC) sampling, which may limit its application in large scale genome-wide analysis. RESULTS: Here, we rewrote the underlying code of COBRA 3.0 using C/C++, and developed a toolbox, termed FastMM, to effectively conduct constraint-based metabolic modeling. The results showed that FastMM is 2~400 times faster than COBRA 3.0 in performing flux balance analysis and knockout analysis and returns consistent outputs. When applied to MCMC sampling, FastMM is 8 times faster than COBRA 3.0. FastMM is also faster than some efficient metabolic modeling applications, such as Cobrapy and Fast-SL. In addition, we developed a Matlab/Octave interface for fast metabolic modeling. This interface was fully compatible with COBRA 3.0, enabling users to easily perform complex applications for metabolic modeling. For example, users who do not have deep constraint-based metabolic model knowledge can just type one command in Matlab/Octave to perform personalized metabolic modeling. Users can also use the advance and multiple threading parameters for complex metabolic modeling. Thus, we provided an efficient and user-friendly solution to perform large scale genome-wide metabolic modeling. For example, FastMM can be applied to the modeling of individual cancer metabolic profiles of hundreds to thousands of samples in the Cancer Genome Atlas (TCGA). CONCLUSION: FastMM is an efficient and user-friendly toolbox for large-scale personalized constraint-based metabolic modeling. It can serve as a complementary and invaluable improvement to the existing functionalities in COBRA 3.0. FastMM is under GPL license and can be freely available at GitHub site: https://github.com/GonghuaLi/FastMM.

Correction to: FastMM: an efficient toolbox for personalized constraint-based metabolic modeling.

An amendment to this paper has been published and can be accessed via the original article.

KERIS: kaleidoscope of gene responses to inflammation between species.

A cornerstone of modern biomedical research is the use of animal models to study disease mechanisms and to develop new therapeutic approaches. In order to help the research community to better explore the similarities and differences of genomic response between human inflammatory diseases and murine models, we developed KERIS: kaleidoscope of gene responses to inflammation between species (available at http://www.igenomed.org/keris/). As of June 2016, KERIS includes comparisons of the genomic response of six human inflammatory diseases (burns, trauma, infection, sepsis, endotoxin and acute respiratory distress syndrome) and matched mouse models, using 2257 curated samples from the Inflammation and the Host Response to Injury Glue Grant studies and other representative studies in Gene Expression Omnibus. A researcher can browse, query, visualize and compare the response patterns of genes, pathways and functional modules across different diseases and corresponding murine models. The database is expected to help biologists choosing models when studying the mechanisms of particular genes and pathways in a disease and prioritizing the translation of findings from disease models into clinical studies.

Inhibition of Hif1α prevents both trauma-induced and genetic heterotopic ossification.

Pathologic extraskeletal bone formation, or heterotopic ossification (HO), occurs following mechanical trauma, burns, orthopedic operations, and in patients with hyperactivating mutations of the type I bone morphogenetic protein receptor ACVR1 (Activin type 1 receptor). Extraskeletal bone forms through an endochondral process with a cartilage intermediary prompting the hypothesis that hypoxic signaling present during cartilage formation drives HO development and that HO precursor cells derive from a mesenchymal lineage as defined by Paired related homeobox 1 (Prx). Here we demonstrate that Hypoxia inducible factor-1α (Hif1α), a key mediator of cellular adaptation to hypoxia, is highly expressed and active in three separate mouse models: trauma-induced, genetic, and a hybrid model of genetic and trauma-induced HO. In each of these models, Hif1α expression coincides with the expression of master transcription factor of cartilage, Sox9 [(sex determining region Y)-box 9]. Pharmacologic inhibition of Hif1α using PX-478 or rapamycin significantly decreased or inhibited extraskeletal bone formation. Importantly, de novo soft-tissue HO was eliminated or significantly diminished in treated mice. Lineage-tracing mice demonstrate that cells forming HO belong to the Prx lineage. Burn/tenotomy performed in lineage-specific Hif1α knockout mice (Prx-Cre/Hif1α(fl:fl)) resulted in substantially decreased HO, and again lack of de novo soft-tissue HO. Genetic loss of Hif1α in mesenchymal cells marked by Prx-cre prevents the formation of the mesenchymal condensations as shown by routine histology and immunostaining for Sox9 and PDGFRα. Pharmacologic inhibition of Hif1α had a similar effect on mesenchymal condensation development. Our findings indicate that Hif1α represents a promising target to prevent and treat pathologic extraskeletal bone.

Strategic Targeting of Multiple BMP Receptors Prevents Trauma-Induced Heterotopic Ossification.

Trauma-induced heterotopic ossification (tHO) is a condition of pathologic wound healing, defined by the progressive formation of ectopic bone in soft tissue following severe burns or trauma. Because previous studies have shown that genetic variants of HO, such as fibrodysplasia ossificans progressiva (FOP), are caused by hyperactivating mutations of the type I bone morphogenetic protein receptor (T1-BMPR) ACVR1/ALK2, studies evaluating therapies for HO have been directed primarily toward drugs for this specific receptor. However, patients with tHO do not carry known T1-BMPR mutations. Here we show that, although BMP signaling is required for tHO, no single T1-BMPR (ACVR1/ALK2, BMPR1a/ALK3, or BMPR1b/ALK6) alone is necessary for this disease, suggesting that these receptors have functional redundancy in the setting of tHO. By utilizing two different classes of BMP signaling inhibitors, we developed a translational approach to treatment, integrating treatment choice with existing diagnostic options. Our treatment paradigm balances either immediate therapy with reduced risk for adverse effects (Alk3-Fc) or delayed therapy with improved patient selection but greater risk for adverse effects (LDN-212854).

Molecular indexing enables quantitative targeted RNA sequencing and reveals poor efficiencies in standard library preparations.

We present a simple molecular indexing method for quantitative targeted RNA sequencing, in which mRNAs of interest are selectively captured from complex cDNA libraries and sequenced to determine their absolute concentrations. cDNA fragments are individually labeled so that each molecule can be tracked from the original sample through the library preparation and sequencing process. Multiple copies of cDNA fragments of identical sequence become distinct through labeling, and replicate clones created during PCR amplification steps can be identified and assigned to their distinct parent molecules. Selective capture enables efficient use of sequencing for deep sampling and for the absolute quantitation of rare or transient transcripts that would otherwise escape detection by standard sequencing methods. We have also constructed a set of synthetic barcoded RNA molecules, which can be introduced as controls into the sample preparation mix and used to monitor the efficiency of library construction. The quantitative targeted sequencing revealed extremely low efficiency in standard library preparations, which were further confirmed by using synthetic barcoded RNA molecules. This finding shows that standard library preparation methods result in the loss of rare transcripts and highlights the need for monitoring library efficiency and for developing more efficient sample preparation methods.

The identification of novel potential injury mechanisms and candidate biomarkers in renal allograft rejection by quantitative proteomics.

Early transplant dysfunction and failure because of immunological and nonimmunological factors still presents a significant clinical problem for transplant recipients. A critical unmet need is the noninvasive detection and prediction of immune injury such that acute injury can be reversed by proactive immunosuppression titration. In this study, we used iTRAQ -based proteomic discovery and targeted ELISA validation to discover and validate candidate urine protein biomarkers from 262 renal allograft recipients with biopsy-confirmed allograft injury. Urine samples were randomly split into a training set of 108 patients and an independent validation set of 154 patients, which comprised the clinical biopsy-confirmed phenotypes of acute rejection (AR) (n = 74), stable graft (STA) (n = 74), chronic allograft injury (CAI) (n = 58), BK virus nephritis (BKVN) (n = 38), nephrotic syndrome (NS) (n = 8), and healthy, normal control (HC) (n = 10). A total of 389 proteins were measured that displayed differential abundances across urine specimens of the injury types (p < 0.05) with a significant finding that SUMO2 (small ubiquitin-related modifier 2) was identified as a "hub" protein for graft injury irrespective of causation. Sixty-nine urine proteins had differences in abundance (p < 0.01) in AR compared with stable graft, of which 12 proteins were up-regulated in AR with a mean fold increase of 2.8. Nine urine proteins were highly specific for AR because of their significant differences (p < 0.01; fold increase >1.5) from all other transplant categories (HLA class II protein HLA-DRB1, KRT14, HIST1H4B, FGG, ACTB, FGB, FGA, KRT7, DPP4). Increased levels of three of these proteins, fibrinogen beta (FGB; p = 0.04), fibrinogen gamma (FGG; p = 0.03), and HLA DRB1 (p = 0.003) were validated by ELISA in AR using an independent sample set. The fibrinogen proteins further segregated AR from BK virus nephritis (FGB p = 0.03, FGG p = 0.02), a finding that supports the utility of monitoring these urinary proteins for the specific and sensitive noninvasive diagnosis of acute renal allograft rejection.

Genomic responses in mouse models poorly mimic human inflammatory diseases.

A cornerstone of modern biomedical research is the use of mouse models to explore basic pathophysiological mechanisms, evaluate new therapeutic approaches, and make go or no-go decisions to carry new drug candidates forward into clinical trials. Systematic studies evaluating how well murine models mimic human inflammatory diseases are nonexistent. Here, we show that, although acute inflammatory stresses from different etiologies result in highly similar genomic responses in humans, the responses in corresponding mouse models correlate poorly with the human conditions and also, one another. Among genes changed significantly in humans, the murine orthologs are close to random in matching their human counterparts (e.g., R(2) between 0.0 and 0.1). In addition to improvements in the current animal model systems, our study supports higher priority for translational medical research to focus on the more complex human conditions rather than relying on mouse models to study human inflammatory diseases.

Detecting differential protein expression in large-scale population proteomics.

MOTIVATION: Mass spectrometry (MS)-based high-throughput quantitative proteomics shows great potential in large-scale clinical biomarker studies, identifying and quantifying thousands of proteins in biological samples. However, there are unique challenges in analyzing the quantitative proteomics data. One issue is that the quantification of a given peptide is often missing in a subset of the experiments, especially for less abundant peptides. Another issue is that different MS experiments of the same study have significantly varying numbers of peptides quantified, which can result in more missing peptide abundances in an experiment that has a smaller total number of quantified peptides. To detect as many biomarker proteins as possible, it is necessary to develop bioinformatics methods that appropriately handle these challenges. RESULTS: We propose a Significance Analysis for Large-scale Proteomics Studies (SALPS) that handles missing peptide intensity values caused by the two mechanisms mentioned above. Our model has a robust performance in both simulated data and proteomics data from a large clinical study. Because varying patients' sample qualities and deviating instrument performances are not avoidable for clinical studies performed over the course of several years, we believe that our approach will be useful to analyze large-scale clinical proteomics data. AVAILABILITY AND IMPLEMENTATION: R codes for SALPS are available at http://www.stanford.edu/%7eclairesr/software.html.

Benchmarking outcomes in the critically injured burn patient.

OBJECTIVE: To determine and compare outcomes with accepted benchmarks in burn care at 6 academic burn centers. BACKGROUND: Since the 1960s, US morbidity and mortality rates have declined tremendously for burn patients, likely related to improvements in surgical and critical care treatment. We describe the baseline patient characteristics and well-defined outcomes for major burn injuries. METHODS: We followed 300 adults and 241 children from 2003 to 2009 through hospitalization, using standard operating procedures developed at study onset. We created an extensive database on patient and injury characteristics, anatomic and physiological derangement, clinical treatment, and outcomes. These data were compared with existing benchmarks in burn care. RESULTS: Study patients were critically injured, as demonstrated by mean % total body surface area (TBSA) (41.2 ± 18.3 for adults and 57.8 ± 18.2 for children) and presence of inhalation injury in 38% of the adults and 54.8% of the children. Mortality in adults was 14.1% for those younger than 55 years and 38.5% for those aged 55 years and older. Mortality in patients younger than 17 years was 7.9%. Overall, the multiple organ failure rate was 27%. When controlling for age and % TBSA, presence of inhalation injury continues to be significant. CONCLUSIONS: This study provides the current benchmark for major burn patients. Mortality rates, notwithstanding significant % TBSA and presence of inhalation injury, have significantly declined compared with previous benchmarks. Modern day surgical and medically intensive management has markedly improved to the point where we can expect patients younger than 55 years with severe burn injuries and inhalation injury to survive these devastating conditions.

Human transcriptome array for high-throughput clinical studies.

A 6.9 million-feature oligonucleotide array of the human transcriptome [Glue Grant human transcriptome (GG-H array)] has been developed for high-throughput and cost-effective analyses in clinical studies. This array allows comprehensive examination of gene expression and genome-wide identification of alternative splicing as well as detection of coding SNPs and noncoding transcripts. The performance of the array was examined and compared with mRNA sequencing (RNA-Seq) results over multiple independent replicates of liver and muscle samples. Compared with RNA-Seq of 46 million uniquely mappable reads per replicate, the GG-H array is highly reproducible in estimating gene and exon abundance. Although both platforms detect similar expression changes at the gene level, the GG-H array is more sensitive at the exon level. Deeper sequencing is required to adequately cover low-abundance transcripts. The array has been implemented in a multicenter clinical program and has generated high-quality, reproducible data. Considering the clinical trial requirements of cost, sample availability, and throughput, the GG-H array has a wide range of applications. An emerging approach for large-scale clinical genomic studies is to first use RNA-Seq to the sufficient depth for the discovery of transcriptome elements relevant to the disease process followed by high-throughput and reliable screening of these elements on thousands of patient samples using custom-designed arrays.

High-throughput VDJ sequencing for quantification of minimal residual disease in chronic lymphocytic leukemia and immune reconstitution assessment.

The primary cause of poor outcome following allogeneic hematopoietic cell transplantation (HCT) for chronic lymphocytic leukemia (CLL) is disease recurrence. Detection of increasing minimal residual disease (MRD) following HCT may permit early intervention to prevent clinical relapse; however, MRD quantification remains an uncommon diagnostic test because of logistical and financial barriers to widespread use. Here we describe a method for quantifying CLL MRD using widely available consensus primers for amplification of all Ig heavy chain (IGH) genes in a mixture of peripheral blood mononuclear cells, followed by high-throughput sequencing (HTS) for disease-specific IGH sequence quantification. To achieve accurate MRD quantification, we developed a systematic bioinformatic methodology to aggregate cancer clone sequence variants arising from systematic and random artifacts occurring during IGH-HTS. We then compared the sensitivity of IGH-HTS, flow cytometry, and allele-specific oligonucleotide PCR for MRD quantification in 28 samples collected from 6 CLL patients following allogeneic HCT. Using amplimer libraries generated with consensus primers from patient blood samples, we demonstrate the sensitivity of IGH-HTS with 454 pyrosequencing to be 10(-5), with a high correlation between quantification by allele-specific oligonucleotide PCR and IGH-HTS (r = 0.85). From the same dataset used to quantify MRD, IGH-HTS also allowed us to profile IGH repertoire reconstitution after HCT-information not provided by the other MRD methods. IGH-HTS using consensus primers will broaden the availability of MRD quantification in CLL and other B cell malignancies, and this approach has potential for quantitative evaluation of immune diversification following transplant and nontransplant therapies.

Inference for longitudinal data with nonignorable nonmonotone missing responses.

For the analysis of longitudinal data with nonignorable and nonmonotone missing responses, a full likelihood method often requires intensive computation, especially when there are many follow-up times. The authors propose and explore a Monte Carlo method, based on importance sampling, for approximating the maximum likelihood estimators. The finite-sample properties of the proposed estimators are studied using simulations. An application of the proposed method is also provided using longitudinal data on peptide intensities obtained from a proteomics experiment of trauma patients.

Targeted DNA-seq and RNA-seq of Reference Samples with Short-read and Long-read Sequencing.

Next-generation sequencing (NGS) has revolutionized genomic research by enabling high-throughput, cost-effective genome and transcriptome sequencing accelerating personalized medicine for complex diseases, including cancer. Whole genome/transcriptome sequencing (WGS/WTS) provides comprehensive insights, while targeted sequencing is more cost-effective and sensitive. In comparison to short-read sequencing, which still dominates the field due to high speed and cost-effectiveness, long-read sequencing can overcome alignment limitations and better discriminate similar sequences from alternative transcripts or repetitive regions. Hybrid sequencing combines the best strengths of different technologies for a more comprehensive view of genomic/transcriptomic variations. Understanding each technology's strengths and limitations is critical for translating cutting-edge technologies into clinical applications. In this study, we sequenced DNA and RNA libraries of reference samples using various targeted DNA and RNA panels and the whole transcriptome on both short-read and long-read platforms. This study design enables a comprehensive analysis of sequencing technologies, targeting protocols, and library preparation methods. Our expanded profiling landscape establishes a reference point for assessing current sequencing technologies, facilitating informed decision-making in genomic research and precision medicine.

Development of a genomic metric that can be rapidly used to predict clinical outcome in severely injured trauma patients.

OBJECTIVE: Many patients have complicated recoveries following severe trauma due to the development of organ injury. Physiological and anatomical prognosticators have had limited success in predicting clinical trajectories. We report on the development and retrospective validation of a simple genomic composite score that can be rapidly used to predict clinical outcomes. DESIGN: Retrospective cohort study. SETTING: Multi-institutional level 1 trauma centers. PATIENTS: Data were collected from 167 severely traumatized (injury severity score >15) adult (18-55 yr) patients. METHODS: Microarray-derived genomic data obtained from 167 severely traumatized patients over 28 days were assessed for differences in messenger RNA abundance among individuals with different clinical trajectories. Once a set of genes was identified based on differences in expression over the entire study period, messenger RNA abundance from these subjects obtained in the first 24 hours was analyzed in a blinded fashion using a rapid multiplex platform, and genomic data reduced to a single metric. RESULTS: From the existing genomic dataset, we identified 63 genes whose leukocyte expression differed between an uncomplicated and complicated clinical outcome over 28 days. Using a multiplex approach that can quantitate messenger RNA abundance in less than 12 hours, we reassessed total messenger RNA abundance from the first 24 hours after trauma and reduced the genomic data to a single composite score using the difference from reference. This composite score showed good discriminatory capacity to distinguish patients with a complicated outcome (area under a receiver-operator curve, 0.811; p <0.001). This was significantly better than the predictive power of either Acute Physiology and Chronic Health Evaluation II or new injury severity score scoring systems. CONCLUSIONS: A rapid genomic composite score obtained in the first 24 hours after trauma can retrospectively identify trauma patients who are likely to develop complicated clinical trajectories. A novel platform is described in which this genomic score can be obtained within 12 hours of blood collection, making it available for clinical decision making.

Determination of burn patient outcome by large-scale quantitative discovery proteomics.

OBJECTIVES: Emerging proteomics techniques can be used to establish proteomic outcome signatures and to identify candidate biomarkers for survival following traumatic injury. We applied high-resolution liquid chromatography-mass spectrometry and multiplex cytokine analysis to profile the plasma proteome of survivors and nonsurvivors of massive burn injury to determine the proteomic survival signature following a major burn injury. DESIGN: Proteomic discovery study. SETTING: Five burn hospitals across the United States. PATIENTS: Thirty-two burn patients (16 nonsurvivors and 16 survivors), 19-89 years old, were admitted within 96 hours of injury to the participating hospitals with burns covering more than 20% of the total body surface area and required at least one surgical intervention. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We found differences in circulating levels of 43 proteins involved in the acute-phase response, hepatic signaling, the complement cascade, inflammation, and insulin resistance. Thirty-two of the proteins identified were not previously known to play a role in the response to burn. Interleukin-4, interleukin-8, granulocyte macrophage colony-stimulating factor, monocyte chemotactic protein-1, and ß2-microglobulin correlated well with survival and may serve as clinical biomarkers. CONCLUSIONS: These results demonstrate the utility of these techniques for establishing proteomic survival signatures and for use as a discovery tool to identify candidate biomarkers for survival. This is the first clinical application of a high-throughput, large-scale liquid chromatography-mass spectrometry-based quantitative plasma proteomic approach for biomarker discovery for the prediction of patient outcome following burn, trauma, or critical illness.

JETTA: junction and exon toolkits for transcriptome analysis.

SUMMARY: High-throughput genome-wide studies of alternatively spliced mRNA transcripts have become increasingly important in clinical research. Consequently, easy-to-use software tools are required to process data from these studies, for example, using exon and junction arrays. Here, we introduce JETTA, an integrated software package for the calculation of gene expression indices as well as the identification and visualization of alternative splicing events. We demonstrate the software using data of human liver and muscle samples hybridized on an exon-junction array. AVAILABILITY: JETTA and its demonstrations are freely available at http://igenomed.stanford.edu/~junhee/JETTA/index.html

Down-regulation of glutatione S-transferase α 4 (hGSTA4) in the muscle of thermally injured patients is indicative of susceptibility to bacterial infection.

Patients with severe burns are highly susceptible to bacterial infection. While immunosuppression facilitates infection, the contribution of soft tissues to infection beyond providing a portal for bacterial entry remains unclear. We showed previously that glutathione S-transferase S1 (gstS1), an enzyme with conjugating activity against the lipid peroxidation byproduct 4-hydroxynonenal (4HNE), is important for resistance against wound infection in Drosophila muscle. The importance of the mammalian functional counterpart of GstS1 in the context of wounds and infection has not been investigated. Here we demonstrate that the presence of a burn wound dramatically affects expression of both human (hGSTA4) and mouse (mGsta4) 4HNE scavengers. hGSTA4 is down-regulated significantly within 1 wk of thermal burn injury in the muscle and fat tissues of patients from the large-scale collaborative Inflammation and the Host Response to Injury multicentered study. Similarly, mGsta4, the murine GST with the highest catalytic efficiency for 4HNE, is down-regulated to approximately half of normal levels in mouse muscle immediately postburn. Consequently, 4HNE protein adducts are increased 4- to 5-fold in mouse muscle postburn. Using an open wound infection model, we show that deletion of mGsta4 renders mice more susceptible to infection with the prevalent wound pathogen Pseudomonas aeruginosa, while muscle hGSTA4 expression negatively correlates with burn wound infection episodes per patient. Our data suggest that hGSTA4 down-regulation and the concomitant increase in 4HNE adducts in human muscle are indicative of susceptibility to infection in individuals with severely thermal injuries.