Wenqian Zhang

BACKGROUND: Gene expression profiling is being widely applied in cancer research to identify biomarkers for clinical endpoint prediction. Since RNA-seq provides a powerful tool for transcriptome-based applications beyond the limitations of microarrays, we sought to systematically evaluate the performance of RNA-seq-based and microarray-based classifiers in this MAQC-III/SEQC study for clinical endpoint prediction using neuroblastoma as a model. RESULTS: We generate gene expression profiles from 498 primary neuroblastomas using both RNA-seq and 44 k microarrays. Characterization of the neuroblastoma transcriptome by RNA-seq reveals that more than 48,000 genes and 200,000 transcripts are being expressed in this malignancy. We also find that RNA-seq provides much more detailed information on specific transcript expression patterns in clinico-genetic neuroblastoma subgroups than microarrays. To systematically compare the power of RNA-seq and microarray-based models in predicting clinical endpoints, we divide the cohort randomly into training and validation sets and develop 360 predictive models on six clinical endpoints of varying predictability. Evaluation of factors potentially affecting model performances reveals that prediction accuracies are most strongly influenced by the nature of the clinical endpoint, whereas technological platforms (RNA-seq vs. microarrays), RNA-seq data analysis pipelines, and feature levels (gene vs. transcript vs. exon-junction level) do not significantly affect performances of the models. CONCLUSIONS: We demonstrate that RNA-seq outperforms microarrays in determining the transcriptomic characteristics of cancer, while RNA-seq and microarray-based models perform similarly in clinical endpoint prediction. Our findings may be valuable to guide future studies on the development of gene expression-based predictive models and their implementation in clinical practice.

INTRODUCTION The Saccharomyces cerevisiae 2.0 project (Sc2.0) aims to modify the yeast genome with a series of densely spaced designer changes. Both a synthetic yeast chromosome arm (synIXR) and the entirely synthetic chromosome (synIII) function with high fitness in yeast. For designer genome synthesis projects, precise engineering of the physical sequence to match the specified design is important for the systematic evaluation of underlying design principles. Yeast can maintain nuclear chromosomes as rings, occurring by chance at repeated sequences, although the cyclized format is unfavorable in meiosis given the possibility of dicentric chromosome formation from meiotic recombination. Here, we describe the de novo synthesis of synthetic yeast chromosome V (synV) in the “Build-A-Genome China” course, perfectly matching the designer sequence and bearing loxPsym sites, distinguishable watermarks, and all the other features of the synthetic genome. We generated a ring synV derivative with user-specified cyclization coordinates and characterized its performance in mitosis and meiosis. RATIONALE Systematic evaluation of underlying Sc2.0 design principles requires that the final assembled synthetic genome perfectly match the designed sequence. Given the size of yeast chromosomes, synthetic chromosome construction is performed iteratively, and new mutations and unpredictable events may occur during synthesis; even a very small number of unintentional nucleotide changes across the genome could have substantial effects on phenotype. Therefore, precisely matching the physical sequence to the designed sequence is crucial for verification of the design principles in genome synthesis. Ring chromosomes can extend those design principles to provide a model for genomic rearrangement, ring chromosome evolution, and human ring chromosome disorders. RESULTS We chemically synthesized, assembled, and incorporated designer chromosome synV (536,024 base pairs) of S. cerevisiae according to Sc2.0 principles, based on the complete nucleotide sequence of native yeast chromosome V (576,874 base pairs). This work was performed as part of the “Build-A-Genome China” course in Tianjin University. We corrected all mutations found—including duplications, substitutions, and indels—in the initial synV strain by using integrative cotransformation of the precise desired changes and by means of a clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9)–based method. Altogether, 3331 corrected base pairs were required to match to the designed sequence. We generated a strain that exactly matches all designer sequence changes that displays high fitness under a variety of culture conditions. All corrections were verified with whole-genome sequencing; RNA sequencing revealed only minor changes in gene expression—most notably, decreases in expression of genes relocated near synthetic telomeres as a result of design. We constructed a functional circular synV (ring_synV) derivative in yeast by precisely joining both chromosome ends (telomeres) at specified coordinates. The ring chromosome showed restoration of subtelomeric gene expression levels. The ring_synV strain exhibited fitness comparable with that of the linear synV strain, revealed no change in sporulation frequency, but notably reduced spore viability. In meiosis, heterozygous or homozygous diploid ring_wtV and ring_synV chromosomes behaved similarly, exhibiting substantially higher frequency of the formation of zero-spore tetrads, a type that was not seen in the rod chromosome diploids. Rod synV chromosomes went through meiosis with high spore viability, despite no effort having been made to preserve meiotic competency in the design of synV. CONCLUSION The perfect designer-matched synthetic chromosome V provides strategies to edit sequence variants and correct unpredictable events, such as off-target integration of extra copies of synthetic DNA elsewhere in the genome. We also constructed a ring synthetic chromosome derivative and evaluated its fitness and stability in yeast. Both synV and synVI can be circularized and can power yeast cell growth without affecting fitness when gene content is maintained. These fitness and stability phenotypes of the ring synthetic chromosome in yeast provide a model system with which to probe the mechanism of human ring chromosome disorders. Synthesis, cyclization, and characterization of synV . ( A ) Synthetic chromosome V (synV, 536,024 base pairs) was designed in silico from native chromosome V (wtV, 576,874 base pairs), with extensive genotype modification designed to be phenotypically neutral. ( B ) CRISPR/Cas9 strategy for multiplex repair. ( C ) Colonies of wtV, synV, and ring_synV strains.

A lanthanide coordination polymer Tb0.957Eu0.043cpda was synthesized as a ratiometric and colorimetric luminescent thermometer. The high triplet excited state energy of a linker enables Tb0.957Eu0.043cpda to detect and visualize temperature over a wide range from cryogenic to room temperature (40-300 K).

BACKGROUND: Gene expression microarray has been the primary biomarker platform ubiquitously applied in biomedical research, resulting in enormous data, predictive models, and biomarkers accrued. Recently, RNA-seq has looked likely to replace microarrays, but there will be a period where both technologies co-exist. This raises two important questions: Can microarray-based models and biomarkers be directly applied to RNA-seq data? Can future RNA-seq-based predictive models and biomarkers be applied to microarray data to leverage past investment? RESULTS: We systematically evaluated the transferability of predictive models and signature genes between microarray and RNA-seq using two large clinical data sets. The complexity of cross-platform sequence correspondence was considered in the analysis and examined using three human and two rat data sets, and three levels of mapping complexity were revealed. Three algorithms representing different modeling complexity were applied to the three levels of mappings for each of the eight binary endpoints and Cox regression was used to model survival times with expression data. In total, 240,096 predictive models were examined. CONCLUSIONS: Signature genes of predictive models are reciprocally transferable between microarray and RNA-seq data for model development, and microarray-based models can accurately predict RNA-seq-profiled samples; while RNA-seq-based models are less accurate in predicting microarray-profiled samples and are affected both by the choice of modeling algorithm and the gene mapping complexity. The results suggest continued usefulness of legacy microarray data and established microarray biomarkers and predictive models in the forthcoming RNA-seq era.