Daniel M. Klass

Bio: Daniel M. Klass is an academic researcher from Hoffmann-La Roche. The author has contributed to research in topics: Nucleic acid & RNA-binding protein. The author has an hindex of 11, co-authored 22 publications receiving 1371 citations. Previous affiliations of Daniel M. Klass include University of Wisconsin-Madison & Stanford University.

Integrated digital error suppression for improved detection of circulating tumor DNA

Abstract: High-throughput sequencing of circulating tumor DNA (ctDNA) promises to facilitate personalized cancer therapy. However, low quantities of cell-free DNA (cfDNA) in the blood and sequencing artifacts currently limit analytical sensitivity. To overcome these limitations, we introduce an approach for integrated digital error suppression (iDES). Our method combines in silico elimination of highly stereotypical background artifacts with a molecular barcoding strategy for the efficient recovery of cfDNA molecules. Individually, these two methods each improve the sensitivity of cancer personalized profiling by deep sequencing (CAPP-Seq) by about threefold, and synergize when combined to yield ∼15-fold improvements. As a result, iDES-enhanced CAPP-Seq facilitates noninvasive variant detection across hundreds of kilobases. Applied to non-small cell lung cancer (NSCLC) patients, our method enabled biopsy-free profiling of EGFR kinase domain mutations with 92% sensitivity and >99.99% specificity at the variant level, and with 90% sensitivity and 96% specificity at the patient level. In addition, our approach allowed monitoring of NSCLC ctDNA down to 4 in 10(5) cfDNA molecules. We anticipate that iDES will aid the noninvasive genotyping and detection of ctDNA in research and clinical settings.

Distinct biological subtypes and patterns of genome evolution in lymphoma revealed by circulating tumor DNA.

Abstract: Patients with diffuse large B cell lymphoma (DLBCL) exhibit marked diversity in tumor behavior and outcomes, yet the identification of poor-risk groups remains challenging. In addition, the biology underlying these differences is incompletely understood. We hypothesized that characterization of mutational heterogeneity and genomic evolution using circulating tumor DNA (ctDNA) profiling could reveal molecular determinants of adverse outcomes. To address this hypothesis, we applied cancer personalized profiling by deep sequencing (CAPP-Seq) analysis to tumor biopsies and cell-free DNA samples from 92 lymphoma patients and 24 healthy subjects. At diagnosis, the amount of ctDNA was found to strongly correlate with clinical indices and was independently predictive of patient outcomes. We demonstrate that ctDNA genotyping can classify transcriptionally defined tumor subtypes, including DLBCL cell of origin, directly from plasma. By simultaneously tracking multiple somatic mutations in ctDNA, our approach outperformed immunoglobulin sequencing and radiographic imaging for the detection of minimal residual disease and facilitated noninvasive identification of emergent resistance mutations to targeted therapies. In addition, we identified distinct patterns of clonal evolution distinguishing indolent follicular lymphomas from those that transformed into DLBCL, allowing for potential noninvasive prediction of histological transformation. Collectively, our results demonstrate that ctDNA analysis reveals biological factors that underlie lymphoma clinical outcomes and could facilitate individualized therapy.

Positional cloning of Sorcs1, a type 2 diabetes quantitative trait locus.

Abstract: We previously mapped the type 2 diabetes mellitus-2 locus (T2dm2), which affects fasting insulin levels, to distal chromosome 19 in a leptin-deficient obese F2 intercross derived from C57BL/6 (B6) and BTBR T+ tf/J (BTBR) mice1. Introgression of a 7-Mb segment of the B6 chromosome 19 into the BTBR background (strain 1339A) replicated the reduced insulin linked to T2dm2. The 1339A mice have markedly impaired insulin secretion in vivo and disrupted islet morphology. We used subcongenic strains derived from 1339A to localize the T2dm2 quantitative trait locus (QTL) to a 242-kb segment comprising the promoter, first exon and most of the first intron of the Sorcs1 gene. This was the only gene in the 1339A strain for which we detected amino acid substitutions and expression level differences between mice carrying B6 and BTBR alleles of this insert, thereby identifying variation within the Sorcs1 gene as underlying the phenotype associated with the T2dm2 locus. SorCS1 binds platelet-derived growth factor, a growth factor crucial for pericyte recruitment to the microvasculature, and may thus have a role in expanding or maintaining the islet vasculature. Our identification of the Sorcs1 gene provides insight into the pathway underlying the pathophysiology of obesity-induced type 2 diabetes mellitus.

Proteome-Wide Search Reveals Unexpected RNA-Binding Proteins in Saccharomyces cerevisiae

Abstract: The vast landscape of RNA-protein interactions at the heart of post-transcriptional regulation remains largely unexplored Indeed it is likely that, even in yeast, a substantial fraction of the regulatory RNA-binding proteins (RBPs) remain to be discovered Systematic experimental methods can play a key role in discovering these RBPs - most of the known yeast RBPs lack RNA-binding domains that might enable this activity to be predicted We describe here a proteome-wide approach to identify RNA-protein interactions based on in vitro binding of RNA samples to yeast protein microarrays that represent over 80% of the yeast proteome We used this procedure to screen for novel RBPs and RNA-protein interactions A complementary mass spectrometry technique also identified proteins that associate with yeast mRNAs Both the protein microarray and mass spectrometry methods successfully identify previously annotated RBPs, suggesting that other proteins identified in these assays might be novel RBPs Of 35 putative novel RBPs identified by either or both of these methods, 12, including 75% of the eight most highly-ranked candidates, reproducibly associated with specific cellular RNAs Surprisingly, most of the 12 newly discovered RBPs were enzymes Functional characteristics of the RNA targets of some of the novel RBPs suggest coordinated post-transcriptional regulation of subunits of protein complexes and a possible link between mRNA trafficking and vesicle transport Our results suggest that many more RBPs still remain to be identified and provide a set of candidates for further investigation

Quantitative proteomic analysis reveals concurrent RNA–protein interactions and identifies new RNA-binding proteins in Saccharomyces cerevisiae

Abstract: A growing body of evidence supports the existence of an extensive network of RNA-binding proteins (RBPs) whose combinatorial binding affects the post-transcriptional fate of every mRNA in the cell-yet we still do not have a complete understanding of which proteins bind to mRNA, which of these bind concurrently, and when and where in the cell they bind. We describe here a method to identify the proteins that bind to RNA concurrently with an RBP of interest, using quantitative mass spectrometry combined with RNase treatment of affinity-purified RNA-protein complexes. We applied this method to the known RBPs Pab1, Nab2, and Puf3. Our method significantly enriched for known RBPs and is a clear improvement upon previous approaches in yeast. Our data reveal that some reported protein-protein interactions may instead reflect simultaneous binding to shared RNA targets. We also discovered more than 100 candidate RBPs, and we independently confirmed that 77% (23/30) bind directly to RNA. The previously recognized functions of the confirmed novel RBPs were remarkably diverse, and we mapped the RNA-binding region of one of these proteins, the transcriptional coactivator Mbf1, to a region distinct from its DNA-binding domain. Our results also provided new insights into the roles of Nab2 and Puf3 in post-transcriptional regulation by identifying other RBPs that bind simultaneously to the same mRNAs. While existing methods can identify sets of RBPs that interact with common RNA targets, our approach can determine which of those interactions are concurrent-a crucial distinction for understanding post-transcriptional regulation.

fastp: an ultra-fast all-in-one FASTQ preprocessor.

Abstract: Motivation Quality control and preprocessing of FASTQ files are essential to providing clean data for downstream analysis. Traditionally, a different tool is used for each operation, such as quality control, adapter trimming and quality filtering. These tools are often insufficiently fast as most are developed using high-level programming languages (e.g. Python and Java) and provide limited multi-threading support. Reading and loading data multiple times also renders preprocessing slow and I/O inefficient. Results We developed fastp as an ultra-fast FASTQ preprocessor with useful quality control and data-filtering features. It can perform quality control, adapter trimming, quality filtering, per-read quality pruning and many other operations with a single scan of the FASTQ data. This tool is developed in C++ and has multi-threading support. Based on our evaluation, fastp is 2-5 times faster than other FASTQ preprocessing tools such as Trimmomatic or Cutadapt despite performing far more operations than similar tools. Availability and implementation The open-source code and corresponding instructions are available at https://github.com/OpenGene/fastp.

fastp: an ultra-fast all-in-one FASTQ preprocessor

Abstract: Motivation: Quality control and preprocessing of FASTQ files are essential to providing clean data for downstream analysis. Traditionally, a different tool is used for each operation, such as quality control, adapter trimming, and quality filtering. These tools are often insufficiently fast as most are developed using high level programming languages (e.g., Python and Java) and provide limited multithreading support. Reading and loading data multiple times also renders preprocessing slow and I/O inefficient. Results: We developed fastp as an ultra-fast FASTQ preprocessor with useful quality control and data-filtering features. It can perform quality control, adapter trimming, quality filtering, per read quality cutting, and many other operations with a single scan of the FASTQ data. It also supports unique molecular identifier preprocessing, poly tail trimming, output splitting, and base correction for paired-end data. It can automatically detect adapters for single-end and paired-end FASTQ data. This tool is developed in C++ and has multithreading support. Based on our evaluation, fastp is 2 to 5 times faster than other FASTQ preprocessing tools such as Trimmomatic or Cutadapt despite performing far more operations than similar tools. Availability and Implementation: The open-source code and corresponding instructions are available at https://github.com/OpenGene/fastp

Liquid biopsies come of age: towards implementation of circulating tumour DNA

Abstract: Improvements in genomic and molecular methods are expanding the range of potential applications for circulating tumour DNA (ctDNA), both in a research setting and as a 'liquid biopsy' for cancer management. Proof-of-principle studies have demonstrated the translational potential of ctDNA for prognostication, molecular profiling and monitoring. The field is now in an exciting transitional period in which ctDNA analysis is beginning to be applied clinically, although there is still much to learn about the biology of cell-free DNA. This is an opportune time to appraise potential approaches to ctDNA analysis, and to consider their applications in personalized oncology and in cancer research.

A census of human RNA-binding proteins.

Abstract: Post-transcriptional gene regulation (PTGR) concerns processes involved in the maturation, transport, stability and translation of coding and non-coding RNAs. RNA-binding proteins (RBPs) and ribonucleoproteins coordinate RNA processing and PTGR. The introduction of large-scale quantitative methods, such as next-generation sequencing and modern protein mass spectrometry, has renewed interest in the investigation of PTGR and the protein factors involved at a systems-biology level. Here, we present a census of 1,542 manually curated RBPs that we have analysed for their interactions with different classes of RNA, their evolutionary conservation, their abundance and their tissue-specific expression. Our analysis is a critical step towards the comprehensive characterization of proteins involved in human RNA metabolism.