Richard D. LeDuc

De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis

De novo assembly of RNA-seq data enables researchers to study transcriptomes without the need for a genome sequence; this approach can be usefully applied, for instance, in research on non-model organisms of ecological and evolutionary importance, cancer samples or the microbiome. In this protocol we describe the use of the Trinity platform for de novo transcriptome assembly from RNA-seq data in non-model organisms. We also present Trinity-supported companion utilities for downstream applications, including RSEM for transcript abundance estimation, R/Bioconductor packages for identifying differentially expressed transcripts across samples and approaches to identify protein-coding genes. In the procedure, we provide a workflow for genome-independent transcriptome analysis leveraging the Trinity platform. The software, documentation and demonstrations are freely available from http://trinityrnaseq.sourceforge.net. The run time of this protocol is highly dependent on the size and complexity of data to be analyzed. The example data set analyzed in the procedure detailed herein can be processed in less than 5 h.

Mapping intact protein isoforms in discovery mode using top-down proteomics

A full description of the human proteome relies on the challenging task of detecting mature and changing forms of protein molecules in the body. Large-scale proteome analysis has routinely involved digesting intact proteins followed by inferred protein identification using mass spectrometry. This ‘bottom-up’ process affords a high number of identifications (not always unique to a single gene). However, complications arise from incomplete or ambiguous characterization of alternative splice forms, diverse modifications (for example, acetylation and methylation) and endogenous protein cleavages, especially when combinations of these create complex patterns of intact protein isoforms and species. ‘Top-down’ interrogation of whole proteins can overcome these problems for individual proteins, but has not been achieved on a proteome scale owing to the lack of intact protein fractionation methods that are well integrated with tandem mass spectrometry. Here we show, using a new four-dimensional separation system, identification of 1,043 gene products from human cells that are dispersed into more than 3,000 protein species created by post-translational modification (PTM), RNA splicing and proteolysis. The overall system produced greater than 20-fold increases in both separation power and proteome coverage, enabling the identification of proteins up to 105 kDa and those with up to 11 transmembrane helices. Many previously undetected isoforms of endogenous human proteins were mapped, including changes in multiply modified species in response to accelerated cellular ageing (senescence) induced by DNA damage. Integrated with the latest version of the Swiss-Prot database, the data provide precise correlations to individual genes and proof-of-concept for large-scale interrogation of whole protein molecules. The technology promises to improve the link between proteomics data and complex phenotypes in basic biology and disease research.

ADAMTS7B, the Full-length Product of the ADAMTS7 Gene, Is a Chondroitin Sulfate Proteoglycan Containing a Mucin Domain

We have characterized ADAMTS7B, the authentic full-length protein product of the ADAMTS7 gene. ADAMTS7B has a domain organization similar to that of ADAMTS12, with a total of eight thrombospondin type 1 repeats in its ancillary domain. Of these, seven are arranged in two distinct clusters that are separated by a mucin domain. Unique to the ADAMTS family, ADAMTS7B is modified by attachment of the glycosaminoglycan chondroitin sulfate within the mucin domain, thus rendering it a proteoglycan. Glycosaminoglycan addition has potentially important implications for ADAMTS7B cellular localization and for substrate recognition. Although not an integral membrane protein, ADAMTS7B is retained near the cell surface of HEK293F cells via interactions involving both the ancillary domain and the prodomain. ADAMTS7B undergoes removal of the prodomain by a multistep furin-dependent mechanism. At least part of the final processing event, i.e. cleavage following Arg220 (mouse sequence annotation), occurs at the cell surface. ADAMTS7B is an active metalloproteinase as shown by its ability to cleave α2-macroglobulin, but it does not cleave specific peptide bonds in versican and aggrecan attacked by ADAMTS proteases. Together with ADAMTS12, whose primary structure also predicts a mucin domain, ADAMTS7B constitutes a unique subgroup of the ADAMTS family.

ProSight Lite: Graphical software to analyze top-down mass spectrometry data

Many top‐down proteomics experiments focus on identifying and localizing PTMs and other potential sources of “mass shift” on a known protein sequence. A simple application to match ion masses and facilitate the iterative hypothesis testing of PTM presence and location would assist with the data analysis in these experiments. ProSight Lite is a free software tool for matching a single candidate sequence against a set of mass spectrometric observations. Fixed or variable modifications, including both PTMs and a select number of glycosylations, can be applied to the amino acid sequence. The application reports multiple scores and a matching fragment list. Fragmentation maps can be exported for publication in either portable network graphic (PNG) or scalable vector graphic (SVG) format. ProSight Lite can be freely downloaded from http://prosightlite.northwestern.edu, installs and updates from the web, and requires Windows 7 or a higher version.

The first pilot project of the consortium for top-down proteomics: a status report.

Pilot Project #1—the identification and characterization of human histone H4 proteoforms by top‐down MS—is the first project launched by the Consortium for Top‐Down Proteomics (CTDP) to refine and validate top‐down MS. Within the initial results from seven participating laboratories, all reported the probability‐based identification of human histone H4 (UniProt accession P62805) with expectation values ranging from 10−13 to 10−105. Regarding characterization, a total of 74 proteoforms were reported, with 21 done so unambiguously; one new PTM, K79ac, was identified. Inter‐laboratory comparison reveals aspects of the results that are consistent, such as the localization of individual PTMs and binary combinations, while other aspects are more variable, such as the accurate characterization of low‐abundance proteoforms harboring >2 PTMs. An open‐access tool and discussion of proteoform scoring are included, along with a description of general challenges that lie ahead including improved proteoform separations prior to mass spectrometric analysis, better instrumentation performance, and software development.

Applying label-free quantitation to top down proteomics.

With the prospect of resolving whole protein molecules into their myriad proteoforms on a proteomic scale, the question of their quantitative analysis in discovery mode comes to the fore. Here, we demonstrate a robust pipeline for the identification and stringent scoring of abundance changes of whole protein forms <30 kDa in a complex system. The input is ∼100–400 μg of total protein for each biological replicate, and the outputs are graphical displays depicting statistical confidence metrics for each proteoform (i.e., a volcano plot and representations of the technical and biological variation). A key part of the pipeline is the hierarchical linear model that is tailored to the original design of the study. Here, we apply this new pipeline to measure the proteoform-level effects of deleting a histone deacetylase (rpd3) in S. cerevisiae. Over 100 proteoform changes were detected above a 5% false positive threshold in WT vs the Δrpd3 mutant, including the validating observation of hyperacetylation of histone H4 and both H2B isoforms. Ultimately, this approach to label-free top down proteomics in discovery mode is a critical technical advance for testing the hypothesis that whole proteoforms can link more tightly to complex phenotypes in cell and disease biology than do peptides created in shotgun proteomics.

Revealing Novel Telomere Proteins Using in Vivo Cross-linking, Tandem Affinity Purification, and Label-free Quantitative LC-FTICR-MS

Telomeres are DNA-protein structures that protect chromosome ends from the actions of the DNA repair machinery. When telomeric integrity is compromised, genomic instability ensues. Considerable effort has focused on identification of telomere-binding proteins and elucidation of their functions. To date, protein identification has relied on classical immunoprecipitation and mass spectrometric approaches, primarily under conditions that favor isolation of proteins with strong or long lived interactions that are present at sufficient quantities to visualize by SDS-PAGE. To facilitate identification of low abundance and transiently associated telomere-binding proteins, we developed a novel approach that combines in vivo protein-protein cross-linking, tandem affinity purification, and stringent sequential endoprotease digestion. Peptides were identified by label-free comparative nano-LC-FTICR-MS. Here, we expressed an epitope-tagged telomere-binding protein and utilized a modified chromatin immunoprecipitation approach to cross-link associated proteins. The resulting immunoprecipitant contained telomeric DNA, establishing that this approach captures bona fide telomere binding complexes. To identify proteins present in the immunocaptured complexes, samples were reduced, alkylated, and digested with sequential endoprotease treatment. The resulting peptides were purified using a microscale porous graphite stationary phase and analyzed using nano-LC-FTICR-MS. Proteins enriched in cells expressing HA-FLAG-TIN2 were identified by label-free quantitative analysis of the FTICR mass spectra from different samples and ion trap tandem mass spectrometry followed by database searching. We identified all of the proteins that constitute the telomeric shelterin complex, thus validating the robustness of this approach. We also identified 62 novel telomere-binding proteins. These results demonstrate that DNA-bound protein complexes, including those present at low molar ratios, can be identified by this approach. The success of this approach will allow us to create a more complete understanding of telomere maintenance and have broad applicability.

Trinity RNA-Seq assembler performance optimization

RNA-sequencing is a technique to study RNA expression in biological material. It is quickly gaining popularity in the field of transcriptomics. Trinity is a software tool that was developed for efficient de novo reconstruction of transcriptomes from RNA-Seq data. In this paper we first conduct a performance study of Trinity and compare it to previously published data from 2011. The version from 2011 is much slower than many other de novo assemblers and biologists have thus been forced to choose between quality and speed. We examine the runtime behavior of Trinity as a whole as well as its individual components and then optimize the most performance critical parts. We find that standard best practices for HPC applications can also be applied to Trinity, especially on systems with large amounts of memory. When combining best practices for HPC applications along with our specific performance optimization, we can decrease the runtime of Trinity by a factor of 3.9. This brings the runtime of Trinity in line with other de novo assemblers while maintaining superior quality. The purpose of this paper is to describe a series of improvements to Trinity, quantify the execution improvements achieved, and document the new version of the software.

The C‑Score: A Bayesian Framework to Sharply Improve Proteoform Scoring in High-Throughput Top Down Proteomics

The automated processing of data generated by top down proteomics would benefit from improved scoring for protein identification and characterization of highly related protein forms (proteoforms). Here we propose the “C-score” (short for Characterization Score), a Bayesian approach to the proteoform identification and characterization problem, implemented within a framework to allow the infusion of expert knowledge into generative models that take advantage of known properties of proteins and top down analytical systems (e.g., fragmentation propensities, “off-by-1 Da” discontinuous errors, and intelligent weighting for site-specific modifications). The performance of the scoring system based on the initial generative models was compared to the current probability-based scoring system used within both ProSightPC and ProSightPTM on a manually curated set of 295 human proteoforms. The current implementation of the C-score framework generated a marked improvement over the existing scoring system as measured by the area under the curve on the resulting ROC chart (AUC of 0.99 versus 0.78).

Proteomic analyses of native brain KV4.2 channel complexes

Somatodendritic A-type (IA) voltage-gated K+ (KV) channels are key regulators of neuronal excitability, functioning to control action potential waveforms, repetitive firing and the responses to synaptic inputs. Rapidly activating and inactivating somatodendritic IA channels are encoded by KV4 α subunits and accumulating evidence suggests that these channels function as components of macromolecular protein complexes. Mass spectrometry (MS)-based proteomic approaches were developed and exploited here to identify potential components and regulators of native brain KV4.2-encoded IA channel complexes. Using anti-KV4.2 specific antibodies, KV4.2 channel complexes were immunoprecipitated from adult wild type mouse brain. Parallel control experiments were performed on brain samples isolated from (KV4.2-/-) mice harboring a targeted disruption of the KCND2 (KV4.2) locus. Three proteomic strategies were employed: an in-gel approach, coupled to one-dimensional liquid chromatography-tandem MS (1D-LC-MS/MS), and two in-solution approaches, followed by 1D- or 2D-LC-MS/MS. The targeted in-gel 1D-LC-MS/MS analyses demonstrated the presence of the Kv4 α subunits (KV4.2, KV4.3 and KV4.1) and the KV4 accessory, KChIP (KChIP1-4) and DPP (DPP6 and 10), proteins in native brain KV4.2 channel complexes. The more comprehensive, in-solution approach, coupled to 2D-LC-MS/MS, also called Multidimensional Protein Identification Technology (MudPIT), revealed that additional regulatory proteins, including the Kv channel accessory subunit KVβ1, are also components of native brain KV4.2 channel complexes. Additional biochemical and functional approaches will be required to elucidate the physiological roles of these newly identified KV4 interacting proteins.