J. Will Thompson

Proteomic analysis of S-nitrosylation and denitrosylation by resin-assisted capture.

We have modified the biotin switch assay for protein S-nitrosothiols (SNOs), using resin-assisted capture (SNO-RAC). Compared with existing methodologies, SNO-RAC requires fewer steps, detects high-mass S-nitrosylated proteins more efficiently, and facilitates identification and quantification of S-nitrosylated sites by mass spectrometry. When combined with iTRAQ labeling, SNO-RAC revealed that intracellular proteins may undergo rapid denitrosylation on a global scale. This methodology is readily adapted to analyzing diverse cysteine-based protein modifications, including S-acylation.

Convergent transcriptional specializations in the brains of humans and song-learning birds.

INTRODUCTION Vocal learning, the ability to imitate sounds, is a trait that has undergone convergent evolution in several lineages of birds and mammals, including song-learning birds and humans. This behavior requires cortical and striatal vocal brain regions, which form unique connections in vocal-learning species. These regions have been found to have specialized gene expression within some species, but the patterns of specialization across vocal-learning bird and mammal species have not been systematically explored. Identifying molecular brain similarities across species. Brain region gene expression specializations were hierarchically organized into specialization trees of each species (blue lines), including for circuits that control learned vocalizations (highlighted green, purple, and orange regions). A set of comparative genomic algorithms found the most similarly specialized regions between songbird and human (orange lines), some of which are convergently evolved. RATIONALE The sequencing of genomes representing all major vocal-learning and vocal-nonlearning avian lineages has allowed us to develop the genomic tools to measure anatomical gene expression across species. Here, we asked whether behavioral and anatomical convergence is associated with gene expression convergence in the brains of vocal-learning birds and humans. RESULTS We developed a computational approach that discovers homologous and convergent specialized anatomical gene expression profiles. This includes generating hierarchically organized gene expression specialization trees for each species and a dynamic programming algorithm that finds the optimal alignment between species brain trees. We applied this approach to brain region gene expression databases of thousands of samples and genes that we and others generated from multiple species, including humans and song-learning birds (songbird, parrot, and hummingbird) as well as vocal-nonlearning nonhuman primates (macaque) and birds (dove and quail). Our results confirmed the recently revised understanding of the relationships between avian and mammalian brains. We further found that songbird Area X, a striatal region necessary for vocal learning, was most similar to a part of the human striatum activated during speech production. The RA (robust nucleus of the arcopallium) analog of song-learning birds, necessary for song production, was most similar to laryngeal motor cortex regions in humans that control speech production. More than 50 genes contributed to their convergent specialization and were enriched in motor control and neural connectivity functions. These patterns were not found in vocal nonlearners, but songbird RA was similar to layer 5 of primate motor cortex for another set of genes, supporting previous hypotheses about the similarity of these cell types between bird and mammal brains. CONCLUSION Our approach can accurately and quantitatively identify functionally and molecularly analogous brain regions between species separated by as much as 310 million years from a common ancestor. We were able to identify analogous brain regions for song and speech between birds and humans, and broader homologous brain regions in which these specialized song and speech regions are located, for tens to hundreds of genes. These genes now serve as candidates involved in developing and maintaining the unique connectivity and functional properties of vocal-learning brain circuits shared across species. The finding that convergent neural circuits for vocal learning are accompanied by convergent molecular changes of multiple genes in species separated by millions of years from a common ancestor indicates that brain circuits for complex traits may have limited ways in which they could have evolved from that ancestor. Song-learning birds and humans share independently evolved similarities in brain pathways for vocal learning that are essential for song and speech and are not found in most other species. Comparisons of brain transcriptomes of song-learning birds and humans relative to vocal nonlearners identified convergent gene expression specializations in specific song and speech brain regions of avian vocal learners and humans. The strongest shared profiles relate bird motor and striatal song-learning nuclei, respectively, with human laryngeal motor cortex and parts of the striatum that control speech production and learning. Most of the associated genes function in motor control and brain connectivity. Thus, convergent behavior and neural connectivity for a complex trait are associated with convergent specialized expression of multiple genes.

Sepsis: An integrated clinico-metabolomic model improves prediction of death in sepsis

A molecular signature, derived from integrated analysis of clinical data, the metabolome, and the proteome in prospective human studies, improved the prediction of death in patients with sepsis, potentially identifying a subset of patients who merit intensive treatment. Understanding Survival of the Fittest in Sepsis Differentiating mild infections from life-threatening ones is a complex decision that is made millions of times a year in U.S. emergency rooms. Should a patient be sent home with antibiotics and chicken soup? Or should he or she be hospitalized for intensive treatment? Sepsis—a serious infection that is associated with a generalized inflammatory response—is one of the leading causes of death. In two prospective clinical studies reported by Langley et al., patients arriving at four urban emergency departments with symptoms of sepsis were evaluated clinically and by analysis of their plasma proteome and metabolome. Survivors and nonsurvivors at 28 days were compared, and a molecular signature was detected that appeared to differentiate these outcomes—even as early as the time of hospital arrival. The signature was part of a large set of differences between these groups, showing that better energy-producing fatty acid catabolism was associated with survival of the fittest in sepsis. A test developed from the signature was able to predict sepsis survival and nonsurvival reproducibly and better than current methods. This test could help to make all important decisions in the emergency room more accurate. Sepsis is a common cause of death, but outcomes in individual patients are difficult to predict. Elucidating the molecular processes that differ between sepsis patients who survive and those who die may permit more appropriate treatments to be deployed. We examined the clinical features and the plasma metabolome and proteome of patients with and without community-acquired sepsis, upon their arrival at hospital emergency departments and 24 hours later. The metabolomes and proteomes of patients at hospital admittance who would ultimately die differed markedly from those of patients who would survive. The different profiles of proteins and metabolites clustered into the following groups: fatty acid transport and β-oxidation, gluconeogenesis, and the citric acid cycle. They differed consistently among several sets of patients, and diverged more as death approached. In contrast, the metabolomes and proteomes of surviving patients with mild sepsis did not differ from survivors with severe sepsis or septic shock. An algorithm derived from clinical features together with measurements of five metabolites predicted patient survival. This algorithm may help to guide the treatment of individual patients with sepsis.

Site-specific analysis of protein S-acylation by resin-assisted capture

Protein S-acylation is a major posttranslational modification whereby a cysteine thiol is converted to a thioester. A prototype is S-palmitoylation (fatty acylation), in which a protein undergoes acylation with a hydrophobic 16 carbon lipid chain. Although this modification is a well-recognized determinant of protein function and localization, current techniques to study cellular S-acylation are cumbersome and/or technically demanding. We recently described a simple and robust methodology to rapidly identify S-nitrosylation sites in proteins via resin-assisted capture (RAC) and provided an initial description of the applicability of the technique to S-acylated proteins (acyl-RAC). Here we expand on the acyl-RAC assay, coupled with mass spectrometry-based proteomics, to characterize both previously reported and novel sites of endogenous S-acylation. Acyl-RAC should therefore find general applicability in studies of both global and individual protein S-acylation in mammalian cells.

Ion mobility derived collision cross sections to support metabolomics applications.

Metabolomics is a rapidly evolving analytical approach in life and health sciences. The structural elucidation of the metabolites of interest remains a major analytical challenge in the metabolomics workflow. Here, we investigate the use of ion mobility as a tool to aid metabolite identification. Ion mobility allows for the measurement of the rotationally averaged collision cross-section (CCS), which gives information about the ionic shape of a molecule in the gas phase. We measured the CCSs of 125 common metabolites using traveling-wave ion mobility-mass spectrometry (TW-IM-MS). CCS measurements were highly reproducible on instruments located in three independent laboratories (RSD < 5% for 99%). We also determined the reproducibility of CCS measurements in various biological matrixes including urine, plasma, platelets, and red blood cells using ultra performance liquid chromatography (UPLC) coupled with TW-IM-MS. The mean RSD was < 2% for 97% of the CCS values, compared to 80% of retention times. Finally, as proof of concept, we used UPLC–TW-IM-MS to compare the cellular metabolome of epithelial and mesenchymal cells, an in vitro model used to study cancer development. Experimentally determined and computationally derived CCS values were used as orthogonal analytical parameters in combination with retention time and accurate mass information to confirm the identity of key metabolites potentially involved in cancer. Thus, our results indicate that adding CCS data to searchable databases and to routine metabolomics workflows will increase the identification confidence compared to traditional analytical approaches.

The protein expression landscape of the Arabidopsis root

Because proteins are the major functional components of cells, knowledge of their cellular localization is crucial to gaining an understanding of the biology of multicellular organisms. We have generated a protein expression map of the Arabidopsis root providing the identity and cell type-specific localization of nearly 2,000 proteins. Grouping proteins into functional categories revealed unique cellular functions and identified cell type-specific biomarkers. Cellular colocalization provided support for numerous protein–protein interactions. With a binary comparison, we found that RNA and protein expression profiles are weakly correlated. We then performed peak integration at cell type-specific resolution and found an improved correlation with transcriptome data using continuous values. We performed GeLC-MS/MS (in-gel tryptic digestion followed by liquid chromatography-tandem mass spectrometry) proteomic experiments on mutants with ectopic and no root hairs, providing complementary proteomic data. Finally, among our root hair-specific proteins we identified two unique regulators of root hair development.

Quantitative proteomics reveals metabolic and pathogenic properties of Chlamydia trachomatis developmental forms

Chlamydia trachomatis is an obligate intracellular pathogen responsible for ocular and genital infections of significant public health importance. C. trachomatis undergoes a biphasic developmental cycle alternating between two distinct forms: the infectious elementary body (EB), and the replicative but non‐infectious reticulate body (RB). The molecular basis for these developmental transitions and the metabolic properties of the EB and RB forms are poorly understood as these bacteria have traditionally been difficult to manipulate through classical genetic approaches. Using two‐dimensional liquid chromatography – tandem mass spectrometry (LC/LC‐MS/MS) we performed a large‐scale, label‐free quantitative proteomic analysis of C. trachomatis LGV‐L2 EB and RB forms. Additionally, we carried out LC‐MS/MS to analyse the membranes of the pathogen‐containing vacuole (‘inclusion’). We developed a label‐free quantification approaches to measure protein abundance in a mixed‐proteome background which we applied for EB and RB quantitative analysis. In this manner, we catalogued the relative distribution of > 54% of the predicted proteins in the C. trachomatis LGV‐L2 proteome. Proteins required for central metabolism and glucose catabolism were predominant in the EB, whereas proteins associated with protein synthesis, ATP generation and nutrient transport were more abundant in the RB. These findings suggest that the EB is primed for a burst in metabolic activity upon entry, whereas the RB form is geared towards nutrient utilization, a rapid increase in cellular mass, and securing the resources for an impending transition back to the EB form. The most revealing difference between the two forms was the relative deficiency of cytoplasmic factors required for efficient type III secretion (T3S) in the RB stage at 18 h post infection, suggesting a reduced T3S capacity or a low frequency of active T3S apparatus assembled on a ‘per organism’ basis. Our results show that EB and RB proteomes are streamlined to fulfil their predicted biological functions: maximum infectivity for EBs and replicative capacity for RBs.

Ion Mobility-Derived Collision Cross Section As an Additional Measure for Lipid Fingerprinting and Identification

Despite recent advances in analytical and computational chemistry, lipid identification remains a significant challenge in lipidomics. Ion-mobility spectrometry provides an accurate measure of the molecules’ rotationally averaged collision cross-section (CCS) in the gas phase and is thus related to ionic shape. Here, we investigate the use of CCS as a highly specific molecular descriptor for identifying lipids in biological samples. Using traveling wave ion mobility mass spectrometry (MS), we measured the CCS values of over 200 lipids within multiple chemical classes. CCS values derived from ion mobility were not affected by instrument settings or chromatographic conditions, and they were highly reproducible on instruments located in independent laboratories (interlaboratory RSD < 3% for 98% of molecules). CCS values were used as additional molecular descriptors to identify brain lipids using a variety of traditional lipidomic approaches. The addition of CCS improved the reproducibility of analysis in a liquid chromatography-MS workflow and maximized the separation of isobaric species and the signal-to-noise ratio in direct-MS analyses (e.g., “shotgun” lipidomics and MS imaging). These results indicate that adding CCS to databases and lipidomics workflows increases the specificity and selectivity of analysis, thus improving the confidence in lipid identification compared to traditional analytical approaches. The CCS/accurate-mass database described here is made publicly available.

Restraint of apoptosis during mitosis through interdomain phosphorylation of caspase-2

The apoptotic initiator caspase‐2 has been implicated in oocyte death, in DNA damage‐ and heat shock‐induced death, and in mitotic catastrophe. We show here that the mitosis‐promoting kinase, cdk1–cyclin B1, suppresses apoptosis upstream of mitochondrial cytochrome c release by phosphorylating caspase‐2 within an evolutionarily conserved sequence at Ser 340. Phosphorylation of this residue, situated in the caspase‐2 interdomain, prevents caspase‐2 activation. S340 was susceptible to phosphatase 1 dephosphorylation, and an interaction between phosphatase 1 and caspase‐2 detected during interphase was lost in mitosis. Expression of S340A non‐phosphorylatable caspase‐2 abrogated mitotic suppression of caspase‐2 and apoptosis in various settings, including oocytes induced to undergo cdk1‐dependent maturation. Moreover, U2OS cells treated with nocodazole were found to undergo mitotic catastrophe more readily when endogenous caspase‐2 was replaced with the S340A mutant to lift mitotic inhibition. These data demonstrate that for apoptotic stimuli transduced by caspase‐2, cell death is prevented during mitosis through the inhibitory phosphorylation of caspase‐2 and suggest that under conditions of mitotic arrest, cdk1–cyclin B1 activity must be overcome for apoptosis to occur.

Proteomic Profiling of a Layered Tissue Reveals Unique Glycolytic Specializations of Photoreceptor Cells

The retina is a highly ordered tissue whose outermost layers are formed by subcellular compartments of photoreceptors generating light-evoked electrical responses. We studied protein distributions among individual photoreceptor compartments by separating the entire photoreceptor layer of a flat-mounted frozen retina into a series of thin tangential cryosections and analyzing protein compositions of each section by label-free quantitative mass spectrometry. Based on 5038 confidently identified peptides assigned to 896 protein database entries, we generated a quantitative proteomic database (a “map”) correlating the distribution profiles of identified proteins with the profiles of marker proteins representing individual compartments of photoreceptors and adjacent cells. We evaluated the applicability of several common peptide-to-protein quantification algorithms in the context of our database and found that the highest reliability was obtained by summing the intensities of all peptides representing a given protein, using at least the 5–6 most intense peptides when applicable. We used this proteome map to investigate the distribution of glycolytic enzymes, critical in fulfilling the extremely high metabolic demands of photoreceptor cells, and obtained two major findings. First, unlike the majority of neurons rich in hexokinase I, but similar to other highly metabolically active cells, photoreceptors express hexokinase II. Hexokinase II has a very high catalytic activity when associated with mitochondria, and indeed we found it colocalized with mitochondria in photoreceptors. Second, photoreceptors contain very little triosephosphate isomerase, an enzyme converting dihydroxyacetone phosphate into glyceraldehyde-3-phosphate. This may serve as a functional adaptation because dihydroxyacetone phosphate is a major precursor in phospholipid biosynthesis, a process particularly active in photoreceptors because of the constant renewal of their light-sensitive membrane disc stacks. Overall, our approach for proteomic profiling of very small tissue amounts at a resolution of a few microns, combining cryosectioning and liquid chromatography-tandem MS, can be applied for quantitative investigation of proteomes where spatial resolution is paramount.