Pressurized Pepsin Digestion in Proteomics: AN AUTOMATABLE ALTERNATIVE TO TRYPSIN FOR INTEGRATED TOP-DOWN BOTTOM-UP PROTEOMICS*

Integrated top-down bottom-up proteomics combined with on-line digestion has great potential to improve the characterization of protein isoforms in biological systems and is amendable to high throughput proteomics experiments. Bottom-up proteomics ultimately provides the peptide sequences derived from the tandem MS analyses of peptides after the proteome has been digested. Top-down proteomics conversely entails the MS analyses of intact proteins for more effective characterization of genetic variations and/or post-translational modifications. Herein, we describe recent efforts toward efficient integration of bottom-up and top-down LC-MS-based proteomics strategies. Since most proteomics separations utilize acidic conditions, we exploited the compatibility of pepsin (where the optimal digestion conditions are at low pH) for integration into bottom-up and top-down proteomics work flows. Pressure-enhanced pepsin digestions were successfully performed and characterized with several standard proteins in either an off-line mode using a Barocycler or an on-line mode using a modified high pressure LC system referred to as a fast on-line digestion system (FOLDS). FOLDS was tested using pepsin and a whole microbial proteome, and the results were compared against traditional trypsin digestions on the same platform. Additionally, FOLDS was integrated with a RePlay configuration to demonstrate an ultrarapid integrated bottom-up top-down proteomics strategy using a standard mixture of proteins and a monkey pox virus proteome.In-depth characterization and quantitation of protein isoforms, including post-translationally modified proteins, are challenging goals of contemporary proteomics. Traditionally, top-down (1, 2) and bottom-up (3, 4) proteomics have been two distinct analytical paths for liquid-based proteomics analysis. Top-down proteomics is the mass spectrometry (MS)-based characterization of intact proteins, whereas bottom-up proteomics requires a chemical or enzymatic proteolytic digestion of all proteins into peptides prior to MS analysis. Both strategies have their own strengths and challenges and can be thought of as complementary rather than competing analytical techniques.In a top-down proteomics approach, proteins are usually separated by one- or two-dimensional liquid chromatography (LC) and identified using high performance MS (5, 6). This approach is very attractive because it allows the identification of protein isoforms arising from various amino acid modifications, genetic variants (e.g. single nucleotide polymorphisms), mRNA splice variants, and multisite modifications (7) (e.g. specific histone modifications) as well as characterization of proteolytic processing events. However, there are several challenges that have limited the broad application of the approach. Typically, intact proteins are less soluble than their peptide complement, which effectively results in greater losses during various stages of sample handling (i.e. limited sensitivity). Similarly, proteins above ∼40–50 kDa in size are more difficult to ionize, detect, and dissociate in most high throughput MS work flows. Additionally, major challenges associated with MS data interpretation and sensitivity, especially for higher molecular mass proteins (>100 kDa) and highly hydrophobic proteins (e.g. integral membrane proteins), remain largely unsolved, thus limiting the applicability of top-down proteomics on a large scale.Bottom-up proteomics approaches have broad application because peptides are easier to separate and analyze via LC coupled with tandem mass spectrometry (MS/MS), offering a basis for more comprehensive protein identification. As this method relies on protein digestion (which produces multiple peptides for each protein), the sample complexity can become exceedingly large, requiring several dimensions of chromatographic separations (e.g. strong cation exchange and/or high pH reversed phase) prior to the final LC separation (typically reversed phase (RP)¹ C₁₈), which is oftentimes directly coupled with the mass spectrometer (3, 8). In general, the bottom-up analysis rarely achieves 100% sequence coverage of the original proteins, which can result in an incorrect/incomplete assessment of protein isoforms and combinatorial PTMs. Additionally, the digested peptides are not detected with uniform efficiency, which challenges and distorts protein quantification efforts.Because the data obtained from top-down and bottom-up work flows are complementary, several attempts have been made to integrate the two strategies (9, 10). Typically, these efforts have utilized extensive fractionation of the intact protein separation followed by bottom-up analysis of the collected fractions. Results so far have encouraged us to consider on-line digestion methods for integrating top-down and bottom-up proteomics in a higher throughput fashion. Such an on-line digestion approach would not only benefit in terms of higher sample throughput and improved overall sensitivity but would also allow a better correlation between the observed intact protein and its peptide digestion products, greatly aiding data analysis and protein characterization efforts.So far, however, none of the on-line integrated methods have proven robust enough for routine high throughput analyses. One of the reasons for this limited success relates to the choice of the proteolytic enzyme used for the bottom-up segment. Trypsin is by far the most widely used enzyme for proteome analyses because it is affordable (relative to other proteases), it has been well characterized for proteome research, and it offers a nice array of detectable peptides due to a fairly even distribution of lysines and arginines across most proteins. However, protein/peptide RPLC separations (optimal at low pH) are fundamentally incompatible with on-line trypsin digestion (optimal at pH ∼ 8) (11, 12). Therefore, on-line coupling of trypsin digestion and RPLC separations is fraught with technological challenges, and proposed solutions (12) have not proven to be robust enough for integration into demanding high throughput platforms.Our approach to this challenge was to investigate alternative proteases that may be more compatible with automated on-line digestion, peptide separation, and MS detection. Pepsin, which is acid-compatible (i.e. it acts in the stomach to initially aid in the digestion of food) (13), is a particularly promising candidate. This protease has previously been successfully used for the targeted analyses of protein complexes, hydrogen/deuterium exchange experiments (14, 15), and characterization of biopharmaceuticals (16, 17). Generally, pepsin preferentially cleaves the peptide bond located on the N-terminal side of hydrophobic amino acids, such as leucine and phenylalanine, although with less specificity than the preferential cleavage observed for trypsin at arginine and lysine. The compatibility of pepsin with typical LC-MS operation makes it an ideal choice for the development of novel approaches combining protein digestion, protein/peptide separation, and MS-based protein/peptide identification.To develop an automated system capable of simultaneously capturing top-down and bottom-up data, enzyme kinetics of the chosen protease must be extremely fast (because one cannot wait hours as is typical when performing off-line proteolysis). Another requirement is the use of immobilized enzyme or a low enough concentration of the enzyme such that autolysis products do not obscure the detection of substrate peptides. The latter was a concern when using pepsin because prior hydrogen/deuterium exchange experiments used enzyme:substrate ratios up to 1:2 (18, 19). To test whether or not such a large concentration of pepsin was necessary, we performed pepsin digestion at ratios of 1:20. Many alternative energy inputs into the system were considered for speeding up the digestion. For instance, it has been shown that an input of ultrasonic energy could accelerate the reaction rate of a typical trypsin digestion while using small amounts of a protease (20). Because ultrasonic energy results in an increase of temperature and microenvironments of high pressure, it has been hypothesized that the higher temperature was the component responsible for the enhanced enzyme activity (21). López-Ferrer et al. (22, 23), however, have demonstrated that application of higher pressure with incorporation of a Barocycler alone can make trypsin display faster enzyme kinetics. This phenomenon can easily be integrated with an LC separation (which already operates at elevated pressure) to enable an automatable ultrarapid on-line digestion LC-MS proteomics platform. Herein, we refer to this platform as the fast on-line digestion system (FOLDS) (23). Although FOLDS has been described before using trypsin, here the system is characterized with pepsin, and the results obtained are compared with results attainable with trypsin. Like trypsin, pepsin produced efficient protein digestion in just a few minutes when placed under pressure. Because of the natural maximal activity of pepsin at low pH, the FOLDS can be incorporated with a RePlay (Advion Biosciences, Ithaca, NY) system, and this powerful combination is what ultimately makes the integration of top-down and bottom-up proteomics analyses possible. The integrated analysis begins with a chromatographic separation of intact proteins. The separated proteins are then split into two streams. One stream proceeds directly to the mass spectrometer for MS and/or tandem MS analysis. The second stream is split into a long capillary where the chromatographic separation of the proteins is maintained, but their arrival to the mass spectrometer for detection is delayed. This is in essence the concept of RePlay (24, 25). Herein, we have taken the RePlay a step further by implementing our FOLDS technology into the second split delayed stream of proteins. While these delayed proteins travel down the long and narrow capillary, we exposed them to pepsin where, in combination with the pressure, the proteins are quickly and reproducibly digested. These peptide fragments are subsequently subjected to MS and/or tandem MS analysis. The FOLDS RePlay system allows the rapid and robust incorporation of the integrated top-down bottom-up proteomics work flow with the ability to not only identify proteins but also to sequence multisite/combinatorial PTMs because all detected peptides (from the FOLDS analysis) are confined to the original chromatographic peak of the protein they were derived from. The analysis of protein mixtures using this integrated strategy reduces the total amount of samples required to obtain both the top-down and bottom-up data, increases throughput, and improves protein sequence coverage.     

Chlamydia trachomatis co-opts GBF1 and CERT to acquire host sphingomyelin for distinct roles during intracellular development

The obligate intracellular pathogen Chlamydia trachomatis replicates within a membrane-bound inclusion that acquires host sphingomyelin (SM), a process that is essential for replication as well as inclusion biogenesis. Previous studies demonstrate that SM is acquired by a Brefeldin A (BFA)-sensitive vesicular trafficking pathway, although paradoxically, this pathway is dispensable for bacterial replication. This finding suggests that other lipid transport mechanisms are involved in the acquisition of host SM. In this work, we interrogated the role of specific components of BFA-sensitive and BFA-insensitive lipid trafficking pathways to define their contribution in SM acquisition during infection. We found that C. trachomatis hijacks components of both vesicular and non-vesicular lipid trafficking pathways for SM acquisition but that the SM obtained from these separate pathways is being utilized by the pathogen in different ways. We show that C. trachomatis selectively co-opts only one of the three known BFA targets, GBF1, a regulator of Arf1-dependent vesicular trafficking within the early secretory pathway for vesicle-mediated SM acquisition. The Arf1/GBF1-dependent pathway of SM acquisition is essential for inclusion membrane growth and stability but is not required for bacterial replication. In contrast, we show that C. trachomatis co-opts CERT, a lipid transfer protein that is a key component in non-vesicular ER to trans-Golgi trafficking of ceramide (the precursor for SM), for C. trachomatis replication. We demonstrate that C. trachomatis recruits CERT, its ER binding partner, VAP-A, and SM synthases, SMS1 and SMS2, to the inclusion and propose that these proteins establish an on-site SM biosynthetic factory at or near the inclusion. We hypothesize that SM acquired by CERT-dependent transport of ceramide and subsequent conversion to SM is necessary for C. trachomatis replication whereas SM acquired by the GBF1-dependent pathway is essential for inclusion growth and stability. Our results reveal a novel mechanism by which an intracellular pathogen redirects SM biosynthesis to its replicative niche.     

ULK1 inhibits the kinase activity of mTORC1 and cell proliferation

ULK1 (Unc51-like kinase, hATG1) is a Ser/Thr kinase that plays a key role in inducing autophagy in response to starvation. ULK1 is phosphorylated and negatively regulated by the mammalian target of rapamycin complex 1 (mTORC1). Previous studies have shown that ULK1 is not only a downstream effector of mTORC1 but also a negative regulator of mTORC1 signaling. ( 1-3) Here, we investigated how ULK1 regulates mTORC1 signaling, and found that ULK1 inhibits the kinase activity of mTORC1 and cell proliferation. Deficiency or knockdown of ULK1 or its homolog ULK2 enhanced mTORC1 signaling, cell proliferation rates and accumulation of cell mass, whereas overexpression of ULK1 had the opposite effect. Knockdown of Atg13, the binding partner of ULK1 and ULK2, mimicked the effects of ULK1 or ULK2 deficiency or knockdown. Both insulin and leucine stimulated mTORC1 signaling to a greater extent when ULK1 or ULK2 was deficient or knocked down. In contrast, Atg5 deficiency did not have a significant effect on mTORC1 signaling and cell proliferation. The stimulatory effect of ULK1 knockdown on mTORC1 signaling occurred even in the absence of tuberous sclerosis complex 2 (TSC2), the negative regulator of mTORC1 signaling. In addition, ULK1 was found to bind raptor, induce its phosphorylation, and inhibit the kinase activity of mTORC1. These results demonstrate that ULK1 negatively regulates the kinase activity of mTORC1 and cell proliferation in a manner independent of Atg5 and TSC2. The inhibition of mTORC1 by ULK1 may be important to coordinately regulate cell growth and autophagy with optimized utilization of cellular energy.     

Surrogate reporters for enrichment of cells with nuclease-induced mutations

Zinc-finger nucleases (ZFNs) and TAL-effector nucleases (TALENs) are powerful tools for creating genetic modifications in eukaryotic cells and organisms. But wild-type and mutant cells that contain genetic modifications induced by these programmable nucleases are often phenotypically indistinguishable, hampering isolation of mutant cells. Here we show that transiently transfected episomal reporters encoding fluorescent proteins can be used as surrogate genes for the efficient enrichment of endogenous gene-modified cells by flow cytometry.     

The use of non-invasive molecular techniques to confirm the presence of mountain bongo <Emphasis Type="Italic">Tragelaphus eurycerus isaaci</Emphasis> populations in Kenya and preliminary inference of their mitochondrial genetic variation

The mountain bongo antelope Tragelaphus eurycerus isaaci has rapidly declined in recent decades, due to a combination of hunting, habitat degradation and disease. Endemic to Kenya, mountain bongo populations have shrunk to approximately 100 individuals now mainly confined to the Aberdares mountain ranges. Indirect observation of bongo signs (e.g. tracks, dung) can be misleading, thus methods to ensure reliable species identification, such as DNA-based techniques, are necessary to effectively study and monitor this species. We assessed bongo presence in four mountain habitats in Kenya (Mount Kenya National Park, Aberdare National Park, Eburu and Mau forests) and carried out a preliminary analysis of genetic variation by examining 466 bp of the first domain of the mtDNA control region using DNA extracted from faecal samples. Of the 201 dung samples collected in the field, 102 samples were molecularly identified as bongo, 97 as waterbuck, one as African buffalo and one as Aders’ duiker. Overall species-identification accuracy by experienced trackers was 64%, with very high error of commission when identifying bongo sign (37%), and high error of omission for waterbuck sign (82%), suggesting that the two species’ signs are easily confused. Despite high variation in the mtDNA control region in most antelope species, our results suggest low genetic variation in mountain bongo as only two haplotypes were detected in 102 samples analyzed. In contrast, the analysis of 63 waterbuck samples from the same sites revealed 21 haplotypes. Nevertheless, further examination using nuclear DNA markers (e.g. microsatellites) in a multi-locus approach is still required, especially because the use of mitochondrial DNA can result in population overestimation as distinct dung samples can potentially be originated from the same individual.     

Genome-wide DNA methylation profiles in noncancerous gastric mucosae with regard to Helicobacter pylori infection and the presence of gastric cancer

Background and Aims: To determine genome‐wide DNA methylation profiles induced by Helicobacter pylori (H. pylori) infection and to identify methylation markers in H. pylori‐induced gastric carcinogenesis. Methods: Gastric mucosae obtained from controls (n = 20) and patients with gastric cancer (n = 28) were included. A wide panel of CpG sites in cancer‐related genes (1505 CpG sites in 807 genes) was analyzed using Illumina bead array technology. Validation of the results of Illumina bead array technique was performed using methylation‐specific PCR method for four genes (MOS, DCC, CRK, and PTPN6). Results: The Illumina bead array showed that a total of 359 CpG sites (269 genes) were identified as differentially methylated by H. pylori infection (p < .0001). The correlation between methylation‐specific PCR and bead array analysis was significant (p < .0001, Spearman coefficient = 0.5054). Methylation profiles in noncancerous gastric mucosae of the patients with gastric cancer showed quite distinct patterns according to the presence or absence of the current H. pylori infection; however, 10 CpG sites were identified to be hypermethylated and three hypomethylated in association with the presence of gastric cancer regardless of H. pylori infection (p < .01). Conclusions: Genome‐wide methylation profiles showed a number of genes differentially methylated by H. pylori infection. Methylation profiles in noncancerous gastric mucosae from the patients with gastric cancer can be affected by H. pylori‐induced gastritis. Differentially methylated CpG sites in this study needs to be validated in a larger population using quantitative methylation‐specific PCR method.     

Automated genome-wide visual profiling of cellular proteins involved in HIV infection

Recent genome-wide RNAi screens have identified >842 human genes that affect the human immunodeficiency virus (HIV) cycle. The list of genes implicated in infection differs between screens, and there is minimal overlap. A reason for this variance is the interdependence of HIV infection and host cell function, producing a multitude of indirect or pleiotropic cellular effects affecting the viral infection during RNAi screening. To overcome this, the authors devised a 15-dimensional phenotypic profile to define the viral infection block induced by CD4 silencing in HeLa cells. They demonstrate that this phenotypic profile excludes nonspecific, RNAi-based side effects and viral replication defects mediated by silencing of housekeeping genes. To achieve statistical robustness, the authors used automatically annotated RNAi arrays for seven independent genome-wide RNAi screens. This identified 56 host genes, which reliably reproduced CD4-like phenotypes upon HIV infection. The factors include 11 known HIV interactors and 45 factors previously not associated with HIV infection. As proof of concept, the authors confirmed that silencing of PAK1, Ku70, and RNAseH2A impaired HIV replication in Jurkat cells. In summary, multidimensional, visual profiling can identify genes required for HIV infection.