Tau PTM Profiles Identify Patient Heterogeneity and Stages of Alzheimer's Disease.

To elucidate the role of Tau isoforms and post-translational modification (PTM) stoichiometry in Alzheimer's disease (AD), we generated a high-resolution quantitative proteomics map of 95 PTMs on multiple isoforms of Tau isolated from postmortem human tissue from 49 AD and 42 control subjects. Although Tau PTM maps reveal heterogeneity across subjects, a subset of PTMs display high occupancy and frequency for AD, suggesting importance in disease. Unsupervised analyses indicate that PTMs occur in an ordered manner, leading to Tau aggregation. The processive addition and minimal set of PTMs associated with seeding activity was further defined by analysis of size-fractionated Tau. To summarize, features in the Tau protein critical for disease intervention at different stages of disease are identified, including enrichment of 0N and 4R isoforms, underrepresentation of the C terminus, an increase in negative charge in the proline-rich region (PRR), and a decrease in positive charge in the microtubule binding domain (MBD).

MassIVE.quant: a community resource of quantitative mass spectrometry-based proteomics datasets.

MassIVE.quant is a repository infrastructure and data resource for reproducible quantitative mass spectrometry-based proteomics, which is compatible with all mass spectrometry data acquisition types and computational analysis tools. A branch structure enables MassIVE.quant to systematically store raw experimental data, metadata of the experimental design, scripts of the quantitative analysis workflow, intermediate input and output files, as well as alternative reanalyses of the same dataset.

Biomarker Candidates for Tumors Identified from Deep-Profiled Plasma Stem Predominantly from the Low Abundant Area.

The plasma proteome has the potential to enable a holistic analysis of the health state of an individual. However, plasma biomarker discovery is difficult due to its high dynamic range and variability. Here, we present a novel automated analytical approach for deep plasma profiling and applied it to a 180-sample cohort of human plasma from lung, breast, colorectal, pancreatic, and prostate cancers. Using a controlled quantitative experiment, we demonstrate a 257% increase in protein identification and a 263% increase in significantly differentially abundant proteins over neat plasma. In the cohort, we identified 2732 proteins. Using machine learning, we discovered biomarker candidates such as STAT3 in colorectal cancer and developed models that classify the diseased state. For pancreatic cancer, a separation by stage was achieved. Importantly, biomarker candidates came predominantly from the low abundance region, demonstrating the necessity to deeply profile because they would have been missed by shallow profiling.

Combining Precursor and Fragment Information for Improved Detection of Differential Abundance in Data Independent Acquisition.

In bottom-up, label-free discovery proteomics, biological samples are acquired in a data-dependent (DDA) or data-independent (DIA) manner, with peptide signals recorded in an intact (MS1) and fragmented (MS2) form. While DDA has only the MS1 space for quantification, DIA contains both MS1 and MS2 at high quantitative quality. DIA profiles of complex biological matrices such as tissues or cells can contain quantitative interferences, and the interferences at the MS1 and the MS2 signals are often independent. When comparing biological conditions, the interferences can compromise the detection of differential peptide or protein abundance and lead to false positive or false negative conclusions.We hypothesized that the combined use of MS1 and MS2 quantitative signals could improve our ability to detect differentially abundant proteins. Therefore, we developed a statistical procedure incorporating both MS1 and MS2 quantitative information of DIA. We benchmarked the performance of the MS1-MS2-combined method to the individual use of MS1 or MS2 in DIA using four previously published controlled mixtures, as well as in two previously unpublished controlled mixtures. In the majority of the comparisons, the combined method outperformed the individual use of MS1 or MS2. This was particularly true for comparisons with low fold changes, few replicates, and situations where MS1 and MS2 were of similar quality. When applied to a previously unpublished investigation of lung cancer, the MS1-MS2-combined method increased the coverage of known activated pathways.Since recent technological developments continue to increase the quality of MS1 signals (e.g. using the BoxCar scan mode for Orbitrap instruments), the combination of the MS1 and MS2 information has a high potential for future statistical analysis of DIA data.

Analysis of 1508 Plasma Samples by Capillary-Flow Data-Independent Acquisition Profiles Proteomics of Weight Loss and Maintenance.

Comprehensive, high throughput analysis of the plasma proteome has the potential to enable holistic analysis of the health state of an individual. Based on our own experience and the evaluation of recent large-scale plasma mass spectrometry (MS) based proteomic studies, we identified two outstanding challenges: slow and delicate nano-flow liquid chromatography (LC) and irreproducibility of identification of data-dependent acquisition (DDA). We determined an optimal solution reducing these limitations with robust capillary-flow data-independent acquisition (DIA) MS. This platform can measure 31 plasma proteomes per day. Using this setup, we acquired a large-scale plasma study of the diet, obesity and genes dietary (DiOGenes) comprising 1508 samples. Proving the robustness, the complete acquisition was achieved on a single analytical column. Totally, 565 proteins (459 identified with two or more peptide sequences) were profiled with 74% data set completeness. On average 408 proteins (5246 peptides) were identified per acquisition (319 proteins in 90% of all acquisitions). The workflow reproducibility was assessed using 34 quality control pools acquired at regular intervals, resulting in 92% data set completeness with CVs for protein measurements of 10.9%.The profiles of 20 apolipoproteins could be profiled revealing distinct changes. The weight loss and weight maintenance resulted in sustained effects on low-grade inflammation, as well as steroid hormone and lipid metabolism, indicating beneficial effects. Comparison to other large-scale plasma weight loss studies demonstrated high robustness and quality of biomarker candidates identified. Tracking of nonenzymatic glycation indicated a delayed, slight reduction of glycation in the weight maintenance phase. Using stable-isotope-references, we could directly and absolutely quantify 60 proteins in the DIA.In conclusion, we present herein the first large-scale plasma DIA study and one of the largest clinical research proteomic studies to date. Application of this fast and robust workflow has great potential to advance biomarker discovery in plasma.

Phosphorylation of ACTN4 Leads to Podocyte Vulnerability and Proteinuric Glomerulosclerosis.

BACKGROUND: Genetic mutations in α-actinin-4 (ACTN4)-an important actin crosslinking cytoskeletal protein that provides structural support for kidney podocytes-have been linked to proteinuric glomerulosclerosis in humans. However, the effect of post-translational modifications of ACTN4 on podocyte integrity and kidney function is not known. METHODS: Using mass spectrometry, we found that ACTN4 is phosphorylated at serine (S) 159 in human podocytes. We used phosphomimetic and nonphosphorylatable ACTN4 to comprehensively study the effects of this phosphorylation in vitro and in vivo. We conducted x-ray crystallography, F-actin binding and bundling assays, and immunofluorescence staining to evaluate F-actin alignment. Microfluidic organ-on-a-chip technology was used to assess for detachment of podocytes simultaneously exposed to fluid flow and cyclic strain. We then used CRISPR/Cas9 to generate mouse models and assessed for renal injury by measuring albuminuria and examining kidney histology. We also performed targeted mass spectrometry to determine whether high extracellular glucose or TGF-ß levels increase phosphorylation of ACTN4. RESULTS: Compared with the wild type ACTN4, phosphomimetic ACTN4 demonstrated increased binding and bundling activity with F-actin in vitro. Phosphomimetic Actn4 mouse podocytes exhibited more spatially correlated F-actin alignment and a higher rate of detachment under mechanical stress. Phosphomimetic Actn4 mice developed proteinuria and glomerulosclerosis after subtotal nephrectomy. Moreover, we found that exposure to high extracellular glucose or TGF-ß stimulates phosphorylation of ACTN4 at S159 in podocytes. CONCLUSIONS: These findings suggest that increased phosphorylation of ACTN4 at S159 leads to biochemical, cellular, and renal pathology that is similar to pathology resulting from human disease-causing mutations in ACTN4. ACTN4 may mediate podocyte injury as a consequence of both genetic mutations and signaling events that modulate phosphorylation.

Systematic Comparison of Strategies for the Enrichment of Lysosomes by Data Independent Acquisition.

In mammalian cells, the lysosome is the main organelle for the degradation of macromolecules and the recycling of their building blocks. Correct lysosomal function is essential, and mutations in every known lysosomal hydrolase result in so-called lysosomal storage disorders, a group of rare and often fatal inherited diseases. Furthermore, it is becoming more and more apparent that lysosomes play also decisive roles in other diseases, such as cancer and common neurodegenerative disorders. This leads to an increasing interest in the proteomic analysis of lysosomes for which enrichment is a prerequisite. In this study, we compared the four most common strategies for the enrichment of lysosomes using data-independent acquisition. We performed centrifugation at 20,000 × g to generate an organelle-enriched pellet, two-step sucrose density gradient centrifugation, enrichment by superparamagnetic iron oxide nanoparticles (SPIONs), and immunoprecipitation using a 3xHA tagged version of the lysosomal membrane protein TMEM192. Our results show that SPIONs and TMEM192 immunoprecipitation outperform the other approaches with enrichment factors of up to 118-fold for certain proteins relative to whole cell lysates. Furthermore, we achieved an increase in identified lysosomal proteins and a higher reproducibility in protein intensities for label-free quantification in comparison to the other strategies.

Comparison of Protein Quantification in a Complex Background by DIA and TMT Workflows with Fixed Instrument Time.

Label-free quantification (LFQ) and isobaric labeling quantification (ILQ) are among the most popular protein quantification workflows in discovery proteomics. Here, we compared the TMT SPS/MS3 10-plex workflow to a label free single shot data-independent acquisition (DIA) workflow on a controlled sample set. The sample set consisted of ten samples derived from 10 biological replicates of mouse cerebelli spiked with the UPS2 protein standard in five different concentrations. For a fair comparison, we matched the instrument time for the two workflows. The LC-MS data were acquired at two facilities to assess interlaboratory reproducibility. Both methods resulted in a high proteome coverage (>5000 proteins) with low missing values on protein level (<2%). The TMT workflow led to 15-20% more identified proteins and a slightly better quantitative precision, whereas the quantitative accuracy was better for the DIA method. The quantitative performance was benchmarked by the number of true positives (UPS2 proteins) within the top 100 candidates. TMT and DIA showed a similar performance. The quantitative performance of the DIA data stayed in a similar range when searching the spectra against a fasta database directly, instead of using a project-specific library. Our experiments also demonstrated that both workflows are readily transferrable between facilities.

Abundance-based classifier for the prediction of mass spectrometric peptide detectability upon enrichment (PPA).

The function of a large percentage of proteins is modulated by post-translational modifications (PTMs). Currently, mass spectrometry (MS) is the only proteome-wide technology that can identify PTMs. Unfortunately, the inability to detect a PTM by MS is not proof that the modification is not present. The detectability of peptides varies significantly making MS potentially blind to a large fraction of peptides. Learning from published algorithms that generally focus on predicting the most detectable peptides we developed a tool that incorporates protein abundance into the peptide prediction algorithm with the aim to determine the detectability of every peptide within a protein. We tested our tool, "Peptide Prediction with Abundance" (PPA), on in-house acquired as well as published data sets from other groups acquired on different instrument platforms. Incorporation of protein abundance into the prediction allows us to assess not only the detectability of all peptides but also whether a peptide of interest is likely to become detectable upon enrichment. We validated the ability of our tool to predict changes in protein detectability with a dilution series of 31 purified proteins at several different concentrations. PPA predicted the concentration dependent peptide detectability in 78% of the cases correctly, demonstrating its utility for predicting the protein enrichment needed to observe a peptide of interest in targeted experiments. This is especially important in the analysis of PTMs. PPA is available as a web-based or executable package that can work with generally applicable defaults or retrained from a pilot MS data set.

MStern Blotting-High Throughput Polyvinylidene Fluoride (PVDF) Membrane-Based Proteomic Sample Preparation for 96-Well Plates.

We describe a 96-well plate compatible membrane-based proteomic sample processing method, which enables the complete processing of 96 samples (or multiples thereof) within a single workday. This method uses a large-pore hydrophobic PVDF membrane that efficiently adsorbs proteins, resulting in fast liquid transfer through the membrane and significantly reduced sample processing times. Low liquid transfer speeds have prevented the useful 96-well plate implementation of FASP as a widely used membrane-based proteomic sample processing method. We validated our approach on whole-cell lysate and urine and cerebrospinal fluid as clinically relevant body fluids. Without compromising peptide and protein identification, our method uses a vacuum manifold and circumvents the need for digest desalting, making our processing method compatible with standard liquid handling robots. In summary, our new method maintains the strengths of FASP and simultaneously overcomes one of the major limitations of FASP without compromising protein identification and quantification.

FLEXITau: Quantifying Post-translational Modifications of Tau Protein in Vitro and in Human Disease.

Tauopathies, including Alzheimer's disease (AD), are associated with the aggregation of modified microtubule associated protein tau. This pathological state of tau is often referred to as "hyperphosphorylated". Due to limitations in technology, an accurate quantitative description of this state is lacking. Here, a mass spectrometry-based assay, FLEXITau, is presented to measure phosphorylation stoichiometry and provide an unbiased quantitative view of the tau post-translational modification (PTM) landscape. The power of this assay is demonstrated by measuring the state of hyperphosphorylation from tau in a cellular model for AD pathology, mapping, and calculating site occupancies for over 20 phosphorylations. We further employ FLEXITau to define the tau PTM landscape present in AD post-mortem brain. As shown in this study, the application of this assay provides mechanistic understanding of tau pathology that could lead to novel therapeutics, and we envision its further use in prognostic and diagnostic approaches for tauopathies.

Comprehensive absolute quantification of the cytosolic proteome of Bacillus subtilis by data independent, parallel fragmentation in liquid chromatography/mass spectrometry (LC/MS(E)).

In the growing field of systems biology, the knowledge of protein concentrations is highly required to truly understand metabolic and adaptational networks within the cells. Therefore we established a workflow relying on long chromatographic separation and mass spectrometric analysis by data independent, parallel fragmentation of all precursor ions at the same time (LC/MS(E)). By prevention of discrimination of co-eluting low and high abundant peptides a high average sequence coverage of 40% could be achieved, resulting in identification of almost half of the predicted cytosolic proteome of the Gram-positive model organism Bacillus subtilis (>1,050 proteins). Absolute quantification was achieved by correlation of average MS signal intensities of the three most intense peptides of a protein to the signal intensity of a spiked standard protein digest. Comparative analysis with heavily labeled peptides (AQUA approach) showed the use of only one standard digest is sufficient for global quantification. The quantification results covered almost four orders of magnitude, ranging roughly from 10 to 150,000 copies per cell. To prove this method for its biological relevance selected physiological aspects of B. subtilis cells grown under conditions requiring either amino acid synthesis or alternatively amino acid degradation were analyzed. This allowed both in particular the validation of the adjustment of protein levels by known regulatory events and in general a perspective of new insights into bacterial physiology. Within new findings the analysis of "protein costs" of cellular processes is extremely important. Such a comprehensive and detailed characterization of cellular protein concentrations based on data independent, parallel fragmentation in liquid chromatography/mass spectrometry (LC/MS(E)) data has been performed for the first time and should pave the way for future comprehensive quantitative characterization of microorganisms as physiological entities.

Advancing Urinary Protein Biomarker Discovery by Data-Independent Acquisition on a Quadrupole-Orbitrap Mass Spectrometer.

The promises of data-independent acquisition (DIA) strategies are a comprehensive and reproducible digital qualitative and quantitative record of the proteins present in a sample. We developed a fast and robust DIA method for comprehensive mapping of the urinary proteome that enables large scale urine proteomics studies. Compared to a data-dependent acquisition (DDA) experiments, our DIA assay doubled the number of identified peptides and proteins per sample at half the coefficients of variation observed for DDA data (DIA = ∼8%; DDA = ∼16%). We also tested different spectral libraries and their effects on overall protein and peptide identifications and their reproducibilities, which provided clear evidence that sample type-specific spectral libraries are preferred for reliable data analysis. To show applicability for biomarker discovery experiments, we analyzed a sample set of 87 urine samples from children seen in the emergency department with abdominal pain. The whole set was analyzed with high proteome coverage (∼1300 proteins/sample) in less than 4 days. The data set revealed excellent biomarker candidates for ovarian cyst and urinary tract infection. The improved throughput and quantitative performance of our optimized DIA workflow allow for the efficient simultaneous discovery and verification of biomarker candidates without the requirement for an early bias toward selected proteins.

Quantitative prediction of genome-wide resource allocation in bacteria.

Predicting resource allocation between cell processes is the primary step towards decoding the evolutionary constraints governing bacterial growth under various conditions. Quantitative prediction at genome-scale remains a computational challenge as current methods are limited by the tractability of the problem or by simplifying hypotheses. Here, we show that the constraint-based modeling method Resource Balance Analysis (RBA), calibrated using genome-wide absolute protein quantification data, accurately predicts resource allocation in the model bacterium Bacillus subtilis for a wide range of growth conditions. The regulation of most cellular processes is consistent with the objective of growth rate maximization except for a few suboptimal processes which likely integrate more complex objectives such as coping with stressful conditions and survival. As a proof of principle by using simulations, we illustrated how calibrated RBA could aid rational design of strains for maximizing protein production, offering new opportunities to investigate design principles in prokaryotes and to exploit them for biotechnological applications.

SweetSEQer, simple de novo filtering and annotation of glycoconjugate mass spectra.

The past 15 years have seen significant progress in LC-MS/MS peptide sequencing, including the advent of successful de novo and database search methods; however, analysis of glycopeptide and, more generally, glycoconjugate spectra remains a much more open problem, and much annotation is still performed manually. This is partly because glycans, unlike peptides, need not be linear chains and are instead described by trees. In this study, we introduce SweetSEQer, an extremely simple open source tool for identifying potential glycopeptide MS/MS spectra. We evaluate SweetSEQer on manually curated glycoconjugate spectra and on negative controls, and we demonstrate high quality filtering that can be easily improved for specific applications. We also demonstrate a high overlap between peaks annotated by experts and peaks annotated by SweetSEQer, as well as demonstrate inferred glycan graphs consistent with canonical glycan tree motifs. This study presents a novel tool for annotating spectra and producing glycan graphs from LC-MS/MS spectra. The tool is evaluated and shown to perform similarly to an expert on manually curated data.

An exclusion list based label-free proteome quantification approach using an LTQ Orbitrap.

RATIONALE: Label-based mass spectrometry is a powerful tool for large-scale protein identification and quantification. However, it requires the chemical or metabolic incorporation of the labeled compound(s) which can be difficult to attain, e.g. for non-cultivable organisms or scarce sample, such as biopsies. Therefore, we set out to develop and validate an efficient label-free liquid chromatography/tandem mass spectrometry (LC/MS/MS) workflow based on optimized instrument settings and incremental exclusion lists. METHODS: To increase the number of quantified peptides an incremental exclusion list was incorporated along with optimized instrument settings for the used LTQ Orbitrap. As a proof of concept, label-free quantification data from this optimized approach were compared to the results of control measurements without exclusion lists and of an in vivo metabolic labeling GeLC/MS/MS experiment. The data were drawn from Staphylococcus aureus whole cell lysates of non-stressed and nitric oxide (NO)-stressed cells. RESULTS: Compared to MS analysis without exclusion lists the new approach resulted in an increased number of identified peptides, enabling label-free quantification of more than 990 S. aureus proteins. With respect to the number of quantified proteins and differences in protein levels between the control and NO-treated samples the results of the new method were consistent with those of the GeLC/MS/MS experiment. CONCLUSIONS: The application of exclusion lists and optimized instrument settings in LC/MS/MS analysis significantly enhances the sensitivity and resolution of label-free protein identification and quantification. Therefore, the new workflow is a powerful alternative to label-based quantification methods.

Different syngeneic tumors show distinctive intrinsic tumor-immunity and mechanisms of actions (MOA) of anti-PD-1 treatment.

Cancers are immunologically heterogeneous. A range of immunotherapies target abnormal tumor immunity via different mechanisms of actions (MOAs), particularly various tumor-infiltrate leukocytes (TILs). We modeled loss of function (LOF) in four common anti-PD-1 antibody-responsive syngeneic tumors, MC38, Hepa1-6, CT-26 and EMT-6, by systematical depleting a series of TIL lineages to explore the mechanisms of tumor immunity and treatment. CD8+-T-cells, CD4+-T-cells, Treg, NK cells and macrophages were individually depleted through either direct administration of anti-marker antibodies/reagents or using DTR (diphtheria toxin receptor) knock-in mice, for some syngeneic tumors, where specific subsets were depleted following diphtheria toxin (DT) administration. These LOF experiments revealed distinctive intrinsic tumor immunity and thus different MOAs in their responses to anti-PD-1 antibody among different syngeneic tumors. Specifically, the intrinsic tumor immunity and the associated anti-PD-1 MOA were predominately driven by CD8+ cytotoxic TILs (CTL) in all syngeneic tumors, excluding Hepa1-6 where CD4+ Teff TILs played a key role. TIL-Treg also played a critical role in supporting tumor growth in all four syngeneic models as well as M2-macrophages. Pathway analysis using pharmacodynamic readouts of immuno-genomics and proteomics on MC38 and Hepa1-6 also revealed defined, but distinctive, immune pathways of activation and suppression between the two, closely associated with the efficacy and consistent with TIL-pharmacodynamic readouts. Understanding tumor immune-pathogenesis and treatment MOAs in the different syngeneic animal models, not only assists the selection of the right model for evaluating new immunotherapy of a given MOA, but also can potentially help to understand the potential disease mechanisms and strategize optimal immune-therapies in patients.

Urine Proteomics for Noninvasive Monitoring of Biomarkers in Bronchopulmonary Dysplasia.

INTRODUCTION: Current techniques to diagnose and/or monitor critically ill neonates with bronchopulmonary dysplasia (BPD) require invasive sampling of body fluids, which is suboptimal in these frail neonates. We tested our hypothesis that it is feasible to use noninvasively collected urine samples for proteomics from extremely low gestational age newborns (ELGANs) at risk for BPD to confirm previously identified proteins and biomarkers associated with BPD. METHODS: We developed a robust high-throughput urine proteomics methodology that requires only 50 µL of urine. We utilized the methodology with a proof-of-concept study validating proteins previously identified in invasively collected sample types such as blood and/or tracheal aspirates on urine collected within 72 h of birth from ELGANs (gestational age [26 ± 1.2] weeks) who were admitted to a single Neonatal Intensive Care Unit (NICU), half of whom eventually developed BPD (n = 21), while the other half served as controls (n = 21). RESULTS: Our high-throughput urine proteomics approach clearly identified several BPD-associated changes in the urine proteome recapitulating expected blood proteome changes, and several urinary proteins predicted BPD risk. Interestingly, 16 of the identified urinary proteins are known targets of drugs approved by the Food and Drug Administration. CONCLUSION: In addition to validating numerous proteins, previously found in invasively collected blood, tracheal aspirate, and bronchoalveolar lavage, that have been implicated in BPD pathophysiology, urine proteomics also suggested novel potential therapeutic targets. Ease of access to urine could allow for sequential proteomic evaluations for longitudinal monitoring of disease progression and impact of therapeutic intervention in future studies.

Global, in situ analysis of the structural proteome in individuals with Parkinson's disease to identify a new class of biomarker.

Parkinson's disease (PD) is a prevalent neurodegenerative disease for which robust biomarkers are needed. Because protein structure reflects function, we tested whether global, in situ analysis of protein structural changes provides insight into PD pathophysiology and could inform a new concept of structural disease biomarkers. Using limited proteolysis-mass spectrometry (LiP-MS), we identified 76 structurally altered proteins in cerebrospinal fluid (CSF) of individuals with PD relative to healthy donors. These proteins were enriched in processes misregulated in PD, and some proteins also showed structural changes in PD brain samples. CSF protein structural information outperformed abundance information in discriminating between healthy participants and those with PD and improved the discriminatory performance of CSF measures of the hallmark PD protein α-synuclein. We also present the first analysis of inter-individual variability of a structural proteome in healthy individuals, identifying biophysical features of variable protein regions. Although independent validation is needed, our data suggest that global analyses of the human structural proteome will guide the development of novel structural biomarkers of disease and enable hypothesis generation about underlying disease processes.

The dynamic protein partnership of RNA polymerase in Bacillus subtilis.

In prokaryotes, transcription results from the activity of a 400 kDa RNA polymerase (RNAP) protein complex composed of at least five subunits (2α, ß, ß', ω). To ensure adequate responses to changing environmental cues, RNAP activity is tightly controlled by means of interacting regulatory proteins. Here, we report the affinity-purification of the Bacillus subtilis RNAP complexes from cells in different growth states and stress conditions, and the quantitative assessment by mass spectrometry of the dynamic changes in the composition of the RNAP complex. The stoichiometry of RNA polymerase was determined by a comparison of two mass spectrometry-based quantification methods: a label-based and a label-free method. The validated label-free method was then used to quantify the proteins associated with RNAP. The levels of sigma factors bound to RNAP varied during growth and exposure to stress. Elongation factors, helicases such as HelD and PcrA, and novel unknown proteins were also associated with RNAP complexes. The content in 6S RNAs of purified RNAP complexes increased at the onset of the stationary phase. These quantitative variations in the protein and RNA composition of the RNAP complexes well correlate with the known physiology of B. subtilis cells under different conditions.