Simplifying MS1 and MS2 spectra to achieve lower mass error, more dynamic range, and higher peptide identification confidence on the Bruker timsTOF Pro.

For bottom-up proteomic analysis, the goal of analytical pipelines that process the raw output of mass spectrometers is to detect, characterise, identify, and quantify peptides. The initial steps of detecting and characterising features in raw data must overcome some considerable challenges. The data presents as a sparse array, sometimes containing billions of intensity readings over time. These points represent both signal and chemical or electrical noise. Depending on the biological sample's complexity, tens to hundreds of thousands of peptides may be present in this vast data landscape. For ion mobility-based LC-MS analysis, each peptide is comprised of a grouping of hundreds of single intensity readings in three dimensions: mass-over-charge (m/z), mobility, and retention time. There is no inherent information about any associations between individual points; whether they represent a peptide or noise must be inferred from their structure. Peptides each have multiple isotopes, different charge states, and a dynamic range of intensity of over six orders of magnitude. Due to the high complexity of most biological samples, peptides often overlap in time and mobility, making it very difficult to tease apart isotopic peaks, to apportion the intensity of each and the contribution of each isotope to the determination of the peptide's monoisotopic mass, which is critical for the peptide's identification. Here we describe four algorithms for the Bruker timsTOF Pro that each play an important role in finding peptide features and determining their characteristics. These algorithms focus on separate characteristics that determine how candidate features are detected in the raw data. The first two algorithms deal with the complexity of the raw data, rapidly clustering raw data into spectra that allows isotopic peaks to be resolved. The third algorithm compensates for saturation of the instrument's detector thereby recovering lost dynamic range, and lastly, the fourth algorithm increases confidence of peptide identifications by simplification of the fragment spectra. These algorithms are effective in processing raw data to detect features and extracting the attributes required for peptide identification, and make an important contribution to an analytical pipeline by detecting features that are higher quality and better segmented from other peptides in close proximity. The software has been developed in Python using Numpy and Pandas and made freely available with an open-source MIT license to facilitate experimentation and further improvement (DOI 10.5281/zenodo.6513126). Data are available via ProteomeXchange with identifier PXD030706.

Person-Specific Biomolecular Coronas Modulate Nanoparticle Interactions with Immune Cells in Human Blood.

When nanoparticles interact with human blood, a multitude of plasma components adsorb onto the surface of the nanoparticles, forming a biomolecular corona. Corona composition is known to be influenced by the chemical composition of nanoparticles. In contrast, the possible effects of variations in the human blood proteome between healthy individuals on the formation of the corona and its subsequent interactions with immune cells in blood are unknown. Herein, we prepared and examined a matrix of 11 particles (including organic and inorganic particles of three sizes and five surface chemistries) and plasma samples from 23 healthy donors to form donor-specific biomolecular coronas (personalized coronas) and investigated the impact of the personalized coronas on particle interactions with immune cells in human blood. Among the particles examined, poly(ethylene glycol) (PEG)-coated mesoporous silica (MS) particles, irrespective of particle size (800, 450, or 100 nm in diameter), displayed the widest range (up to 60-fold difference) of donor-dependent variance in immune cell association. In contrast, PEG particles (after MS core removal) of 860, 518, or 133 nm in diameter displayed consistent stealth behavior (negligible cell association), irrespective of plasma donor. For comparison, clinically relevant PEGylated doxorubicin-encapsulated liposomes (Doxil) (74 nm in diameter) showed significant variance in association with monocytes and B cells across all plasma donors studied. An in-depth proteomic analysis of each biomolecular corona studied was performed, and the results were compared against the nanoparticle-blood cell association results, with individual variance in the proteome driving differential association with specific immune cell types. We identified key immunoglobulin and complement proteins that explicitly enriched or depleted within the corona and which strongly correlated with the cell association pattern observed across the 23 donors. This study demonstrates how plasma variance in healthy individuals significantly influences the blood immune cell interactions of nanoparticles.

A missense mutation in the MLKL brace region promotes lethal neonatal inflammation and hematopoietic dysfunction.

MLKL is the essential effector of necroptosis, a form of programmed lytic cell death. We have isolated a mouse strain with a single missense mutation, MlklD139V, that alters the two-helix 'brace' that connects the killer four-helix bundle and regulatory pseudokinase domains. This confers constitutive, RIPK3 independent killing activity to MLKL. Homozygous mutant mice develop lethal postnatal inflammation of the salivary glands and mediastinum. The normal embryonic development of MlklD139V homozygotes until birth, and the absence of any overt phenotype in heterozygotes provides important in vivo precedent for the capacity of cells to clear activated MLKL. These observations offer an important insight into the potential disease-modulating roles of three common human MLKL polymorphisms that encode amino acid substitutions within or adjacent to the brace region. Compound heterozygosity of these variants is found at up to 12-fold the expected frequency in patients that suffer from a pediatric autoinflammatory disease, chronic recurrent multifocal osteomyelitis (CRMO).

TRAF2 regulates TNF and NF-κB signalling to suppress apoptosis and skin inflammation independently of Sphingosine kinase 1.

TRAF2 is a component of TNF superfamily signalling complexes and plays an essential role in the regulation and homeostasis of immune cells. TRAF2 deficient mice die around birth, therefore its role in adult tissues is not well-explored. Furthermore, the role of the TRAF2 RING is controversial. It has been claimed that the atypical TRAF2 RING cannot function as a ubiquitin E3 ligase but counterclaimed that TRAF2 RING requires a co-factor, sphingosine-1-phosphate, that is generated by the enzyme sphingosine kinase 1, to function as an E3 ligase. Keratinocyte-specific deletion of Traf2, but not Sphk1 deficiency, disrupted TNF mediated NF-κB and MAP kinase signalling and caused epidermal hyperplasia and psoriatic skin inflammation. This inflammation was driven by TNF, cell death, non-canonical NF-κB and the adaptive immune system, and might therefore represent a clinically relevant model of psoriasis. TRAF2 therefore has essential tissue specific functions that do not overlap with those of Sphk1.

Activation of the pseudokinase MLKL unleashes the four-helix bundle domain to induce membrane localization and necroptotic cell death.

Necroptosis is considered to be complementary to the classical caspase-dependent programmed cell death pathway, apoptosis. The pseudokinase Mixed Lineage Kinase Domain-Like (MLKL) is an essential effector protein in the necroptotic cell death pathway downstream of the protein kinase Receptor Interacting Protein Kinase-3 (RIPK3). How MLKL causes cell death is unclear, however RIPK3-mediated phosphorylation of the activation loop in MLKL trips a molecular switch to induce necroptotic cell death. Here, we show that the MLKL pseudokinase domain acts as a latch to restrain the N-terminal four-helix bundle (4HB) domain and that unleashing this domain results in formation of a high-molecular-weight, membrane-localized complex and cell death. Using alanine-scanning mutagenesis, we identified two clusters of residues on opposing faces of the 4HB domain that were required for the 4HB domain to kill cells. The integrity of one cluster was essential for membrane localization, whereas MLKL mutations in the other cluster did not prevent membrane translocation but prevented killing; this demonstrates that membrane localization is necessary, but insufficient, to induce cell death. Finally, we identified a small molecule that binds the nucleotide binding site within the MLKL pseudokinase domain and retards MLKL translocation to membranes, thereby preventing necroptosis. This inhibitor provides a novel tool to investigate necroptosis and demonstrates the feasibility of using small molecules to target the nucleotide binding site of pseudokinases to modulate signal transduction.

RIPK1 regulates RIPK3-MLKL-driven systemic inflammation and emergency hematopoiesis.

Upon ligand binding, RIPK1 is recruited to tumor necrosis factor receptor superfamily (TNFRSF) and Toll-like receptor (TLR) complexes promoting prosurvival and inflammatory signaling. RIPK1 also directly regulates caspase-8-mediated apoptosis or, if caspase-8 activity is blocked, RIPK3-MLKL-dependent necroptosis. We show that C57BL/6 Ripk1(-/-) mice die at birth of systemic inflammation that was not transferable by the hematopoietic compartment. However, Ripk1(-/-) progenitors failed to engraft lethally irradiated hosts properly. Blocking TNF reversed this defect in emergency hematopoiesis but, surprisingly, Tnfr1 deficiency did not prevent inflammation in Ripk1(-/-) neonates. Deletion of Ripk3 or Mlkl, but not Casp8, prevented extracellular release of the necroptotic DAMP, IL-33, and reduced Myd88-dependent inflammation. Reduced inflammation in the Ripk1(-/-)Ripk3(-/-), Ripk1(-/-)Mlkl(-/-), and Ripk1(-/-)Myd88(-/-) mice prevented neonatal lethality, but only Ripk1(-/-)Ripk3(-/-)Casp8(-/-) mice survived past weaning. These results reveal a key function for RIPK1 in inhibiting necroptosis and, thereby, a role in limiting, not only promoting, inflammation.

Modification of PATase by L/F-transferase generates a ClpS-dependent N-end rule substrate in Escherichia coli.

The N-end rule pathway is conserved from bacteria to man and determines the half-life of a protein based on its N-terminal amino acid. In Escherichia coli, model substrates bearing an N-degron are recognised by ClpS and degraded by ClpAP in an ATP-dependent manner. Here, we report the isolation of 23 ClpS-interacting proteins from E. coli. Our data show that at least one of these interacting proteins--putrescine aminotransferase (PATase)--is post-translationally modified to generate a primary N-degron. Remarkably, the N-terminal modification of PATase is generated by a new specificity of leucyl/phenylalanyl-tRNA-protein transferase (LFTR), in which various combinations of primary destabilising residues (Leu and Phe) are attached to the N-terminal Met. This modification (of PATase), by LFTR, is essential not only for its recognition by ClpS, but also determines the stability of the protein in vivo. Thus, the N-end rule pathway, through the ClpAPS-mediated turnover of PATase may have an important function in putrescine homeostasis. In addition, we have identified a new element within the N-degron, which is required for substrate delivery to ClpA.

Structural basis of N-end rule substrate recognition in Escherichia coli by the ClpAP adaptor protein ClpS.

In Escherichia coli, the ClpAP protease, together with the adaptor protein ClpS, is responsible for the degradation of proteins bearing an amino-terminal destabilizing amino acid (N-degron). Here, we determined the three-dimensional structures of ClpS in complex with three peptides, each having a different destabilizing residue--Leu, Phe or Trp--at its N terminus. All peptides, regardless of the identity of their N-terminal residue, are bound in a surface pocket on ClpS in a stereo-specific manner. Several highly conserved residues in this binding pocket interact directly with the backbone of the N-degron peptide and hence are crucial for the binding of all N-degrons. By contrast, two hydrophobic residues define the volume of the binding pocket and influence the specificity of ClpS. Taken together, our data suggest that ClpS has been optimized for the binding and delivery of N-degrons containing an N-terminal Phe or Leu.

Conserved residues in the N-domain of the AAA+ chaperone ClpA regulate substrate recognition and unfolding.

Protein degradation in the cytosol of Escherichia coli is carried out by a variety of different proteolytic machines, including ClpAP. The ClpA component is a hexameric AAA+ (ATPase associated with various cellular activities) chaperone that utilizes the energy of ATP to control substrate recognition and unfolding. The precise role of the N-domains of ClpA in this process, however, remains elusive. Here, we have analysed the role of five highly conserved basic residues in the N-domain of ClpA by monitoring the binding, unfolding and degradation of several different substrates, including short unstructured peptides, tagged and untagged proteins. Interestingly, mutation of three of these basic residues within the N-domain of ClpA (H94, R86 and R100) did not alter substrate degradation. In contrast mutation of two conserved arginine residues (R90 and R131), flanking a putative peptide-binding groove within the N-domain of ClpA, specifically compromised the ability of ClpA to unfold and degrade selected substrates but did not prevent substrate recognition, ClpS-mediated substrate delivery or ClpP binding. In contrast, a highly conserved tyrosine residue lining the central pore of the ClpA hexamer was essential for the degradation of all substrate types analysed, including both folded and unstructured proteins. Taken together, these data suggest that ClpA utilizes two structural elements, one in the N-domain and the other in the pore of the hexamer, both of which are required for efficient unfolding of some protein substrates.