Brian D O'Donovan

Given the global surge in autoimmune diseases, it is critical to evaluate emerging therapeutic interventions. Despite numerous new targeted immunomodulatory therapies, comprehensive approaches to apply and evaluate the effects of these treatments longitudinally are lacking. Here, we leveraged advances in programmable-phage immunoprecipitation methodology to explore the modulation, or lack thereof, of autoantibody profiles, proteome-wide, in both health and disease. Using a custom set of over 730,000 human-derived peptides, we demonstrated that each individual, regardless of disease state, possesses a distinct and complex constellation of autoreactive antibodies. For each individual, the set of resulting autoreactivites constituted a unique immunological fingerprint, or “autoreactome,” that was remarkably stable over years. Using the autoreactome as a primary output, we evaluated the relative effectiveness of various immunomodulatory therapies in altering autoantibody repertoires. We found that therapies targeting B cell maturation antigen (BCMA) profoundly altered an individual’s autoreactome, while anti-CD19 and anti-CD20 therapies had minimal effects. These data both confirm that the autoreactome comprises autoantibodies secreted by plasma cells and strongly suggest that BCMA or other plasma cell–targeting therapies may be highly effective in treating currently refractory autoantibody-mediated diseases.

Proteolysis is a major post-translational regulator of biology both inside and outside of cells. Broad identification of optimal cleavage sites and natural substrates of proteases is critical for drug discovery and to understand protease biology. Here we present a method that employs two genetically encoded substrate phage display libraries coupled with next generation sequencing (SPD-NGS) that allows up to 10,000-fold deeper sequence coverage of the typical 6 to 8 residue protease cleavage sites compared to state-of-the-art synthetic peptide libraries or proteomics. We applied SPD-NGS to two classes of proteases, the intracellular caspases 2, 3, 6, 7 and 8, and the ectodomains of the membrane sheddases, ADAMs 10 and 17. The first library (Lib 10AA) was used to determine substrate cleavage motifs. Lib 10AA contains a highly diverse randomized 10-mer substrate peptide sequences (10 ⁹ unique members) that was displayed mono-valently on filamentous phage and bound to magnetic beads via an N-terminal biotin. The protease was allowed to cleave the SPD beads, and the released phage subjected to up to three total rounds of positive selection followed by next generation sequencing (NGS). This allowed us to identify from 10 ⁴ to 10 ⁵ unique cleavage sites over a 1000-fold dynamic range of NGS counts (ranging from 3-4000), and produced consensus and optimal cleavage motifs based positional sequencing scoring matrices that closely matched synthetic peptide data. A second SPD-NGS library (Lib hP) was constructed that allowed us to identify candidate human proteome sequences. Lib hP displayed virtually the entire human proteome tiled in contiguous 49AA sequences with 25AA overlaps (nearly 1 million members). After three rounds of positive selection we identified up to 10 ⁴ natural linear cut sites depending on the protease and captured most of the examples previously identified by proteomics (ranging from 30 to 1500) and predicted 10 to 100-fold more. Structural bioinformatics was used to facilitate the identification of candidate natural protein substrates. SPD-NGS is rapid, reproducible, simple to perform and analyze, inexpensive, renewable, with unprecedented depth of coverage for substrate sequences. SPD-NGS is an important tool for protease biologists interested protease specificity for specific assays and inhibitors and to facilitate identification of natural protein substrates.

Paraneoplastic neurological disorders (PNDs) are immune-mediated diseases of the nervous system understood to manifest as part of a misdirected anti-tumor immune response. Identifying PND-associated autoantibodies and their cognate antigens can assist with proper diagnosis and treatment while also enhancing our understanding of tumor-associated immune processes, triggers for autoimmune disease, and the functional significance of onconeuronal proteins. Here, we employed an enhanced version of phage display immunoprecipitation and sequencing (PhIP-Seq) leveraging a library of over 731,000 unique phage clones tiling across the entire human proteome to detect autoantibodies and create high-resolution epitope profiles in serum and CSF samples from patients suffering from two common PNDs, the anti-Yo (n = 36 patients) and anti-Hu syndromes (n = 44 patients). All patient samples positive for anti-Yo antibody by a validated clinical assay yielded polyspecific enrichment of phage presenting peptides from the canonical anti-Yo (CDR2 and CDR2L) antigens, while 38% of anti-Hu patients (17/44) had a serum and/or CSF sample that significantly enriched peptides deriving from the ELAVL family of proteins, the anti-Hu autoantigenic target. The anti-Hu antibodies showed a remarkably convergent antigenic signature across 15/17 patients corresponding to residues surrounding and including the degenerate motif, RLDxLL, shared by ELAVL2, 3 and 4. Lastly, PhIP-Seq identified several known and novel autoantigens in these same patient samples, representing potential biomarkers that could aid in the diagnosis and prognosis of PND and cancer.

Importance: Identifying infectious causes of subacute and chronic meningitis can be challenging. Enhanced, unbiased diagnostic approaches are needed. Objective: To present a case series of patients with diagnostically challenging subacute and chronic meningitis in whom metagenomic next-generation sequencing (mNGS) of cerebrospinal fluid (CSF), supported by a statistical framework generated from mNGS sequencing of non-infectious patients and environmental controls, identified a pathogen. Design: Case series. Using mNGS data from the CSF of 94 non-infectious neuroinflammatory cases and 24 water and reagent controls, we developed and implemented a weighted scoring metric based on z-scores at the species and genus level for both nucleotide and protein databases to prioritize and rank mNGS results. We performed mNGS on total RNA extracted from CSF of patients with subacute or chronic meningitis and highlight seven cases representing a diverse array of pathogens. Setting: A multi-center study of mNGS pathogen discovery in patients with suspected neuroinflammatory conditions. Participants: Patients with diagnostically challenging subacute or chronic meningitis enrolled in a research study of mNGS performed on CSF. Intervention: mNGS was performed on total RNA extracted from CSF (0.25-0.5 mL). A weighted z-score was used to filter out environmental contaminants and facilitate efficient data triage and analysis. Main Outcomes: 1) Pathogens identified by mNGS and 2) ability of a statistical model to prioritize, rank, and simplify mNGS results. Results: mNGS identified parasitic worms, fungi and viruses in seven subjects: Taenia solium (n=2), Cryptococcus neoformans , human immunodeficiency virus-1, Aspergillus oryzae , Histoplasma capsulatum , and Candida dubliniensis . Evaluating mNGS data with a weighted z-score based scoring algorithm effectively separated bona fide pathogen sequences from spurious environmental sequences. Conclusions and Relevance: mNGS of CSF identified a diversity of microbial pathogens in patients with diagnostically challenging subacute or chronic meningitis, including a case of subarachnoid neurocysticercosis that defied diagnosis for one year, the first case of CNS vasculitis caused by Aspergillus oryzae , and the fourth reported case of Candida dubliniensis meningitis. Filtering metagenomic data with a scoring algorithm greatly clarified data interpretation and highlights the difficulties attributing biological significance to organisms that may be present in control samples used for metagenomic sequencing studies. Key Points: Question: How can metagenomic next-generation sequencing of cerebrospinal fluid be leveraged to aid in the diagnosis of patients with subacute or chronic meningitis? Findings: Metagenomic next-generation sequencing identified parasitic worms, fungi and viruses in a case series of seven subjects. A database of water-only and healthy patient controls enabled application of a z-score based scoring algorithm to effectively separate bona fide pathogen sequences from spurious environmental sequences. Meaning: Our scoring algorithm greatly simplified data interpretation in a series of patients with a wide range of challenging infectious causes of subacute or chronic meningitis identified by metagenomic next-generation sequencing.

RATIONALE: Current microbiologic diagnostics often fail to identify the etiology of lower respiratory tract infections (LRTI) in hematopoietic cellular transplant recipients (HCT), which precludes the implementation of targeted therapies. OBJECTIVES: To address the need for improved LRTI diagnostics, we evaluated the utility of metagenomic next generation sequencing (mNGS) of bronchoalveolar lavage (BAL) to detect microbial pathogens in HCT patients with acute respiratory illnesses. METHODS: We enrolled 22 post-HCT adults ages 19-69 years with acute respiratory illnesses who underwent BAL at the University of Michigan between January 2012 and May 2013. mNGS was performed on BAL fluid to detect microbes and simultaneously assess the host transcriptional response. Results were compared against conventional microbiologic assays. MEASUREMENTS & MAIN RESULTS: mNGS demonstrated 100% sensitivity for detecting respiratory microbes (human metapneumovirus, respiratory syncytial virus, Stenotrophomonas maltophilia , human herpesvirus 6 and cytomegalovirus) when compared to standard testing. Previously unrecognized LRTI pathogens were identified in six patients for whom standard testing was negative (human coronavirus 229E, human rhinovirus A, Corynebacterium propinquum and Streptococcus mitis ); findings were confirmed by independent PCR and 16S rRNA sequencing. Relative to patients without infection, patients with infection had increased expression of immunity related genes (p=0.022) and significantly lower diversity of their respiratory microbiome (p=0.017). CONCLUSIONS: Compared to conventional diagnostics, mNGS enhanced detection of pathogens in BAL fluid from HCT patients. Furthermore, our results suggest that combining unbiased microbial pathogen detection with assessment of host gene biomarkers of immune response may hold promise for enhancing the diagnosis of post-HCT respiratory infections.