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Citrullination is an important post-translational modification implicated in many diseases including rheumatoid arthritis (RA), Alzheimer’s disease, and cancer. Neutrophil and mast cells have different expression profiles for protein-arginine deiminases (PADs), and ionomycin-induced activation makes them an ideal cellular model to study proteins susceptible to citrullination. We performed high-resolution mass spectrometry and stringent data filtration to identify citrullination sites in neutrophil and mast cells treated with and without ionomycin. We identified a total of 833 validated citrullination sites on 395 proteins. Several of these citrullinated proteins are important components of pathways involved in innate immune responses. Using this benchmark primary sequence data set, we developed machine learning models to predict citrullination in neutrophil and mast cell proteins. We show that our models predict citrullination likelihood with 0.735 and 0.766 AUCs (area under the receiver operating characteristic curves), respectively, on independent validation sets. In summary, this study provides the largest number of validated citrullination sites in neutrophil and mast cell proteins. The use of our novel motif analysis approach to predict citrullination sites will facilitate the discovery of novel protein substrates of protein-arginine deiminases (PADs), which may be key to understanding immunopathologies of various diseases.

There is an urgent need for robust and high-throughput methods for SARS-CoV-2 detection in suspected patient samples to facilitate disease management, surveillance, and control. Although nucleic acid detection methods such as reverse transcription polymerase chain reaction (RT-PCR) are the gold standard, during the current pandemic, the deployment of RT-PCR tests has been extremely slow, and key reagents such as PCR primers and RNA extraction kits are at critical shortages. Rapid point-of-care viral antigen detection methods have been previously employed for the diagnosis of respiratory viruses such as influenza and respiratory syncytial viruses. Therefore, the direct detection of SARS-CoV-2 viral antigens in patient samples could also be used for diagnosis of active infection, and alternative methodologies for specific and sensitive viral protein detection should be explored. Targeted mass spectrometry techniques have enabled the identification and quantitation of a defined subset of proteins/peptides at single amino acid resolution with attomole level sensitivity and high reproducibility. Herein, we report a targeted mass spectrometry assay for the detection of SARS-CoV-2 spike protein and nucleoprotein in a relevant biological matrix. Recombinant full-length spike protein and nucleoprotein were digested and proteotypic peptides were selected for parallel reaction monitoring (PRM) quantitation using a high-resolution Orbitrap instrument. A spectral library, which contained seven proteotypic peptides (four from spike protein and three from nucleoprotein) and the top three to four transitions, was generated and evaluated. From the original spectral library, we selected two best performing peptides for the final PRM assay. The assay was evaluated using mock test samples containing inactivated SARS-CoV-2 virions, added to in vitro derived mucus. The PRM assay provided a limit of detection of ∼200 attomoles and a limit of quantitation of ∼ 390 attomoles. Extrapolating from the test samples, the projected titer of virus particles necessary for the detection of SARS-CoV-2 spike and nucleoprotein detection was approximately 2 × 105viral particles/mL, making it an attractive alternative to RT-PCR assays. Potentially, mass spectrometry-based methods for viral antigen detection may deliver higher throughput and could serve as a complementary diagnostic tool to RT-PCR. Furthermore, this assay could be used to evaluate the presence of SARS-CoV-2 in archived or recently collected biological fluids, in vitro-derived research materials, and wastewater samples.

The synthesis and inhibitory potencies against botulinum neurotoxin serotype A light chain (BoNT/A LC) using in vitro HPLC based enzymatic assay for various steroidal, benzothiophene, thiophene, and adamantane 4-aminoquinoline derivatives are described. In addition, the compounds were evaluated for the activity against BoNT/A holotoxin in mouse embryonic stem cell derived motor neurons. Steroidal derivative 16showed remarkable protection (up to 89% of uncleaved SNAP-25) even when administered 30 min postintoxication. This appears to be the first example of LC inhibitors antagonizing BoNT intoxication in mouse embryonic stem cell derived motor neurons (mES-MNs) in a postexposure model. Oral administration of 16was well tolerated in the mouse up to 600 mg/kg, q.d. Although adequate unbound drug levels were not achieved at this dose, the favorable in vitro ADMET results strongly support further work in this series.

Introduction:A long‐term goal of our group is to develop proteomic‐based approaches to the detection and use of protein biomarkers for improvement in diagnosis, prognosis, and tailoring of treatment for head and neck squamous cell cancer (HNSCC). We have previously demonstrated that protein expression profiling of serum can identify multiple protein biomarker events that can serve as molecular fingerprints for the assessment of HNSCC disease state and prognosis.

Initial clinically oriented applications of emerging proteomic technologies that aim to identify biomarkers for head and neck squamous cell carcinoma diagnostics have yielded promising results. The development of new proteomic diagnostics remains critical for the early detection of head and neck squamous cell carcinoma at more treatable stages. Prognostic markers for disease recurrence and treatment sensitivities are also required. In this overview of current biomarker identification strategies for head and neck squamous cell carcinoma, different combinations of mass spectrometry platforms, laser capture microscopy and 2D gel electrophoresis procedures are summarized as applied to readily available clinical specimens (tissue, blood and saliva). Issues related to assay reproducibility, management of large data sets and future improvements in clinical proteomics are also addressed.

The distribution of dissolved DNA concentrations and some microbial variables were compared in an oligo-mesotrophic river (the Crystal River) and a phosphate-rich eutrophic river (the Alafia River) in Southwest Florida over a 15 month period. Concentrations of phosphate and nitrate in the Alafia River averaged 135 and 18.2 times the respective phosphate and nitrate concentrations of the oligo-mesotrophic Crystal River. The seasonal average dissolved DNA concentration for the Alafia River exceeded that of the Crystal River by a factor of 1.8 (8.2 µg 1-1 compared to 4.6 µg 1-1, respectively). The greatest concentrations of dissolved DNA in the Alafia River were found in areas that contained the largest populations of phytoplankton and bacteria (a reservoir formed from an abandoned phosphate mining pit and two downstream stations near the mouth of the river). Differences in dissolved DNA concentrations between these environments and more pristine environments (i.e. all Crystal River Stations and upstream Alafia River stations) were of the same order of magnitude (1.8 to 2.2-fold) as the differences in bacterial abundance and activity, but considerably less than differences in phytoplankton abundance and activity between such environments. Seasonal variations in dissolved DNA concentrations in the Crystal River corresponded to seasonal variations in microbial populations, with minimal values in January and greater values in July. In the Alafia River, lowest concentrations for dissolved DNA occurred in July during the wet season, when seasonal flooding of area of leaf litter yielded high levels of dissolved organic carbon (DOC) which were low in dissolved DNA. These results suggest that: 1) in situ planktonic activity is a greater source of dissolved DNA than allochthonous or terrestrial sources of DOC; 2) factors that control the magnitude of heterotrophic bacterial populations are more likely to control dissolved DNA levels than factors regulating autotrophic population activity and abundance; 3) differences in dissolved DNA between eutrophic and oligo-mesotrophic environments are often much smaller than the differences in nutrient concentration between such environments.