Ultra-deep and quantitative saliva proteome reveals dynamics of the oral microbiome

Background: The oral cavity is home to one of the most diverse microbial communities of the human body and a major entry portal for pathogens. Its homeostasis is maintained by saliva, which fulfills key functions including lubrication of food, pre-digestion, and bacterial defense. Consequently, disruptions in saliva secretion and changes in the oral microbiome contribute to conditions such as tooth decay and respiratory tract infections. Here we set out to quantitatively map the saliva proteome in great depth with a rapid and in-depth mass spectrometry-based proteomics workflow. Methods: We used recent improvements in mass spectrometry (MS)-based proteomics to develop a rapid workflow for mapping the saliva proteome quantitatively and at great depth. Standard clinical cotton swabs were used to collect saliva form eight healthy individuals at two different time points, allowing us to study interindividual differences and interday changes of the saliva proteome. To accurately identify microbial proteins, we developed a method called "split by taxonomy id" that prevents peptides shared by humans and bacteria or between different bacterial phyla to contribute to protein identification. Results: Microgram protein amounts retrieved from cotton swabs resulted in more than 3700 quantified human proteins in 100-min gradients or 5500 proteins after simple fractionation. Remarkably, our measurements also quantified more than 2000 microbial proteins from 50 bacterial genera. Co-analysis of the proteomics results with next-generation sequencing data from the Human Microbiome Project as well as a comparison to MALDI-TOF mass spectrometry on microbial cultures revealed strong agreement. The oral microbiome differs between individuals and changes drastically upon eating and tooth brushing. Conclusion: Rapid shotgun and robust technology can now simultaneously characterize the human and microbiome contributions to the proteome of a body fluid and is therefore a valuable complement to genomic studies. This opens new frontiers for the study of host-pathogen interactions and clinical saliva diagnostics

MaxDIA enables library-based and library-free data-independent acquisition proteomics

MaxDIA is a software platform for analyzing data-independent acquisition (DIA) proteomics data within the MaxQuant software environment. Using spectral libraries, MaxDIA achieves deep proteome coverage with substantially better coefficients of variation in protein quantification than other software. MaxDIA is equipped with accurate false discovery rate (FDR) estimates on both library-to-DIA match and protein levels, including when using whole-proteome predicted spectral libraries. This is the foundation of discovery DIA—hypothesis-free analysis of DIA samples without library and with reliable FDR control. MaxDIA performs three- or four-dimensional feature detection of fragment data, and scoring of matches is augmented by machine learning on the features of an identification. MaxDIA’s bootstrap DIA workflow performs multiple rounds of matching with increasing quality of recalibration and stringency of matching to the library. Combining MaxDIA with two new technologies—BoxCar acquisition and trapped ion mobility spectrometry—both lead to deep and accurate proteome quantification.publishedVersio

Direct identification of clinically relevant neoepitopes presented on native human melanoma tissue by mass spectrometry

Although mutations may represent attractive targets for immunotherapy, direct identification of mutated peptide ligands isolated from human leucocyte antigens (HLA) on the surface of native tumour tissue has so far not been successful. Using advanced mass spectrometry (MS) analysis, we survey the melanoma-associated immunopeptidome to a depth of 95,500 patient-presented peptides. We thereby discover a large spectrum of attractive target antigen candidates including cancer testis antigens and phosphopeptides. Most importantly, we identify peptide ligands presented on native tumour tissue samples harbouring somatic mutations. Four of eleven mutated ligands prove to be immunogenic by neoantigen-specific T-cell responses. Moreover, tumour-reactive T cells with specificity for selected neoantigens identified by MS are detected in the patient's tumour and peripheral blood. We conclude that direct identification of mutated peptide ligands from primary tumour material by MS is possible and yields true neoepitopes with high relevance for immunotherapeutic strategies in cancer

Ultra-deep and quantitative saliva proteome reveals dynamics of the oral microbiome

Background: The oral cavity is home to one of the most diverse microbial communities of the human body and a major entry portal for pathogens. Its homeostasis is maintained by saliva, which fulfills key functions including lubrication of food, pre-digestion, and bacterial defense. Consequently, disruptions in saliva secretion and changes in the oral microbiome contribute to conditions such as tooth decay and respiratory tract infections. Here we set out to quantitatively map the saliva proteome in great depth with a rapid and in-depth mass spectrometry-based proteomics workflow. Methods: We used recent improvements in mass spectrometry (MS)-based proteomics to develop a rapid workflow for mapping the saliva proteome quantitatively and at great depth. Standard clinical cotton swabs were used to collect saliva form eight healthy individuals at two different time points, allowing us to study interindividual differences and interday changes of the saliva proteome. To accurately identify microbial proteins, we developed a method called "split by taxonomy id" that prevents peptides shared by humans and bacteria or between different bacterial phyla to contribute to protein identification. Results: Microgram protein amounts retrieved from cotton swabs resulted in more than 3700 quantified human proteins in 100-min gradients or 5500 proteins after simple fractionation. Remarkably, our measurements also quantified more than 2000 microbial proteins from 50 bacterial genera. Co-analysis of the proteomics results with next-generation sequencing data from the Human Microbiome Project as well as a comparison to MALDI-TOF mass spectrometry on microbial cultures revealed strong agreement. The oral microbiome differs between individuals and changes drastically upon eating and tooth brushing. Conclusion: Rapid shotgun and robust technology can now simultaneously characterize the human and microbiome contributions to the proteome of a body fluid and is therefore a valuable complement to genomic studies. This opens new frontiers for the study of host-pathogen interactions and clinical saliva diagnostics

Progress and challenges in mass spectrometry-based analysis of antibody repertoires

Humoral immunity is divided into the cellular B cell and protein-level antibody responses. High-throughput sequencing has advanced our understanding of both these fundamental aspects of B cell immunology as well as aspects pertaining to vaccine and therapeutics biotechnology. Although the protein-level serum and mucosal antibody repertoire make major contributions to humoral protection, the sequence composition and dynamics of antibody repertoires remain underexplored. This limits insight into important immunological and biotechnological parameters such as the number of antigen-specific antibodies, which are for example, relevant for pathogen neutralization, microbiota regulation, severity of autoimmunity, and therapeutic efficacy. High-resolution mass spectrometry (MS) has allowed initial insights into the antibody repertoire. We outline current challenges in MS-based sequence analysis of antibody repertoires and propose strategies for their resolution

Additional file 1: of Ultra-deep and quantitative saliva proteome reveals dynamics of the oral microbiome

Supplementary Figures S1-S9. (PDF 13519 kb

Translatome and transcriptome analysis of TMA20 (MCT-1) and TMA64 (eIF2D) knockout yeast strains

TMA20 (MCT-1), TMA22 (DENR) and TMA64 (eIF2D) are eukaryotic translation factors involved in ribosome recycling and re-initiation. They operate with P-site bound tRNA in post-termination or (re-)initiation translation complexes, thus participating in the removal of 40S ribosomal subunit from mRNA stop codons after termination and controlling translation re-initiation on mRNAs with upstream open reading frames (uORFs), as well as de novo initiation on some specific mRNAs. Here we report ribosomal profiling data of S.cerevisiae strains with individual deletions of TMA20, TMA64 or both TMA20 and TMA64 genes. We provide RNA-Seq and Ribo-Seq data from yeast strains grown in the rich YPD or minimal SD medium. We illustrate our data by plotting differential distribution of ribosomal-bound mRNA fragments throughout uORFs in 5′-untranslated region (5′ UTR) of GCN4 mRNA and on mRNA transcripts encoded in MAT locus in the mutant and wild-type strains, thus providing a basis for investigation of the role of these factors in the stress response, mating and sporulation. We also document a shift of transcription start site of the APC4 gene which occurs when the neighboring TMA64 gene is replaced by the standard G418-resistance cassette used for the creation of the Yeast Deletion Library. This shift results in dramatic deregulation of the APC4 gene expression, as revealed by our Ribo-Seq data, which can be probably used to explain strong genetic interactions of TMA64 with genes involved in the cell cycle and mitotic checkpoints. Raw RNA-Seq and Ribo-Seq data as well as all gene counts are available in NCBI Gene Expression Omnibus (GEO) repository under GEO accession GSE122039 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE122039)

MaxDIA enables library-based and library-free data-independent acquisition proteomics

MaxDIA is a software platform for analyzing data-independent acquisition (DIA) proteomics data within the MaxQuant software environment. Using spectral libraries, MaxDIA achieves deep proteome coverage with substantially better coefficients of variation in protein quantification than other software. MaxDIA is equipped with accurate false discovery rate (FDR) estimates on both library-to-DIA match and protein levels, including when using whole-proteome predicted spectral libraries. This is the foundation of discovery DIA—hypothesis-free analysis of DIA samples without library and with reliable FDR control. MaxDIA performs three- or four-dimensional feature detection of fragment data, and scoring of matches is augmented by machine learning on the features of an identification. MaxDIA’s bootstrap DIA workflow performs multiple rounds of matching with increasing quality of recalibration and stringency of matching to the library. Combining MaxDIA with two new technologies—BoxCar acquisition and trapped ion mobility spectrometry—both lead to deep and accurate proteome quantification