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Mammalian Gut Microbiome Metaproteomics

Ninety percent of the cells and 99% of the genes in the human body are not human at all, but microbial. The vast majority of these organisms live in the gastro-intestinal tract where they can reach concentrations of 10^12 cfu/ml. The Elias lab is collaborating with the Sonnenburg Lab (microbiology and Immunology, Stanford) to applying our proteomic techniques to the study of the human gut microbiome. By applying mass-spectrometry based proteomics and developing specialized computational tools to handle large, poorly-defined systems, we provide a new way to observe and quantify the expression of the millions of genes present in the system. Using germ-free mouse models (Sonnenburg Lab), we can make experimental perturbations on defined communities to study issues ranging from diet and obesity to infection and antibiotics. In particular, we are interested in how proteins secreted by the host affect and are affected by the resident microbiota.

MHC I Associated Protein Turnover Using Quantitative Mass Spectrometry

Protein degradation and immune system surveillance are tightly interconnected. Aside from recycling proteins to recover amino acids for future biosynthesis, protein degradation is essential for reporting pathogenic peptides to the immune system. In a cell, degraded peptides, often of viral origin, are loaded onto MHC I molecules and then presented to the immune system at the cell surface. Self peptides derived from endogenous proteins that activate an immune response, however, can lead to autoimmune diseases, but can also provide a route towards sensitive cancer diagnosis or treatment. Still, little is known about the exact nature of peptide antigen presentation, in health and disease and it is therefore of great interest to investigate MHC I associated presentation and turnover. By using quantitative mass spectrometry and ribosome profiling methods we want to shed light into protein turnover affecting autoimmunity.

Proteome Turnover In Aging And Infectious Disease

Protein degradation and immune system surveillance are tightly interconnected. Aside from recycling proteins to recover amino acids for future biosynthesis, protein degradation is essential for reporting pathogenic peptides to the immune system. In a cell, degraded peptides, often of viral origin, are loaded onto MHC I molecules and then presented to the immune system at the cell surface. Self peptides derived from endogenous proteins that activate an immune response, however, can lead to autoimmune diseases, but can also provide a route towards sensitive cancer diagnosis or treatment. Still, little is known about the exact nature of peptide antigen presentation, in health and disease and it is therefore of great interest to investigate MHC I associated presentation and turnover. By using quantitative mass spectrometry and ribosome profiling methods we want to shed light into protein turnover affecting autoimmunity.

Software

Label-assisted de novo peptide sequencing (LADS)

Publication: Application of de Novo Sequencing to Large-Scale Complex Proteomics Data Sets
People: Arun Devabhaktuni, Sam Pearlman, Sarah Lin

Computational tools that search databases of known proteins for the peptides that best match observed mass spectra make modern proteomics possible. However, for some applications where the underlying source proteome is either unknown or unwieldy, a better option is to identify peptide sequences directly from observed spectra, i.e., de novo. We have developed a computational strategy known as LADS that lets us discover peptides in this way, and are applying it to the discovery of antigenic MHC-presented peptides, and to the gut microbiome.

TagGraph

Publication: TagGraph reveals vast protein modification landscapes from large tandem mass spectrometry datasets
People:  Arun Devabhaktuni, Sam Pearlman, Sarah Lin
Although mass spectrometry is well suited to identifying thousands of potential protein post-translational modifications (PTMs), it has historically been biased towards just a few. To measure the entire set of PTMs across diverse proteomes, software must overcome the dual challenges of covering enormous search spaces and distinguishing correct from incorrect spectrum interpretations. Here, we describe TagGraph, a computational tool that overcomes both challenges with an unrestricted string-based search method that is as much as 350-fold faster than existing approaches, and a probabilistic validation model that we optimized for PTM assignments. We applied TagGraph to a published human proteomic dataset of 25 million mass spectra and tripled confident spectrum identifications compared to its original analysis. We identified thousands of modification types on almost 1 million sites in the proteome. We show alternative contexts for highly abundant yet understudied PTMs such as proline hydroxylation, and its unexpected association with cancer mutations. By enabling broad characterization of PTMs, TagGraph informs as to how their functions and regulation intersect.

Motif-Z

People:  Xueheng Zhao
A length-independent computational tool for ab initio motif-discovery Amino acid motifs are the foundation for many protein-protein and protein-peptide interactions. Although such motifs may have a semi-variable structure (e.g., AxxBxxC and AxxxBxxxxC), most motif discovery algorithms endeavor to discover fixed-length motifs from fixed-length input sequences. Motif-Z adapts the motif discovery strategy employed in the Motif-X algorithm to return flexible-length motifs from a variety of input sequence lengths. Motif-Z is suited to discover motifs from peptide antigens presented by MHC-I complexes.