Faculty Bibliography

Male mammals must simultaneously produce prodigious numbers of sperm and maintain an adequate reserve of stem cells to ensure continuous production of gametes throughout life. Failures in the mechanisms responsible for balancing germ cell differentiation and spermatogonial stem cell (SSC) self-renewal can result in infertility. We discovered a novel requirement for Ubiquitous Expressed Transcript (UXT) in spermatogenesis by developing the first knockout mouse model for this gene. Constitutive deletion of Uxt is embryonic lethal, while conditional knockout in the male germline results in a Sertoli cell-only phenotype during the first wave of spermatogenesis that does not recover in the adult. This phenotype begins to manifest between 6 and 7 days post-partum, just before meiotic entry. Gene expression analysis revealed that Uxt deletion downregulates the transcription of genes governing SSC self-renewal, differentiation, and meiosis, consistent with its previously defined role as a transcriptional co-factor. Our study has revealed the first in vivo function for UXT in the mammalian germline as a regulator of distinct transcriptional programs in SSCs and differentiating spermatogonia.

We introduce the Interaction Factor (IF), a measure for quantifying the interaction of molecular clusters in super-resolution microscopy images. The IF is robust in the sense that it is independent of cluster density, and it only depends on the extent of the pair-wise interaction between different types of molecular clusters in the image. The IF for a single or a collection of images is estimated by first using stochastic modelling where the locations of clusters in the images are repeatedly randomized to estimate the distribution of the overlaps between the clusters in the absence of interaction (IF = 0). Second, an analytical form of the relationship between IF and the overlap (which has the random overlap as its only parameter) is used to estimate the IF for the experimentally observed overlap. The advantage of IF compared to conventional methods to quantify interaction in microscopy images is that it is insensitive to changing cluster density and is an absolute measure of interaction, making the interpretation of experiments easier. We validate the IF method by using both simulated and experimental data and provide an ImageJ plugin for determining the IF of an image.

Proteogenomics is a research area that combines areas as proteomics and genomics in a multi-omics setup using both mass spectrometry and high-throughput sequencing technologies. Currently, the main goals of the field are to aid genome annotation or to unravel the proteome complexity. Mass spectrometry based identifications of matching or homologues peptides can further refine gene models. Also, the identification of novel proteoforms is also made possible based on detection of novel translation initiation sites (cognate or near-cognate), novel transcript isoforms, sequence variation or novel (small) open reading frames in intergenic or un-translated genic regions by analyzing high-throughput sequencing data from RNAseq or ribosome profiling experiments. Other proteogenomics studies using a combination of proteomics and genomics techniques focus on antibody sequencing, the identification of immunogenic peptides or venom peptides. Over the years, a growing amount of bioinformatics tools and databases became available to help streamlining these cross-omics studies. Some of these solutions only help in specific steps of the proteogenomics studies, e.g. building custom sequence databases (based on next generation sequencing output) for mass spectrometry fragmentation spectrum matching. Over the last few years a handful integrative tools also became available that can execute complete proteogenomics analyses. Some of these are presented as stand-alone solutions, whereas others are implemented in a web-based framework such as Galaxy. In this review we aimed at sketching a comprehensive overview of all the bioinformatics solutions that are available for this growing research area. (c) 2015 Wiley Periodicals, Inc. Mass Spec Rev.

Cancer forms specialized microenvironmental niches that promote local invasion and colonization. Engrafted patient-derived xenografts (PDXs) locally invade and colonize naive stroma in mice while enabling unambiguous molecular discrimination of human proteins in the tumor from mouse proteins in the microenvironment. To characterize how patient breast tumors form a niche and educate naive stroma, subcutaneous breast cancer PDXs were globally profiled by species-specific quantitative proteomics. Regulation of PDX stromal proteins by breast tumors was extensive, with 35% of the stromal proteome altered by tumors consistently across different animals and passages. Differentially regulated proteins in the stroma clustered into six signatures, which included both known and previously unappreciated contributors to tumor invasion and colonization. Stromal proteomes were coordinately regulated; however, the sets of proteins altered by each tumor were highly distinct. Integrated analysis of tumor and stromal proteins, a comparison made possible in these xenograft models, indicated that the known hallmarks of cancer contribute pleiotropically to establishing and maintaining the microenvironmental niche of the tumor. Education of the stroma by the tumor is therefore an intrinsic property of breast tumors that is highly individualized, yet proceeds by consistent, nonrandom, and defined tumor-promoting molecular alterations.

Identifying genomic variants is a fundamental first step toward the understanding of the role of inherited and acquired variation in disease. The accelerating growth in the corpus of sequencing data that underpins such analysis is making the data-download bottleneck more evident, placing substantial burdens on the research community to keep pace. As a result, the search for alternative approaches to the traditional "download and analyze" paradigm on local computing resources has led to a rapidly growing demand for cloud-computing solutions for genomics analysis. Here, we introduce the Genome Variant Investigation Platform (GenomeVIP), an open-source framework for performing genomics variant discovery and annotation using cloud- or local high-performance computing infrastructure. GenomeVIP orchestrates the analysis of whole-genome and exome sequence data using a set of robust and popular task-specific tools, including VarScan, GATK, Pindel, BreakDancer, Strelka, and Genome STRiP, through a web interface. GenomeVIP has been used for genomic analysis in large-data projects such as the TCGA PanCanAtlas and in other projects, such as the ICGC Pilots, CPTAC, ICGC-TCGA DREAM Challenges, and the 1000 Genomes SV Project. Here, we demonstrate GenomeVIP's ability to provide high-confidence annotated somatic, germline, and de novo variants of potential biological significance using publicly available data sets.

Optic gliomas are brain tumors characterized by slow growth, progressive loss of vision, and limited therapeutic options. Optic gliomas contain various amounts of myxoid matrix, which can represent most of the tumor mass. We sought to investigate biological function and protein structure of the myxoid matrix in optic gliomas to identify novel therapeutic targets. We reviewed histological features and clinical imaging properties, analyzed vasculature by immunohistochemistry and electron microscopy, and performed liquid chromatography-mass spectrometry on optic gliomas, which varied in the amount of myxoid matrix. We found that although subtypes of optic gliomas are indistinguishable on imaging, the microvascular network of pilomyxoid astrocytoma, a subtype of optic glioma with abundant myxoid matrix, is characterized by the presence of endothelium-free channels in the myxoid matrix. These tumors show normal perfusion by clinical imaging and lack histological evidence of hemorrhage organization or thrombosis. The myxoid matrix is composed predominantly of the proteoglycan versican and its linking protein, a vertebrate hyaluronan and proteoglycan link protein 1. We propose that pediatric optic gliomas can maintain blood supply without endothelial cells by using invertebrate-like channels, which we termed primitive myxoid vascularization. Enzymatic targeting of the proteoglycan versican/hyaluronan and proteoglycan link protein 1 rich myxoid matrix, which is in direct contact with circulating blood, can provide novel therapeutic avenues for optic gliomas of childhood.

Background: There are no predictive biomarkers of ipilimumab (IPI) toxicity. Of metastatic melanoma (MM) patients (pts) receiving IPI (3mg/kg), 35% require systemic therapies to treat immune-related adverse events (irAEs) and 20% must terminate treatment (Horvat et al., JCO 2015). Here we tested the hypothesis that a pre-existing autoantibody (autoAb) profile is predictive of IPI irAEs. Methods: We measured autoAb levels in pre- and post-treatment sera from mm pts who received IPI (3mg/kg) monotherapy on a proteome microarray containing ~20,000 unique full-length human proteins (HuProt array, CDI Laboratories). Clinical data were prospectively collected with protocol-driven follow-up. IrAEs were categorized by CTCAE guidelines as none (grade 0), mild (grade 1-2), or severe (grade 3-4). AutoAb levels were standardized using median quantile normalization and considered positive hits if > 2-SD above the peak array signal and differed by >=2 fold with p < 0.05 between toxicity groups (Non-parametric Analysis/Wilcox test). Results: Seventy-eight sera from 37 mm pts were analyzed. Antibodies against CTLA-4 were significantly elevated post IPI treatment (p < 0.0001), validating the assay. The pre-treatment levels of 190 IgG autoAbs were significantly diferent in pts who experienced irAEs (n = 28) compared to those with no irAEs (n = 9). Comparison of severe irAE (n = 9) and no irAE (n = 9) groups revealed 129 IgG autoAbs that significantly differed in pre-treatment sera. Localization and pathway analysis (UniProt, KEGG, Reactome) showed 81/190 (43%) of the autoAbs targeted nuclear and mitochondrial antigens and were enriched in metabolic pathways (p = 0.015). AutoAbs associated with irAEs did not correlate with treatment response. Conclusions: AutoAbs to antigens enriched in metabolic pathways prior to treatment may predict IPI-induced toxicity in MM. The subcellular localization of targeted antigens could explain the autoimmune toxicities associated with IPI. Studies in larger cohorts and in pts receiving other checkpoint inhibitors and/or combination therapies are essential to determine the validity of the data. If validated, our results would support the discovery of the first toxicity predictor in cancer immunotherapy

Association of aberrant glycosylation with melanoma progression is based mainly on analyses of cell lines. Here we present a systems-based study of glycomic changes and corresponding enzymes associated with melanoma metastasis in patient samples. Upregulation of core fucosylation (FUT8) and downregulation of alpha-1,2 fucosylation (FUT1, FUT2) were identified as features of metastatic melanoma. Using both in vitro and in vivo studies, we demonstrate FUT8 is a driver of melanoma metastasis which, when silenced, suppresses invasion and tumor dissemination. Glycoprotein targets of FUT8 were enriched in cell migration proteins including the adhesion molecule L1CAM. Core fucosylation impacted L1CAM cleavage and the ability of L1CAM to support melanoma invasion. FUT8 and its targets represent therapeutic targets in melanoma metastasis.

With combined technological advancements in high-throughput next-generation sequencing and deep mass spectrometry-based proteomics, proteogenomics, i.e., the integrative analysis of proteomic and genomic data, has emerged as a new research field. Early efforts in the field were focused on improving protein identification using sample-specific genomic and transcriptomic sequencing data. More recently, integrative analysis of quantitative measurements from genomic and proteomic studies have identified novel insights into gene expression regulation, cell signaling, and disease. Many methods and tools have been developed or adapted to enable an array of integrative proteogenomic approaches and in this article, we systematically classify published methods and tools into four major categories, (1) Sequence-centric proteogenomics; (2) Analysis of proteogenomic relationships; (3) Integrative modeling of proteogenomic data; and (4) Data sharing and visualization. We provide a comprehensive review of methods and available tools in each category and highlight their typical applications.