Biomedical subjects

Chris Sander

Publications and source records attributed to Chris Sander.

At least 19 recordsLinked to original sources

PubMed2026

AI proteomics: from protein identification to virtual cells.

Artificial intelligence (AI) is transforming scientific research, including proteomics. In this Perspective, we highlight key mass spectrometry (MS)-based proteomics areas where AI is driving innovation, ranging from protein identification to building AI virtual cells. These include improving peptide and protein identification and quantification; characterizing protein-protein interactions and protein complexes; advancing spatial and perturbation proteomics; integrating multi-omics data; and, ultimately, enabling AI virtual cells. Finally, we call for global collaboration among data producers, data consumers and other stakeholders to establish an AI-friendly ecosystem for MS-based proteomics, laying the foundation for transformative advancements in proteomics driven by AI.

Proteomics↗

PubMed2006

cPath: open source software for collecting, storing, and querying biological pathways.

BACKGROUND: Biological pathways, including metabolic pathways, protein interaction networks, signal transduction pathways, and gene regulatory networks, are currently represented in over 220 diverse databases. These data are crucial for the study of specific biological processes, including human diseases. Standard exchange formats for pathway information, such as BioPAX, CellML, SBML and PSI-MI, enable convenient collection of this data for biological research, but mechanisms for common storage and communication are required. RESULTS: We have developed cPath, an open source database and web application for collecting, storing, and querying biological pathway data. cPath makes it easy to aggregate custom pathway data sets available in standard exchange formats from multiple databases, present pathway data to biologists via a customizable web interface, and export pathway data via a web service to third-party software, such as Cytoscape, for visualization and analysis. cPath is software only, and does not include new pathway information. Key features include: a built-in identifier mapping service for linking identical interactors and linking to external resources; built-in support for PSI-MI and BioPAX standard pathway exchange formats; a web service interface for searching and retrieving pathway data sets; and thorough documentation. The cPath software is freely available under the LGPL open source license for academic and commercial use. CONCLUSION: cPath is a robust, scalable, modular, professional-grade software platform for collecting, storing, and querying biological pathways. It can serve as the core data handling component in information systems for pathway visualization, analysis and modeling.

Computational Biology↗

PubMed2006

CancerGenes: a gene selection resource for cancer genome projects.

The genome sequence framework provided by the human genome project allows us to precisely map human genetic variations in order to study their association with disease and their direct effects on gene function. Since the description of tumor suppressor genes and oncogenes several decades ago, both germ-line variations and somatic mutations have been established to be important in cancer-in terms of risk, oncogenesis, prognosis and response to therapy. The Cancer Genome Atlas initiative proposed by the NIH is poised to elucidate the contribution of somatic mutations to cancer development and progression through the re-sequencing of a substantial fraction of the total collection of human genes-in hundreds of individual tumors and spanning several tumor types. We have developed the CancerGenes resource to simplify the process of gene selection and prioritization in large collaborative projects. CancerGenes combines gene lists annotated by experts with information from key public databases. Each gene is annotated with gene name(s), functional description, organism, chromosome number, location, Entrez Gene ID, GO terms, InterPro descriptions, gene structure, protein length, transcript count, and experimentally determined transcript control regions, as well as links to Entrez Gene, COSMIC, and iHOP gene pages and the UCSC and Ensembl genome browsers. The user-friendly interface provides for searching, sorting and intersection of gene lists. Users may view tabulated results through a web browser or may dynamically download them as a spreadsheet table. CancerGenes is available at http://cbio.mskcc.org/cancergenes.

Databases, Genetic↗

PubMed2006

A novel class of small RNAs bind to MILI protein in mouse testes.

Small RNAs bound to Argonaute proteins recognize partially or fully complementary nucleic acid targets in diverse gene-silencing processes. A subgroup of the Argonaute proteins--known as the 'Piwi family'--is required for germ- and stem-cell development in invertebrates, and two Piwi members--MILI and MIWI--are essential for spermatogenesis in mouse. Here we describe a new class of small RNAs that bind to MILI in mouse male germ cells, where they accumulate at the onset of meiosis. The sequences of the over 1,000 identified unique molecules share a strong preference for a 5' uridine, but otherwise cannot be readily classified into sequence families. Genomic mapping of these small RNAs reveals a limited number of clusters, suggesting that these RNAs are processed from long primary transcripts. The small RNAs are 26-31 nucleotides (nt) in length--clearly distinct from the 21-23 nt of microRNAs (miRNAs) or short interfering RNAs (siRNAs)--and we refer to them as 'Piwi-interacting RNAs' or piRNAs. Orthologous human chromosomal regions also give rise to small RNAs with the characteristics of piRNAs, but the cloned sequences are distinct. The identification of this new class of small RNAs provides an important starting point to determine the molecular function of Piwi proteins in mammalian spermatogenesis.

Animals↗

PubMed2006

Signal processing in the TGF-beta superfamily ligand-receptor network.

The TGF-beta pathway plays a central role in tissue homeostasis and morphogenesis. It transduces a variety of extracellular signals into intracellular transcriptional responses that control a plethora of cellular processes, including cell growth, apoptosis, and differentiation. We use computational modeling to show that coupling of signaling with receptor trafficking results in a highly versatile signal-processing unit, able to sense by itself absolute levels of ligand, temporal changes in ligand concentration, and ratios of multiple ligands. This coupling controls whether the response of the receptor module is transient or permanent and whether or not different signaling channels behave independently of each other. Our computational approach unifies seemingly disparate experimental observations and suggests specific changes in receptor trafficking patterns that can lead to phenotypes that favor tumor progression.

Computer Simulation↗

PubMed2006

Pathguide: a pathway resource list.

Pathguide: the Pathway Resource List (http://pathguide.org) is a meta-database that provides an overview of more than 190 web-accessible biological pathway and network databases. These include databases on metabolic pathways, signaling pathways, transcription factor targets, gene regulatory networks, genetic interactions, protein-compound interactions, and protein-protein interactions. The listed databases are maintained by diverse groups in different locations and the information in them is derived either from the scientific literature or from systematic experiments. Pathguide is useful as a starting point for biological pathway analysis and for content aggregation in integrated biological information systems.

Databases, Genetic↗

PubMed2006

Prediction of human microRNA targets.

MicroRNAs (miRNAs) are small, nonprotein-coding RNAs that regulate gene expression. Although hundreds of human miRNA genes have been discovered, the functions of most of these are unknown. Computational predictions indicate that miRNAs, which account for at least 1% of human protein-coding genes, regulate protein production for thousands of or possibly all of human genes. We discuss the functions of mammalian miRNAs and the experimental and computational methods used to detect and predict human miRNA target genes. Anticipating their impact on genome-wide discovery of miRNA targets, we describe the various computational tools and web-based resources available to predict miRNA targets.

3' Untranslated Regions↗

PubMed2005

MicroRNA profiling of the murine hematopoietic system.

BACKGROUND: MicroRNAs (miRNAs) are a class of recently discovered noncoding RNA genes that post-transcriptionally regulate gene expression. It is becoming clear that miRNAs play an important role in the regulation of gene expression during development. However, in mammals, expression data are principally based on whole tissue analysis and are still very incomplete. RESULTS: We used oligonucleotide arrays to analyze miRNA expression in the murine hematopoietic system. Complementary oligonucleotides capable of hybridizing to 181 miRNAs were immobilized on a membrane and probed with radiolabeled RNA derived from low molecular weight fractions of total RNA from several different hematopoietic and neuronal cells. This method allowed us to analyze cell type-specific patterns of miRNA expression and to identify miRNAs that might be important for cell lineage specification and/or cell effector functions. CONCLUSION: This is the first report of systematic miRNA gene profiling in cells of the hematopoietic system. As expected, miRNA expression patterns were very different between hematopoietic and non-hematopoietic cells, with further subtle differences observed within the hematopoietic group. Interestingly, the most pronounced similarities were observed among fully differentiated effector cells (Th1 and Th2 lymphocytes and mast cells) and precursors at comparable stages of differentiation (double negative thymocytes and pro-B cells), suggesting that in addition to regulating the process of commitment to particular cellular lineages, miRNAs might have an important general role in the mechanism of cell differentiation and maintenance of cell identity.

Animals↗

PubMed2005

Antisense-mediated depletion reveals essential and specific functions of microRNAs in Drosophila development.

MicroRNAs are small noncoding RNAs that control gene function posttranscriptionally through mRNA degradation or translational inhibition. Much has been learned about the processing and mechanism of action of microRNAs, but little is known about their biological function. Here, we demonstrate that injection of 2'O-methyl antisense oligoribonucleotides into early Drosophila embryos leads to specific and efficient depletion of microRNAs and thus permits systematic loss-of-function analysis in vivo. Twenty-five of the forty-six embryonically expressed microRNAs show readily discernible defects; pleiotropy is moderate and family members display similar yet distinct phenotypes. Processes under microRNA regulation include cellularization and patterning in the blastoderm, morphogenesis, and cell survival. The largest microRNA family in Drosophila (miR-2/6/11/13/308) is required for suppressing embryonic apoptosis; this is achieved by differential posttranscriptional repression of the proapoptotic factors hid, grim, reaper, and sickle. Our findings demonstrate that microRNAs act as specific and essential regulators in a wide range of developmental processes.

Animals↗

PubMed2005

The developmental miRNA profiles of zebrafish as determined by small RNA cloning.

MicroRNAs (miRNAs) represent a family of small, regulatory, noncoding RNAs that are found in plants and animals. Here, we describe the miRNA profile of the zebrafish Danio rerio resolved in a developmental and cell-type-specific manner. The profiles were obtained from larger-scale sequencing of small RNA libraries prepared from developmentally staged zebrafish, and two adult fibroblast cell lines derived from the caudal fin (ZFL) and the liver epithelium (SJD). We identified a total of 154 distinct miRNAs expressed from 343 miRNA genes. Other experimental/computational sources support an additional 10 miRNAs encoded by 19 genes. The miRNAs can be classified into 87 distinct families. Cross-species comparison indicates that 81 families are conserved in mammals, 17 of which also have at least one member conserved in an invertebrate. Our analysis reveals that the zygotes are essentially devoid of miRNAs and that their expression begins during the blastula period with a zebrafish-specific family of miRNAs encoded by closely spaced multicopy genes. Computational predictions of zebrafish miRNA targets are provided that take into account the depth of evolutionary conservation. Besides miRNAs, we identified a prominent class of repeat-associated small interfering RNAs (rasiRNAs).

Animals↗

PubMed2005

Pathway information for systems biology.

Pathway information is vital for successful quantitative modeling of biological systems. The almost 170 online pathway databases vary widely in coverage and representation of biological processes, making their use extremely difficult. Future pathway information systems for querying, visualization and analysis must support standard exchange formats to successfully integrate data on a large scale. Such integrated systems will greatly facilitate the constructive cycle of computational model building and experimental verification that lies at the heart of systems biology.

Animals↗

PubMed2005

Identification of microRNAs of the herpesvirus family.

Epstein-Barr virus (EBV or HHV4), a member of the human herpesvirus (HHV) family, has recently been shown to encode microRNAs (miRNAs). In contrast to most eukaryotic miRNAs, these viral miRNAs do not have close homologs in other viral genomes or in the genome of the human host. To identify other miRNA genes in pathogenic viruses, we combined a new miRNA gene prediction method with small-RNA cloning from several virus-infected cell types. We cloned ten miRNAs in the Kaposi sarcoma-associated virus (KSHV or HHV8), nine miRNAs in the mouse gammaherpesvirus 68 (MHV68) and nine miRNAs in the human cytomegalovirus (HCMV or HHV5). These miRNA genes are expressed individually or in clusters from either polymerase (pol) II or pol III promoters, and share no substantial sequence homology with one another or with the known human miRNAs. Generally, we predicted miRNAs in several large DNA viruses, and we could neither predict nor experimentally identify miRNAs in the genomes of small RNA viruses or retroviruses.

Chromosome Mapping↗

PubMed2004

Human MicroRNA targets.

MicroRNAs (miRNAs) interact with target mRNAs at specific sites to induce cleavage of the message or inhibit translation. The specific function of most mammalian miRNAs is unknown. We have predicted target sites on the 3' untranslated regions of human gene transcripts for all currently known 218 mammalian miRNAs to facilitate focused experiments. We report about 2,000 human genes with miRNA target sites conserved in mammals and about 250 human genes conserved as targets between mammals and fish. The prediction algorithm optimizes sequence complementarity using position-specific rules and relies on strict requirements of interspecies conservation. Experimental support for the validity of the method comes from known targets and from strong enrichment of predicted targets in mRNAs associated with the fragile X mental retardation protein in mammals. This is consistent with the hypothesis that miRNAs act as sequence-specific adaptors in the interaction of ribonuclear particles with translationally regulated messages. Overrepresented groups of targets include mRNAs coding for transcription factors, components of the miRNA machinery, and other proteins involved in translational regulation, as well as components of the ubiquitin machinery, representing novel feedback loops in gene regulation. Detailed information about target genes, target processes, and open-source software for target prediction (miRanda) is available at http://www.microrna.org. Our analysis suggests that miRNA genes, which are about 1% of all human genes, regulate protein production for 10% or more of all human genes.

3' Untranslated Regions↗

PubMed2004

miR-122, a mammalian liver-specific microRNA, is processed from hcr mRNA and may downregulate the high affinity cationic amino acid transporter CAT-1.

These studies show that miR-122, a 22-nucleotide microRNA, is derived from a liver-specific noncoding polyadenylated RNA transcribed from the gene hcr. The exact sequence of miR-122 as well as the adjacent secondary structure within the hcr mRNA are conserved from mammalian species back to fish. Levels of miR-122 in the mouse liver increase to half maximal values around day 17 of embryogenesis, and reach near maximal levels of 50,000 copies per average cell before birth. Lewis et al. (2003) predicted the cationic amino acid transporter (CAT-1 or SLC7A1) as a miR-122 target. CAT-1 protein and its mRNA are expressed in all mammalian tissues but with lower levels in adult liver. Furthermore, during mouse liver development CAT-1 mRNA decreases in an almost inverse correlation with miR-122. Eight potential miR-122 target sites were predicted within the human CAT-1 mRNA, with six in the 3'-untranslated region. Using a reporter construct it was found that just three of the predicted sites, linked in a 400-nucleotide sequence from human CAT-1, acted with synergy and were sufficient to strongly inhibit protein synthesis and reduce mRNA levels. In summary, these studies followed the accumulation during development of miR-122 from its mRNA precursor, hcr, through to identification of what may be a specific mRNA target, CAT-1.

Animals↗

PubMed2004

Identification of virus-encoded microRNAs.

RNA silencing processes are guided by small RNAs that are derived from double-stranded RNA. To probe for function of RNA silencing during infection of human cells by a DNA virus, we recorded the small RNA profile of cells infected by Epstein-Barr virus (EBV). We show that EBV expresses several microRNA (miRNA) genes. Given that miRNAs function in RNA silencing pathways either by targeting messenger RNAs for degradation or by repressing translation, we identified viral regulators of host and/or viral gene expression.

Animals↗

PubMed2004

Global mapping of the yeast genetic interaction network.

A genetic interaction network containing approximately 1000 genes and approximately 4000 interactions was mapped by crossing mutations in 132 different query genes into a set of approximately 4700 viable gene yeast deletion mutants and scoring the double mutant progeny for fitness defects. Network connectivity was predictive of function because interactions often occurred among functionally related genes, and similar patterns of interactions tended to identify components of the same pathway. The genetic network exhibited dense local neighborhoods; therefore, the position of a gene on a partially mapped network is predictive of other genetic interactions. Because digenic interactions are common in yeast, similar networks may underlie the complex genetics associated with inherited phenotypes in other organisms.

Amino Acid Sequence↗

PubMed2004

The HUPO PSI's molecular interaction format--a community standard for the representation of protein interaction data.

A major goal of proteomics is the complete description of the protein interaction network underlying cell physiology. A large number of small scale and, more recently, large-scale experiments have contributed to expanding our understanding of the nature of the interaction network. However, the necessary data integration across experiments is currently hampered by the fragmentation of publicly available protein interaction data, which exists in different formats in databases, on authors' websites or sometimes only in print publications. Here, we propose a community standard data model for the representation and exchange of protein interaction data. This data model has been jointly developed by members of the Proteomics Standards Initiative (PSI), a work group of the Human Proteome Organization (HUPO), and is supported by major protein interaction data providers, in particular the Biomolecular Interaction Network Database (BIND), Cellzome (Heidelberg, Germany), the Database of Interacting Proteins (DIP), Dana Farber Cancer Institute (Boston, MA, USA), the Human Protein Reference Database (HPRD), Hybrigenics (Paris, France), the European Bioinformatics Institute's (EMBL-EBI, Hinxton, UK) IntAct, the Molecular Interactions (MINT, Rome, Italy) database, the Protein-Protein Interaction Database (PPID, Edinburgh, UK) and the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING, EMBL, Heidelberg, Germany).

Database Management Systems↗

PubMed2004

Detection of activity centers in cellular pathways using transcript profiling.

We present a new computational method for identifying regulated pathway components in transcript profiling (TP) experiments by evaluating transcriptional activity in the context of known biological pathways. We construct a graph representing thousands of protein functional relationships by integrating knowledge from public databases and review articles. We use the notion of distance in a graph to define pathway neighborhoods. The pathways perturbed in an experiment are then identified as the subgraph induced by the genes, referred to as activity centers, having significant density of transcriptional activity in their functional neighborhoods. We illustrate the predictive power of this approach by performing and analyzing an experiment of TP53 overexpression in NCI-H125 cells. The detected activity centers are in agreement with the known TP53 activation effects and our independent experimental results. We also apply the method to a serum starvation experiment using HEY cells and investigate the predicted activity of the transcription factor MYC. Finally, we discuss interesting properties of the activity center approach and its possible applications beyond the comparison of two experiments.

Algorithms↗