Michael Fero

Gene Expression Patterns in Renal Cell Carcinoma Assessed by Complementary DNA Microarray

Renal cell carcinoma comprises several histological types with different clinical behavior. Accurate pathological characterization is important in the clinical management of these tumors. We describe gene expression profiles in 41 renal tumors determined by using DNA microarrays containing 22,648 unique cDNAs representing 17,083 different UniGene Clusters, including 7230 characterized human genes. Differences in the patterns of gene expression among the different tumor types were readily apparent; hierarchical cluster analysis of the tumor samples segregated histologically distinct tumor types solely based on their gene expression patterns. Conventional renal cell carcinomas with clear cells showed a highly distinctive pattern of gene expression. Papillary carcinomas formed a tightly clustered group, as did tumors arising from the distal nephron and the normal kidney samples. Surprisingly, conventional renal cell carcinomas with granular cytoplasm were heterogeneous, and did not resemble any of the conventional carcinomas with clear cytoplasm in their pattern of gene expression. Characterization of renal cell carcinomas based on gene expression patterns provides a revised classification of these tumors and has the potential to supply significant biological and clinical insights.

The External RNA Controls Consortium: a progress report

Standard controls and best practice guidelines advance acceptance of data from research, preclinical and clinical laboratories by providing a means for evaluating data quality. The External RNA Controls Consortium (ERCC) is developing commonly agreed-upon and tested controls for use in expression assays, a true industry-wide standard control.Standard controls and best practice guidelines advance acceptance of data from research, preclinical and clinical laboratories by providing a means for evaluating data quality. The External RNA Controls Consortium (ERCC) is developing commonly agreed-upon and tested controls for use in expression assays, a true industry-wide standard control.

The essential genome of a bacterium

Caulobacter crescentus is a model organism for the integrated circuitry that runs a bacterial cell cycle. Full discovery of its essential genome, including non‐coding, regulatory and coding elements, is a prerequisite for understanding the complete regulatory network of a bacterial cell. Using hyper‐saturated transposon mutagenesis coupled with high‐throughput sequencing, we determined the essential Caulobacter genome at 8 bp resolution, including 1012 essential genome features: 480 ORFs, 402 regulatory sequences and 130 non‐coding elements, including 90 intergenic segments of unknown function. The essential transcriptional circuitry for growth on rich media includes 10 transcription factors, 2 RNA polymerase sigma factors and 1 anti‐sigma factor. We identified all essential promoter elements for the cell cycle‐regulated genes. The essential elements are preferentially positioned near the origin and terminus of the chromosome. The high‐resolution strategy used here is applicable to high‐throughput, full genome essentiality studies and large‐scale genetic perturbation experiments in a broad class of bacterial species.

Assembly of the Caulobacter cell division machine

Cytokinesis in Gram‐negative bacteria is mediated by a multiprotein machine (the divisome) that invaginates and remodels the inner membrane, peptidoglycan and outer membrane. Understanding the order of divisome assembly would inform models of the interactions among its components and their respective functions. We leveraged the ability to isolate synchronous populations of Caulobacter crescentus cells to investigate assembly of the divisome and place the arrival of each component into functional context. Additionally, we investigated the genetic dependence of localization among divisome proteins and the cell cycle regulation of their transcript and protein levels to gain insight into the control mechanisms underlying their assembly. Our results revealed a picture of divisome assembly with unprecedented temporal resolution. Specifically, we observed (i) initial establishment of the division site, (ii) recruitment of early FtsZ‐binding proteins, (iii) arrival of proteins involved in peptidoglycan remodelling, (iv) arrival of FtsA, (v) assembly of core divisome components, (vi) initiation of envelope invagination, (vii) recruitment of polar markers and cytoplasmic compartmentalization and (viii) cell separation. Our analysis revealed differences in divisome assembly among Caulobacter and other bacteria that establish a framework for identifying aspects of bacterial cytokinesis that are widely conserved from those that are more variable.

Caulobacter PopZ forms a polar subdomain dictating sequential changes in pole composition and function

The bacterium Caulobacter crescentus has morphologically and functionally distinct cell poles that undergo sequential changes during the cell cycle. We show that the PopZ oligomeric network forms polar ribosome exclusion zones that change function during cell cycle progression. The parS/ParB chromosomal centromere is tethered to PopZ at one pole prior to the initiation of DNA replication. During polar maturation, the PopZ‐centromere tether is broken, and the PopZ zone at that pole then switches function to act as a recruitment factor for the ordered addition of multiple proteins that promote the transformation of the flagellated pole into a stalked pole. Stalked pole assembly, in turn, triggers the initiation of chromosome replication, which signals the formation of a new PopZ zone at the opposite cell pole, where it functions to anchor the newly duplicated centromere that has traversed the long axis of the cell. We propose that pole‐specific control of PopZ function co‐ordinates polar development and cell cycle progression by enabling independent assembly and tethering activities at the two cell poles.

High-throughput identification of protein localization dependency networks

Bacterial cells are highly organized with many protein complexes and DNA loci dynamically positioned to distinct subcellular sites over the course of a cell cycle. Such dynamic protein localization is essential for polar organelle development, establishment of asymmetry, and chromosome replication during the Caulobacter crescentus cell cycle. We used a fluorescence microscopy screen optimized for high-throughput to find strains with anomalous temporal or spatial protein localization patterns in transposon-generated mutant libraries. Automated image acquisition and analysis allowed us to identify genes that affect the localization of two polar cell cycle histidine kinases, PleC and DivJ, and the pole-specific pili protein CpaE, each tagged with a different fluorescent marker in a single strain. Four metrics characterizing the observed localization patterns of each of the three labeled proteins were extracted for hundreds of cell images from each of 854 mapped mutant strains. Using cluster analysis of the resulting set of 12-element vectors for each of these strains, we identified 52 strains with mutations that affected the localization pattern of the three tagged proteins. This information, combined with quantitative localization data from epitasis experiments, also identified all previously known proteins affecting such localization. These studies provide insights into factors affecting the PleC/DivJ localization network and into regulatory links between the localization of the pili assembly protein CpaE and the kinase localization pathway. Our high-throughput screening methodology can be adapted readily to any sequenced bacterial species, opening the potential for databases of localization regulatory networks across species, and investigation of localization network phylogenies.

Automated Quantitative Live Cell Fluorescence Microscopy

Advances in microscopy automation and image analysis have given biologists the tools to attempt large scale systems-level experiments on biological systems using microscope image readout. Fluorescence microscopy has become a standard tool for assaying gene function in RNAi knockdown screens and protein localization studies in eukaryotic systems. Similar high throughput studies can be attempted in prokaryotes, though the difficulties surrounding work at the diffraction limit pose challenges, and targeting essential genes in a high throughput way can be difficult. Here we will discuss efforts to make live-cell fluorescent microscopy based experiments using genetically encoded fluorescent reporters an automated, high throughput, and quantitative endeavor amenable to systems-level experiments in bacteria. We emphasize a quantitative data reduction approach, using simulation to help develop biologically relevant cell measurements that completely characterize the cell image. We give an example of how this type of data can be directly exploited by statistical learning algorithms to discover functional pathways.

A method for detecting and correcting feature misidentification on expression microarrays

BackgroundMuch of the microarray data published at Stanford is based on mouse and human arrays produced under controlled and monitored conditions at the Brown and Botstein laboratories and at the Stanford Functional Genomics Facility (SFGF). Nevertheless, as large datasets based on the Stanford Human array began to accumulate, a small but significant number of discrepancies were detected that required a serious attempt to track down the original source of error. Due to a controlled process environment, sufficient data was available to accurately track the entire process leading to up to the final expression data. In this paper, we describe our statistical methods to detect the inconsistencies in microarray data that arise from process errors, and discuss our technique to locate and fix these errors.ResultsTo date, the Brown and Botstein laboratories and the Stanford Functional Genomics Facility have together produced 40,000 large-scale (10–50,000 feature) cDNA microarrays. By applying the heuristic described here, we have been able to check most of these arrays for misidentified features, and have been able to confidently apply fixes to the data where needed. Out of the 265 million features checked in our database, problems were detected and corrected on 1.3 million of them.ConclusionProcess errors in any genome scale high throughput production regime can lead to subsequent errors in data analysis. We show the value of tracking multi-step high throughput operations by using this knowledge to detect and correct misidentified data on gene expression microarrays.