Cancer-associated fibroblasts are the main contributors to epithelial-to-mesenchymal signatures in the tumor microenvironment.

Epithelial-to-mesenchymal transition (EMT) is associated with tumor initiation, metastasis, and drug resistance. However, the mechanisms underlying these associations are largely unknown. We studied several tumor types to identify the source of EMT gene expression signals and a potential mechanism of resistance to immuno-oncology treatment. Across tumor types, EMT-related gene expression was strongly associated with expression of stroma-related genes. Based on RNA sequencing of multiple patient-derived xenograft models, EMT-related gene expression was enriched in the stroma versus parenchyma. EMT-related markers were predominantly expressed by cancer-associated fibroblasts (CAFs), cells of mesenchymal origin which produce a variety of matrix proteins and growth factors. Scores derived from a 3-gene CAF transcriptional signature (COL1A1, COL1A2, COL3A1) were sufficient to reproduce association between EMT-related markers and disease prognosis. Our results suggest that CAFs are the primary source of EMT signaling and have potential roles as biomarkers and targets for immuno-oncology therapies.

Fc-Optimized Anti-CCR8 Antibody Depletes Regulatory T Cells in Human Tumor Models.

FOXP3+ regulatory T cells (Treg) play a critical role in mediating tolerance to self-antigens and can repress antitumor immunity through multiple mechanisms. Therefore, targeted depletion of tumor-resident Tregs is warranted to promote effective antitumor immunity while preserving peripheral homeostasis. Here, we propose the chemokine receptor CCR8 as one such optimal tumor Treg target. CCR8 was expressed by Tregs in both murine and human tumors, and unlike CCR4, a Treg depletion target in the clinic, CCR8 was selectively expressed on suppressive tumor Tregs and minimally expressed on proinflammatory effector T cells (Teff). Preclinical mouse tumor modeling showed that depletion of CCR8+ Tregs through an FcyR-engaging anti-CCR8 antibody, but not blockade, enabled dose-dependent, effective, and long-lasting antitumor immunity that synergized with PD-1 blockade. This depletion was tumor Treg-restricted, sparing CCR8+ T cells in the spleen, thymus, and skin of mice. Importantly, Fc-optimized, nonfucosylated (nf) anti-human CCR8 antibodies specifically depleted Tregs and not Teffs in ex vivo tumor cultures from primary human specimens. These findings suggest that anti-CCR8-nf antibodies may deliver optimal tumor-targeted Treg depletion in the clinic, providing long-term antitumor memory responses while limiting peripheral toxicities. SIGNIFICANCE: These findings show that selective depletion of regulatory T cells with an anti-CCR8 antibody can improve antitumor immune responses as a monotherapy or in combination with other immunotherapies. GRAPHICAL ABSTRACT: http://cancerres.aacrjournals.org/content/canres/81/11/2983/F1.large.jpg.

Analyses of PD-L1 and Inflammatory Gene Expression Association with Efficacy of Nivolumab ± Ipilimumab in Gastric Cancer/Gastroesophageal Junction Cancer.

PURPOSE: In advanced gastric cancer/gastroesophageal junction cancer (GC/GEJC), there is a need to identify biomarkers of response to therapies, such as immune checkpoint inhibitors. PATIENTS AND METHODS: In post hoc exploratory analyses from CheckMate 032 (GC/GEJC cohort), we evaluated associations between nivolumab ± ipilimumab (NIVO ± IPI) efficacy and programmed death ligand 1 (PD-L1) expression, defined by tumor cells (% TC) or combined positive score (CPS; sum of PD-L1-staining TCs + immune cells, divided by total viable TCs, × 100) using the Dako PD-L1 IHC 28-8 pharmDx assay, or inflammatory gene expression. RESULTS: There was a trend toward increased efficacy (objective response and overall survival) when PD-L1 expression was determined by CPS compared with % TC at higher cutoffs of ≥5 and ≥10 in the pooled analysis of all treatment regimens. In this analysis, 19% and 26% of patients with PD-L1-positive tumors at a CPS cutoff of ≥5 and ≥10, respectively, had an objective response compared with 8% and 9% of patients at the equivalent % TC cutoffs. Longer survival was demonstrated in patients with PD-L1-positive (defined by CPS cutoffs of ≥5 and ≥10) versus PD-L1-negative status. Similar results were observed in the NIVO 1 mg/kg + IPI 3 mg/kg subgroup. Multiple inflammatory gene signatures/transcripts, including a signature consisting of four genes (CD274, CD8A, LAG3, and STAT1), showed associations with response to NIVO ± IPI. CONCLUSIONS: This study suggests a greater association of PD-L1 expression by CPS with NIVO ± IPI efficacy compared with % TC PD-L1 expression in patients with GC/GEJC. Inflammatory signatures were also associated with NIVO ± IPI response, warranting further investigation.See related commentary by Moutafi and Rimm, p. 3812.

Insights Gained from Single-Cell Analysis of Immune Cells in the Tumor Microenvironment.

Understanding tumor immune microenvironments is critical for identifying immune modifiers of cancer progression and developing cancer immunotherapies. Recent applications of single-cell RNA sequencing (scRNA-seq) in dissecting tumor microenvironments have brought important insights into the biology of tumor-infiltrating immune cells, including their heterogeneity, dynamics, and potential roles in both disease progression and response to immune checkpoint inhibitors and other immunotherapies. This review focuses on the advances in knowledge of tumor immune microenvironments acquired from scRNA-seq studies across multiple types of human tumors, with a particular emphasis on the study of phenotypic plasticity and lineage dynamics of immune cells in the tumor environment. We also discuss several imminent questions emerging from scRNA-seq observations and their potential solutions on the horizon.

CD96 Is an Immune Checkpoint That Regulates CD8+ T-cell Antitumor Function.

CD96 is a novel target for cancer immunotherapy shown to regulate NK cell effector function and metastasis. Here, we demonstrated that blocking CD96 suppressed primary tumor growth in a number of experimental mouse tumor models in a CD8+ T cell-dependent manner. DNAM-1/CD226, Batf3, IL12p35, and IFNγ were also critical, and CD96-deficient CD8+ T cells promoted greater tumor control than CD96-sufficient CD8+ T cells. The antitumor activity of anti-CD96 therapy was independent of Fc-mediated effector function and was more effective in dual combination with blockade of a number of immune checkpoints, including PD-1, PD-L1, TIGIT, and CTLA-4. We consistently observed coexpression of PD-1 with CD96 on CD8+ T lymphocytes in tumor-infiltrating leukocytes both in mouse and human cancers using mRNA analysis, flow cytometry, and multiplex IHF. The combination of anti-CD96 with anti-PD-1 increased the percentage of IFNγ-expressing CD8+ T lymphocytes. Addition of anti-CD96 to anti-PD-1 and anti-TIGIT resulted in superior antitumor responses, regardless of the ability of the anti-TIGIT isotype to engage FcR. The optimal triple combination was also dependent upon CD8+ T cells and IFNγ. Overall, these data demonstrate that CD96 is an immune checkpoint on CD8+ T cells and that blocking CD96 in combination with other immune-checkpoint inhibitors is a strategy to enhance T-cell activity and suppress tumor growth.

Genome-wide association analysis identifies genetic correlates of immune infiltrates in solid tumors.

Therapeutic options for the treatment of an increasing variety of cancers have been expanded by the introduction of a new class of drugs, commonly referred to as checkpoint blocking agents, that target the host immune system to positively modulate anti-tumor immune response. Although efficacy of these agents has been linked to a pre-existing level of tumor immune infiltrate, it remains unclear why some patients exhibit deep and durable responses to these agents while others do not benefit. To examine the influence of tumor genetics on tumor immune state, we interrogated the relationship between somatic mutation and copy number alteration with infiltration levels of 7 immune cell types across 40 tumor cohorts in The Cancer Genome Atlas. Levels of cytotoxic T, regulatory T, total T, natural killer, and B cells, as well as monocytes and M2 macrophages, were estimated using a novel set of transcriptional signatures that were designed to resist interference from the cellular heterogeneity of tumors. Tumor mutational load and estimates of tumor purity were included in our association models to adjust for biases in multi-modal genomic data. Copy number alterations, mutations summarized at the gene level, and position-specific mutations were evaluated for association with tumor immune infiltration. We observed a strong relationship between copy number loss of a large region of chromosome 9p and decreased lymphocyte estimates in melanoma, pancreatic, and head/neck cancers. Mutations in the oncogenes PIK3CA, FGFR3, and RAS/RAF family members, as well as the tumor suppressor TP53, were linked to changes in immune infiltration, usually in restricted tumor types. Associations of specific WNT/beta-catenin pathway genetic changes with immune state were limited, but we noted a link between 9p loss and the expression of the WNT receptor FZD3, suggesting that there are interactions between 9p alteration and WNT pathways. Finally, two different cell death regulators, CASP8 and DIDO1, were often mutated in head/neck tumors that had higher lymphocyte infiltrates. In summary, our study supports the relevance of tumor genetics to questions of efficacy and resistance in checkpoint blockade therapies. It also highlights the need to assess genome-wide influences during exploration of any specific tumor pathway hypothesized to be relevant to therapeutic response. Some of the observed genetic links to immune state, like 9p loss, may influence response to cancer immune therapies. Others, like mutations in cell death pathways, may help guide combination therapeutic approaches.

Gene expression analyses of mouse aortic endothelium in response to atherogenic stimuli.

OBJECTIVE: Endothelial cells are central to the initiation of atherosclerosis, yet there has been limited success in studying their gene expression in the mouse aorta. To address this, we developed a method for determining the global transcriptional changes that occur in the mouse endothelium in response to atherogenic conditions and applied it to investigate inflammatory stimuli. APPROACH AND RESULTS: We characterized a method for the isolation of endothelial cell RNA with high purity directly from mouse aortas and adapted this method to allow for the treatment of aortas ex vivo before RNA collection. Expression array analysis was performed on endothelial cell RNA isolated from control and hyperlipidemic prelesion mouse aortas, and 797 differentially expressed genes were identified. We also examined the effect of additional atherogenic conditions on endothelial gene expression, including ex vivo treatment with inflammatory stimuli, acute hyperlipidemia, and age. Of the 14 most highly differentially expressed genes in endothelium from prelesion aortas, 8 were also perturbed significantly by ≥ 1 atherogenic conditions: 2610019E17Rik, Abca1, H2-Ab1, H2-D1, Pf4, Ppbp, Pvrl2, and Tnnt2. CONCLUSIONS: We demonstrated that RNA can be isolated from mouse aortic endothelial cells after in vivo and ex vivo treatments of the murine vessel wall. We applied these methods to identify a group of genes, many of which have not been described previously as having a direct role in atherosclerosis, that were highly regulated by atherogenic stimuli and may play a role in early atherogenesis.

Unraveling inflammatory responses using systems genetics and gene-environment interactions in macrophages.

Many common diseases have an important inflammatory component mediated in part by macrophages. Here we used a systems genetics strategy to examine the role of common genetic variation in macrophage responses to inflammatory stimuli. We examined genome-wide transcript levels in macrophages from 92 strains of the Hybrid Mouse Diversity Panel. We exposed macrophages to control media, bacterial lipopolysaccharide (LPS), or oxidized phospholipids. We performed association mapping under each condition and identified several thousand expression quantitative trait loci (eQTL), gene-by-environment interactions, and eQTL "hot spots" that specifically control LPS responses. We used siRNA knockdown of candidate genes to validate an eQTL hot spot in chromosome 8 and identified the gene 2310061C15Rik as a regulator of inflammatory responses in macrophages. We have created a public database where the data presented here can be used as a resource for understanding many common inflammatory traits that are modeled in the mouse and for the dissection of regulatory relationships between genes.

An immune-active tumor microenvironment favors clinical response to ipilimumab.

PURPOSE: Ipilimumab, a fully human monoclonal antibody specific to CTLA-4, has been shown to improve overall survival in metastatic melanoma patients. As a consequence of CTLA-4 blockade, ipilimumab treatment is associated with proliferation and activation of peripheral T cells. To better understand various tumor-associated components that may influence the clinical outcome of ipilimumab treatment, gene expression profiles of tumors from patients treated with ipilimumab were characterized. EXPERIMENTAL DESIGN: Gene expression profiling was performed on tumor biopsies collected from 45 melanoma patients before and 3 weeks after the start of treatment in a phase II clinical trial. RESULTS: Analysis of pre-treatment tumors indicated that patients with high baseline expression levels of immune-related genes were more likely to respond favorably to ipilimumab. Furthermore, ipilimumab appeared to induce two major changes in tumors from patients who exhibited clinical activity: genes involved in immune response showed increased expression, whereas expression of genes for melanoma-specific antigens and genes involved in cell proliferation decreased. These changes were associated with the total lymphocyte infiltrate in tumors, and there was a suggestion of association with prolonged overall survival in these patients. Many IFN-γ-inducible genes and Th1-associated markers showed increased expression after ipilimumab treatment, suggesting an accumulation of this particular type of T cell at the tumor sites, which might play an important role in mediating the antitumor activity of ipilimumab. CONCLUSIONS: These results support the proposed mechanism of action of ipilimumab, suggesting that cell-mediated immune responses play an important role in the antitumor activity of ipilimumab.

Biochemical and transcriptional profiling to triage additional activities in a series of IGF-1R/IR inhibitors.

Therapeutic development of a targeted agent involves a series of decisions over additional activities that may be ignored, eliminated or pursued. This paper details the concurrent application of two methods that provide a spectrum of information about the biological activity of a compound: biochemical profiling on a large panel of kinase assays and transcriptional profiling of mRNA responses. Our mRNA profiling studies used a full dose range, identifying subsets of transcriptional responses with differing EC(50)s which may reflect distinct targets. Profiling data allowed prioritization for validation in xenograft models, generated testable hypotheses for active compounds, and informed decisions on the general utility of the series.

SDRS--an algorithm for analyzing large-scale dose-response data.

SUMMARY: Dose-response information is critical to understanding drug effects, yet analytical methods for dose-response assays cannot cope with the dimensionality of large-scale screening data such as the microarray profiling data. To overcome this limitation, we developed and implemented the Sigmoidal Dose Response Search (SDRS) algorithm, a grid search-based method designed to handle large-scale dose-response data. This method not only calculates the pharmacological parameters for every assay, but also provides built-in statistic that enables downstream systematic analyses, such as characterizing dose response at the transcriptome level. AVAILABILITY: Bio::SDRS is freely available from CPAN (www.cpan.org). CONTACTS: ruiruji@gmail.com; bruc@acm.org SUPPLEMENTARY INFORMATION: Supplementary data is available at Bioinformatics online.

Exome sequencing reveals comprehensive genomic alterations across eight cancer cell lines.

It is well established that genomic alterations play an essential role in oncogenesis, disease progression, and response of tumors to therapeutic intervention. The advances of next-generation sequencing technologies (NGS) provide unprecedented capabilities to scan genomes for changes such as mutations, deletions, and alterations of chromosomal copy number. However, the cost of full-genome sequencing still prevents the routine application of NGS in many areas. Capturing and sequencing the coding exons of genes (the "exome") can be a cost-effective approach for identifying changes that result in alteration of protein sequences. We applied an exome-sequencing technology (Roche Nimblegen capture paired with 454 sequencing) to identify sequence variation and mutations in eight commonly used cancer cell lines from a variety of tissue origins (A2780, A549, Colo205, GTL16, NCI-H661, MDA-MB468, PC3, and RD). We showed that this technology can accurately identify sequence variation, providing ∼95% concordance with Affymetrix SNP Array 6.0 performed on the same cell lines. Furthermore, we detected 19 of the 21 mutations reported in Sanger COSMIC database for these cell lines. We identified an average of 2,779 potential novel sequence variations/mutations per cell line, of which 1,904 were non-synonymous. Many non-synonymous changes were identified in kinases and known cancer-related genes. In addition we confirmed that the read-depth of exome sequence data can be used to estimate high-level gene amplifications and identify homologous deletions. In summary, we demonstrate that exome sequencing can be a reliable and cost-effective way for identifying alterations in cancer genomes, and we have generated a comprehensive catalogue of genomic alterations in coding regions of eight cancer cell lines. These findings could provide important insights into cancer pathways and mechanisms of resistance to anti-cancer therapies.

Comparative analysis of proteome and transcriptome variation in mouse.

The relationships between the levels of transcripts and the levels of the proteins they encode have not been examined comprehensively in mammals, although previous work in plants and yeast suggest a surprisingly modest correlation. We have examined this issue using a genetic approach in which natural variations were used to perturb both transcript levels and protein levels among inbred strains of mice. We quantified over 5,000 peptides and over 22,000 transcripts in livers of 97 inbred and recombinant inbred strains and focused on the 7,185 most heritable transcripts and 486 most reliable proteins. The transcript levels were quantified by microarray analysis in three replicates and the proteins were quantified by Liquid Chromatography-Mass Spectrometry using O(18)-reference-based isotope labeling approach. We show that the levels of transcripts and proteins correlate significantly for only about half of the genes tested, with an average correlation of 0.27, and the correlations of transcripts and proteins varied depending on the cellular location and biological function of the gene. We examined technical and biological factors that could contribute to the modest correlation. For example, differential splicing clearly affects the analyses for certain genes; but, based on deep sequencing, this does not substantially contribute to the overall estimate of the correlation. We also employed genome-wide association analyses to map loci controlling both transcript and protein levels. Surprisingly, little overlap was observed between the protein- and transcript-mapped loci. We have typed numerous clinically relevant traits among the strains, including adiposity, lipoprotein levels, and tissue parameters. Using correlation analysis, we found that a low number of clinical trait relationships are preserved between the protein and mRNA gene products and that the majority of such relationships are specific to either the protein levels or transcript levels. Surprisingly, transcript levels were more strongly correlated with clinical traits than protein levels. In light of the widespread use of high-throughput technologies in both clinical and basic research, the results presented have practical as well as basic implications.

A high-resolution association mapping panel for the dissection of complex traits in mice.

Systems genetics relies on common genetic variants to elucidate biologic networks contributing to complex disease-related phenotypes. Mice are ideal model organisms for such approaches, but linkage analysis has been only modestly successful due to low mapping resolution. Association analysis in mice has the potential of much better resolution, but it is confounded by population structure and inadequate power to map traits that explain less than 10% of the variance, typical of mouse quantitative trait loci (QTL). We report a novel strategy for association mapping that combines classic inbred strains for mapping resolution and recombinant inbred strains for mapping power. Using a mixed model algorithm to correct for population structure, we validate the approach by mapping over 2500 cis-expression QTL with a resolution an order of magnitude narrower than traditional QTL analysis. We also report the fine mapping of metabolic traits such as plasma lipids. This resource, termed the Hybrid Mouse Diversity Panel, makes possible the integration of multiple data sets and should prove useful for systems-based approaches to complex traits and studies of gene-by-environment interactions.

Transcriptional profiling of the dose response: a more powerful approach for characterizing drug activities.

The dose response curve is the gold standard for measuring the effect of a drug treatment, but is rarely used in genomic scale transcriptional profiling due to perceived obstacles of cost and analysis. One barrier to examining transcriptional dose responses is that existing methods for microarray data analysis can identify patterns, but provide no quantitative pharmacological information. We developed analytical methods that identify transcripts responsive to dose, calculate classical pharmacological parameters such as the EC50, and enable an in-depth analysis of coordinated dose-dependent treatment effects. The approach was applied to a transcriptional profiling study that evaluated four kinase inhibitors (imatinib, nilotinib, dasatinib and PD0325901) across a six-logarithm dose range, using 12 arrays per compound. The transcript responses proved a powerful means to characterize and compare the compounds: the distribution of EC50 values for the transcriptome was linked to specific targets, dose-dependent effects on cellular processes were identified using automated pathway analysis, and a connection was seen between EC50s in standard cellular assays and transcriptional EC50s. Our approach greatly enriches the information that can be obtained from standard transcriptional profiling technology. Moreover, these methods are automated, robust to non-optimized assays, and could be applied to other sources of quantitative data.

Gene set enrichment analysis.

Set enrichment analytical methods have become commonplace tools applied to the analysis and interpretation of biological data. The statistical techniques are used to identify categorical biases within lists of genes, proteins, or metabolites. The goal is to discover the shared functions or properties of the biological items represented within the lists. Application of these methods can provide great biological insight, including the discovery of participation in the same biological activity or pathway, shared interacting genes or regulators, common cellular compartmentalization, or association with disease. The methods require ordered or unordered lists of biological items as input, understanding of the reference set from which the lists were selected, categorical classifiers describing the items, and a statistical algorithm to assess bias of each classifier. Due to the complexity of most algorithms and the number of calculations performed, computer software is almost always used for execution of the algorithm, as well as for presentation of the results. This chapter will provide an overview of the statistical methods used to perform an enrichment analysis. Guidelines for assembly of the requisite information will be presented, with a focus on careful definition of the sets used by the statistical algorithms. The need for multiple test correction when working with large libraries of classifiers is emphasized, and we outline several options for performing the corrections. Finally, interpreting the results of such analysis will be discussed along with examples of recent research utilizing the techniques.

HBEGF, SRA1, and IK: Three cosegregating genes as determinants of cardiomyopathy.

Human dilated cardiomyopathy (DCM), a disorder of the cardiac muscle, causes considerable morbidity and mortality and is one of the major causes of sudden cardiac death. Genetic factors play a role in the etiology and pathogenesis of DCM. Disease-associated genetic variations identified to date have been identified in single families or single sporadic patients and explain a minority of the etiology of DCM. We show that a 600-kb region of linkage disequilibrium (LD) on 5q31.2-3, harboring multiple genes, is associated with cardiomyopathy in three independent Caucasian populations (combined P-value = 0.00087). Functional assessment in zebrafish demonstrates that at least three genes, orthologous to loci in this LD block, HBEGF, IK, and SRA1, result independently in a phenotype of myocardial contractile dysfunction when their expression is reduced with morpholino antisense reagents. Evolutionary analysis across multiple vertebrate genomes suggests that this heart failure-associated LD block emerged by a series of genomic rearrangements across amphibian, avian, and mammalian genomes and is maintained as a cluster in mammals. Taken together, these observations challenge the simple notion that disease phenotypes can be traced to altered function of a single locus within a haplotype and suggest that a more detailed assessment of causality can be necessary.

Construction of a reference gene association network from multiple profiling data: application to data analysis.

MOTIVATION: Gene expression profiling is an important tool for gaining insight into biology. Novel strategies are required to analyze the growing archives of microarray data and extract useful information from them. One area of interest is in the construction of gene association networks from collections of profiling data. Various approaches have been proposed to construct gene networks using profiling data, and these networks have been used in functional inference as well as in data visualization. Here, we investigated a non-parametric approach to translate profiling data into a gene network. We explored the characteristics and utility of the resulting network and investigated the use of network information in analysis of variance models and hypothesis testing. RESULTS: Our work is composed of two parts: gene network construction and partitioning and hypothesis testing using sub-networks as groups. In the first part, multiple independently collected microarray datasets from the Gene Expression Omnibus data repository were analyzed to identify probe pairs that are positively co-regulated across the samples. A co-expression network was constructed based on a reciprocal ranking criteria and a false discovery rate analysis. We named this network Reference Gene Association (RGA) network. Then, the network was partitioned into densely connected sub-networks of probes using a multilevel graph partitioning algorithm. In the second part, we proposed a new, MANOVA-based approach that can take individual probe expression values as input and perform hypothesis testing at the sub-network level. We applied this MANOVA methodology to two published studies and our analysis indicated that the methodology is both effective and sensitive for identifying transcriptional sub-networks or pathways that are perturbed across treatments.

Nearest Neighbor Networks: clustering expression data based on gene neighborhoods.

BACKGROUND: The availability of microarrays measuring thousands of genes simultaneously across hundreds of biological conditions represents an opportunity to understand both individual biological pathways and the integrated workings of the cell. However, translating this amount of data into biological insight remains a daunting task. An important initial step in the analysis of microarray data is clustering of genes with similar behavior. A number of classical techniques are commonly used to perform this task, particularly hierarchical and K-means clustering, and many novel approaches have been suggested recently. While these approaches are useful, they are not without drawbacks; these methods can find clusters in purely random data, and even clusters enriched for biological functions can be skewed towards a small number of processes (e.g. ribosomes). RESULTS: We developed Nearest Neighbor Networks (NNN), a graph-based algorithm to generate clusters of genes with similar expression profiles. This method produces clusters based on overlapping cliques within an interaction network generated from mutual nearest neighborhoods. This focus on nearest neighbors rather than on absolute distance measures allows us to capture clusters with high connectivity even when they are spatially separated, and requiring mutual nearest neighbors allows genes with no sufficiently similar partners to remain unclustered. We compared the clusters generated by NNN with those generated by eight other clustering methods. NNN was particularly successful at generating functionally coherent clusters with high precision, and these clusters generally represented a much broader selection of biological processes than those recovered by other methods. CONCLUSION: The Nearest Neighbor Networks algorithm is a valuable clustering method that effectively groups genes that are likely to be functionally related. It is particularly attractive due to its simplicity, its success in the analysis of large datasets, and its ability to span a wide range of biological functions with high precision.

Structural and functional characterization of CFE88: evidence that a conserved and essential bacterial protein is a methyltransferase.

CFE88 is a conserved essential gene product from Streptococcus pneumoniae. This 227-residue protein has minimal sequence similarity to proteins of known 3D structure. Sequence alignment models and computational protein threading studies suggest that CFE88 is a methyltransferase. Characterization of the conformation and function of CFE88 has been performed by using several techniques. Backbone atom and limited side-chain atom NMR resonance assignments have been obtained. The data indicate that CFE88 has two domains: an N-terminal domain with 163 residues and a C-terminal domain with 64 residues. The C-terminal domain is primarily helical, while the N-terminal domain has a mixed helical/extended (Rossmann) fold. By aligning the experimentally observed elements of secondary structure, an initial unrefined model of CFE88 has been constructed based on the X-ray structure of ErmC' methyltransferase (Protein Data Bank entry 1QAN). NMR and biophysical studies demonstrate binding of S-adenosyl-L-homocysteine (SAH) to CFE88; these interactions have been localized by NMR to the predicted active site in the N-terminal domain. Mutants that target this predicted active site (H26W, E46R, and E46W) have been constructed and characterized. Overall, our results both indicate that CFE88 is a methyltransferase and further suggest that the methyltransferase activity is essential for bacterial survival.