Indiana University-Purdue University Indianapolis (IUPUI) / SETMAR is a chimeric protein that originates from the fusion of a SET domain to the mariner Hsmar1 transposase. This fusion event occurred approximately 50 million years ago, after the split of an anthropoid primate ancestor from the prosimians. Thus, SETMAR is only expressed in anthropoid primates, such as humans, apes, and New World monkeys. Evolutionary sequence analyses have revealed that the DNA-binding domain, one of the two functional domains in the Hsmar1 transposase, has been subjected to a strong purifying selection. Consistent with these analyses, SETMAR retains robust binding specificity to its ancestral terminal inverted repeat (TIR) DNA. In the human genome, this TIR sequence is dispersed in over 1500 perfect or nearly perfect sites. Given that many DNA-binding domains of transcriptional regulators are derived from transposases, we hypothesized that SETMAR may play a role in gene regulation. In this thesis, we determined the crystal structures of the DNA-binding domain bound to both its ancestral TIR DNA and a variant TIR DNA sequence at 2.37 and 3.07 Å, respectively. Overall, the DNA-binding domain contains two helix-turn-helix (HTH) motifs linked by two AT-hook motifs and dimerizes through its HTH1 motif. In both complexes, minor groove interactions with the AT-hook motifs are similar, and major groove interactions with HTH1 involve a single residue. However, four residues from HTH2 participate in nucleobase-specific interactions with the TIR and only two with the variant DNA sequence. Despite these differences in nucleobase-specific interactions, the DNA-binding affinities of SETMAR to TIR or variant TIR differ by less than two-fold. From cell-based studies, we found that SETMAR represses firefly luciferase gene expression while the DNA-binding deficient mutant does not. A chromatin immunoprecipitation assay further confirms that SETMAR binds the TIR sequence in cells. Collectively, our studies suggest that SETMAR functions in gene regulation.

Indiana University-Purdue University Indianapolis (IUPUI) / The literature corresponding to disordered proteins has been on a rise. As the number of publications increase, the time and effort needed to manually identify the relevant publications and protein information to add to centralized repository (called DisProt) is becoming arduous and critical. Existing search facilities on PubMed can retrieve a seemingly large number of publications based on keywords and does not have any support for ranking them based on the probability of the protein names mentioned in a given abstract being added to DisProt. This thesis explores a novel system of using disorder predictors and context based dictionary methods to quickly identify publications on disordered proteins from the PubMed database. NLProt, which is built around Support Vector Machines, is used to identify protein names and PONDR-FIT which is an Artificial Neural Network based meta- predictor is used for identifying protein disorder. The work done in this thesis is of immediate significance in identifying disordered protein names. We have tested the new system on 100 abstracts from DisProt [these abstracts were found to be relevant to disordered proteins and were added to DisProt manually by the annotators.] This system had an accuracy of 87% on this test set. We then took another 100 recently added abstracts from PubMed and ran our algorithm on them. This time it had an accuracy of 68%. We suggested improvements to increase the accuracy and believe that this system can be applied for identifying disordered proteins from literature.

Indiana University-Purdue University Indianapolis (IUPUI) / Histones are highly basic proteins which when digested by trypsin are hard to analyze using mass spectrometry. Because histones are basic nuclear proteins, a nuclei prep followed by acid extraction is the best purification strategy to increase overall abundance of purified histones. Blocking the lysine residues and cleaving with trypsin is a useful technique to increase detection of histone peptides using MudPIT. In particular, carbamylation and propionylation are the best two methods to block lysine residues. Using both propionylation and carbamylation along with no treatment has been shown to increase the identification of unmodified and modified histone peptides when coupled with MudPIT analysis.

Indiana University-Purdue University Indianapolis (IUPUI) / Proteins are nano-machines that work inside every living organism. Functional disruption of one or several proteins is the cause for many diseases. However, the functions for most proteins are yet to be annotated because inexpensive sequencing techniques dramatically speed up discovery of new protein sequences (265 million and counting) and experimental examinations of every protein in all its possible functional categories are simply impractical. Thus, it is necessary to develop computational function-prediction tools that complement and guide experimental studies. In this study, we developed a series of predictors for highly accurate prediction of proteins with DNA-binding, RNA-binding and carbohydrate-binding capability. These predictors are a template-based technique that combines sequence and structural information with predicted binding affinity. Both sequence and structure-based approaches were developed. Results indicate the importance of binding affinity prediction for improving sensitivity and precision of function prediction. Application of these methods to the human genome and structure genome targets demonstrated its usefulness in annotating proteins of unknown functions and discovering moon-lighting proteins with DNA,RNA, or carbohydrate binding function. In addition, we also investigated disruption of protein functions by naturally occurring genetic variations due to insertions and deletions (INDELS). We found that protein structures are the most critical features in recognising disease-causing non-frame shifting INDELs. The predictors for function predictions are available at http://sparks-lab.org/spot, and the predictor for classification of non-frame shifting INDELs is available at http://sparks-lab.org/ddig.

Chen, Zhang. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (p. 109-114). / Abstracts in English and Chinese. / ABSTRACT --- p.I / 摘要 --- p.III / ACKNOWLEDGEMENTS --- p.IV / PUBLICATIONS --- p.VI / CONTRIBUTORS --- p.VII / TABLE OF CONTENTS --- p.VIII / LIST OF TABLES --- p.XIII / LIST OF FIGURES --- p.XIV / LIST OF ABBREVIATIONS --- p.XVI / Chapter SECTION I : --- BACKGROUND --- p.1 / Chapter CHAPTER 1: --- PRENATAL DIAGNOSIS OF FETAL TRISOMY BY NEXT GENERATION SEQUENCING TECHNOLOGY --- p.2 / Chapter 1.1 --- FETAL TRISOMY --- p.2 / Chapter 1.2 --- CONVENTIONAL PRENATAL DIAGNOSIS OF FETAL TRISOMIES --- p.3 / Chapter 1.3 --- CELL FREE FETAL D N A AND ITS APPLICATION IN PRENATAL DIAGNOSIS --- p.5 / Chapter 1.4 --- NEXT GENERATION SEQUENCING TECHNOLOGY --- p.5 / Chapter 1.5 --- SUBSTANTIAL BIAS IN THE NEXT GENERATION SEQUENCING PLATFORM --- p.9 / Chapter 1.6 --- PRENATAL DIAGNOSIS OF TRISOMY BY NEXT GENERATION SEQUENCING --- p.10 / Chapter 1.7 --- AIMS OF THIS THESIS --- p.11 / Chapter SECTION I I : --- MATERIALS AND METHODS --- p.13 / Chapter CHAPTER 2: --- METHODS FOR NONINVASIVE PRENATAL DIAGNOSIS OF FETAL TRISOMY MATERNAL PLASMA DNA SEQUENCING --- p.14 / Chapter 2.1 --- STUDY DESIGN AND PARTICIPANTS --- p.14 / Chapter 2.1.1 --- Ethics Statement --- p.14 / Chapter 2.1.2 --- "Study design, setting and participants" --- p.14 / Chapter 2.2 --- MATERNAL PLASMA D N A SEQUENCING --- p.17 / Chapter 2.3 --- SEQUENCING DATA ANALYSIS --- p.18 / Chapter SECTION I I I : --- TRISOMY 13 AND 18 DETECTION BY THE T21 BIOINFORMATICS ANALYSIS PIPELINE --- p.21 / Chapter CHAPTER 3: --- THE T21 BIOINFORMATICS ANALYSIS PIPELINE FOR TRISOMY 13 AND 18 DETECTION --- p.22 / Chapter 3.1 --- INTRODUCTION --- p.22 / Chapter 3.2 --- METHODS --- p.23 / Chapter 3.2.1 --- Bioinformatics analysis pipeline for trisomy 13 and 18 detection --- p.23 / Chapter 3.3 --- RESULTS --- p.23 / Chapter 3.3.1 --- Performance of the T21 bioinformatics analysis pipeline for trisomy 13 and 18 detection --- p.23 / Chapter 3.3.2 --- The precision of quantifying chrl 3 and chrl 8 --- p.27 / Chapter 3.4 --- DISCUSSION --- p.29 / Chapter SECTION IV : --- IMPROVING THE T21 BIOINFORMATICS ANALYSIS PIPELINE FOR TRISOMY 13 AND 18 DETECTION --- p.30 / Chapter CHAPTER 4: --- IMPROVING THE ALIGNMENT --- p.31 / Chapter 4.1 --- INTRODUCTION --- p.31 / Chapter 4.2 --- METHODS --- p.32 / Chapter 4.2.1 --- Allowing mismatches in the index sequences --- p.32 / Chapter 4.2.2 --- Calculating the mappability of the human reference genome --- p.33 / Chapter 4.2.3 --- Aligning reads to the non-repeat masked human reference genome --- p.34 / Chapter 4.2.4 --- Trisomy 13 and 18 detection --- p.34 / Chapter 4.3 --- RESULTS --- p.34 / Chapter 4.3.1 --- Increasing read numbers by allowing mismatches in the index sequences --- p.34 / Chapter 4.3.2 --- Increasing read numbers by using the non-masked reference genome for alignment . --- p.38 / Chapter 4.3.3 --- Allowing mismatches in the read alignment --- p.42 / Chapter 4.3.4 --- The performance of trisomy 13 and 18 detection after improving the alignment --- p.47 / Chapter 4.4 --- DISCUSSION --- p.50 / Chapter CHAPTER 5: --- REDUCING THE GC BIAS BY CORRECTION OF READ COUNTS --- p.53 / Chapter 5.1 --- INTRODUCTION --- p.53 / Chapter 5.2 --- METHODS --- p.54 / Chapter 5.2.1 --- Read alignment --- p.54 / Chapter 5.2.2 --- Calculating the correlation between GC content and read counts --- p.55 / Chapter 5.2.3 --- GC correction in read counts --- p.55 / Chapter 5.2.4 --- Trisomy 13 and 18 detection --- p.56 / Chapter 5.3 --- RESULTS --- p.56 / Chapter 5.3.1 --- GC bias in plasma DNA sequencing --- p.56 / Chapter 5.3.2 --- Correcting the GC bias in read counts by linear regression --- p.59 / Chapter 5.3.3 --- Correcting the GC bias in read counts by LOESS regression --- p.65 / Chapter 5.3.4 --- Bin size --- p.72 / Chapter 5.4 --- DISCUSSION --- p.75 / Chapter CHAPTER 6: --- REDUCING THE GC BIAS BY MODIFYING THE GENOMIC REPRESENTATION CALCULATION --- p.77 / Chapter 6.1 --- INTRODUCTION --- p.77 / Chapter 6.2 --- METHODS --- p.78 / Chapter 6.2.1 --- Modifying the genomic representation calculation --- p.78 / Chapter 6.2.2 --- Trisomy 13 and 18 detection --- p.78 / Chapter 6.2.3 --- Combining GC correction and modified genomic representation --- p.78 / Chapter 6.3 --- RESULTS --- p.79 / Chapter 6.3.1 --- Reducing the GC bias by modifying genomic representation calculation --- p.79 / Chapter 6.3.2 --- Combining GC correction and modified genomic representation --- p.86 / Chapter 6.4 --- DISCUSSION --- p.89 / Chapter CHAPTER 7: --- IMPROVING THE STATISTICS FOR TRISOMY 13 AND 18 DETECTION --- p.91 / Chapter 7.1 --- INTRODUCTION --- p.91 / Chapter 7.2 --- METHODS --- p.92 / Chapter 7.2.1 --- Comparing chrl 3 or chrl8 with other chromosomes within the sample --- p.92 / Chapter 7.2.2 --- Comparing chrl 3 or chrl 8 with the artificial chromosomes --- p.92 / Chapter 7.3 --- RESULTS --- p.93 / Chapter 7.3.1 --- Determining the trisomy 13 and 18 status by comparing chromosomes within the samples --- p.93 / Chapter 7.3.2 --- Determining the trisomy 13 and 18 status by comparing chrl3 or chrl 8 with artificial chromosomes --- p.97 / Chapter 7.4 --- DISCUSSION --- p.100 / Chapter SECTION V : --- CONCLUDING REMARKS --- p.102 / Chapter CHAPTER 8: --- CONCLUSION AND FUTURE PERSPECTIVES --- p.103 / Chapter 8.1 --- THE PERFORMANCE OF THE T21 BIOINFORMATICS ANALYSIS PIPELINE DEVELOPED FOR TRISOMY 21 DETECTION IS SUBOPTIMAL FOR TRISOMY 13 AND 18 DETECTION --- p.103 / Chapter 8.2 --- THE ALIGNMENT COULD BE IMPROVED BY ALLOWING ONE MISMATCH IN THE INDEX AND USING THE NON-REPEAT MASKED HUMAN REFERENCE GENOME AS THE ALIGNMENT REFERENCE --- p.104 / Chapter 8.3 --- THE PRECISION OF QUANTIFYING CHR13 AND CHR18 COULD BE IMPROVED BY THE G C CORRECTION OR THE MODIFIED GENOMIC REPRESENTATION --- p.104 / Chapter 8.4 --- THE STATISTICS FOR TRISOMY 13 AND 18 DETECTION COULD BE IMPROVED BY COMPARING CHR13 OR CHR18 WITH ARTIFICIAL CHROMOSOMES WITHIN THE SAMPLE --- p.105 / Chapter 8.5 --- PROSPECTS FOR FUTURE WORK --- p.106 / REFERENCE --- p.109

Thesis (PhD)--Macquarie University, Division of Environmental & Life Sciences, Dept. of Chemistry & Biomolecular Sciences, 2009. / Bibliography: p. 200-212. / Introduction -- Shotgun proteomic analysis of rat liver membrane proteins -- A combination of immobilised pH gradients improve membrane proteomics -- Affects of tumor-induced inflammation on membrane proteins abundance in the mouse liver -- Affects of tumor-induced inflammation on biochemical pathways in the mouse liver -- General discussion -- References. / The aim of this thesis was to develop a proteomics methodology that improves the identification of membrane proteomes from mammalian liver. Shotgun proteomics is a method that allows the analysis of proteins from cells, tissues and organs and provides comprehensive characterisation of proteomes of interest. The method developed in this thesis uses separation of peptides from trypsin digested membrane proteins by immobilised pH gradient isoelectric focusing (IPG-IEF) as the first dimension of two dimensional shotgun proteomics. In this thesis, peptide IPG-IEF was shown to be a highly reproducible, high resolution analytical separation that provided the identification of over 4,000 individual protein identifications from rat liver membrane samples. Furthermore, this shotgun proteomics strategy provided the identification of approximately 1,100 integral membrane proteins from the rat liver. The advantages of using peptide IPG-IEF as a shotgun proteomics separation dimension in conjunction with label-free quantification was applied to a biological question: namely, does the presence of a spatially unrelated benign tumor affect the abundance of mouse liver proteins. IPG-IEF shotgun proteomics provided comprehensive coverage of the mouse liver membrane proteome with 1,569 quantified proteins. In addition, the presence of an Englebreth-Holm-Swarm sarcoma induced changes in abundance of proteins in the mouse liver, including many integral membrane proteins. Changes in the abundance of liver proteins was observed in key liver metabolic processes such as fatty acid metabolism, fatty acid transport, xenobiotic metabolism and clearance. These results provide compelling evidence that the developed shotgun proteomics methodology allows for the comprehensive analysis of mammalian liver membrane proteins and detailed some of the underlying changes in liver metabolism induced by the presence of a tumor. This model may reflect changes that could occur in the livers of cancer patients and has implications for drug treatments. / Mode of access: World Wide Web. / 609 p. ill. (some col.)

Le suppresseur de tumeur p53 permet à la cellule de se défendre contre différentes formes de stress. Il joue un rôle de barrière s'opposant à la tumorigenèse: en effet la perte de p53 chez la souris prédispose grandement ces animaux à développer des tumeurs; de même le locus p53 est inactivé dans près de 50% des tumeurs humaines.<p>p53 constitue un facteur de transcription qui se lie à des séquences particulières de l'ADN et active l'expression des gènes adjacents. L'expression orchestrée de ces gènes conduit, directement ou indirectement et suivant le contexte cellulaire, soit à la mort de la cellule soit à l'inhibition de la division cellulaire.<p>Les mécanismes moléculaires médiant ces deux activités biologiques essentielles de p53, de même que les mécanismes influençant le choix de la réponse cellulaire, sont encore mal compris. L'importance de p53 dans ce choix reste également à démontrer.<p>Afin de contribuer à la compréhension de ces mécanismes, le modèle murin déficient pour Mdm4, un régulateur négatif de l'activité de p53, a été choisi. L'inactivation de Mdm4 chez la souris conduit en effet à l'activation ectopique de p53 in vivo et l'induction de deux types de réponse: apoptose dans le neuroépithélium et arrêt de la prolifération cellulaire dans les tissus non neuronaux. Le profil d'expression des gènes dans les tissus neuronaux et non neuronaux a donc été comparé entre embryons de souris sauvage et mdm4-/- par la technique d'hybridation de biopuces à ADN. Les résultats obtenus suggèrent que le type de réponse dépend du type cellulaire et non de p53 lui-même. En effet les profils d'expression des gènes dans les tissus neuronaux (conditions d'apoptose) et non neuronaux (conditions d'arrêt de la prolifération cellulaire) chez l'embryon de souris mdm4-/- sont comparables.<p><p>Nous nous sommes ensuite particulièrement intéressés à deux nouveaux gènes dont l'expression est augmentée dans les embryons mdm4-/-. Dans un premier temps, leur induction transcriptionnelle chez l'embryon de souris mdm4-/- a été confirmée par différentes techniques et il a été vérifié qu'ils constituaient tous deux des cibles directes de p53 induites suite à un stress génotoxique.<p>Le premier gène code Dapk1, une protéine suppresseur de tumeur pro-apoptotique présentant une activité de type sérine/thréonine kinase. Ce travail a permis d'établir que Dapk1 participait à une boucle de rétroaction du contrôle de l'activité de p53.<p>Le deuxième gène identifié code la protéine Ptprv, un récepteur transmembranaire présentant une activité de type tyrosine phosphatase. En vue d'étudier la signification physiologique de l'induction transcriptionnelle de ptprv suite à l'activation de p53, des expériences effectuées à partir de matériel biologique issu de souris déficientes pour Ptprv ont été réalisées. Ces expériences confirment le rôle essentiel de Ptprv comme médiateur de l'arrêt du cycle cellulaire en phase G1 induit par p53 suite à un stress génotoxique, à la fois in vitro et in vivo. Par contre, Ptprv ne semble pas influencer l'apoptose induite suite à l'activation de p53. Ce travail a également permis d'établir le rôle essentiel de Ptprv dans la suppression de tumeurs induites chez la souris par activation constitutive de l'oncogène Ras.<p> / Doctorat en sciences, Spécialisation biologie moléculaire / info:eu-repo/semantics/nonPublished

The selection of the forcefield is a crucial issue in any MD related work and there is no clear indication as to which of the many available forcefields is the best for protein analysis. Many recent literature surveys indicate that MD work may be hindered by two limitations, namely conformational sampling and forcefields used (inaccuracies in the potential energy function may bias the simulation toward incorrect conformations). However, the advances in computing infrastructures, theoretical and computing aspects of MD have paved the way to carry out a sampling on a sufficiently longtime scale, putting a need for the accuracies in the forcefield. Because there are established differences in MD results when using forcefields, we have sought to ask how we could assess common mobility segments from a protein by analysis of trajectories using three forcefields in a similar environment. This is important because, disparate fluctuations appear to be more at flexible regions compared to stiff regions; in particular, flexible regions are more relevant to functional activities of the protein molecule. Therefore, we have tried to assess the similarity in the dynamics using three well-known forcefields ENCAD, CHARMM27 and AMBERFF99SB for 61 monomeric proteins and identify the properties of dynamic residues, which may be important for function. The comparison of popular forcefields with different parameterization philosophy may give hints to improve some of the currently existing agnostics in forcefields and characterization of mobile regions based on dynamics of proteins with diverse folds. These may also give some signature on the proteins at the level of dynamics in relation to function, which can be used in protein engineering studies. Nanosecond level MD simulation(30ns) on 61 monomeric proteins were carried out using CHARMM and AMBER forcefields and the trajectories with ENCAD forcefield obtained from Dynameomics database. The trajectories were first analyzed to check whether structural and dynamic properties from the three forcefields similar choosing few parameters in each case. The gross dynamic properties calculated (root mean square deviation (RMSD), TM-score derived RMSD, radius of gyration and accessible surface area) indicated similarity in many proteins. Flexibility index analysis on 17 proteins, which showed a notable difference in the flexibility, indicated that tertiary interactions (fraction of nonnative stable hydrogen bonds and salt bridges) might be responsible for the difference in the flexibility index. The normalized subspace overlap and shape overlap score taken based on the covariance matrices derived from trajectories indicated that majority of the proteins show a range between 0.3-0.5 indicating that the first principal components from these proteins in different combinations may not match well. These results indicate that although dynamic properties in general are similar in many proteins. However, flexibility index and normalized subspace overlap score indicate that subspaces on the first principal component in many proteins may not match completely. The number of proteins showing a better correlation is higher in CHARMM-AMBER combinations than the other two. The structural features from trajectories have been computed in terms of fraction of secondary structure, hydrogen bonds, salt bridges and native contacts. Although secondary structures and native contacts are well preserved during the simulations, the tertiary interactions (hydrogen bonds) are lost in many proteins and may be responsible for the difference in the some of properties among forcefields. Comparison of simulation results to experimental structures in terms of Root mean square fluctuations, Accessible surface area and radius of gyration indicates that the simulations results are on par with the ones derived from experimental structures. We have tried to assess the flexibility in the proteins using normalized Root mean square fluctuations (nRMSF), which for a residue is the ratio of RMSF from simulation to that of crystal structure. We have selected a threshold for this nRMSF to indicate the mobile regions in a protein based on secondary structure analysis. Based on the threshold of nRMSF and conformational properties (deviation in the dihedral angles), we have classified the residue and evaluated the properties of rigid hinge residues and corresponding mobile residues in terms of residue propensity, secondary structure preference and accessible surface area ranges. Since the rigid dynamic residues represent the inherent mobility, they might be important for function. Therefore, we have tried to assess the functional relevance considering the dynamic mobile residues from each protein from each forcefield simulation with the residues important for the function (taken from literature and databases). It is observed that some residues found to be mobile from the simulation are found to match with the experimental ones, although in many cases the number of these mobile residues is higher compared to the experimental ones. In summary, an analysis of protein simulation trajectories using three forcefields on a set of monomeric protein has shown that the gross structural properties and secondary structures from many proteins remain similar, but there are differences as may be seen from flexibility index. However correlation in parameters from CHARMM and AMBER force field is better compared to other two combinations. The differences seen in some of structural properties may arise mainly due to the loss of few tertiary interactions as indicated by the fraction of native hydrogen bonds and salt bridges. Based on the nRMSF, mobile segments obtained from the simulations were identified, and some of the mobile segments are found to match the functionally important residues from the experimental ones. Our work indicates that there are still some differences in the properties from the simulations, which indicates that care must be exercised when choosing a forcefield, especially assessing the functionally relevant residues from the simulations.

Indiana University-Purdue University Indianapolis (IUPUI) / The C-terminal domain (CTD) of the RNA polymerase II (RNAPII) subunit Rpb1 must exist in a hypophosphorylated state prior to forming a competent transcription initiation complex. However, during transcription, specific kinases and phosphatases act on the RNAPII CTD to regulate its phosphorylation state, which serves to recruit sequence-specific and general transcription factors at the appropriate stage of transcription. A key phosphatase involved in this process, Rtr1 (Regulator of Transcription 1), was shown to regulate a key step important for transcription elongation and termination. Although the role that Rtr1 plays in regulating RNAPII transcription has been described, the mechanism involved in the recruitment of Rtr1 to RNAPII during transcription has not been elucidated in yeast. Consequently, the present work utilized both affinity purification schemes in Saccharomyces cerevisiae and mass spectrometry to identify key Rtr1-interacting proteins and post-translational modifications that potentially play a role in recruiting Rtr1 to RNAPII. In addition to RNAPII subunits, which were the most consistently enriched Rtr1-interacting proteins, seven proteins were identified that are potentially involved in Rtr1 recruitment. These included PAF complex subunits (Cdc73, Ctr9, Leo1), the heat shock protein Hsc82, the GTPase Npa3, the ATPase Rpt6, and Spn1. Indirect evidence was also uncovered that implicates that the CTDK-I complex, a kinase involved in RNAPII CTD phosphorylation, is important in facilitating interactions between Rtr1, RNAPII, and select transcription factors. Additionally, a putative phosphorylation site was identified on Ser217 of Rtr1 that may also play a role in its recruitment to RNAPII during transcription.

We used flow cytometry in a retrospective study of pleomorphic adenoma and carcinoma arising in pleomorphic adenoma, using paraffin-embedded tissue, to assess the relationship among proliferative activity, ploidy, and recurrence or malignant transformation. Twenty-four specimens obtained from 22 tumors were acceptable for analysis (co-efficient of variation, < or = 7.0), including multiple samples from two tumors. Fourteen tumors (13 benign and one malignant) were diploid. Six tumors were aneuploid: four benign pleomorphic adenomas and two carcinomas arising in pleomorphic adenoma. Two tetraploid tumors were malignant recurrences from the same patient. Of the recurrent tumors (nine benign and four malignant), 54% were aneuploid. The highest S-phase fractions were observed in recurrent and malignant pleomorphic adenomas. Immunostaining with p105, a nuclear proliferation antigen, revealed increased proliferative activity in a majority of pleomorphic adenomas. Increased proliferative activity and aneuploidy occurred in benign pleomorphic adenomas.