This review article focuses on the various aspects of translational research, where research on human subjects can ultimately enhance the diagnosis and treatment of future patients. While we will use specific examples relating to the asbestos related cancer mesothelioma, it should be stressed that the general approach outlined throughout this review is readily applicable to other diseases with an underlying molecular basis. Through the integration of molecular-based technologies, systematic tissue procurement and medical informatics, we now have the ability to identify clinically applicable "genotype"-"phenotype" associations across cohorts of patients that can rapidly be translated into useful diagnostic and treatment strategies. This review will touch on the various steps in the translational pipeline, and highlight some of the most essential elements as well as possible roadblocks that can impact success of the program. Critical issues with regard to Institutional Review Board (IRB) and Health Insurance Portability and Accountability Act (HIPAA) compliance, data standardization, sample procurement, quality control (QC), quality assurance (QA), data analysis, preclinical models and clinical trials are addressed. The various facets of the translational pipeline have been incorporated into a fully integrated computational system, appropriately named Dx2Tx. This system readily allows for the identification of new diagnostic tests, the discovery of biomarkers and drugable targets, and prediction of optimal treatments based upon the underlying molecular basis of the disease. Our systematic approach to translational medicine has been designed to achieve a vision shared by numerous "ambitious" investigators who are focused on applying bench-side discoveries to the clinical setting Figure . We sharWhile a degree of project specificity must be incorporated into the design of any research effort, many of the components of translational research are shared across apparently disparate disease areas Figure . AlthougEffective translational research requires a multi-disciplinary and team approach. Our initial concept meetings include HIPAA/IRB advisors, research nurses, surgeons , pathologists and members of diagnostic service labs, information technologists  and statisticians in addition to the principal investigators. Each member of the translational research team is critical to the overall productivity and success of the pipeline. In acknowledgement of the effectiveness of this approach, we would advise other establishments to involve key personnel across the various disciplines early. We also believe that smaller dedicated teams can often be more efficient and less bureaucratic than oversized consortium.The development of research protocols on human subjects that involve specimen and/or data collection must be approved by the investigational review boards (IRB). The IRB governs patient safety and risk for the hospitals and/or universities/institutions. In accordance with federal regulations, one of the requirements states that each human subject must be thoroughly informed about the research to be undertaken on their sample and/or data during a consenting process. The specificity of the consent should be such that it outlines the research protocol, study procedures and risks and benefits and meets the federal, state and local requirements, so that the subject can make an informed decision regarding participation in the study. In retrospect, a well informed patient can help facilitate the procurement of clinical data and samples in an efficient and longitudinal manner..While the typical testing of a hypothesis requires the collection of only a subset of the available clinical data , the emerging field of medical informatics and electronic charts allows for the potential collection of vast quantities of standardized clinical data that potentially harbors invaluable information with regard to additional "traits" or "phenotypes". As such, we currently collect upward of 9000 potential data fields per patient within our specific IRB-approved protocols, all of which can be populated during the normal course of patient care and stored within either electronic or paper records for each subject. In addition, each patient receives a standardized self-reporting questionnaire addressing histories . Any protected fields are restricted through a security portal, which can be considered a suitable clinical gatekeeper  Figure . This seIn addition to IRB and HIPAA issues, patient recruitment can also be a major hurdle. The teamwork approach is critical here, together with patient/physician outreach and screening efforts as necessary. Our experience is the vast majority of patients are enthusiastic about inclusion in research studies so long as they are informed of the opportunity, particularly since we are careful to follow standard-of-care. For the most part, we are finding a willingness to participate for the hope of betterment for the whole, whether it is potentially helping their own families or others with their disease. At this level, the physician is the central figure and is uniquely positioned to introduce the protocol and support patient participation in the study. For studies where low participation is expected (predominantly due to low incidence), we typically include an IRB-approved public educational effort  that maximizes recruitment. Our clinical research nurses work closely with the physician(s) and their office scheduling staff for notification of potential candidates in advance wherever possible. This team approach with the physician advocating the effort allows the greatest accrual particularly when there are several geographic sites for meeting potential protocol volunteers. On site consenting of high risk or suspected individuals within the outpatient clinic and hospital settings where these patients are typically evaluated should to be considered to enhance accrual. For patients with known disease, the best environment for accrual occurs in a multidisciplinary setting in which the key physician works closely with the medical oncologist, radiation oncologist, nurses, physician assistants, residents and research staff. This is particularly important for longitudinal sampling of patients on treatment protocols to minimize the loss of follow-up data. The logistics of a coordinated approach may differ between a university medical school setting and a community based practice due to the competing obligations of a busy private practice. The multidisciplinary approach is optimal in a single setting in which patients can be seen by the various disciplines, thereby, reducing the extent of patient relocation between specialty offices and allowing for centralized specimen and data collection. Nonetheless, in community-based practice, we have introduced standardized mechanisms allowing us to consent patients and collect clinical samples and data from multiple sites.There are now abundant examples where clinical researchers are using the revolutionary "omics" technologies to demonstrate a clear association between the molecular make-up ("genotype") of clinical samples and well-defined clinical characteristics ("phenotype"). Near-complete genotypes can now be obtained through multiplex technologies such as sequencing, mutational screening (such as single nucleotide polymorphism (SNP) analysis) and gene/protein expression profiling. The absence of well annotated clinical information , however, is evident from most studies reported to date. Rather than taking the reductionist approach at the time of data collection, our group instead chooses to gather all information relating to an individual's medical history as well as follow-up data as it becomes available and as approved by the IRB. It is relatively trivial to reduce the clinical data pool retrospectively during analysis as deemed necessary. Our Dx2Tx system date/time stamps individual events for a given subject/sample, such that multiple events can be temporally associated. The first step is to gather the information in its rawest form from a standardized source. This could be as complicated as a centralized medical informatics database  housing rich longitudinal clinical data that can be expressed in Health Level 7 standards for data portability), or it could be as uncomplicated as a locally maintained excel spreadsheet or access database. Regardless of the source, we attempt to collect as much information regarding a subject's history, diagnosis, treatment, and response assessment as possible. As highlighted above, the critical elements are reliability of the source data and standardization. While we do perform statistical analysis on parsed text from open string comments, we attempt to force objective measurements  wherever feasible. In the absence of a centralized clinical database utilizing standardized clinical nomenclature and data, the responsibility falls on the clinical members of the team to interpret clinical data within isolated databases and/or paper charts. Clearly, the navigation from paper to electronic medical records, and the generation of middleware that can link disparate databases, will greatly alleviate this burden that rapidly becomes a major rate-limiting step in the translational pipeline. Coupled with voice recognition and electronic data recording with inbuilt QC check-points, standardized digital data entry should become the norm in the not so distant future.Clinical data permitted under protocol should be, where possible, prospectively accumulated. The accuracy of the data, particularly those parameters such as the pathologic characteristics, clinical staging, dates of intervention, dates of intermediate endpoints (such as disease progression or death), must be unquestionable in order to identify clinical and molecular correlations among diverse datasets. The accuracy of these data will depend to a large extent upon the frequency of follow-up for parameters such as disease progression, and the degree to which the patients' status is investigated longitudinally. Patients on clinical trials, where the procedures and follow-up are specified as part of the protocol, will have the most robust and standardized data since interval radiographic and physical examinations and/or other clinical procedures and methods of data collection must be adhered to in order to avoid protocol violation. Moreover, the approach of the physician(s) who follows the patient and the availability of an existing mechanism that readily allows the retrieval of prospective data (i.e. a database) will greatly influence the accuracy of the data. Within oncology, staging issues are some of the more difficult to resolve, especially if inadequate staging is performed in the operating room (OR) or if one must rely entirely on clinical or radiographic data. For the majority of organ systems, the oncological staging system is specified in the AJCC Staging Handbook, but it is the responsibility of the investigator to know whether the pathologic and intraoperative details necessary for accurate staging were adequately performed. These include but are not limited to accurate size measurement of the primary tumor, careful intraoperative description of abutment or invasion of nearby structures, verification of negative margins, and extent of lymph node involvement. The patients used in the examples below were all part of either Phase II or Phase III mesothelioma protocols, with computerized tomographic surveillance after surgery every 3–4 months until death. As such, the accuracy of the clinical data was optimized. One surgeon performed the operations, obtained aggressive intraoperative staging in all cases, and took the responsibility for collecting samples and data from all participants in the study. This approach led to the development of a consistent standard of care philosophy for the surgical management of mesothelioma.Our Dx2Tx system performs detailed statistical analysis on both clinical and molecular data, thus leaving little room for error when it comes to standardized data collection and entry. Physicians are uniquely positioned to organize the various aspects of data collection. This endeavor begins with the design of a clinical protocol for the procurement of specimens and the collection of data from a patient population. In addition to identifying the experimental group of patients (i.e. those with a condition or trait of interest), it is important to ensure the inclusion of the proper control populations wherever possible. With respect to our mesothelioma protocols, the correct controls for early diagnostic strategies include age-matched individuals exposed to similar doses of asbestos in order to compare with equivalent individuals diagnosed with the established disease. The recruitment of these control subjects ideally begins in parallel with the recruitment of the experimental group. In the absence of the correct control group, the investigator must identify cohorts of individuals within existing cooperative group mechanisms, SPORES, or the Early Detection Research Network (EDRN). In order to circumvent this problem, the Karmanos Cancer Institute has established a National Center for Vermiculite and Asbestos Related Cancers through a cooperative clinical trial with the Center for Environmental Medicine in Southeast Michigan. This center, under informed consent, evaluates individuals who have been exposed to asbestos in the workplace or at home, and depending on history and pulmonary function data, these individuals undergo computerized tomographic scanning to establish a baseline radiographic evaluation of asbestos exposure and risk. These individuals also give written permission for periodic sample collection including blood and urine for studies of marker assessment for asbestos related malignancies.A major focus of our research is to identify the molecular causes of differential therapeutic response across patient populations (pharmacogenetics). It is generally appreciated that patients and their disease show significant variations in response to a given treatment . It is tWe have introduced standard procurement procedures for various physiological samples including tissue, urine and blood, all of which are now routinely collected under our IRB-approved protocols. These have been optimized for both ease and reliability of clinical collection as well as maintaining the integrity of the sample for subsequent histopathological and molecular analysis. DNA from peripheral blood mononuclear lymphocytes (PBML) obtained from whole blood can be screened by SNP analysis, to identify possible genetic markers of a clinical event -4. Once The precise flow of the clinical sample from the patient to the researcher will depend largely on the disease in question and pre-existing departmental procedures. For example, in a study of pancreatic cancer, urine is provided by the patient prior to the procedure, since we have found that intra-procedural catheterization can result in blood contamination that effects subsequent proteomic analysis. Blood is drawn from an IV access for collection of plasma and PBML, while surgically resected tissue is snap frozen on site (see below). However, a multiple myeloma research protocol requires the collection of bone marrow aspirates from both inpatient and outpatient clinics, which are placed into tissue culture media and rapidly transported on ice to the Flow Cytometry Molecular Diagnostic Laboratory for immunophenotyping and cytometric sorting of plasma cells. The sorted fraction is then archived in freezing media for subsequent DNA, RNA and/or protein analysis. Irrespective of procedure, it is important to maintain a log book that tracks the flow of a sample from the subject to freeze. Procurement time is particularly important for RNA and some protein analysis, since biomolecular degradation can be a significant factor.Prior to collection, our procurement team (lab personnel and research nurses) pre-label collection tubes and containers with anonymous identifier tags that are freezer safe. Several kits that include equipment for blood draws, urine collection and tissue procurement are preassembled and made available to the research staff to ensure rapid response for obtaining specimens. We have an on-call list of several members for the research team who can reach the pick-up point within 30 minutes of receiving a collection call. Through a coordinated multidisciplinary approach, consenting and scheduling is typically obtained well in advance in either the outpatient or inpatient setting.At the Karmanos Institute, we have found that the easiest way to insure proper specimen harvest from patients with solid tumors is for the surgeon to divide the specimen directly in the OR and supervise its collection and distribution to the laboratory for archiving. However at the Van Andel Research Institute, we require oversight from the Hospital Department of Pathology to release surgically resected samples within the OR to the research team. The critical issue is to ensure suitable material is harvested in the correct fashion to allow accurate histopathological diagnosis. In no way should the research effort impede this quality-of-care. As a safety margin, we hold all frozen tissue for a period of time until an official diagnosis has been reported. In addition, wherever possible we embed samples in OCT freezing media, and generate hematoxylin/eosin (H/E)-stained histopathology slides for each research sample. This not only provides high quality histopathology slides for possible diagnostic back-up, but also allows us to correlate our molecular findings with the histology of the same working sample. In addition to entering pertinent data regarding the official diagnostic pathology report, we have also developed standardized report templates within Dx2Tx that accompany each research sample, in which the pathological image is associated with the corresponding data addressing critical variables such as relative amount of each cell type, stage, morphology etc. As with all data entered into Dx2Tx, these metadata are directly amenable to subsequent statistical analysis (see below).At the time of resection, solid tissue samples are immediately frozen in either liquid nitrogen or, for sites with no supply, a dry ice-chilled isopentane bath. Samples are transported in aluminum foil, and stored at -80°C until further processing. It should also be pointed out that many of our protocols involve the collection of tissue biopsies in addition to surgical resections. Since it is physically difficult to split a biopsy , we typically acquire additional samples beyond what are required for accurate pathological diagnosis. If these additional samples are required by the research team, it is important to disclose the supplementary procedures to the patient in the informed consent. While obviously smaller in size compared to surgical resections, we procure these specimens as fresh tissue as described above. It is important to note, some of our molecular technologies allow for the use of as few as 500 cells for complete multiplex analysis. Since biopsy material will doubtless be the predominant source of tissue for future molecular-based diagnostics, it is important to develop protocols to address smaller sample size and possible mis-sampling issues. In addition, as a result of not restricting samples to excess surgical specimens, biopsies can rapidly increase sample accrual from larger cohorts of patients.Urine and blood samples are transported on wet ice to the research labs, while frozen tissue is transferred on dry ice. Upon arrival in the laboratory, urine is typically divided into suitably sized aliquots and frozen at -80°C. Blood is fractionated into plasma or serum , while PBML are isolated by centrifugation and Ficoll/Hipaque density gradient centrifugation and cryogenically frozen. Fresh frozen tissue specimens are blocked in OCT if possible and stored at -80°C until further use. All specimens are associated with the donating subject within Dx2Tx, and archived in a simple grid system within dedicated and alarmed -80°C freezers. The location and use of each aliquot is tracked within Dx2Tx to readily allow for subsequent retrieval.). We enter these variables into Dx2Tx in association with the corresponding experimental procedure such that they can be analyzed together with the derived molecular and clinical data. In this fashion, important QC and QA parameters can be interrelated with molecular and clinical data. Experiments that do not meet certain QC/QA criteria can be excluded from the analysis if the data suggest that the variable(s) is a major confounder. In this fashion, Dx2Tx serves as an electronic notebook, the entries into which can be routinely recorded and statistically analyzed. This feature will be particularly important as potential applications advance towards clinical utility, for example through ensuring compliance with the FDA and accrediting agencies such as JACHO, CMS and CAP, and while maintaining the necessary state, federal, and insurance applications required to provide clinical diagnostic services.The tissue collection protocols outlined above should be evaluated for compatibility for the molecular analysis to be performed. We typically perform SNP analysis on DNA isolated from the PBML fraction of whole blood, gene expression (mRNA) analysis on tissue, and proteomic analysis of blood plasma/serum, urine and tissue. The detailed discussion of each of these molecular protocols is beyond the scope of this review  and detection (such as mass spectrometry) elements. Coupled with extensive clinical content, this collectively presents significant challenges for data storage and retrieval, as well as statistical analysis. The most frequent frustration of new users of the "omics" technologies remains "What are the data telling me?"Before proceeding with specific examples, it is important to grasp the concepts of normalization and data massaging or "filtering" . To compOne problem in dealing with vast amounts of clinical and/or multiplexed molecular data from a relatively small sample population is that many of the observed correlations could (not necessarily do) occur by random chance. One method investigators use to minimize the probability of this so called "false discovery" is to reduce the complexity of the data through successive rounds of filtering. Depending on the application, we do use some filtering under certain circumstances. However, as with normalization, this is done during data analysis and not prior to database entry. In this fashion, we can filter and analyze the data "on the fly", allowing the user to evaluate the effect of filtering the raw data. For example, if we were looking for a specific set of genes that are found highly expressed in mesothelioma relative to other tumor types, we may eliminate genes in mesothelioma samples below a certain expression (intensity) threshold, and those above a certain level in other tumor types. This would maximize the likelihood of identifying sensitive and specific mesothelioma genes, and minimize the gene pool and hence probability of false discovery. We also use fold-change filters depending on the application. For example, we may only be interested in diagnostic biomarkers that display at least a 4-fold increase in mesothelioma tumor tissue relative to other tumor types and normal mesothelium. In addition, genes with certain characteristics  to identify relationships between variables  within large datasets ,14. Thes), we also map results to molecular pathways using detailed pathway mapping software now available. Taking this approach, the coordinated expression of genes/proteins can be seen to map to specific molecular networks, therefore providing important information as to which pathways maybe activated or in-activated in association with a clinical event. For example, when all the genes differentially expressed in recurrent mesotheliomas relative to non-recurring tumors at a defined statistical significance (p < 0.001) are mapped using the MetaCore™ software (MetaCore™ ), a clear signaling pathway associated with cell proliferation is identified that appears to be hyper-activated in aggressive mesothelioma tumors  within the complex clinical and molecular datasets that could be translated into clinical diagnostic applications, we typically begin with unsupervised clustering techniques such as hierarchical clustering as shown above in Figures Once a hypothesis has been generated, Dx2Tx identifies samples against which the hypothesis can be tested. Certain inclusion and exclusion eligibility criteria can be considered and used to filter the content of the database to identify subjects/samples/experiments with certain characteristics. Dx2Tx also allows samples to be selected based upon the extent of any attribute(s) Figure . For exaOnce the sample population is selected, we test the hypothesis across the series of selected samples in a 2-step process. A subset of the samples  are selected  from each subgroup (for example disease versus control) to develop a discrimination algorithm that identifies statistical correlates of the feature in question. It is worthwhile to note that Dx2Tx identifies clinical, experimental and molecular correlates of the selected feature(s), thereby integrating both clinical and molecular data into the potential diagnostic algorithm. The user can exclude any attribute from the input to the training algorithm. In a second cross-validation test, the trained algorithm is applied to the remainder of the samples , to determine if the test could have accurately predicted the nature of the remaining samples. The outcome of the test is plotted using a receiver operator characteristic (ROC) curve to determine the accuracy of the test. The ROC curve is a way to visualize and quantify the effectiveness of a procedure by graphing the true positive rate (Y axis) against the false positive rate (X-axis). The area under the curve (AUC) provides an approximation of the accuracy of the test. A procedure with no effectiveness (AUC = 50%) would show a random 1:1 line, indicating that for every true positive, the procedure also generated a false positive. Generally, an AUC of 90–100% is considered excellent, while an AUC of 80–90% is good. The ideal diagnostic test would of course identify all true positives before encountering a false positive (AUC = 100%).In this working example, the hypothesis generated from analysis of unsupervised clustering of gene expression data from mesothelioma tumors is that survival time of patients can be predicted based upon the underlying genomic signatures of the tumor. Thus, patients with the shortest and longest survival time following surgery were placed into two groups. Each group was then randomly divided into 2 additional groups, the training set and the test set. The discriminating clinical and molecular features are first identified using a standard t-statistic for numerical data and chi squared for binary (including text) data. This test statistic is then used in a weighted voting metric . Data ar). These so called "companion diagnostics", which could accompany therapeutic agents and assist in treatment decisions, are also being developed for specific agents that display varying degrees of efficacy and toxicity across sample populations. With the accurate capture of longitudinal clinical data including toxicity and response assessment, associations between clinical response and molecular features of either the patient and/or the disease tissue should be readily identifiable within complex retrospective datasets. For example, we have identified a genomic signature within plasma cells isolated from multiple myeloma patients that correlates with tumor response to the drug melphalan. This genomic signature is currently being applied to additional patient samples using cross-validation statistics as outlined above, to determine the accuracy of this possible companion diagnostic application. As with most single agent treatment regimens, drug resistance in the area of oncology represents a significant problem in the treatment of the disease. Therefore, pre-treatment tests could conceivably identify the patients who would benefit the most and least from treatment.In addition to the identification of potential diagnostic applications, we have a major focus on identifying new treatment targets, and/or improving therapeutic strategies involving existing treatments. Patient variation, with respect to treatment response (efficacy and toxicity), is a well documented phenomenon . ThroughOur research also includes the discovery of possible early surrogate markers of therapeutic index. This ideally requires the collection of clinical specimens and data both pre and post treatment. In conjunction with the treatment of cell lines in culture and molecular analysis of livers and kidneys from treated mice, early biomarkers of efficacy and toxicity have been identified in association with several treatment regimens. If these biomarkers of response can be validated in retrospective patients, they could be written into future clinical studies to provide an early indication of therapeutic effect. These biomarkers may ultimately be used as surrogate markers to determine discontinuation or modification of protocols to maximize therapeutic index in prospective trials.There are currently a number of drugs in development, in clinical trials or that have recently received FDA approval that target specific molecular aberrations -23. UnliThe FDA approval process of the EGFR inhibitor Iressa has received a great deal of attention . RecentlIn addition to pursuing existing drug targets, we are also attempting to identify novel targets that may warrant future drug discovery efforts. We typically attempt to validate only candidate genes/proteins that have or are predicted to have "drugable" characteristics. To determine whether a potential target is drugable, we are currently using some relatively simple criteria based upon the classes of drug targets that have been actively pursued by pharmaceutical/biotech companies to date. For example, we have annotated the publicly available drug targets described above using gene ontology and literature mining tools within Dx2Tx , and ideThe majority of the preceding discussion has focused on the identification of molecular correlates of disease, and identifying those that may represent diagnostic biomarkers and/or treatment targets for intervention. A large proportion of our translational research effort is dedicated to functional validation, where through various means, the expression and/or activity of the target are modified and the functional consequences addressed. While determining the function of a diagnostic biomarker is not necessary, the functional consequence of target gene/protein disruption is essential when establishing the true therapeutic value of a potential target. An ideal therapeutic target would be one that is causative of the disease, and inhibitors against which thus cause disease regression. The following discussion will focus on a high-throughput means by which we assess the functional significance of target gene/protein disruption in murine models of various human malignancies.There are a variety of approaches one can take to interfere with gene/protein expression and/or function with the purpose of demonstrating a definitive role in a biological process . These iWe have developed an avian retroviral vector that can deliver siRNA to cells expressing the viral receptor (TVA) [35]. TVA expression can be directed to specific cells in vitro through exogenous transfection/infection, or in vivo through transgenic technology . This syin vivo significance of experimental findings in a relevant preclinical setting. We particularly focus on the development of orthotopic xenograft models, in which human tumor cells are implanted into their corresponding site of origin within an immune compromised mouse. In this fashion, human tumors form in the tissue site of origin that more closely resemble the human counterpart with respect to biological behavior. These orthotopic models better recapitulate the various stages of tumor progression and more accurately reflect responsiveness to various therapeutic intervention strategies [37]. Coupled with in vitro and in vivo delivery of siRNA against multiple target genes, these preclinical models readily allow us to evaluate the requirement of proposed target genes in tumor progression. Through these studies, we are beginning to identify the optimal combination targeting strategies that may lead to the eventual treatment of aggressive cancers.Improved murine models that depict specific features of the human disease in question are essential to validate the In the vast majority of cases in which molecular data is being correlated with clinical events, a hypothesis-driven inquiry is tested against archived clinically documented specimens. The majority of these trials are asking either a classification question (i.e. what profile sets this tumor apart from other tumors), a prognostication pattern (what group of genomic/proteomic patterns will predict time to progression or time to death), early detection , or response to therapy (is there a de novo set of genomic/proteomic parameters which predict either response or resistance to a given therapy). For classification phenomena, the investigator must currently rely on pathologic differences to stratify tumors into clusters which, upon genomic/proteomic analysis, have consistent and congruent distinguishing features. This is probably the easiest of analyses and relies mainly on established architectural and morphologic differences between tumors instead of clinical behavior and endpoints. Nevertheless, as with all discovery test-sets, validation prospectively must be performed in order to test the accuracy of the classification algorithms. This requires prospective application of the algorithm to blinded samples, and comparing the predicted outcome with the actual observed pathology.For the early detection, prognostication, and prediction of therapeutic response, one must link potential diagnostic tests to newly developing trials to allow validation of the established hypothesis. Before undertaking such an effort, however, the investigator must realize that the data were typically derived from a set of patients within a particular treatment regimen. For example, the patients presented in this article were characterized as having specimen procurement prior to definitive cytoreductive surgery followed by adjuvant therapy, and therefore a prospective trial which conforms to this treatment regimen should ideally be designed to avoid confounding variables. For the validation of diagnostics that predict time to recurrence or survival following treatment, a phase II trial of surgery and/or postoperative adjuvant therapy could be designed in which samples are harvested at the time of surgery. The diagnostic test would then be performed on the clinical specimen to obtain a prediction of patient outcome, and the patients' clinical course followed to see if there is prospective validation of the test. These analyses, however, must be performed in patient groups not weighted towards either high or low risk patients, and indeed, some stratification of clinical parameters should be specified at the outset in order to make reasonable comparisons. This is especially true if the molecular data is to be validated as part of a Phase III randomized trial comparing two treatment regimens after surgery. As the content of Dx2Tx expands to include clinical trial data, analysis can be performed on only those patients that conform to specified clinical parameters. Therefore, with the complete and standardized collection of clinical data from a large population of patients receiving various treatment regimens both on and off of protocol, established hypotheses can be further tested on additional retrospective subjects; in a sense a virtual trial that begins to more closely resemble the carefully controlled prospective clinical trial.in vitro discovery of genomic/proteomic patterns defined in cell lines treated with the corresponding agent(s).The ideal clinical trials addressing the validity of molecular data to predict a clinical parameter are those involving patients with no prior diagnosis or treatments, who subsequently receive a single therapeutic regimen. Preferably, both pre- and post-treatment specimens are obtained from these patients. Such a pure trial could, for example, test whether a predefined genomic/proteomic pattern could predict a poor outcome despite favorable clinical parameters. Trials of patients receiving initial chemotherapy could also help to define the fidelity of the omics technologies for the prediction of chemosensitivity. These hypotheses may have been derived from a retrospective set of patients receiving the same chemotherapy or targeted therapy for which outcomes were known, or from the Early detection clinical trials using genomic and/or proteomic technologies will typically take the longest to validate. After the discovery of proteins as candidate markers from analyses of serum, urine, or other fluids in patients with early stage malignancies compared to the appropriate high risk group, cohorts of high risk individuals (e.g. asbestos exposed or tobacco smokers) must be identified who are willing to have collections of the appropriate specimens longitudinally at given intervals. Moreover, in initial trials of these markers, the investigator must define at what time to disclose the value of the measured biomarker. One possibility would be to allow the prospective clinical trial to reach a certain endpoint  and then reveal the results of the marker to see whether there is predictive value. Another way would be to define the value of the marker at follow-up intervals during the course of the trial and, in the face of radiographic or physical findings, initiate an invasive workup to "find" the predicted cancer. It is generally agreed that, although a two stage validation effort takes longer , such a model is currently preferred.With respect to novel treatment strategies predicted from retrospective analysis and/or through the preclinical studies defined above, the next logical step is to attempt to validate the retrospective data which initially pointed the investigators to these potential treatment options. If the new therapy has never been used in humans before, it is important to define the maximum tolerated dose of the treatment. This is defined as the dose at which, in a Phase I clinical trial, a defined fraction  of the patient cohort develop dose limiting toxicity. Once the dose or treatment strategy is found to be acceptable, a Phase II trial is designed to provide a measure of the activity of the agent(s) and to begin to define whether there is any benefit of the regimen in certain patient cohorts. The use of tools such as Dx2Tx could be invaluable in the future tracking and analysis of such trials, especially if samples are prospectively harvested at various times pre and post treatment. For example, in the Phase I/II trial, genomic/proteomic correlates of clinical toxicity could be readily identified by melding the clinical data with "omic" data from the patients who have undue toxicity. Molecular pathways that intuitively result in toxicity could be identified which could possibly be abrogated with another agent. With respect to determination of therapeutic efficacy, our present means of defining "clinical correlates of response" is essentially a "best guess" or subjective prediction of what clinical markers may indicate that the new agent is yielding a therapeutic effect. A more objective measure of downstream events resulting from an efficacious agent would result from analysis of the molecular data in tandem with clinical information for the responders compared to the non-responders. Dx2Tx would be able to then define the most important markers of response, both clinical and molecular, which could then be validated in a Phase III trial assessing agent efficacy.It is unknown when an integrated clinical/molecular evaluation of the suspected or afflicted cancer patient will be of use to the end user, the practicing physician. Certainly, many of the same arguments that are used for and against "genetic testing" in other diseases may be used for such a global approach to oncology. There is no doubt however, the ability to define clinical behavior in a more efficacious and predictive manner, other than the archaic prognostic indicators which we use today, will help clinicians initiate and/or alter treatment course earlier and guide clinicians toward informed discussions with their patients in order to make treatment and/or surveillance decisions. If indeed the fidelity of combinatorial molecular and clinical medicine proves to be satisfactory, we may then be able to spare patients unnecessary treatment interventions which are currently doomed to failure. Moreover, the medical oncologist will be able to choose the correct cytotoxic and/or targeted therapy based on a global clinical-molecular snap-shot, which should translate into more favorable health economic policies and patient outcomes.Throughout this review, we have highlighted the importance of designing research protocols involving human subjects that permit the collection of not only clinical specimens, but also extensive standardized clinical data in a longitudinal fashion. Through the merging of clinical and molecular data, non-biased patterns can be discovered that could translate into novel diagnostic and/or treatment opportunities. We have introduced a methodical approach for archiving and mining these seemingly disparate data sources that we believe can accelerate the translational research discovery pipeline. While there are clearly improvements to be made in the systematic collection of accurate and nationally standardized clinical data, we believe the integration of medical informatics and the molecular technologies can convert the once visionary concept of molecular-based medicine into a present reality.CW – A provisional patent has been filed on the Dx2Tx system described in this article.HP – This author declares that he has no competing interests.


For ≈ 24 years the AIDS pandemic has claimed ≈ 30 million lives, causing ≈ 14,000 new HIV-1 infections daily worldwide in 2003. About 80% of infections occur by heterosexual transmission. In the absence of vaccines, topical microbicides, expected to block virus transmission, offer hope for controlling the pandemic. Antiretroviral chemotherapeutics have decreased AIDS mortality in industrialized countries, but only minimally in developing countries. To prevent an analogous dichotomy, microbicides should be: acceptable; accessible; affordable; and accelerative in transition from development to marketing. Already marketed pharmaceutical excipients or foods, with established safety records and adequate anti-HIV-1 activity, may provide this option.Fruit juices were screened for inhibitory activity against HIV-1 IIIB using CD4 and CXCR4 as cell receptors. The best juice was tested for inhibition of: (1) infection by HIV-1 BaL, utilizing CCR5 as the cellular coreceptor; and (2) binding of gp120 IIIB and gp120 BaL, respectively, to CXCR4 and CCR5. To remove most colored juice components, the adsorption of the effective ingredient(s) to dispersible excipients and other foods was investigated. A selected complex was assayed for inhibition of infection by primary HIV-1 isolates.HIV-1 entry inhibitors from pomegranate juice adsorb onto corn starch. The resulting complex blocks virus binding to CD4 and CXCR4/CCR5 and inhibits infection by primary virus clades A to G and group O.These results suggest the possibility of producing an anti-HIV-1 microbicide from inexpensive, widely available sources, whose safety has been established throughout centuries, provided that its quality is adequately standardized and monitored. The global AIDS epidemic has proceeded relentlessly for ≈ 24 years with no promising prophylactic intervention in sight. In 2003 there were 5 million new HIV infections, and 3 million AIDS deaths . To dateAnti-HIV-1 vaccines applicable to global immunization programs are not expected to become available for many years. Thus, other prevention strategies are urgently needed. This includes educational efforts and application of mechanical and/or chemical barrier methods. The latter correspond to microbicides, i.e. topical formulations designed to block HIV-1 infection  when applied vaginally  before intercourse ,5-7. ConTreatment with anti-retroviral drugs has decreased mortality from AIDS in industrialized countries but has had a minimal effect so far in developing countries . To avoi®; Sadaf® Foods, Los Angeles, CA; additional ingredients: fructose, citric acid); PJ4 ; PJ5 ; PJ6 ; PJ7 . Starches used were: PURE-DENT® B815 Corn Starch NF, PURE-DENT® B816 Corn Starch USP, Spress® B825 Pregelatinized corn starch NF, Spress® B820 Pregelatinized corn starch NF, INSTANT PURE-COTE™ B792 Food starch-modified, INSCOSITY™ B656 Food starch-modified ; PURITY® 21 corn starch NF and PURITY® 826 corn starch NF ; Remyline AX-DR Waxy rice starch and Remy DR native rice starch, medium grind ; ARGO® corn starch ; STALEY® pure food powder starch ; STARCH 1500 pregelatinized starch NF . The following polymers were used: polyethylene glycols (PEG) 1000 NF, 1500 NF and 8000 NF; and hydroxypropyl methylcellulose, 50 cps, USP ; Carbopol 974P-NF ; Carbophil, Noveon AA1 ; and Pharmaburst B2 . Fattibase was from Paddock Laboratories, Inc., Minneapolis, MN.Pomegranate juices (PJ) were purchased in local New York City stores; their origin is given in parentheses: PJ1 ; PJ2 was prepared from fresh ripe pomegranates in our laboratory; PJ3  and BaL (2.47 × 1010 virus particles/ml) were from Advanced Biotechnologies, Inc., Columbia, MD. Primary HIV-1 isolates, MT-2 cells, HeLa-CD4-LTR-β-gal and U373-MAGI-CCR5E cells (both contributed by Dr. Michael Emerman) and Cf2Th/synCCR5 cells (contributed by Dr. Tajib Mirzabekov and Dr. Joseph Sodroski) were obtained from the AIDS Research and Reference Reagent Program operated by McKesson BioServices Corporation, Rockville, MD. CEMx174 5.25M7 cells, transduced with an HIV-1 long terminal repeat (LTR)-green fluorescent protein and luciferase reporter construct, expressing CD4 and CXCR4 and CCR5 coreceptors . Consistently, PJs from distinct geographical areas had the highest inhibitory activity , grapefruit, honey, lemon, lime, pineapple, pomegranate and red beet (10% reconstituted dry powder)] were assayed for inhibition of infection by HIV-1 IIIB of cells expressing the CD4 and CXCR4 receptors and coreceptors. Most juices (4-fold diluted) had no inhibitory activity, except blueberry, cranberry, grape and lime juice, respectively [endpoints for 50% inhibition of infection , the inhibitory effect of PJ on binding to gp120 IIIB of antibodies to peptides derived from the amino acid sequence of gp120 was studied. The binding of antibodies to peptides (102–126), 303–338), (306–338), 361–392), 386–417), (391–425), (411–445) and (477–508) was significantly (≥ 50%) inhibited  from PJ. After striving intermittently for over four years to accomplish this, it was discovered that the inhibitor(s) of gp120-CD4 binding can be adsorbed effectively (≥ 99%) onto a selected brand of corn starch Fig. , resultiPJ-S21, to the same extent as the original PJ, inhibited the binding of gp120 IIIB-CD4 complexes to cells expressing CXCR4, as determined by flow cytometry Fig. . SimilarTo be considered as a topical microbicide, PJ-S21 must be formulated to withstand storage in a tropical environment. Accelerated thermal stability studies revealed that a water suspension of PJ-S21 maintained only 4, 11, and 33%, respectively, of its original activity (measured by inhibition of gp120-CD4 binding) when stored for 30 min at 60°C, and one week at 50°C or 40°C. On the other hand, a dried PJ-S21 powder remained fully active after storage at 50°C for 12 weeks . Consequently, anhydrous formulations should be preferred for further development.Three such formulations were prepared: two kinds of suppositories, melting at 37°C, and a tablet (for compositions see Methods section). The inhibitory activity of PJ-S21 was fully preserved after 12 weeks storage at 50°C within tablets, and at 30°C within the suppositories (the highest temperature considered to prevent melting). Data showing the inhibition of infection by HIV-1 IIIB and BaL respectively, by PJ-S21 and its formulations  are sumA microbicide can be considered potentially successful, only if it has antiviral activity against primary virus isolates belonging to distinct virus clades and phenotypes. PJ-S21 meets this requirement since it inhibited infection by primary HIV-1 strains of all clades tested having R5 and X4R5  phenotypes  interfering with gp120 binding to CD4 Fig.  blocked residues ; and othex vivo [Blocking of CD4 binding sites on HIV-1 gp120 by monoclonal antibodies or a CD4-IgG2 recombinant protein has been shown to be sufficient to inhibit HIV-1 infection of human cervical tissue ex vivo  and in pex vivo . TherefoThe application of PJ-S21 as a topical anti-HIV-1 microbicide requires reasonable uniformity among batches produced at distinct times and locations. Similarities in gp120-CD4 binding inhibitory activity among distinct freshly prepared and commercial juices stored for unknown periods , presumably, because of irreversible binding of the PJ inhibitors. Interestingly, there are only a few references available regarding the use of starch as an adsorbent for different compounds: flavors [Particular attention must be devoted to the selection of starch, a pharmaceutical excipient generally used in vaginal formulations , for eff flavors ,60, dyes flavors -63, low- flavors , lipids  flavors ,66, prot flavors  and iodi flavors .in vitro  i.e. ≥ 100-fold higher than the dose needed for blocking HIV-1 infection hallenge . This quIn an alternative approach to formulation development, PJ-S21 can be incorporated into a water dispersible film (similar to the widely available "breath control" strips) or into water dispersible sponges  which arPJ-S21 can be classified as an AAAA candidate microbicide: Acceptable; Accessible; Affordable; and Accelerative in transition from development to marketing. Thus, PJ-S21 would be expected to circumvent some problems associated with antiretroviral drugs and possibly some of the other candidate microbicides, i.e. uncertainty related to potential side effects, investment and time needed to establish inexpensive large scale production, and monopoly of supply.50(90), effective dose(s) for 50% (90%) inhibition of infection; ELISA, enzyme linked immunosorbent assays; FBS, fetal bovine serum; FLSC, a full length single chain protein consisting of BaL gp120 linked with the D1D2 domains of CD4 by a 20 amino acid linker; HIV-1, human immunodeficiency virus type 1; HRP, horseradish peroxidase; LTR, long terminal repeat; PBMCs, peripheral blood mononuclear cells; PBS, phosphate buffered saline; PEG, polyethylene glycols; PJ, pomegranate juice; S21, PURITY® 21 corn starch NF grade; SF, seminal fluid.AIDS, acquired immunodeficiency syndrome; BSA, bovine serum albumin; EDThe authors declare that they have no competing interests.ARN developed the concepts representing the basis of the manuscript and designed most experiments. NS contributed to the development of experimental techniques and carried out experiments other than infectivity assays. YYL did all the tissue culture work and viral infectivity assays. AKD did all the molecular modeling studies and contributed to the development of cell based enzyme linked immunosorbent assays.The pre-publication history for this paper can be accessed here:


Each of the cells in our body is a complex machine. Within each cell, thousands of proteins and other molecules interact to produce the highly organized cellular events that are needed for life. However, there is no cellular line manager telling the individual proteins where to go and what to do. It's rather like an ant colony. No one tells the individual ants how to build a nest. They just do it. Similarly, no one tells the individual actin molecules, for example, to get together and polymerize to form the cytoskeleton that is essential for cell movement and other cellular processes. It just happens.Ant colonies and actin polymerization are both examples of emergent behavior. Broadly defined, emergent behavior is when a collection of individuals interact without central control to produce results that are not explicitly programmed. Scientists are only just beginning to understand how complex behaviors of this type can emerge from a myriad of individual interactions, many of which are well-defined experimentally.Listeria monocytogenes—in their study of the complexities of cellular actin polymerization. This rod-shaped microbe, which lurks in well-ripened brie and other unpasteurized cheeses, can cause the sometimes fatal disease listeriosis, particularly in young babies or people with weakened immune systems.Jonathan Alberts and Garrett Odell have turned to an unlikely ally—the bacterium Listeria microbes in food reach a person's gut, they penetrate the cells lining the gut, reproduce, and then spread from cell to cell without ever exposing themselves to the extracellular environment. In this way, they avoid the host's immune system. One particular protein produced by Listeria is central to this sneaky intracellular lifestyle: ActA. Expression of ActA allows the microbes to hijack the machinery in the host cell that controls the growth of actin networks. Some of the cellular actin, instead of forming the cytoskeleton of the cell, polymerizes around the bacterium, forming a dense “comet tail” of actin that generates a force to move the bacterium around the cell and push it into neighboring cells. This clandestine use of our cellular machinery for actin polymerization is far simpler to understand than the elaborate use our cells normally make of it, so the study of Listeria propulsion provides a scientific stepping-stone to understanding the involvement of the actin cytoskeleton in normal cell movements.When Listeria cells do not move smoothly. Instead, the microbes move jerkily, with runs interspersed with pauses. The simulation faithfully reproduces this type of movement and offers a mechanistic explanation for it.Alberts and Odell have used established data on the biochemical and mechanical details of actin polymerization and the physiological concentrations of the proteins involved in the process to build a computer simulation of how an actin network assembles around a moving, rod-shaped bacterium. Their “in silico” reconstitution, which considers the behavior of individual actin filaments and requires a cluster of 80 computer processors to be run for several days at a time, produces realistic bacterial motion in terms of speed and persistence, and models the actin tail shape. The model also reproduces smaller scale emergent behavior. Real The approach described by Alberts and Odell can now be used to investigate many other, more complex cell mechanics problems, such as how the cellular movements involved in cell division are achieved. With the availability of detailed biological information and powerful computers, we may at last start to solve the mystery of how interactions among maybe 100,000 gene products can produce the organized cellular processes that cell biologists have been watching under the microscope for years.


Cyclin-dependent kinases (CDKs) are a large family of proteins that function in a variety of key regulatory pathways in eukaryotic cells, including control over the cell cycle and gene transcription. Among the most important and broadly studied of these roles is reversible phosphorylation of the C-terminal domain (CTD) of RNA polymerase II, part of a complex array of CTD/protein interactions that coordinate the RNAP II transcription cycle. The RNAP CTD is strongly conserved in some groups of eukaryotes, but highly degenerate or absent in others; the reasons for these differences in stabilizing selection on CTD structure are not clear. Given the importance of reversible phosphorylation for CTD-based transcription, the distribution and evolutionary history of CDKs may be a key to understanding differences in constraints on CTD structure; however, the origins and evolutionary relationships of CTD kinases have not been investigated thoroughly. Moreover, although the functions of most CDKs are reasonably well studied in mammals and yeasts, very little is known from most other eukaryotes.Trypanosoma or Giardia. Kinases involved in CTD phosphorylation, CDK7, CDK8 and CDK9, all are recovered as well-supported and distinct orthologous families, but their relationships to each other and other CDKs are not well-resolved. Significantly, clear orthologs of CDK7 and CDK8 are restricted to only those organisms belonging to groups in which the RNAP II CTD is strongly conserved.Here we identify 123 CDK family members from animals, plants, yeasts, and four protists from which genome sequences have been completed, and 10 additional CDKs from incomplete genome sequences of organisms with known CTD sequences. Comparative genomic and phylogenetic analyses suggest that cell-cycle CDKs are present in all organisms sampled in this study. In contrast, no clear orthologs of transcription-related CDKs are identified in the most putatively ancestral eukaryotes, The apparent origins of CDK7 and CDK8, or at least their conservation as clearly recognizable orthologous families, correlate with strong stabilizing selection on RNAP II CTD structure. This suggests co-evolution of the CTD and these CTD-directed CDKs. This observation is consistent with the hypothesis that CDK7 and CDK8 originated at about the same time that the CTD was canalized as the staging platform RNAP II transcription. Alternatively, extensive CTD phosphorylation may occur in only a subset of eukaryotes and, when present, this interaction results in greater stabilizing selection on both CTD and CDK sequences. Overall, our results suggest that transcription-related kinases originated after cell-cycle related CDKs, and became more evolutionarily and functionally diverse as transcriptional complexity increased. Cyclin-dependent kinases (CDKs) belong to a large protein family with 13 members described so far in human cells including CDKs1-11, along with PCTAIRE and PFTAIRE kinases named after conserved domain sequences -4. Gener1-Ser2-Pro3-Thr4-Ser5-Pro6-Ser7 [Among the most important and broadly studied roles of CDKs in transcription is the reversible phosphorylation of the C-terminal domain (CTD) of the largest subunit (RPB1) of RNAP II. The CTD consists of multiple repeats of an evolutionarily conserved heptapeptide with the consensus sequence Tyrro6-Ser7 . The numro6-Ser7 ,16 with ro6-Ser7 ,17,18. Bro6-Ser7 ,20. Morero6-Ser7 . Throughro6-Ser7 -24.in vitro; they all have been referred to as 'CTD kinases' [in vivo, CDK1 and CDK2 are primarily cell-cycle related kinases [Drosophila in mammals and its role in Tat-dependent HIV-1 transcription is still unclear [in vitro, the role of the CDK1 in mRNA synthesis in vivo is not, as yet, clearly understood. It has been proposed as a candidate for mitotic RNAP II inactivation by inhibition of CDK7 CTD-kinase activity [To date at least five of the CDKs  have been shown to phosphorylate the CTD kinases' -28. Bothkinases' . The CDK kinases . CDK2 ha unclear ,28. Althactivity .In animals and yeasts, interactions between the CTD and CTD-specific kinases have become a focal point of biochemical and genetic investigations of RNAP II transcription and transcription-linked mRNA processing ,26,30. HTrypanosoma, Plasmodium and Giardia), to explore the evolutionary relationships between the CTD and CTD kinases. We also provide a phylogenetic distribution of CDKs from a wide range of organisms, suggesting new hypotheses regarding the emergence and evolution of different members of the CDK family.Here we present a comparative genomic analysis of CDKs, using complete genomes from members of the "CTD clade" , as well as from other diverse eukaryotic organisms lacking a canonical CTD  and Vandepoele et al. (2002) [We identified 133 CDK family members, 123 from animals, plants, yeasts, and four protists from which genome sequences have been completed, and 10 additional CDKs from incomplete genome sequences of organisms with known CTD sequences Table , only 10. (2002)  (Table 1Drosophila and Caenorhabditis), yeasts, both plants (Arabidopsis and Oryza), Encephalitozoon and Giardia  [In this unrooted tree the highly diversified cell-cycle kinases defined in humans, CDKs1-6, fall into a large cluster with 69% Bayesian support. This grouping includes CDKs from all organisms examined in the study. Among these putative cell-cycle CDKs, some plant and protistan kinases can be assigned with reasonable confidence to specific CDK groups. For example, apparent orthologs of human CDK1 are found in other animals (s (2000) , CDK5 anTrypanosoma brucei and Giardia lamblia. This includes strongly supported clades of presumed orthologs of human CDKs7-11 respectively. A well-defined CDK7 family is recovered, including sequences from yeasts, the microsporidian, plants, and animals. These are the primary groups that make up the "CTD-clade," in which the RNAP II CTD is invariably conserved  [Plasmodium, suggested previously to be a CDK7 [Plasmodium kinase. The a priori hypothesis that PfMRK belongs in the core CDK7 group is strongly rejected with our data set in a likelihood paired-sites test.In contrast to cell-cycle kinases, our phylogenetic results failed to identify a clear ortholog of any transcription-related CDKs from two of the complete genomes examined, ved Fig. . CDK7 shs (2003) ; howevere a CDK7 , does noGiardia, based on nearest sequence similarity to Kin28 in a more limited comparison to CDK sequences from fission yeast [Giardia sequence. The a priori hypothesis that GlCAKlike belongs in the core CDK7 group also is strongly rejected in a likelihood paired-sites test.Likewise, GlCAKlike (gi: 292497120) has been proposed as a CDK7 from on yeast . In our Encephalitozoon is a member of the RNAP II "CTD clade," TBlastN searches of the complete genome of Encephalitozoon found six CDKs but none show a phylogenetic affinity to CDK8.A robust CDK8 family is recovered with strong support values in both distance bootstrap and Bayesian inference. Like CDK7, this family includes putative orthologs only from members of the "CTD-clade," specifically yeasts, animals and plants. Although the microsporidian Encephalitozoon, animals, plants and Plasmodium. This group is divided into two well-defined sub-clades. One of them consists of BUR1 from yeast along with CDK9 orthologs from animals; the other contains CTK1 from yeast, CDC2L5 and CrkRS from human, and apparent orthologs from Drosophila and Caenorhabditis, both plants, and Plasmodium. A putative CDK9 also is found in Encephalitozoon, but falls at the base of the larger CDK9 grouping and does not associate clearly with either subgroup  appear to be transcription-related kinases, PfCrk1 groups with the CDK10/11 family, and PfCrk3 with CDK9.In an effort to understand the broader evolutionary history of CDKs, three deep-branching protists with complete genomes, nce data , a relatDictyostelium discoideum that has a canonical RNAP II CTD, and Leishmania major, Cryptosporidium parvum and Entamoeba histolytica, which all lack a CTD , in a more diverse array of eukaryotes than are transcription-related kinases. This pattern also is seen in a more widely-sampled analyses including CDKs from a number of organisms with incompletely sequenced genomes, including  CTD see . Thus, tTrypanosoma [Drosophila and C. elegans. The CDK5 family has undergone expansion in metazoans, including PFTAIRE and PCTAIRE kinases, and putative orthologs of CDK5 are detected in Plasmodium, Trypanosoma and Giardia. Interestingly, no CDK from plants associates strongly with the CDK5 group, while the CdkB-type kinases, which are specific to plants, branch as sister to a broader CDK1/CDK5 clade. Our overall results suggest that cell-cycle kinases have undergone extensive and independent evolutionary diversification in different eukaryotic lineages, and it may be difficult to classify many of them based on orthologous relationships in phylogenetic analyses. It may be that functional homologies, once established experimentally, will prove to be more consistent criteria for designating CDK groups.Our analyses support well-defined groups for cell-cycle kinases CDK1, CDK4/6 and CDK5. An ortholog of either CDK1 or CDK5 is found in all of the organisms in our study, and these two families appear to be closely related. TbCrk3 was proposed as a functional homolog of CDK1 in panosoma ; here itArabidopsis (AtCdkF), and its apparent orthologs from animals, branching as a sister group. In addition to their role as CTD kinases, members of the CDK7 family in plants, animals and fission yeast can function as a CDK-activating kinase (CAK) [Arabidopsis [Drosophila and Caenorhabiditis, exhibits only CAK activity but no CTD kinase activity. Consistent with the phylogenetic relationships recovered in our analysis, human CCRK and other animal orthologs were recently shown to have CAK activity [Arabidopsis have diversified substantially, and may be regulated in different ways from those in yeast, animals, and even rice.Clear orthologs of CDK7 from animals, plants, yeasts and Microsporidian are strongly supported as a core family, with CDK-activating kinase from se (CAK) ,42. Unlibidopsis . AtCdkF activity . In contactivity . ApparenScCAK1 and SpCSK1 from yeasts also have CAK activity; however, despite their functional similarity to kinases in the CCRK group, they do not group with animal or plant CAKs Fig. ; howeverEncephalitozoon. Our further blast results (unpublished data) failed to identify any of the units of the CDK8/SRB10 (SRBs8-11) sub-module in the Microsporidia suggesting a loss of CDK8/SRB10 unit from these highly reduced parasites.CDK 8 (SRB10 in yeast) is a component of the multi-subunit Mediator complex, which transduces signals from cis regulatory elements to RNAP II; it is proposed to inhibit transcription initiation by phosphorylation of the CTD. CDK8/SRB10 and its partner cyclin C/SRB11, together with SRB8 and SRB9, form a specific sub-module that is variably associated with the RNAP II holoenzyme, and potentially with the free mediator complex . ApparenEncephalitozoon suggests CDK8/SRB10 function is absent from the Microsporidia. The loss of CDK8 from Microsporidia, along with absolute conservation of CDKs7 and 9 in all members of the "CTD-clade" . The latter is the only apparent ortholog of a CTD-directed kinase  identified in our analyses from any organism outside the "CTD-clade." It remains to be determined whether PfCRK3 possesses the P-TEFb function of CTK1, since it is the only protistan sequence present in either CDK9 sub-group, and the RNAP II CTD has not been conserved in apicomplexans or their closest relatives  [Arabidopsis and 8 from Oryza) (Table Arabidopsis and Oryza (Oryza is extremely similar to OsCdkC1 and very likely a second copy of CdkC (CDK9) from Oryza. There is no evidence of biological functions for these kinases as yet, but our results indicate that they are part of a large CDK9 complex specific to plants.In addition to the two previously identified copies of CDK9 (CdkC1 and CdkC2) from  (CdkC1) ,34, our nd Oryza . Os1562.Drosophila, while CDK11 occurs in human, Drosophila and Caenorhabditis. Three putative CDK11 orthologs were found in plants (two from Arabidopsis and one from Oryza). CDK10 has been implicated in the regulation of the G2/M phase of the cell cycle [S. pombe and PfCRK1 from Plasmodium show close relationships to the CDK10/11 family, but no function has yet been determined for these kinases either.In this group, orthologs of CDK10 are found only in human and ll cycle , but a cll cycle . CDK11 all cycle . The cloThe apparent co-evolution between the CTD and certain CTD-specific kinases suggests an explanation for strong stabilizing selection on CTD structure in some eukaryotes, and its complete degeneration in others. Based on the genomes examined in this study, either the origins of CDK7 and CDK8 in an unknown ancestor of the "CTD-clade," or the canalization of reversible phosphorylation of the CTD in some eukaryotic groups but not others, could account for the variation seen in RPB1 C-terminal structure. In either case, once thoroughly "locked" into RNAP II function, the CTD must have recruited other transcription and processing related proteins into a growing machinery of the "transcriptosome" . Our resThis work also provides a new perspective on the overall evolution CDKs and evolutionary relationships among kinase families. Our combined genomic and phylogenetic analyses suggest that transcription-related kinases originated later than cell cycle-related CDKs. Finally, our results point to potential functions for a variety of previously uncharacterized kinases, based on their apparent orthologous relationships to defined CDKs. Additional completed genomes, particularly those from broadly diverse protists , will be critical to address these questions further. Such comparative analyses will be invaluable in helping to guide experimental studies, which ultimately are required to verify the functional properties of each putative CDK.Representatives of all previously identified CDKs from budding yeast and human were obtained from Genbank, and used as probes in TBlastN and PSI-Blast  against Initially, a number of inferred protein sequences were grouped into six subsets according to clear similarities to specific CDK family orthologs. These subgroups first were aligned in CLUSTAL X , and thea priori trees. Trees were sampled from the posterior probability distribution every 100 generations. The empirical burn-in required for likelihoods to converge was less than 100,000 generations; an additional 400,000 generations were run and the first 100,000 were excluded from analysis of Bayesian posterior probabilities. Thus, a total of 4,000 trees were examined to determine the 50% majority-rule consensus tree and Bayesian support values. In addition, 1000 distance (PROTDIST + NEIGHBOR) bootstrap replicates were performed in PHYLIP 3.573 [Maximum-likelihood (ML) estimates of substitution parameters were made with the program TREEPUZZLE-50  assumingIP 3.573 , also usa priori alternative hypotheses regarding CDK7 evolution were compared by KHT likelihood paired-sites tests [Plasmodium or GlCAKlike from Giardia, which previously have been characterized as a CDK7 orthologs [a priori hypotheses of orthologous relationships to CDK7 were significantly worse than the Bayesian consensus tree.Several es tests . Trees wrthologs ,38, to gZG was primarily responsible for database searching and assembly of CDK genes. ZG and JWS performed phylogenetic analysis. ZG drafted the manuscript and figures and JWS contributed editorial revisions. All authors read and approved the final manuscript.Arabidopsis and Oryza. The phylogenetic weight of these latter plant sequences disrupts inferred relationships among CDK9 orthologs as shown in Fig. 1. Support values are from Bayesian inference and only values above 50% are shown. As in Fig. 1, CDK names in red are from groups in which the CTD is not strongly conserved, those in blue from members of the "CTD-clade." Inferred groups of CTD-directed CDKs 7, 8 and 9 are shown on the tree.The single most likely tree, with branch lengths, recovered from 16,000 ML trees in the posterior probability distributions of four separate iterations of Bayesian inference. Thirty-two additional sequences were added to this analysis, and are indicated in bold in Table 1. They represent CDKs identified in incomplete genomes of organisms from which CTD structure is known, as well as a large amplification of apparent plant-specific orthologs of CDK9 from Click here for fileOriginal protein sequence alignment.Click here for fileEdited protein sequence alignment.Click here for file


Have you ever noticed that long-distance runners and sprinters seem specially engineered for their sports? One's built for distance, the other speed. The ability to generate quick bursts of power or sustain long periods of exertion depends primarily on your muscle fiber type ratio , which depends on your genes. To this extent, elite athletes are born, not made. No matter how hard you train or how many performance-enhancing drugs you take, if you're not blessed with the muscle composition of a sprinter, you're not going to break the 100-meter world record in your lifetime. Of course that doesn't prevent those at the highest levels from using the latest performance enhancer to get that extra 1% edge. But wait until trainers hear about the Marathon Mouse. A new study by Ronald Evans and colleagues provides evidence that endurance and running performance can be dramatically enhanced through genetic manipulation.Skeletal muscles come in two basic types: type I, or slow twitch, and type II, or fast twitch. Slow-twitch fibers rely on oxidative (aerobic) metabolism and have abundant mitochondria that generate the stable, long-lasting supplies of adenosine triphosphate, or ATP, needed for long distance.  Fast-twitch fibers, which produce ATP through anaerobic glycolysis, generate rapid, powerful contractions but fatigue easily. Top-flight sprinters have up to 80% type II fibers while long-distance runners have up to 90% type I fibers. Coach potatoes have about the same percentage of both.Endurance training can enhance the metabolic performance of muscle types, and now it appears that training can also induce conversion between fiber types. Specific changes in gene expression trigger this oxidative fiber transformation, but the transcription factor responsible for engineering this shift was unknown. Evans and colleagues suspected that a nuclear receptor called PPARδ—a major regulator of fat burning in fat tissue that is also prevalent in skeletal muscle—might be involved.To investigate this possibility, the authors genetically engineered mice to express an activated form of the PPARδ protein in skeletal muscle. Type I fibers normally express higher levels of PPARδ than type II fibers, and the transgenic mice showed much higher levels of the protein than their normal littermates. The transgenic mice also had much redder muscles than the controls—type I fibers have high levels of myoglobin, the red-pigmented protein that facilitates the movement of oxygen within muscle—and elevated levels of proteins associated with mitochondrial biogenesis and operation. A final line of evidence indicating a type I fiber switch was the elevated level of specialized type I contractile proteins and decreased level of specialized type II contractile proteins in the transgenic mice. Interestingly, these same results were seen when naturally occurring (endogenous) PPARδ levels were stimulated in the normal mice . This suggests that muscle fibers can be transformed into type I endurance fibers by simply activating the endogenous PPARδ pathway.In a weight-conscious world, oxidative fibers are thought to offer resistance against obesity since obese individuals have fewer type I fibers than average-weight individuals. Sure enough, transgenic mice fed a high-fat diet gained far less weight than normal mice fed the same diet, even in the absence of exercise. The transgenic mice had much smaller fat cells, which the authors attribute to enhanced oxidative capacity of the muscle tissue, and improved glucose tolerance.  But what about performance? Remarkably, the marathon mice ran about an hour longer than controls, showing dramatic improvement in both running time and distance—increases of 67% and 92%, respectively.Altogether, these results show that PPARδ drives the conversion of type I muscle fibers by activating pathways that enhance physical performance and protect against obesity. The finding that endurance and running capacity can be genetically manipulated suggests that muscle tissue is far more adaptable than previously thought. Maybe Olympiads can be made after all—but don't give up on training just yet. A full understanding of the molecular basis of muscle fiber determination, including the interactions between PPARδ and its regulatory components, awaits further study.


Modeling the complex development and growth of tumor angiogenesis using mathematics and biological data is a burgeoning area of cancer research. Architectural complexity is the main feature of every anatomical system, including organs, tissues, cells and sub-cellular entities. The vascular system is a complex network whose geometrical characteristics cannot be properly defined using the principles of Euclidean geometry, which is only capable of interpreting regular and smooth objects that are almost impossible to find in Nature. However, fractal geometry is a more powerful means of quantifying the spatial complexity of real objects.s) surface fractal dimension  geometrical complexity of tumor vascular networks, and their behavior during computer-simulated changes in vessel density and distribution.This paper introduces the Ds significantly depends on the number of vessels and their pattern of distribution. This demonstrates that the quantitative evaluation of the 2-D geometrical complexity of tumor vascular systems can be useful not only to measure its complex architecture, but also to model its development and growth.We show that form of branched anatomical structures, in an attempt to define more appropriate methods of describing them quantitatively. This knowledge can be used to predict the aggressiveness of malignant tumors and design compounds that can halt the process of angiogenesis and influence tumor growth.Studying the fractal properties of neovascularity induces reflections upon the real significance of the complex  Under tendothelial cells which, together with pericytes (supportive vascular smooth muscle cells), carry all of the genetic information necessary to form tubes, branches and entire capillary networks.The cyclic nature of the microvascular bed in the corpus luteum provides a unique experimental model for examining the discrete physiological steps of angiogenesis in the life cycle of self-limited; however, this is not true of tumor angiogenesis which, once begun, continues indefinitely until the entire tumor is eradicated or the host dies. Without blood vessels, tumors cannot grow beyond a critical size (1–2 mm) or metastasize to another organ.However, many human diseases (including solid tumors) are driven by persistently up-regulated angiogenesis . In someAngiogenesis is one of the most complex dynamic processes in biology, and is highly regulated by a balance of pro- and anti-angiogenic molecules. It is now widely accepted that the "angiogenic switch" is "off" when the effects of pro-angiogenic molecules is balanced by that of anti-angiogenic molecules, and "on" when the net balance is tipped in favor of angiogenesis ,3. Pro- promotion, mediation and inhibition of angiogenesis, very little is known about its underlying complex dynamics. Vasculature and more generally tubular organs develop in a wide variety of ways involving many cell processes  against log (1/ε), in a fixed range of side-lengths (ε 1-ε 2) empirically evaluated by visualization [where lization -29.t as required for parametric variables. p values of less than 0.05 were considered statistically significant.All of the data are expressed as mean values ± standard deviation, and the results were analysed using the Statistica software package . Unvaried analysis was performed by means of the Student DS values can be obtained for images with the same vessel density  when higher vessel densities were considered in the system  and functions . Complexity is a real quality of organized biological matter that is mainly manifested in the living world as diversity and organization. No two anatomical systems are exactly alike because of the enormous variability not only between the different members of a population, but also between the component parts of an organism. The word complexity has long been used descriptively in order to describe, for example, a large number of genes or cellular interconnections [structure of a system (i.e. an intricate architecture or the existence of many different component parts with varying interactions) or its non-linear functions  [One of the most important and distinctive characteristics of biological systems is the nections , but comer time) .space and time, and greatly biases any quantitative method that tends to idealize it as a smooth and regular Euclidean object.The vascular system is a complex network consisting of branched tubes of different sizes that are irregularly settled in the surrounding environment ,7. This fractal objects because of their irregular shape , self-similar structure, non-integer dimension and dependence on the scale of observation  [However, both normal and tumor vasculature can more properly be considered  effect) ,35-37.DS as a quantitative index of the 2-D spatial complexity of the vascular tree, in order to provide a closer-to-reality measure of this complex anatomical entity  as a subset of the range of theoretically possible morphologies [The theory underlying gularity ,21. The hologies .DS also comes from the fact that, like any other complex biological system, the vascular tree cannot be correctly quantified by measuring its individual properties . DS is a parameter that depends on: a) the number of vessels; b) the spatial relationships between the vascular components; and c) the interactions between the vascular components and the surrounding environment. In other words, its estimate is "ecologically" important because it provides a quantitative index of the "habitat structure".The significance of DS increased with the number of vessels making up the system  Ds can be an estimate of the 2-D geometrical complexity of the vascular system. As 2-D vascular complexity depends on the number of vessels and their distribution pattern, the use of MVD quantification alone to measure the angiogenic dependence of a tumor is strongly biased because the number of vessels does not reflect the number of tumor cells that can be supported by a vessel. Moreover the metabolic needs of cancer cells vary with the tissue of origin and change with tumor progression [gression .b) DS depends on the degree of vessel contiguity and continuity. These two geometrical properties determine what is called the intercapillary distance, and are not only involved in the spatial complexity of tumor vasculature, but also reflect the inviolable demand of a growing tumor for sufficient levels of nutrition and oxygen exchange. Inter-capillary distances are locally defined by the net balance between pro- and anti-angiogenic molecules in each microtissue region, as well as by non-angiogenic factors such as the oxygen and nutrient consumption rates of tumor cells. In normal tissue, vessel density fairly accurately reflects cell metabolic demands because evolutionary pressures have led to close and efficient coupling between vascular supply and metabolic needs. In tumors, the close coupling between vascular density and oxygen or nutrient consumption (i.e. the environment) may be loosened [loosened , thus alloosened ,38.c) DS falls between 0 (corresponding to the Euclidean dimension of a point) and 2 (the dimension of a plane). The more DS tends towards 2, the more the analyzed vascular configuration tends to fill a 2-D space and the greater its geometrical complexity.complex geometry of tumor vasculature and its well-known biological characteristics [In conclusion, the present study indicates that the eristics  mean thaeristics ,39-41.Scientific knowledge develops through the evolution of new concepts, and this process is usually driven by new methodologies that provide previously unavailable observation. The potential broad applicability of the proposed quantitative index makes it possible to explore the range of the morphological variability of vasculature that can be produced in nature, thus increasing its diagnostic importance in cancer research.Ds, Surface fractal dimension; 2-D, two-dimensional; VEGF, Vascular-endothelial growth factor; bFGF, basic fibroblastic growth factor; MVD, microvessel density.The author(s) declare that they have no competing interests.FG conceived, coordinated and designed the study and drafted the manuscript; CR, PC, BF, EEF, EC, MCI participated in designing the study and drafting the manuscript. All of the authors have read and approved the final manuscript.The pre-publication history for this paper can be accessed here:


Understanding how the brain processes and stores information depends in large part on knowing which neurons are involved in a particular process and how they're organized into functional networks. Each of the 10 billion or so neurons in the brain has thousands of connections to other neurons, sending (via axons) or receiving (via dendrites) the signals that allow us to think. Each neuron can transmit signals to both local and distant neurons, and it is by mapping these networks that neuroscientists can discern correlations between neural connectivity and physiological responses and ultimately unveil the computational algorithms underlying brain function. Since the beginning of cellular neuroscience at the end of the 19th century, neuronal connections have been explored by tracing axons and dendrites under the light microscope. But even with the resolution of state-of-the-art light microscopy, this approach works only if a small subset of neurons is stained and thus leaves most of the network hidden.Electron microscopy, on the other hand, can provide the spatial resolution necessary both to resolve processes in densely packed neural “wire bundles” and to identify synapses faithfully, but individual electron microscopic images are restricted to two dimensions. Transmission electron microscopy provides cross-sectional images through tissue, while scanning electron microscopy typically provides the appearance of 3D but in reality maps only the specimen surface and is thus blind to the connections within. It's possible to wrest 3D information from the transmission electron microscope by using tilt-series tomography, but sections can't be much thicker than 1 micron (a millionth of a meter). Data from thicker volumes can be obtained, but the process so far has been so painstaking and time-intensive—it involves, among other labor-intensive tasks, manually reconstructing serial sections—that few undertake it.It should, however, be possible to get similar data with “serial block-face imaging,” which involves repeatedly cutting section after section from a plastic-embedded block of tissue and photographing what's left. Scanning electron microscopy is needed for this task, but sample preparation methods are like those used for transmission electron microscopy, albeit with a few additional steps to enhance contrast.This is exactly what Winfried Denk and Heinz Horstmann have done to obtain “truly 3D datasets” using a method they call “serial block-face scanning electron microscopy” (SBFSEM), for which they constructed a “microtome” that goes inside the scanning electron microscope chamber. The resolution achieved is sufficient to reveal “even the thinnest of axons” and identify synapses. The SBFSEM method can generate stacks of thousands of ultra-thin sections, 50–70 nanometers (a nanometer is a billionth of a meter) thick, generating 3D datasets to reconstruct the topology and circuitry of neurons in brain tissue.The authors' custom-designed microtome holds the tissue block in a way that ensures image alignment and maintains focus; all the while the specimen surface is positioned close enough to the objective lens to allow high-resolution imaging.Denk and Horstmann expect that with this method they might ultimately be able to cut sections thinner than the 50 nanometers that their current setup manages. This then would allow them to cut sections even thinner than what is routinely possible in conventional transmission electron microscopy. While the authors doubt that the lateral resolution will ever reach that of transmission electron microscopy, they also argue that such high resolution may not actually be needed to trace neuronal connectivity. On the other hand, the method accelerates 3D electron microscopic data collection “by several orders of magnitude” by obviating the need for the labor-intensive adjustments to correct alignment and distortion required by other methods, an advance that is crucial for large-volume neuroanatomy and might, in addition, open up many hitherto inaccessible problems to ultra-structural investigations.