ATOMDANCE: Kernel-based denoising and choreographic analysis for protein dynamic comparison.

Comparative methods in molecular evolution and structural biology rely heavily upon the site-wise analysis of DNA sequence and protein structure, both static forms of information. However, it is widely accepted that protein function results from nanoscale nonrandom machine-like motions induced by evolutionarily conserved molecular interactions. Comparisons of molecular dynamics (MD) simulations conducted between homologous sites representative of different functional or mutational states can potentially identify local effects on binding interaction and protein evolution. In addition, comparisons of different (i.e., nonhomologous) sites within MD simulations could be employed to identify functional shifts in local time-coordinated dynamics indicative of logic gating within proteins. However, comparative MD analysis is challenged by the large fraction of protein motion caused by random thermal noise in the surrounding solvent. Therefore, properly denoised MD comparisons could reveal functional sites involving these machine-like dynamics with good accuracy. Here, we introduce ATOMDANCE, a user-interfaced suite of comparative machine learning-based denoising tools designed for identifying functional sites and the patterns of coordinated motion they can create within MD simulations. ATOMDANCE-maxDemon4.0 employs Gaussian kernel functions to compute site-wise maximum mean discrepancy between learned features of motion, thereby assessing denoised differences in the nonrandom motions between functional or evolutionary states (e.g., ligand bound versus unbound, wild-type versus mutant). ATOMDANCE-maxDemon4.0 also employs maximum mean discrepancy to analyze potential random amino acid replacements allowing for a site-wise test of neutral versus nonneutral evolution on the divergence of dynamic function in protein homologs. Finally, ATOMDANCE-Choreograph2.0 employs mixed-model analysis of variance and graph network to detect regions where time-synchronized shifts in dynamics occur. Here, we demonstrate ATOMDANCE's utility for identifying key sites involved in dynamic responses during functional binding interactions involving DNA, small-molecule drugs, and virus-host recognition, as well as understanding shifts in global and local site coordination occurring during allosteric activation of a pathogenic protease.

Evolution of drug resistance drives destabilization of flap region dynamics in HIV-1 protease.

The HIV-1 protease is one of several common key targets of combination drug therapies for human immunodeficiency virus infection and acquired immunodeficiency syndrome. During the progression of the disease, some individual patients acquire drug resistance due to mutational hotspots on the viral proteins targeted by combination drug therapies. It has recently been discovered that drug-resistant mutations accumulate on the "flap region" of the HIV-1 protease, which is a critical dynamic region involved in nonspecific polypeptide binding during invasion and infection of the host cell. In this study, we utilize machine learning-assisted comparative molecular dynamics, conducted at single amino acid site resolution, to investigate the dynamic changes that occur during functional dimerization and drug binding of wild-type and common drug-resistant versions of the main protease. We also use a multiagent machine learning model to identify conserved dynamics of the HIV-1 main protease that are preserved across simian and feline protease orthologs. We find that a key conserved functional site in the flap region, a solvent-exposed isoleucine (Ile50) that controls flap dynamics is functionally targeted by drug resistance mutations, leading to amplified molecular dynamics affecting the functional ability of the flap region to hold the drugs. We conclude that better long-term patient outcomes may be achieved by designing drugs that target protease regions that are less dependent upon single sites with large functional binding effects.

20 years of crystal hits: progress and promise in ultrahigh-throughput crystallization screening.

Diffraction-based structural methods contribute a large fraction of the biomolecular structural models available, providing a critical understanding of macromolecular architecture. These methods require crystallization of the target molecule, which remains a primary bottleneck in crystal-based structure determination. The National High-Throughput Crystallization Center at Hauptman-Woodward Medical Research Institute has focused on overcoming obstacles to crystallization through a combination of robotics-enabled high-throughput screening and advanced imaging to increase the success of finding crystallization conditions. This paper will describe the lessons learned from over 20 years of operation of our high-throughput crystallization services. The current experimental pipelines, instrumentation, imaging capabilities and software for image viewing and crystal scoring are detailed. New developments in the field and opportunities for further improvements in biomolecular crystallization are reflected on.

High-Throughput Screening to Obtain Crystal Hits for Protein Crystallography.

X-ray crystallography is the most commonly employed technique to discern macromolecular structures, but the crucial step of crystallizing a protein into an ordered lattice amenable to diffraction remains challenging. The crystallization of biomolecules is largely experimentally defined, and this process can be labor-intensive and prohibitive to researchers at resource-limited institutions. At the National High-Throughput Crystallization (HTX) Center, highly reproducible methods have been implemented to facilitate crystal growth, including an automated high-throughput 1,536-well microbatch-under-oil plate setup designed to sample a wide breadth of crystallization parameters. Plates are monitored using state-of-the-art imaging modalities over the course of 6 weeks to provide insight into crystal growth, as well as to accurately distinguish valuable crystal hits. Furthermore, the implementation of a trained artificial intelligence scoring algorithm for identifying crystal hits, coupled with an open-source, user-friendly interface for viewing experimental images, streamlines the process of analyzing crystal growth images. Here, the key procedures and instrumentation are described for the preparation of the cocktails and crystallization plates, imaging the plates, and identifying hits in a way that ensures reproducibility and increases the likelihood of successful crystallization.

Ensemble clustering of longitudinal bivariate HIV biomarker profiles to group patients by patterns of disease progression.

This paper describes an ensemble cluster analysis of bivariate profiles of HIV biomarkers, viral load and CD4 cell counts, which jointly measure disease progression. Data are from a prevalent cohort of HIV positive participants in a clinical trial of vitamin supplementation in Botswana. These individuals were HIV positive upon enrollment, but with unknown times of infection. To categorize groups of participants based on their patterns of progression of HIV infection using both biomarkers, we combine univariate shape-based cluster results for multiple biomarkers through the use of ensemble clustering methods. We first describe univariate clustering for each of the individual biomarker profiles, and make use of shape-respecting distances for clustering the longitudinal profile data. In our data, profiles are subject to either missing or irregular measurements as well as unobserved initiation times of the process of interest. Shape-respecting distances that can handle such data issues, preserve time-ordering, and identify similar profile shapes are useful in identifying patterns of disease progression from longitudinal biomarker data. However, their performance with regard to clustering differs by severity of the data issues mentioned above. We provide an empirical investigation of shape-respecting distances (Fréchet and dynamic time warping (DTW)) on benchmark shape data, and use DTW in cluster analysis of biomarker profile observations. These reveal a primary group of 'typical progressors,' as well as a smaller group that shows relatively rapid progression. We then refine the analysis using ensemble clustering for both markers to obtain a single classification. The information from joint evaluation of the two biomarkers combined with ensemble clustering reveals subgroups of patients not identifiable through univariate analyses; noteworthy subgroups are those that appear to represent recently and chronically infected subsets. Supplementary Information: The online version contains supplementary material available at 10.1007/s41060-022-00323-2.

CDC7 kinase (DDK) inhibition disrupts DNA replication leading to mitotic catastrophe in Ewing sarcoma.

Ewing sarcoma is the second most common bone malignancy in children and adolescents. In recent years, a large body of evidence has emerged that suggests Ewing tumors harbor large amounts of replication stress (RS). CDC7, also known as DDK (DBF4-dependent kinase), is a serine/threonine kinase that is involved in a diverse array of cellular functions including the regulation of DNA replication initiation and activation of the RS response. Due to DDK's diverse roles during replication, coupled with the fact that there is an increased level of RS within Ewing tumors, we hypothesized that Ewing sarcoma cells would be particularly vulnerable to DDK inhibition. Here, we report that DDK inhibition resulted a significant reduction in cell viability and the induction of apoptosis, specifically in Ewing sarcoma cells. Treatment with DDK inhibitors dramatically reduced the rate of replication, prolonged S-phase, and led to a pronounced increase in phospho-CDC2 (Y15), indicating delay of mitotic entry. The induction of cell death corresponded to mitotic exit and G1 entry, suggesting improper mitotic progression. In accordance with this, we find that DDK inhibition caused premature mitotic entry resulting in mitotic abnormalities such as anaphase bridges, lagging chromosomes, and cells with >2 poles in Ewing sarcoma cells. This abnormal progression through mitosis resulted in mitotic catastrophe as evidenced by the formation of micronuclei and induction of DNA damage. Together, these findings suggest that DDK activity is required for the faithful and timely completion of DNA replication in Ewing cells and that DDK inhibition may present a viable therapeutic strategy for the treatment of Ewing sarcoma.

Functional binding dynamics relevant to the evolution of zoonotic spillovers in endemic and emergent Betacoronavirus strains.

Comparative functional analysis of the dynamic interactions between various Betacoronavirus mutant strains and broadly utilized target proteins such as ACE2 and CD26, is crucial for a more complete understanding of zoonotic spillovers of viruses that cause diseases such as COVID-19. Here, we employ machine learning to replicated sets of nanosecond scale GPU accelerated molecular dynamics simulations to statistically compare and classify atom motions of these target proteins in both the presence and absence of different endemic and emergent strains of the viral receptor binding domain (RBD) of the S spike glycoprotein. A multi-agent classifier successfully identified functional binding dynamics that are evolutionarily conserved from bat CoV-HKU4 to human endemic/emergent strains. Conserved dynamics regions of ACE2 involve both the N-terminal helices, as well as a region of more transient dynamics encompassing residues K353, Q325 and a novel motif AAQPFLL 386-92 that appears to coordinate their dynamic interactions with the viral RBD at N501. We also demonstrate that the functional evolution of Betacoronavirus zoonotic spillovers involving ACE2 interaction dynamics are likely pre-adapted from two precise and stable binding sites involving the viral bat progenitor strain's interaction with CD26 at SAMLI 291-5 and SS 333-334. Our analyses further indicate that the human endemic strains hCoV-HKU1 and hCoV-OC43 have evolved more stable N-terminal helix interactions through enhancement of an interfacing loop region on the viral RBD, whereas the highly transmissible SARS-CoV-2 variants (B.1.1.7, B.1.351 and P.1) have evolved more stable viral binding via more focused interactions between the viral N501 and ACE2 K353 alone.Communicated by Ramaswamy H. Sarma.

Function and evolution of B-Raf loop dynamics relevant to cancer recurrence under drug inhibition.

Oncogenic mutations in the kinase domain of the B-Raf protein have long been associated with cancers involving the MAPK pathway. One constitutive MAPK activating mutation in B-Raf, the V600E (valine to glutamate) replacement occurring adjacent to a site of threonine phosphorylation (T599) occurs in many types of cancer, and in a large percentage of certain cancers, such as melanoma. Because ATP binding activity and the V600E mutation are both known to alter the physical behavior of the activation loop in the B-Raf ATP binding domain, this system is especially amenable to comparative analyses of molecular dynamics simulations modeling various genetic and drug class variants. Here, we employ machine learning enabled identification of functionally conserved protein dynamics to compare how the binding interactions of four B-Raf inhibitors impact the functional loop dynamics controlling ATP activation. We demonstrate that drug development targeting B-Raf has progressively moved towards ATP competitive inhibitors that demonstrate less tendency to mimic the functionally conserved dynamic changes associated with ATP activation and leading to the side effect of hyperactivation (i.e. inducing MAPK activation in non-tumorous cells in the absence of secondary mutation). We compare the functional dynamic impacts of V600E and other sensitizing and drug resistance causing mutations in the regulatory loops of B-Raf, confirming sites of low mutational tolerance in these regions. Lastly, we investigate V600E sensitivity of B-Raf loop dynamics in an evolutionary context, demonstrating that while sensitivity has an ancient origin with primitive eukaryotes, it was also secondarily increased during early jawed vertebrate evolution.Communicated by Ramaswamy H. Sarma.

Role of endolysosome function in iron metabolism and brain carcinogenesis.

Iron, the most abundant metal in human brain, is an essential microelement that regulates numerous cellular mechanisms. Some key physiological roles of iron include oxidative phosphorylation and ATP production, embryonic neuronal development, formation of iron-sulfur clusters, and the regulation of enzymes involved in DNA synthesis and repair. Because of its physiological and pathological importance, iron homeostasis must be tightly regulated by balancing its uptake, transport, and storage. Endosomes and lysosomes (endolysosomes) are acidic organelles known to contain readily releasable stores of various cations including iron and other metals. Increased levels of ferrous (Fe2+) iron can generate reactive oxygen species (ROS) via Fenton chemistry reactions and these increases can damage mitochondria and genomic DNA as well as promote carcinogenesis. Accumulation of iron in the brain has been linked with aging, diet, disease, and cerebral hemorrhage. Further, deregulation of brain iron metabolism has been implicated in carcinogenesis and may be a contributing factor to the increased incidence of brain tumors around the world. Here, we provide insight into mechanisms by which iron accumulation in endolysosomes is altered by pH and lysosome membrane permeabilization. Such events generate excess ROS resulting in mitochondrial DNA damage, fission, and dysfunction, as well as DNA oxidative damage in the nucleus; all of which promote carcinogenesis. A better understanding of the roles that endolysosome iron plays in carcinogenesis may help better inform the development of strategic therapeutic options for cancer treatment and prevention.

Structural biology in the time of COVID-19: perspectives on methods and milestones.

The global COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has wreaked unprecedented havoc on global society, in terms of a huge loss of life and burden of morbidity, economic upheaval and social disruption. Yet the sheer magnitude and uniqueness of this event has also spawned a massive mobilization of effort in the scientific community to investigate the virus, to develop therapeutics and vaccines, and to understand the public health impacts. Structural biology has been at the center of these efforts, and so it is advantageous to take an opportunity to reflect on the status of structural science vis-à-vis its role in the fight against COVID-19, to register the unprecedented response and to contemplate the role of structural biology in addressing future outbreak threats. As the one-year anniversary of the World Health Organization declaration that COVID-19 is a pandemic has just passed, over 1000 structures of SARS-CoV-2 biomolecules have been deposited in the Worldwide Protein Data Bank (PDB). It is rare to obtain a snapshot of such intense effort in the structural biology arena and is of special interest as the 50th anniversary of the PDB is celebrated in 2021. It is additionally timely as it overlaps with a period that has been termed the 'resolution revolution' in cryoelectron microscopy (CryoEM). CryoEM has recently become capable of producing biomolecular structures at similar resolutions to those traditionally associated with macromolecular X-ray crystallo-graphy. Examining SARS-CoV-2 protein structures that have been deposited in the PDB since the virus was first identified allows a unique window into the power of structural biology and a snapshot of the advantages of the different techniques available, as well as insight into the complementarity of the structural methods.

Functional binding dynamics relevant to the evolution of zoonotic spillovers in endemic and emergent Betacoronavirus strains.

Comparative functional analysis of the dynamic interactions between various Betacoronavirus mutant strains and broadly utilized target proteins such as ACE2 and CD26, is crucial for a more complete understanding of zoonotic spillovers of viruses that cause diseases such as COVID-19. Here, we employ machine learning to replicated sets of nanosecond scale GPU accelerated molecular dynamics simulations to statistically compare and classify atom motions of these target proteins in both the presence and absence of different endemic and emergent strains of the viral receptor binding domain (RBD) of the S spike glycoprotein. Machine learning was used to identify functional binding dynamics that are evolutionarily conserved from bat CoV-HKU4 to human endemic/emergent strains. Conserved dynamics regions of ACE2 involve both the N-terminal helices, as well as a region of more transient dynamics encompassing K353, Q325 and a novel motif AAQPFLL 386-92 that appears to coordinate their dynamic interactions with the viral RBD at N501. We also demonstrate that the functional evolution of Betacoronavirus zoonotic spillovers involving ACE2 interaction dynamics are likely pre-adapted from two precise and stable binding sites involving the viral bat progenitor strain's interaction with CD26 at SAMLI 291-5 and SS 333-334. Our analyses further indicate that the human endemic strains hCoV-HKU1 and hCoV-OC43 have evolved more stable N-terminal helix interactions through enhancement of an interfacing loop region on the viral RBD, whereas the highly transmissible SARS-CoV-2 variants (B.1.1.7, B.1.351 and P.1) have evolved more stable viral binding via more focused interactions between the viral N501 and ACE2 K353 alone.

Exploiting Replication Stress as a Novel Therapeutic Intervention.

Ewing sarcoma is an aggressive pediatric tumor of the bone and soft tissue. The current standard of care is radiation and chemotherapy, and patients generally lack targeted therapies. One of the defining molecular features of this tumor type is the presence of significantly elevated levels of replication stress as compared with both normal cells and many other types of cancers, but the source of this stress is poorly understood. Tumors that harbor elevated levels of replication stress rely on the replication stress and DNA damage response pathways to retain viability. Understanding the source of the replication stress in Ewing sarcoma may reveal novel therapeutic targets. Ewing sarcomagenesis is complex, and in this review, we discuss the current state of our knowledge regarding elevated replication stress and the DNA damage response in Ewing sarcoma, one contributor to the disease process. We will also describe how these pathways are being successfully targeted therapeutically in other tumor types, and discuss possible novel, evidence-based therapeutic interventions in Ewing sarcoma. We hope that this consolidation will spark investigations that uncover new therapeutic targets and lead to the development of better treatment options for patients with Ewing sarcoma. IMPLICATIONS: This review uncovers new therapeutic targets in Ewing sarcoma and highlights replication stress as an exploitable vulnerability across multiple cancers.

A Searchable Database of Crystallization Cocktails in the PDB: Analyzing the Chemical Condition Space.

Nearly 90% of structural models in the Protein Data Bank (PDB), the central resource worldwide for three-dimensional structural information, are currently derived from macromolecular crystallography (MX). A major bottleneck in determining MX structures is finding conditions in which a biomolecule will crystallize. Here, we present a searchable database of the chemicals associated with successful crystallization experiments from the PDB. We use these data to examine the relationship between protein secondary structure and average molecular weight of polyethylene glycol and to investigate patterns in crystallization conditions. Our analyses reveal striking patterns of both redundancy of chemical compositions in crystallization experiments and extreme sparsity of specific chemical combinations, underscoring the challenges faced in generating predictive models for de novo optimal crystallization experiments.

Inhibition of LSD1 in MDS progenitors restores differentiation of CD141Hi conventional dendritic cells.

The use of immunotherapy to treat patients with myelodysplastic syndromes (MDS) shows promise but is limited by our incomplete understanding of the immunologic milieu. In solid tumors, CD141Hi conventional dendritic cells (CD141Hi cDCs) are necessary for antitumor immunosurveillance and the response to immunotherapy. Here, we found that CD141Hi cDCs are reduced in MDS bone marrow and based on the premise established in solid tumors, we hypothesized that reduced numbers of CD141Hi cDCs are associated with inferior overall survival in MDS patients. We found that MDS patients with reduced numbers of CD141Hi cDCs, but not other DC populations, showed reduced overall survival. To examine the basis for reduction in CD141Hi cDCs, we found fewer numbers of progenitors committed to DC differentiation in the MDS bone marrow and these progenitors expressed lower levels of interferon regulatory factor-8 (IRF8), a master regulator of CD141Hi cDC differentiation. To rescue impaired CD141Hi cDC differentiation, we used pharmacologic inhibition of lysine-specific demethylase 1A (LSD1) to promote CD141Hi cDC differentiation by MDS progenitors. These data reveal a previously unrecognized element of the MDS immunologic milieu. Epigenetic regulation of CD141Hi cDC differentiation offers an intriguing opportunity for intervention and a potential adjunct to immunotherapy for patients with MDS.

Reliability and Validity of a Two-Question Version of the World Health Organization's Alcohol, Smoking and Substance Involvement Screening Test: The ASSIST-FC.

OBJECTIVE: The Alcohol, Smoking and Substance Involvement Screening Test (ASSIST), developed for the World Health Organization (WHO), screens for risks associated with the use of tobacco, alcohol, and seven categories of drugs. Although the ASSIST has acceptable psychometric properties, it is relatively long for a screening test. This study was designed to identify a subset of questions from the full ASSIST instrument having comparable psychometric properties for the classification of low-, moderate-, and high-risk substance use. METHOD: The study used three data sets from prior studies using the WHO ASSIST. Samples 1 and 3 were obtained from WHO multisite studies conducted in seven countries. Sample 2 included patient data from a U.S.-based screening and brief intervention program that incorporated the ASSIST into its clinical protocol. Samples 1 and 2 were used to conduct psychometric analyses for combinations of ASSIST items. Sample 3 was used to estimate sensitivity, specificity, and positive and negative predictive value for a two-item ASSIST. RESULTS: Based on correlation statistics, reliability metrics, and validation analyses, a new, two-item version is proposed. The ASSIST-FC contains one question about the frequency (F) of current use and a second question about current or past concern (C) expressed by others. The ASSIST-FC demonstrates no substantial loss in reliability, validity, and predictive ability when statistically compared with the full-length ASSIST. CONCLUSIONS: The ASSIST-FC has advantages for clinical applications in settings where a brief, efficient, reliable screening test is needed to identify patients with hazardous and harmful substance use who would benefit from a brief intervention. It can also be used to identify patients who are manifesting symptoms of substance dependence that would require further diagnostic evaluation.

Qualitative coronary artery calcium assessment on CT lung screening exam helps predict first cardiac events.

RESULTS: A total of 1,513 individuals underwent CTLS. Downstream data, pre-test cardiac risk factors and CAC scores were available for 88.3% (1,336/1,513). The average length of follow-up was 2.64 (SD ±0.72) years. There were a total of 43 events, occurring in 1.55% (6/386) of patients with mild CAC, 3.24% (11/339) of patients with moderate CAC, and 8.90% (26/292) of patients with marked CAC. There were no events among patients with no reported CAC (0/319). Using multivariable logistic modeling, the increased odds of an initial cardiac event was 2.56 (95% CI, 1.76-3.92, P<0.001) for mild CAC, 6.57 (95% CI, 3.10-15.4, P<0.001) for moderate CAC, and 16.8 (95% CI, 5.46-60.3, P<0.001) for marked CAC, as compared to individuals with no CAC. Time to event analysis showed distinct differences among the four CAC categories (P<0.001). CONCLUSIONS: Qualitative coronary artery calcification scoring of CTLS exams may provide a novel method to help select individuals at elevated risk for an initial cardiac event.

A Systems Biology Approach to Understanding the Pathophysiology of High-Grade Serous Ovarian Cancer: Focus on Iron and Fatty Acid Metabolism.

Ovarian cancer (OVC) is the most lethal of the gynecological malignancies, with diagnosis often occurring during advanced stages of the disease. Moreover, a majority of cases become refractory to chemotherapeutic approaches. Therefore, it is important to improve our understanding of the molecular dependencies underlying the disease to identify novel diagnostic and precision therapeutics for OVC. Cancer cells are known to sequester iron, which can potentiate cancer progression through mechanisms that have not yet been completely elucidated. We developed an algorithm to identify novel links between iron and pathways implicated in high-grade serous ovarian cancer (HGSOC), the most common and deadliest subtype of OVC, using microarray gene expression data from both clinical sources and an experimental model. Using our approach, we identified several links between fatty acid (FA) and iron metabolism, and subsequently developed a network for iron involvement in FA metabolism in HGSOC. FA import and synthesis pathways are upregulated in HGSOC and other cancers, but a link between these processes and iron-related genes has not yet been identified. We used the network to derive hypotheses of specific mechanisms by which iron and iron-related genes impact and interact with FA metabolic pathways to promote tumorigenesis. These results suggest a novel mechanism by which iron sequestration by cancer cells can potentiate cancer progression, and may provide novel targets for use in diagnosis and/or treatment of HGSOC.

DCYTB is a predictor of outcome in breast cancer that functions via iron-independent mechanisms.

BACKGROUND: Duodenal cytochrome b (DCYTB) is a ferrireductase that functions together with divalent metal transporter 1 (DMT1) to mediate dietary iron reduction and uptake in the duodenum. DCYTB is also a member of a 16-gene iron regulatory gene signature (IRGS) that predicts metastasis-free survival in breast cancer patients. To better understand the relationship between DCYTB and breast cancer, we explored in detail the prognostic significance and molecular function of DCYTB in breast cancer. METHODS: The prognostic significance of DCYTB expression was evaluated using publicly available microarray data. Signaling Pathway Impact Analysis (SPIA) of microarray data was used to identify potential novel functions of DCYTB. The role of DCYTB was assessed using immunohistochemistry and measurements of iron uptake, iron metabolism, and FAK signaling. RESULTS: High DCYTB expression was associated with prolonged survival in two large independent cohorts, together totaling 1610 patients (cohort #1, p = 1.6e-11, n = 741; cohort #2, p = 1.2e-05, n = 869; log-rank test) as well as in the Gene expression-based Outcome for Breast cancer Online (GOBO) cohort (p < 1.0e-05, n = 1379). High DCYTB expression was also associated with increased survival in homogeneously treated groups of patients who received either tamoxifen or chemotherapy. Immunohistochemistry revealed that DCYTB is localized on the plasma membrane of breast epithelial cells, and that expression is dramatically reduced in high-grade tumors. Surprisingly, neither overexpression nor knockdown of DCYTB affected levels of ferritin H, transferrin receptor, labile iron or total cellular iron in breast cancer cells. Because SPIA pathway analysis of patient microarray data revealed an association between DCYTB and the focal adhesion pathway, we examined the influence of DCYTB on FAK activation in breast cancer cells. These experiments reveal that DCYTB reduces adhesion and activation of focal adhesion kinase (FAK) and its adapter protein paxillin. CONCLUSIONS: DCYTB is an important predictor of outcome and is associated with response to therapy in breast cancer patients. DCYTB does not affect intracellular iron in breast cancer cells. Instead, DCYTB may retard cancer progression by reducing activation of FAK, a kinase that plays a central role in tumor cell adhesion and metastasis.

Proximal tubule proteins are significantly elevated in bladder urine of patients with ureteropelvic junction obstruction and may represent novel biomarkers: A pilot study.

PURPOSE: Ureteropelvic junction obstruction (UPJO) is the major cause of hydronephrosis in children and may lead to renal injury and early renal dysfunction. However, diagnosis of the degree of obstruction and severity of renal injury relies on invasive and often inconclusive renal scans. Biomarkers from voided urine that detect early renal injury are highly desirable because of their noninvasive collection and their potential to assist in earlier and more reliable diagnosis of the severity of obstruction. Early in response to UPJO, increased intrarenal pressure directly impacts the proximal tubule brush border. We hypothesize that single-pass, apically expressed proximal tubule brush border proteins will be shed into the urine early and rapidly and will be reliable noninvasive urinary biomarkers, providing the tools for a more reliable stratification of UPJO patients. MATERIALS AND METHODS: We performed a prospective cohort study at Connecticut Children's Medical Center. Bladder urine samples from 12 UPJO patients were obtained prior to surgical intervention. Control urine samples were collected from healthy pediatric patients presenting with primary nocturnal enuresis. We determined levels of NGAL, KIM-1 (previously identified biomarkers), CD10, CD13, and CD26 (potentially novel biomarkers) by ELISA in control and experimental urine samples. Urinary creatinine levels were used to normalize the urinary protein levels measured by ELISA. RESULTS: Each of the proximal tubule proteins outperformed the previously published biomarkers. No differences in urinary NGAL and KIM-1 levels were observed between control and obstructed patients (p = 0.932 and p = 0.799, respectively). However, levels of CD10, CD13, and CD26 were significantly higher in the voided urine of obstructed individuals when compared with controls (p = 0.002, p = 0.024, and p = 0.007, respectively) (Figure). CONCLUSIONS: Targeted identification of reliable, noninvasive biomarkers of renal injury is critical to aid in diagnosing patients at risk, guiding therapeutic decisions and monitoring treatment efficacy. Proximal tubule brush border proteins are reliably detected in the urine of obstructed patients and may be more effective at predicting UPJO.

Predicting time to threshold for initiating antiretroviral treatment to evaluate cost of treatment as prevention of human immunodeficiency virus.

The goal of this paper is to predict the additional amount of antiretroviral treatment that would be required to implement a policy of treating all HIV-infected people at time of detection of infection rather than at the time that their CD4 T-lymphocyte counts are observed to be below a threshold-the current standard of care. We describe a sampling-based inverse prediction method for predicting time from HIV infection to attainment of the CD4 threshold and apply it to a set of treatment-naive HIV-infected subjects in a village in Botswana who participated in a household survey that collected cross-sectional CD4 counts. The inferential target of interest is the population-level mean time to reaching CD4-based treatment threshold in this group of subjects. To address the challenges arising from the fact that these subject's dates of HIV infection are unknown, we make use of data from an auxiliary cohort study of subjects enrolled shortly after HIV infection in which CD4 counts were measured over time. We use a multiple imputation framework to combine across the different sources of data, and discuss how the methods compensate for the length-biased sampling inherent in cross-sectional screening procedures, such as household surveys. We comment on how the results bear upon analyses of costs of implementation of treatment-for-prevention use of antiretroviral drugs in HIV prevention interventions.