A machine learning classifier using 33 host immune response mRNAs accurately distinguishes viral and non-viral acute respiratory illnesses in nasal swab samples

BACKGROUND

Viral acute respiratory illnesses (viral ARIs) contribute significantly to human morbidity and mortality worldwide, but their successful treatment requires timely diagnosis of viral etiology, which is complicated by overlap in clinical presentation with the non-viral ARIs. Multiple pandemics in the twenty-first century to date have further highlighted the unmet need for effective monitoring of clinically relevant emerging viruses. Recent studies have identified conserved host response to viral infections in the blood.

METHODS

We hypothesize that a similarly conserved host response in nasal samples can be utilized for diagnosis and to rule out viral infection in symptomatic patients when current diagnostic tests are negative. Using a multi-cohort analysis framework, we analyzed 1555 nasal samples across 10 independent cohorts dividing them into training and validation.

RESULTS

Using six of the datasets for training, we identified 119 genes that are consistently differentially expressed in viral ARI patients (N = 236) compared to healthy controls (N = 146) and further down-selected 33 genes for classifier development. The resulting locked logistic regression-based classifier using the 33-mRNAs had AUC of 0.94 and 0.89 in the six training and four validation datasets, respectively. Furthermore, we found that although trained on healthy controls only, in the four validation datasets, the 33-mRNA classifier distinguished viral ARI from both healthy or non-viral ARI samples with > 80% specificity and sensitivity, irrespective of age, viral type, and viral load. Single-cell RNA-sequencing data showed that the 33-mRNA signature is dominated by macrophages and neutrophils in nasal samples.

CONCLUSION

This proof-of-concept signature has potential to be adapted as a clinical point-of-care test ('RespVerity') to improve the diagnosis of viral ARIs.

[Influence of antimicrobial peptide biofunctionalized TiO2 nanotubes on the biological behavior of human keratinocytes and its antibacterial effect]

Objective: To fabricate TiO2 nanotube material functionalized by antimicrobial peptide LL-37, and to explore its effects on biological behaviors such as adhesion and migration of human keratinocytes (HaCaT) and its antibacterial properties. Methods: The TiO2 nanotube array (NT) was constructed on the surface of polished titanium (PT) by anodization, and the antimicrobial peptide LL-37 was loaded on the surface of TiO2 nanotube (LL-37/NT) by physical adsorption. Three samples were selected by simple random sampling in each group. Surface morphology, roughness, hydrophilicity and release characteristics of LL-37 of the samples were analyzed with a field emission scanning electron microscope, an atomic force microscope, a contact angle measuring device and a microplate absorbance reader. HaCaT cells were respectively cultured on the surface of three groups of titanium samples. Each group had 3 replicates. The morphology of cell was observed by field emission scanning electron microscope. The number of cell adhesion was observed by cellular immunofluorescence staining. Cell counting kit-8 (CCK-8) assay was used to detect cell proliferation. Wound scratch assay was used to observe the migration of HaCaT. The above experiments were used to evaluate the effect of each group on the biological behavior of HaCaT cells. To evaluate their antibacterial effects, Porphyromonas gingivalis (Pg) was respectively inoculated on the surface of three groups of titanium samples. Each group had 3 replicates. The morphology of bacteria was observed by field emission scanning electron microscope. Bacterial viability was determined by live/dead bacterial staining. Results: A uniform array of nanotubes could be seen on the surface of titanium samples in LL-37/NT group, and the top of the tube was covered with granular LL-37. Compared with PT group [the roughness was (2.30±0.18) nm, the contact angle was 71.8°±1.7°], the roughness [(20.40±3.10) and (19.10±4.11) nm] and hydrophilicity (the contact angles were 22.4°±3.1° and 25.3°±2.2°, respectively) of titanium samples increased in NT and LL-37/NT group (P<0.001). The results of in vitro release test showed that the release of antimicrobial peptide LL-37 was characterized by early sudden release (1-4 h) and long-term (1-7 d) slow release. With the immunofluorescence, more cell attachment was found on NT and LL-37/NT than that on PT at the first 0.5 and 2.0 h of culture (P<0.05). The results of CCK-8 showed that there was no significant difference in the proliferation of cells among groups at 1, 3 and 5 days after culture. Wound scratch assay showed that compared with PT and NT group, the cell moved fastest on the surface of titanium samples in LL-37/NT group at 24 h of culture [(96.4±4.9)%] (F=35.55, P<0.001). A monolayer cells could be formed and filled with the scratch in 24 h at LL-37/NT group. The results of bacterial test in vitro showed that compared with the PT group, the bacterial morphology in the NT and LL-37/NT groups was significantly wrinkled, and obvious bacterial rupture could be seen on the surface of titanium samples in LL-37/NT group. The results of bacteria staining showed that the green fluorescence intensity of titanium samples in LL-37/NT group was the lowest in all groups (F=66.54,P<0.001). Conclusions: LL-37/NT is beneficial to the adhesion and migration of HaCaT cells and has excellent antibacterial properties, this provides a new strategy for the optimal design of implant neck materials.

A robust gene expression signature for NASH in liver expression data

Non-Alcoholic Fatty Liver Disease (NAFLD) is a progressive liver disease that affects up to 30% of worldwide population, of which up to 25% progress to Non-Alcoholic SteatoHepatitis (NASH), a severe form of the disease that involves inflammation and predisposes the patient to liver cirrhosis. Despite its epidemic proportions, there is no reliable diagnostics that generalizes to global patient population for distinguishing NASH from NAFLD. We performed a comprehensive multicohort analysis of publicly available transcriptome data of liver biopsies from Healthy Controls (HC), NAFLD and NASH patients. Altogether we analyzed 812 samples from 12 different datasets across 7 countries, encompassing real world patient heterogeneity. We used 7 datasets for discovery and 5 datasets were held-out for independent validation. Altogether we identified 130 genes significantly differentially expressed in NASH versus a mixed group of NAFLD and HC. We show that our signature is not driven by one particular group (NAFLD or HC) and reflects true biological signal. Using a forward search we were able to downselect to a parsimonious set of 19 mRNA signature with mean AUROC of 0.98 in discovery and 0.79 in independent validation. Methods for consistent diagnosis of NASH relative to NAFLD are urgently needed. We showed that gene expression data combined with advanced statistical methodology holds the potential to serve basis for development of such diagnostic tests for the unmet clinical need.

A Multi-mRNA Prognostic Signature for Anti-TNFα Therapy Response in Patients with Inflammatory Bowel Disease

BACKGROUND

Anti-TNF-alpha (anti-TNFα) therapies have transformed the care and management of inflammatory bowel disease (IBD). However, they are expensive and ineffective in greater than 50% of patients, and they increase the risk of infections, liver issues, arthritis, and lymphoma. With 1.6 million Americans suffering from IBD and global prevalence on the rise, there is a critical unmet need in the use of anti-TNFα therapies: a test for the likelihood of therapy response. Here, as a proof-of-concept, we present a multi-mRNA signature for predicting response to anti-TNFα treatment to improve the efficacy and cost-to-benefit ratio of these biologics.

METHODS

We surveyed public data repositories and curated four transcriptomic datasets (n = 136) from colonic and ileal mucosal biopsies of IBD patients (pretreatment) who were subjected to anti-TNFα therapy and subsequently adjudicated for response. We applied a multicohort analysis with a leave-one-study-out (LOSO) approach, MetaIntegrator, to identify significant differentially expressed (DE) genes between responders and non-responders and then used a greedy forward search to identify a parsimonious gene signature. We then calculated an anti-TNFα response (ATR) score based on this parsimonious gene signature to predict responder status and assessed discriminatory performance via an area-under-receiver operating-characteristic curve (AUROC).

RESULTS

We identified 324 significant DE genes between responders and non-responders. The greedy forward search yielded seven genes that robustly distinguish anti-TNFα responders from non-responders, with an AUROC of 0.88 (95% CI: 0.70-1). The Youden index yielded a mean sensitivity of 91%, mean specificity of 76%, and mean accuracy of 86%.

CONCLUSIONS

Our findings suggest that there is a robust transcriptomic signature for predicting anti-TNFα response in mucosal biopsies from IBD patients prior to treatment initiation. This seven-gene signature should be further investigated for its potential to be translated into a predictive test for clinical use.

Universal Toxicity Gene Signatures for Early Identification of Drug-Induced Tissue Injuries in Rats

A new safety testing paradigm that relies on gene expression biomarker panels was developed to easily and quickly identify drug-induced injuries across tissues in rats prior to drug candidate selection. Here, we describe the development, qualification, and implementation of gene expression signatures that diagnose tissue degeneration/necrosis for use in early rat safety studies. Approximately 400 differentially expressed genes were first identified that were consistently regulated across 4 prioritized tissues (liver, kidney, heart, and skeletal muscle), following injuries induced by known toxicants. Hundred of these "universal" genes were chosen for quantitative PCR, and the most consistent and robustly responding transcripts selected, resulting in a final 22-gene set from which unique sets of 12 genes were chosen as optimal for each tissue. The approach was extended across 4 additional tissues (pancreas, gastrointestinal tract, bladder, and testes) where toxicities are less common. Mathematical algorithms were generated to convert each tissue's 12-gene expression values to a single metric, scaled between 0 and 1, and a positive threshold set. For liver, kidney, heart, and skeletal muscle, this was established using a training set of 22 compounds and performance determined by testing a set of approximately 100 additional compounds, resulting in 74%-94% sensitivity and 94%-100% specificity for liver, kidney, and skeletal muscle, and 54%-62% sensitivity and 95%-98% specificity for heart. Similar performance was observed across a set of 15 studies for pancreas, gastrointestinal tract, bladder, and testes. Bundled together, we have incorporated these tissue signatures into a 4-day rat study, providing a rapid assessment of commonly seen compound liabilities to guide selection of lead candidates without the necessity to perform time-consuming histopathologic analyses.

[Application of deep convolutional neural networks in the diagnosis of laryngeal squamous cell carcinoma based on narrow band imaging endoscopy]

Objective: To explore the possibility of using artificial intelligence (AI) technology based on convolutional neural network (CNN) to assist the clinical diagnosis of laryngeal squamous cell carcinoma (LSCC) through deep learning algorithm. Methods: A deep CNN was developed and applied in narrow band imaging (NBI) endoscopy of 4 799 patients with laryngeal lesions, including 3 168 males and 1 631 females, aged from 21 to 87 years, from 2015 to 2017 in Beijing Tongren Hospital, Capital Medical University. A simple randomization method was used to select the laryngeal NBI images of 2 427 patients (1 388 benign lesions and 1 039 LSCC lesions) for the training and correction the CNN model. The remaining laryngeal NBI images of 2 372 patients (including 1 276 benign lesions and 1 096 LSCC lesions) were used as validation data set to compare performance between CNN and otolaryngologists. SPSS 21.0 software was used for Chi-square test to calculate the accuracy, sensitivity and specificity of AI and otolaryngologists. The area under the curve (AUC) of receiver operating curve (ROC) was used to evaluate the diagnostic ability of the algorithm for NBI images. Results: The accuracy, sensitivity and specificity for NBI predictions were respectively 90.91% (AUC=0.96), 90.12% and 91.53%, which were equivalent to those for otolaryngologists' predictions (accuracy, sensitivity and specificity were (91.93±3.20)%, (91.33±3.25)% and (93.02±2.59)%, t values were 0.64, 0.75 and 1.17, and P values were 0.32, 0.28 and 0.21, respectively). The diagnostic efficiency of CNN was significantly higher than that of otolaryngologists (0.01 vs. 5.50, t =9.15, P<0.001). Conclusion: AI based on deep CNN is effective for using in the laryngeal NBI image diagnosis, showing a good application prospect in the diagnosis of LSCC.

Multi-cohort analysis of host immune response identifies conserved protective and detrimental modules associated with severity across viruses

Viral infections induce a conserved host response distinct from bacterial infections. We hypothesized that the conserved response is associated with disease severity and is distinct between patients with different outcomes. To test this, we integrated 4,780 blood transcriptome profiles from patients aged 0 to 90 years infected with one of 16 viruses, including SARS-CoV-2, Ebola, chikungunya, and influenza, across 34 cohorts from 18 countries, and single-cell RNA sequencing profiles of 702,970 immune cells from 289 samples across three cohorts. Severe viral infection was associated with increased hematopoiesis, myelopoiesis, and myeloid-derived suppressor cells. We identified protective and detrimental gene modules that defined distinct trajectories associated with mild versus severe outcomes. The interferon response was decoupled from the protective host response in patients with severe outcomes. These findings were consistent, irrespective of age and virus, and provide insights to accelerate the development of diagnostics and host-directed therapies to improve global pandemic preparedness.

Transcriptomic similarities and differences in host response between SARS-CoV-2 and other viral infections

The pandemic 2019 novel coronavirus disease (COVID-19) shares certain clinical characteristics with other acute viral infections. We studied the whole-blood transcriptomic host response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using RNAseq from 24 healthy controls and 62 prospectively enrolled patients with COVID-19. We then compared these data to non-COVID-19 viral infections, curated from 23 independent studies profiling 1,855 blood samples covering six viruses (influenza, respiratory syncytial virus (RSV), human rhinovirus (HRV), severe acute respiratory syndrome coronavirus 1 (SARS-CoV-1), Ebola, dengue). We show gene expression changes in COVID-19 versus non-COVID-19 viral infections are highly correlated (r = 0.74, p < 0.001). However, we also found 416 genes specific to COVID-19. Inspection of top genes revealed dynamic immune evasion and counter host responses specific to COVID-19. Statistical deconvolution of cell proportions maps many cell type proportions concordantly shifting. Discordantly increased in COVID-19 were CD56^bright natural killer cells and M2 macrophages. The concordant and discordant responses mapped out here provide a window to explore the pathophysiology of the host response to SARS-CoV-2.

Biomarkers of genome instability in normal mammalian genomes following drug-induced replication stress

Genome instability is a hallmark of most human cancers and is exacerbated following replication stress. However, the effects that drugs/xenobiotics have in promoting genome instability including chromosomal structural rearrangements in normal cells are not currently assessed in the genetic toxicology battery. Here, we show that drug-induced replication stress leads to increased genome instability in vitro using proliferating primary human cells as well as in vivo in rat bone marrow (BM) and duodenum (DD). p53-binding protein 1 (53BP1, biomarker of DNA damage repair) nuclear bodies were increased in a dose-dependent manner in normal proliferating human mammary epithelial fibroblasts following treatment with compounds traditionally classified as either genotoxic (hydralazine) and nongenotoxic (low-dose aphidicolin, duvelisib, idelalisib, and amiodarone). Comparatively, no increases in 53BP1 nuclear bodies were observed in nonproliferating cells. Negative control compounds (mannitol, alosteron, diclofenac, and zonisamide) not associated with cancer risk did not induce 53BP1 nuclear bodies in any cell type. Finally, we studied the in vivo genomic consequences of drug-induced replication stress in rats treated with 10 mg/kg of cyclophosphamide for up to 14 days followed by polymerase chain reaction-free whole genome sequencing (30X coverage) of BM and DD cells. Cyclophosphamide induced chromosomal structural rearrangements at an average of 90 genes, including 40 interchromosomal/intrachromosomal translocations, within 2 days of treatment. Collectively, these data demonstrate that this drug-induced genome instability test (DiGIT) can reveal potential adverse effects of drugs not otherwise informed by standard genetic toxicology testing batteries. These efforts are aligned with the food and drug administration's (FDA's) predictive toxicology roadmap initiative.

[Predictive value of vascular health indicators on newly cardiovascular events: Preliminary validation of Beijing vascular health stratification system]

OBJECTIVE

To explore the predictive value of carotid femoral artery pulse wave velocity (CF-PWV), carotid radial artery pulse wave velocity (CR-PWV), cardio-ankle vascular index (CAVI), and ankle brachial index (ABI) on coronary heart disease (CHD) and cerebral infarction (CI), and the preliminary validation of Beijing vascular health stratification (BVHS).

METHODS

Subjects with at least 2 in-patient records were included into the study between 2010 and 2017 from Vascular Medicine Center of Peking University Shougang Hospital. Subjects with CHD or CI, and without data of vascular function at baseline were excluded. Eventually, 467 subjects free of CHD [cohort 1, mean age: (63.4±12.3) years, female 42.2%] and 658 subjects free of CI [cohort 2, mean age: (64.3±12.2) years, female 48.7%] at baseline were included. The first in-patient records were as the baseline data, the second in-patient records were as a following-up data. Cox proportional hazard regression was used to establish the predictive models of CHD or CI derived from BVHS by multivariable-adjusted analysis.

RESULTS

The median follow-up time of cohort 1 and cohort 2 was 1.9 years and 2.1 years, respectively. During the follow-up, 164 first CHD events occurred in cohort 1 and 117 first CI events occurred in cohort 2. Four indicators were assessed as continuous variables simultaneously by multivariable-adjusted analysis. In cohort 1, CF-PWV, CR-PWV, ABI, and CAVI reached statistical significance in the multivariable-adjusted models (P<0.05). In cohort 2, only CAVI (P<0.05) was of statistical significance. In addition, the higher CF-PWV became a protector of CHD or CI (P<0.05). The prediction value of BVHS reached the statistical significance for CHD and CI in the unadjusted models (all P<0.05), however, BVHS could only predict the incidence of CHD (P<0.05), but not the incidence of CI (P>0.05) in the multivariable-adjusted models. CF-PWV, CR-PWV, ABI, and CAVI were associated factors of CHD independent of each other (P<0.05), only CAVI (P<0.05) was the risk factor of CI independent of the other three.

CONCLUSION

The different vascular indicators might have different effect on CHD or CI. CAVI might be a stable predictor of both CHD and CI. Higher baseline CF-PWV was not necessarily a risk factor of CHD or CI because of proper vascular health management. BVHS was a potential factor for the prediction of CHD, and further research is needed to explore the prediction value for CI.

Genome-scale metabolic model of the rat liver predicts effects of diet restriction

Mapping network analysis in cells and tissues can provide insights into metabolic adaptations to changes in external environment, pathological conditions, and nutrient deprivation. Here, we reconstructed a genome-scale metabolic network of the rat liver that will allow for exploration of systems-level physiology. The resulting in silico model (iRatLiver) contains 1,882 reactions, 1,448 metabolites, and 994 metabolic genes. We then used this model to characterize the response of the liver’s energy metabolism to a controlled perturbation in diet. Transcriptomics data were collected from the livers of Sprague Dawley rats at 4 or 14 days of being subjected to 15%, 30%, or 60% diet restriction. These data were integrated with the iRatLiver model to generate condition-specific metabolic models, allowing us to explore network differences under each condition. We observed different pathway usage between early and late time points. Network analysis identified several highly connected “hub” genes (Pklr, Hadha, Tkt, Pgm1, Tpi1, and Eno3) that showed differing trends between early and late time points. Taken together, our results suggest that the liver’s response varied with short- and long-term diet restriction. More broadly, we anticipate that the iRatLiver model can be exploited further to study metabolic changes in the liver under other conditions such as drug treatment, infection, and disease.

Time-dependent cellular and transcriptional changes in the osteoblast lineage associated with sclerostin antibody treatment in ovariectomized rats

Inhibition of sclerostin with sclerostin antibody (Scl-Ab) has been shown to stimulate bone formation, decrease bone resorption, and increase bone mass in both animals and humans. To obtain insight into the temporal cellular and transcriptional changes in the osteoblast (OB) lineage associated with long-term Scl-Ab treatment, stereological and transcriptional analyses of the OB lineage were performed on lumbar vertebrae from aged ovariectomized rats. Animals were administered Scl-Ab 3 or 50mg/kg/wk or vehicle (VEH) for up to 26weeks (d183), followed by a treatment-free period (TFP). At 50mg/kg/wk, bone volume (BV/total volume [TV]) increased through d183 and declined during the TFP. Bone formation rate (BFR/bone surface [BS]) and total OB number increased through d29, then progressively declined, coincident with a decrease in total osteoprogenitor (OP) numbers from d29 through d183. Analysis of differentially expressed genes (DEGs) from microarray analysis of mRNA isolated from laser capture microdissection samples enriched for OB, lining cells, and osteocytes (OCy) revealed modules of genes that correlated with BFR/BS, BV/TV, and osteoblastic surface (Ob.S)/BS. Expression change of canonical Wnt target genes was similar in all three cell types at d8, including upregulation of Twist1 and Wisp1. At d29, the pattern of Wnt target gene expression changed in the OCy, with Twist1 returning to VEH level, sustained upregulation of Wisp1, and upregulation of several other Wnt targets that continued into the TFP. Predicted activation of pathways recognized to integrate with and regulate canonical Wnt signaling were also activated at d29 in the OCy. The most significantly affected pathways represented transcription factor signaling known to inhibit cell cycle progression (notably p53) and mitogenesis (notably c-Myc). These changes occurred at the time of peak BFR/BS and continued as BFR/BS declined during treatment, then trended toward VEH level in the TFP. Concurrent with this transcriptional switch was a reduction in OP numbers, an effect that would ultimately limit bone formation. This study confirms that the initial transcriptional response in response to Scl-Ab is activation of canonical Wnt signaling and the data demonstrate that there is induction of additional regulatory pathways in OCy with long-term treatment. The interactions between Wnt and p53/c-Myc signaling may be key in limiting OP populations, thus contributing to self-regulation of bone formation with continued Scl-Ab administration.

Site-specific function and regulation of Osterix in tooth root formation

Congenital diseases of tooth roots, in terms of developmental abnormalities of short and thin root phenotypes, can lead to loss of teeth. A more complete understanding of the genetic molecular pathways and biological processes controlling tooth root formation is required. Recent studies have revealed that Osterix (Osx), a key mesenchymal transcriptional factor participating in both the processes of osteogenesis and odontogenesis, plays a vital role underlying the mechanisms of developmental differences between root and crown. During tooth development, Osx expression has been identified from late embryonic to postnatal stages when the tooth root develops, particularly in odontoblasts and cementoblasts to promote their differentiation and mineralization. Furthermore, the site-specific function of Osx in tooth root formation has been confirmed, because odontoblastic Osx-conditional knockout mice demonstrate primarily short and thin root phenotypes with no apparent abnormalities in the crown (Journal of Bone and Mineral Research 30, 2014 and 742, Journal of Dental Research 94, 2015 and 430). These findings suggest that Osx functions to promote odontoblast and cementoblast differentiation and root elongation only in root, but not in crown formation. Mechanistic research shows regulatory networks of Osx expression, which can be controlled through manipulating the epithelial BMP signalling, mesenchymal Runx2 expression and cellular phosphorylation levels, indicating feasible routes of promoting Osx expression postnatally (Journal of Cellular Biochemistry 114, 2013 and 975). In this regard, a promising approach might be available to regenerate the congenitally diseased root and that regenerative therapy would be the best choice for patients with developmental tooth diseases.

Transcriptional Profiling of Laser Capture Microdissected Subpopulations of the Osteoblast Lineage Provides Insight Into the Early Response to Sclerostin Antibody in Rats

Sclerostin antibody (Scl-Ab) increases bone formation through a process dependent on the activation of canonical Wnt signaling, although the specific signaling in the osteoblast lineage in vivo is largely unknown. To gain insight into the signaling pathways acutely modulated by Scl-Ab, the transcriptional response of subpopulations of the osteoblast lineage was assessed by TaqMan and microarray analyses of mRNA isolated from laser capture microdissection (LCM)-enriched samples from the vertebrae of ovariectomized rats during the first week after Scl-Ab administration. Briefly, 6-month-old Sprague-Dawley rats were ovariectomized and, after 2 months, received a single dose of vehicle (VEH) or 100 mg/kg Scl-Ab (n = 20/group). Lumbar vertebrae were collected at 6, 24, 72, and 168 hours postdose and cryosectioned for LCM. Osteocytes were captured from bone matrix, and osteoblasts and lining cells were captured from bone surfaces based on fluorochrome labeling. mRNA was isolated, amplified, and profiled by TaqMan and microarray. Expression analysis revealed that Scl-Ab caused strikingly similar transcriptional profiles across all three cell types. Only 13 known canonical Wnt target genes, the majority with known functions in bone, showed a significant change in expression by microarray in response to Scl-Ab, with Wisp1 and Twist1 being the most responsive. Coincident with increased expression of Wnt target genes was the upregulation of numerous extracellular matrix (ECM) genes. The acute and progressive upregulation of ECM genes in lining cells supports their activation into matrix-producing osteoblasts, consistent with modeling-based bone formation. A similar transcriptional profile in osteocytes may indicate that Scl-Ab stimulates perilacunar/pericanalicular matrix deposition. Pathway analyses indicated that Scl-Ab regulated a limited number of genes related to cell cycle arrest and B-cell development. These data describe the acute downstream signaling in response to Scl-Ab in vivo and demonstrate selected canonical Wnt target gene activation associated with increased bone formation in all mature osteoblast subpopulations.

A systematic assessment of mitochondrial function identified novel signatures for drug-induced mitochondrial disruption in cells

Mitochondrial perturbation has been recognized as a contributing factor to various drug-induced organ toxicities. To address this issue, we developed a high-throughput flow cytometry-based mitochondrial signaling assay to systematically investigate mitochondrial/cellular parameters known to be directly impacted by mitochondrial dysfunction: mitochondrial membrane potential (MMP), mitochondrial reactive oxygen species (ROS), intracellular reduced glutathione (GSH) level, and cell viability. Modulation of these parameters by a training set of compounds, comprised of established mitochondrial poisons and 60 marketed drugs (30 nM to 1mM), was tested in HL-60 cells (a human pro-myelocytic leukemia cell line) cultured in either glucose-supplemented (GSM) or glucose-free (containing galactose/glutamine; GFM) RPMI-1640 media. Post-hoc bio-informatic analyses of IC50 or EC50 values for all parameters tested revealed that MMP depolarization in HL-60 cells cultured in GSM was the most reliable parameter for determining mitochondrial dysfunction in these cells. Disruptors of mitochondrial function depolarized MMP at concentrations lower than those that caused loss of cell viability, especially in cells cultured in GSM; cellular GSH levels correlated more closely to loss of viability in vitro. Some mitochondrial respiratory chain inhibitors increased mitochondrial ROS generation; however, measuring an increase in ROS alone was not sufficient to identify mitochondrial disruptors. Furthermore, hierarchical cluster analysis of all measured parameters provided confirmation that MMP depletion, without loss of cell viability, was the key signature for identifying mitochondrial disruptors. Subsequent classification of compounds based on ratios of IC50s of cell viability:MMP determined that this parameter is the most critical indicator of mitochondrial health in cells and provides a powerful tool to predict whether novel small molecule entities possess this liability.

Common handling procedures conducted in preclinical safety studies result in minimal hepatic gene expression changes in Sprague-Dawley rats

Gene expression profiling is a tool to gain mechanistic understanding of adverse effects in response to compound exposure. However, little is known about how the common handling procedures of experimental animals during a preclinical study alter baseline gene expression. We report gene expression changes in the livers of female Sprague-Dawley rats following common handling procedures. Baseline gene expression changes identified in this study provide insight on how these changes may affect interpretation of gene expression profiles following compound exposure. Rats were divided into three groups. One group was not subjected to handling procedures and served as controls for both handled groups. Animals in the other two groups were weighed, subjected to restraint in Broome restrainers, and administered water via oral gavage daily for 1 or 4 days with tail vein blood collections at 1, 2, 4, and 8 hours postdose on days 1 and 4. Significantly altered genes were identified in livers of animals following 1 or 4 days of handling when compared to the unhandled animals. Gene changes in animals handled for 4 days were similar to those handled for 1 day, suggesting a lack of habituation. The altered genes were primarily immune function related genes. These findings, along with a correlating increase in corticosterone levels suggest that common handling procedures may cause a minor immune system perturbance.

Cytokines associated with increased erythropoiesis in Sprague-Dawley rats administered a novel hyperglycosylated analog of recombinant human erythropoietin

We previously reported an increased incidence of thrombotic toxicities in Sprague-Dawley rats administered the highest dose level of a hyperglycosylated analog of recombinant human erythropoietin (AMG 114) for 1 month as not solely dependent on high hematocrit (HCT). Thereafter, we identified increased erythropoiesis as a prothrombotic risk factor increased in the AMG 114 high-dose group with thrombotic toxicities, compared to a low-dose group with no toxicities but similar HCT. Here, we identified pleiotropic cytokines as prothrombotic factors associated with AMG 114 dose level. Before a high HCT was achieved, rats in the AMG 114 high, but not the low-dose group, had imbalanced hemostasis (increased von Willebrand factor and prothrombin time, decreased antithrombin III) coexistent with cytokines implicated in thrombosis: monocyte chemotactic protein 1 (MCP-1), MCP-3, tissue inhibitor of metalloproteinases 1, macrophage inhibitory protein-2, oncostatin M, T-cell-specific protein, stem cell factor, vascular endothelial growth factor, and interleukin-11. While no unique pathway to erythropoiesis stimulating agent-related thrombosis was identified, cytokines associated with increased erythropoiesis contributed to a prothrombotic intravascular environment in the AMG 114 high-dose group, but not in lower dose groups with a similar high HCT.

Dose-related Differences in the Pharmacodynamic and Toxicologic Response to a Novel Hyperglycosylated Analog of Recombinant Human Erythropoietin in Sprague-Dawley Rats with Similarly High Hematocrit

We recently reported results that erythropoiesis-stimulating agent (ESA)–related thrombotic toxicities in preclinical species were not solely dependent on a high hematocrit (HCT) but also associated with increased ESA dose level, dose frequency, and dosing duration. In this article, we conclude that sequelae of an increased magnitude of ESA-stimulated erythropoiesis potentially contributed to thrombosis in the highest ESA dose groups. The results were obtained from two investigative studies we conducted in Sprague-Dawley rats administered a low (no thrombotic toxicities) or high (with thrombotic toxicities) dose level of a hyperglycosylated analog of recombinant human erythropoietin (AMG 114), 3 times weekly for up to 9 days or for 1 month. Despite similarly increased HCT at both dose levels, animals in the high-dose group had an increased magnitude of erythropoiesis measured by spleen weights, splenic erythropoiesis, and circulating reticulocytes. Resulting prothrombotic risk factors identified predominantly or uniquely in the high-dose group were higher numbers of immature reticulocytes and nucleated red blood cells in circulation, severe functional iron deficiency, and increased intravascular destruction of iron-deficient reticulocyte/red blood cells. No thrombotic events were detected in rats dosed up to 9 days suggesting a sustained high HCT is a requisite cofactor for development of ESA-related thrombotic toxicities.

A novel statistical algorithm for gene expression analysis helps differentiate pregnane X receptor-dependent and independent mechanisms of toxicity

Genome-wide gene expression profiling has become standard for assessing potential liabilities as well as for elucidating mechanisms of toxicity of drug candidates under development. Analysis of microarray data is often challenging due to the lack of a statistical model that is amenable to biological variation in a small number of samples. Here we present a novel non-parametric algorithm that requires minimal assumptions about the data distribution. Our method for determining differential expression consists of two steps: 1) We apply a nominal threshold on fold change and platform p-value to designate whether a gene is differentially expressed in each treated and control sample relative to the averaged control pool, and 2) We compared the number of samples satisfying criteria in step 1 between the treated and control groups to estimate the statistical significance based on a null distribution established by sample permutations. The method captures group effect without being too sensitive to anomalies as it allows tolerance for potential non-responders in the treatment group and outliers in the control group. Performance and results of this method were compared with the Significant Analysis of Microarrays (SAM) method. These two methods were applied to investigate hepatic transcriptional responses of wild-type (PXR^+/+) and pregnane X receptor-knockout (PXR^−/−) mice after 96 h exposure to CMP013, an inhibitor of β-secretase (β-site of amyloid precursor protein cleaving enzyme 1 or BACE1). Our results showed that CMP013 led to transcriptional changes in hallmark PXR-regulated genes and induced a cascade of gene expression changes that explained the hepatomegaly observed only in PXR^+/+ animals. Comparison of concordant expression changes between PXR^+/+ and PXR^−/− mice also suggested a PXR-independent association between CMP013 and perturbations to cellular stress, lipid metabolism, and biliary transport.

The fate of Cu, Zn, Pb and Cd during the pyrolysis of sewage sludge at different temperatures

In the present study, a sequential extraction procedure, recommended by the Community Bureau of Reference (BCR), was used for the fractionation of the heavy metals Cu, Zn, Pb and Cd in sewage sludge and its residues produced after pyrolysis at different temperatures from 250 to 700 degrees C. The results show that, in the sludge sample, the sum of the percentages of the reducible and oxidizable fractions for all metals except Cu was very high (65.4% for Cd, 85.7% for Pb, 78.7% for Zn), whereas the sum of the percentages of the oxidizable and residual fractions for Cu was very high (88.8%). The same result could be attained in the residues. Statistical analysis shows that at low temperatures the variation in pyrolysis temperature did not effectively contribute to the distribution of metal speciation in the residues. Meanwhile a modified Toxicity Characteristic Leaching Procedure (TCLP) was employed to determine the leachability of these four metals. The result indicates that the TCLP concentration of Cu, Zn, Pb and Cd dropped sharply after the temperature reached 350 degrees C, 550 degrees C, 500 degrees C and 400 degrees C respectively, which means pyrolysis can enhance the stability of these four metals when the temperature is high enough.

Isolation, sequence identification and expression profile of three novel genes Rab2A, Rab3A and Rab7A from Black-boned sheep (Ovis aries)

Complete coding sequences of three Black-boned sheep (Ovis aries) genes Rab2A, Rab3A and Rab7A were amplified using reverse transcription polymerase chain reaction (RT-PCR) based on the conserved sequence information of cattle or other mammals known to be highly homologous to sheep ESTs. The Black-boned sheep Rab2A gene encodes a protein of 226 amino acids which contains the conserved putative RabL2 domain and is highly homologous to the Rab2A proteins of seven other species--cattle (96%), human (83%), Sumatran orangutan (82%), rat (81%), mouse (80%), African clawed frog (72%) and zebrafish (71%). The Black-boned sheep Rab3A gene encodes a protein of 220 amino acids that contains the conserved putative Rab3 domain and is very similar to the Rab3A proteins of four species--cattle (99%), African clawed frog (99%), Western clawed frog (98%) and zebrafish (95%). And the Black-boned sheep Rab7A gene encodes a protein of 207 amino acids that contains the conserved putative Rab7 domain and has high homology with the Rab7A proteins of six other species--human (99%), dog (99%), Sumatran orangutan (99%), zebrafish (97%), rabbit (97%) and African clawed frog (96%). Analysis of the phylogenetic tree has demonstrated that the Black-boned sheep Rab2A, Rab3A and Rab7A proteins share a common ancestor and the tissue expression analysis has shown that the corresponding genes are expressed in a range of tissues including leg muscle, kidney, skin, longissimus dorsi muscle, spleen, heart and liver. Our experiment is the first to provide the primary foundation for a further insight into these three sheep genes.

Temporal pattern of skeletal muscle gene expression following endurance exercise in Alaskan sled dogs

Muscle responses to exercise are complex and include acute responses to exercise-induced injury, as well as longer term adaptive training responses. Using Alaskan sled dogs as an experimental model, changes in muscle gene expression were analyzed to test the hypotheses that important regulatory elements of the muscle's adaptation to exercise could be identified based on the temporal pattern of gene expression. Dogs were randomly assigned to undertake a 160-km run (n=9), or to remain at rest (n=4). Biceps femoris muscle was obtained from the unexercised dogs and two dogs at each of 2, 6, and 12 h after the exercise, and from three dogs 24 h after exercise. RNA was extracted and microarray analysis used to define gene transcriptional changes. The changes in gene expression after exercise occurred in a temporal pattern. Overall, 569, 469, 316, and 223 transcripts were differentially expressed at 2, 6, 12, and 24 h postexercise, respectively, compared with unexercised dogs (based on P<or=0.01 and an absolute fold change of >or=1.5). Increases in a number of known transcriptional regulators, including peroxisome proliferator-activated receptor-alpha, cAMP-responsive element modulator, and CCAAT enhancer binding protein-delta, and potential signaling molecules, including brain-derived neurotrophic factor, dermokine, and suprabasin, were observed 2 h after exercise. Biological functional analysis suggested changes in expression of genes with known functional relationships, including genes involved in muscle remodeling and growth, intermediary metabolism, and immune regulation. Sustained endurance exercise by Alaskan sled dogs induces coordinated changes in gene expression with a clear temporal pattern. RNA expression profiling has the potential to identify novel regulatory mechanisms and responses to exercise stimuli.

Use of diagnostic accuracy as a metric for evaluating laboratory proficiency with microarray assays using mixed-tissue RNA reference samples

Effective use of microarray technology in clinical and regulatory settings is contingent on the adoption of standard methods for assessing performance. The MicroArray Quality Control project evaluated the repeatability and comparability of microarray data on the major commercial platforms and laid the groundwork for the application of microarray technology to regulatory assessments. However, methods for assessing performance that are commonly applied to diagnostic assays used in laboratory medicine remain to be developed for microarray assays. A reference system for microarray performance evaluation and process improvement was developed that includes reference samples, metrics and reference datasets. The reference material is composed of two mixes of four different rat tissue RNAs that allow defined target ratios to be assayed using a set of tissue-selective analytes that are distributed along the dynamic range of measurement. The diagnostic accuracy of detected changes in expression ratios, measured as the area under the curve from receiver operating characteristic plots, provides a single commutable value for comparing assay specificity and sensitivity. The utility of this system for assessing overall performance was evaluated for relevant applications like multi-laboratory proficiency testing programs and single-laboratory process drift monitoring. The diagnostic accuracy of detection of a 1.5-fold change in signal level was found to be a sensitive metric for comparing overall performance. This test approaches the technical limit for reliable discrimination of differences between two samples using this technology. We describe a reference system that provides a mechanism for internal and external assessment of laboratory proficiency with microarray technology and is translatable to performance assessments on other whole-genome expression arrays used for basic and clinical research.

Gene expression profiling of rat liver reveals a mechanistic basis for ritonavir-induced hyperlipidemia

The molecular mechanisms of action of a HIV protease inhibitor, ritonavir, on hepatic function were explored on a genomic scale using microarrays comprising genes expressed in the liver of Sprague-Dawley rats (Rattus norvegicus). Analyses of hepatic transcriptional fingerprints led to the identification of several key cellular pathways affected by ritonavir treatment. These effects were compared to a compendium of gene expression responses for 52 unrelated compounds and to other protease inhibitors, including atazanavir and two experimental compounds. We identified genes involved in cholesterol and fatty acid biosynthesis, as well as genes involved in fatty acid and cholesterol breakdown, whose expressions were regulated in opposite manners by ritonavir and bezafibrate, a hypolipidemic agonist of the peroxisome proliferator-activated receptor alpha. Ritonavir also upregulated multiple proteasomal subunit transcripts as well as genes involved in ubiquitination, consistent with its known inhibitory effect on proteasomal activity. We also tested three other protease inhibitors in addition to ritonavir. Atazanavir did not impact ubiquitin or proteasomal gene expression, although the two other experimental protease inhibitors impacted both proteasomal gene expression and sterol regulatory element-binding protein-activated genes, similar to ritonavir. Identification of key metabolic pathways that are affected by ritonavir and other protease inhibitors will enable us to understand better the downstream effects of protease inhibitors, thus leading to better drug design and an effective method to mitigate the side effects of this important class of HIV therapeutics.

Genomic approach to biomarker identification and its recent applications

This paper discusses selected activities, issues, and challenges in recent development of analytical methods and applications in biomarker identification and validation using state-of-the-art genomic approaches. Molecular profiling via genomics, proteomics, and metabonomics has opened new windows to study disease states and biological systems. It has also provided exciting opportunities for novel applications in clinical research as well as in drug discovery and development. In the past several years, we have witnessed enormous progress resulting particularly from gene expression profiling of mRNA or transcriptomics. After a brief review on technology advances in gene expression profiling using microarrays, I mainly discuss recent developments of the genomic approaches to biomarker identification and validation in two major types of applications. The first type involves examples in cancer diagnostics and prognostics based on tumor gene expression profiling, whereas the second type involves biomarker applications in drug discovery and development. The focus will be on analytical methods and algorithms that have been developed in recent years facilitating biomarker discovery and application by leveraging genome-wide expression profiles derived from microarrays. Technical issues in experimental design, data processing, error modeling, quality control, figures of merit for performance evaluation, and meta-analysis related to biomarker discovery and application are also discussed. A case study of disease outcome prognosis for breast cancer patients based on tumor expression pattern is presented before closing remarks.

PPARalpha siRNA-treated expression profiles uncover the causal sufficiency network for compound-induced liver hypertrophy

Uncovering pathways underlying drug-induced toxicity is a fundamental objective in the field of toxicogenomics. Developing mechanism-based toxicity biomarkers requires the identification of such novel pathways and the order of their sufficiency in causing a phenotypic response. Genome-wide RNA interference (RNAi) phenotypic screening has emerged as an effective tool in unveiling the genes essential for specific cellular functions and biological activities. However, eliciting the relative contribution of and sufficiency relationships among the genes identified remains challenging. In the rodent, the most widely used animal model in preclinical studies, it is unrealistic to exhaustively examine all potential interactions by RNAi screening. Application of existing computational approaches to infer regulatory networks with biological outcomes in the rodent is limited by the requirements for a large number of targeted permutations. Therefore, we developed a two-step relay method that requires only one targeted perturbation for genome-wide de novo pathway discovery. Using expression profiles in response to small interfering RNAs (siRNAs) against the gene for peroxisome proliferator-activated receptor alpha (Ppara), our method unveiled the potential causal sufficiency order network for liver hypertrophy in the rodent. The validity of the inferred 16 causal transcripts or 15 known genes for PPARalpha-induced liver hypertrophy is supported by their ability to predict non-PPARalpha-induced liver hypertrophy with 84% sensitivity and 76% specificity. Simulation shows that the probability of achieving such predictive accuracy without the inferred causal relationship is exceedingly small (p < 0.005). Five of the most sufficient causal genes have been previously disrupted in mouse models; the resulting phenotypic changes in the liver support the inferred causal roles in liver hypertrophy. Our results demonstrate the feasibility of defining pathways mediating drug-induced toxicity from siRNA-treated expression profiles. When combined with phenotypic evaluation, our approach should help to unleash the full potential of siRNAs in systematically unveiling the molecular mechanism of biological events.

Identification of potential pharmacological and toxicological targets differentiating structural analogs by a combination of transcriptional profiling and promoter analysis in LS-180 and Caco-2 adenocarcinoma cell lines

Detecting and understanding the potential for off-target pharmacological effects is critical in the optimization of lead compounds in drug discovery programs. Compound-mediated activation of the pregnane X receptor (PXR; NR1I2), a key regulator for drug metabolism genes, is often monitored to avoid potential drug-drug interactions. Two structural analogs, MRL-1 and MRL-2, were determined to be equivalent PXR activators in trans-activation assays. To differentiate these two PXR activators, their transcriptional effects were examined in PXR-sufficient (LS180) and PXR-deficient (Caco-2) adenocarcinoma cell lines. Both compounds regulated drug-management genes (e.g. CYP3A4, CYP2B6, UGT1A1 and ABCB1) in LS180 cells, but not in PXR-deficient Caco-2 cells. The potency of MRL-1 and MRL-2 on PXR activation was again equivalent as revealed by a set of 113 genes that were regulated by four prototypical PXR agonists (rifampicin, ritonavir, troglitazone and dexamethasone) in the LS180 cells. The specificity of the PXR signature genes was supported by the enrichment of putative PXR binding sites uncovered by sequence-based promoter analyses. Interestingly, an additional off-target activity of MRL-2 was suggested where sterol response element binding protein binding sites were found enriched in a subset of PXR signature genes. These genes, involved in cholesterol and fatty acid synthesis, were significantly regulated by ritonavir, chlorpromazine and MRL-2, which were linked to the manifestation of phospholipidosis. The present study demonstrates the utility of our approach in the differentiation and selection of lead compounds for drug development.

Development of an approach for ab initio estimation of compound-induced liver injury based on global gene transcriptional profiles

Toxicity is a major cause of failure in drug development. A toxicogenomic approach may provide a powerful tool for better assessing the potential toxicity of drug candidates. Several approaches have been reported for predicting hepatotoxicity based on reference compounds with well-studied toxicity mechanisms. We developed a new approach for assessing compound-induced liver injury without prior knowledge of a compound's mechanism of toxicity. Using samples from rodents treated with 49 known liver toxins and 10 compounds without known liver toxicity, we derived a hepatotoxicity score as a single quantitative measurement for assessing the degree of induced liver damage. Combining the sensitivity of the hepatotoxicity score and the power of a machine learning algorithm, we then built a model to predict compound-induced liver injury based on 212 expression profiles. As estimated in an independent data set of 54 expression profiles, the built model predicted compound-induced liver damage with 90.9% sensitivity and 88.4% specificity. Our findings illustrate the feasibility of ab initio estimation of liver toxicity based on transcriptional profiles.

No common denominator for breast cancer lymph node metastasis

The axillary lymph node status is the most powerful prognostic factor for breast cancer patients to date. The molecular mechanisms that control lymph node metastasis, however, remain poorly understood. To define patterns of genes or gene regulatory pathways that drive breast cancer lymph node metastasis, we compared the gene expression profiles of 15 primary breast carcinomas and their matching lymph node metastases using microarrays. In general, primary breast carcinomas and lymph node metastases do not differ at the transcriptional level by a common subset of genes. No classifier or single gene discriminating the group of primary tumours from those of the lymph node metastases could be identified. Also, in a series of 295 breast tumours, no classifier predicting lymph node metastasis could be developed. However, subtle differences in the expression of genes involved in extracellular-matrix organisation and growth factor signalling are detected in individual pairs of matching primary and metastatic tumours. Surprisingly, however, different sets of these genes are either up- or downregulated in lymph node metastases. Our data suggest that breast carcinomas do not use a shared gene set to accomplish lymph node metastasis.

A cell proliferation signature is a marker of extremely poor outcome in a subpopulation of breast cancer patients

Breast cancer comprises a group of distinct subtypes that despite having similar histologic appearances, have very different metastatic potentials. Being able to identify the biological driving force, even for a subset of patients, is crucially important given the large population of women diagnosed with breast cancer. Here, we show that within a subset of patients characterized by relatively high estrogen receptor expression for their age, the occurrence of metastases is strongly predicted by a homogeneous gene expression pattern almost entirely consisting of cell cycle genes (5-year odds ratio of metastasis, 24.0; 95% confidence interval, 6.0-95.5). Overexpression of this set of genes is clearly associated with an extremely poor outcome, with the 10-year metastasis-free probability being only 24% for the poor group, compared with 85% for the good group. In contrast, this gene expression pattern is much less correlated with the outcome in other patient subpopulations. The methods described here also illustrate the value of combining clinical variables, biological insight, and machine-learning to dissect biological complexity. Our work presented here may contribute a crucial step towards rational design of personalized treatment.

A protocol for building and evaluating predictors of disease state based on microarray data

MOTIVATION

Microarray gene expression data are increasingly employed to identify sets of marker genes that accurately predict disease development and outcome in cancer. Many computational approaches have been proposed to construct such predictors. However, there is, as yet, no objective way to evaluate whether a new approach truly improves on the current state of the art. In addition no 'standard' computational approach has emerged which enables robust outcome prediction.

RESULTS

An important contribution of this work is the description of a principled training and validation protocol, which allows objective evaluation of the complete methodology for constructing a predictor. We review the possible choices of computational approaches, with specific emphasis on predictor choice and reporter selection strategies. Employing this training-validation protocol, we evaluated different reporter selection strategies and predictors on six gene expression datasets of varying degrees of difficulty. We demonstrate that simple reporter selection strategies (forward filtering and shrunken centroids) work surprisingly well and outperform partial least squares in four of the six datasets. Similarly, simple predictors, such as the nearest mean classifier, outperform more complex classifiers. Our training-validation protocol provides a robust methodology to evaluate the performance of new computational approaches and to objectively compare outcome predictions on different datasets.

Robustness, scalability, and integration of a wound-response gene expression signature in predicting breast cancer survival

Based on the hypothesis that features of the molecular program of normal wound healing might play an important role in cancer metastasis, we previously identified consistent features in the transcriptional response of normal fibroblasts to serum, and used this "wound-response signature" to reveal links between wound healing and cancer progression in a variety of common epithelial tumors. Here, in a consecutive series of 295 early breast cancer patients, we show that both overall survival and distant metastasis-free survival are markedly diminished in patients whose tumors expressed this wound-response signature compared to tumors that did not express this signature. A gene expression centroid of the wound-response signature provides a basis for prospectively assigning a prognostic score that can be scaled to suit different clinical purposes. The wound-response signature improves risk stratification independently of known clinico-pathologic risk factors and previously established prognostic signatures based on unsupervised hierarchical clustering ("molecular subtypes") or supervised predictors of metastasis ("70-gene prognosis signature").

T lymphocyte activation gene identification by coregulated expression on DNA microarrays

High-capacity methods for assessing gene function have become increasingly important because of the increasing number of newly identified genes emerging from large-scale genome sequencing and cDNA cloning efforts. We investigated the use of DNA microarrays to identify uncharacterized genes specifically involved in human T cell activation. Activation of human peripheral blood T lymphocytes induced significant changes in hundreds of transcripts, but most of these were not unique to T cell activation. Variation of experimental parameters and analysis techniques allowed better enrichment for gene expression changes unique to T cell activation. Best results were achieved by identification of genes that were most highly coregulated with the T-cell-specific transcript interleukin 2 (IL2) in a "compendium" of experiments involving both T cells and other cell types. Among the genes most highly coregulated with IL2 were many genes known to function during T cell activation, together with ESTs of unknown function. Four of these ESTs were extended to novel full-length clones encoding T-cell-regulated proteins with predicted functions in GTP metabolism, cell organization, and signal transduction.

Effects of atmospheric ozone on microarray data quality

A data anomaly was observed that affected the uniformity and reproducibility of fluorescent signal across DNA microarrays. Results from experimental sets designed to identify potential causes (from microarray production to array scanning) indicated that the anomaly was linked to a batch process; further work allowed us to localize the effect to the posthybridization array stringency washes. Ozone levels were monitored and highly correlated with the batch effect. Controlled exposures of microarrays to ozone confirmed this factor as the root cause, and we present data that show susceptibility of a class of cyanine dyes (e.g., Cy5, Alexa 647) to ozone levels as low as 5-10 ppb for periods as short as 10-30 s. Other cyanine dyes (e.g., Cy3, Alexa 555) were not significantly affected until higher ozone levels (> 100 ppb). To address this environmental effect, laboratory ozone levels should be kept below 2 ppb (e.g., with filters in HVAC) to achieve high quality microarray data.

Activators of the rat pregnane X receptor differentially modulate hepatic and intestinal gene expression

Ligand-mediated activation of the pregnane X receptor (PXR, NR1I2) is postulated to affect both hepatic and intestinal gene expression, because of the presence of this nuclear receptor in these important drug metabolizing organs; as such, activation of this receptor may elicit the coordinated regulation of PXR target genes in both tissues. Induction of hepatic and intestinal drug metabolism can contribute to the increased metabolism of drugs, and can result in adverse or undesirable drug-drug interactions. 2(S)-((3,5-bis(Trifluoromethyl)benzyl)-oxy)-3(S)phenyl-4-((3-oxo-1,2,4-triazol-5-yl)methyl)morpholine (L-742694) is a potent activator of the rat PXR and was characterized for its effects on hepatic and intestinal gene expression in female Sprague-Dawley rats by DNA microarray analysis. Transcriptional profiling in liver and small intestine revealed that L-742694 and dexamethasone (DEX) induced the prototypical battery of PXR target genes in liver, including CYP3A, Oatp2, and UGT1A1. In addition, both DEX and L-742694 induced common gene expression profiles that were specific to liver or small intestine, but there was a distinct lack of coordinated gene expression of genes common to both tissues. This pattern of gene regulation occurred in liver and small intestine independent of PXR, constitutive androstane receptor, or hepatic nuclear factor-4alpha expression, suggesting that other factors are involved in controlling the extent of coordinated gene expression in response to a PXR agonist. Overall, these results suggest that ligand-mediated activation of PXR and induction of hepatic, rather than small intestinal, drug metabolism genes would contribute to the increased metabolism of orally administered pharmaceuticals.

Microarray standard data set and figures of merit for comparing data processing methods and experiment designs

MOTIVATION

There is a very large and growing level of effort toward improving the platforms, experiment designs, and data analysis methods for microarray expression profiling. Along with a growing richness in the approaches there is a growing confusion among most scientists as to how to make objective comparisons and choices between them for different applications. There is a need for a standard framework for the microarray community to compare and improve analytical and statistical methods.

RESULTS

We report on a microarray data set comprising 204 in-situ synthesized oligonucleotide arrays, each hybridized with two-color cDNA samples derived from 20 different human tissues and cell lines. Design of the approximately 24 000 60mer oligonucleotides that report approximately 2500 known genes on the arrays, and design of the hybridization experiments, were carried out in a way that supports the performance assessment of alternative data processing approaches and of alternative experiment and array designs. We also propose standard figures of merit for success in detecting individual differential expression changes or expression levels, and for detecting similarities and differences in expression patterns across genes and experiments. We expect this data set and the proposed figures of merit will provide a standard framework for much of the microarray community to compare and improve many analytical and statistical methods relevant to microarray data analysis, including image processing, normalization, error modeling, combining of multiple reporters per gene, use of replicate experiments, and sample referencing schemes in measurements based on expression change.

AVAILABILITY/SUPPLEMENTARY INFORMATION

Expression data and supplementary information are available at http://www.rii.com/publications/2003/HE_SDS.htm

A gene-expression signature as a predictor of survival in breast cancer

BACKGROUND

A more accurate means of prognostication in breast cancer will improve the selection of patients for adjuvant systemic therapy.

METHODS

Using microarray analysis to evaluate our previously established 70-gene prognosis profile, we classified a series of 295 consecutive patients with primary breast carcinomas as having a gene-expression signature associated with either a poor prognosis or a good prognosis. All patients had stage I or II breast cancer and were younger than 53 years old; 151 had lymph-node-negative disease, and 144 had lymph-node-positive disease. We evaluated the predictive power of the prognosis profile using univariable and multivariable statistical analyses.

RESULTS

Among the 295 patients, 180 had a poor-prognosis signature and 115 had a good-prognosis signature, and the mean (+/-SE) overall 10-year survival rates were 54.6+/-4.4 percent and 94.5+/-2.6 percent, respectively. At 10 years, the probability of remaining free of distant metastases was 50.6+/-4.5 percent in the group with a poor-prognosis signature and 85.2+/-4.3 percent in the group with a good-prognosis signature. The estimated hazard ratio for distant metastases in the group with a poor-prognosis signature, as compared with the group with the good-prognosis signature, was 5.1 (95 percent confidence interval, 2.9 to 9.0; P<0.001). This ratio remained significant when the groups were analyzed according to lymph-node status. Multivariable Cox regression analysis showed that the prognosis profile was a strong independent factor in predicting disease outcome.

CONCLUSIONS

The gene-expression profile we studied is a more powerful predictor of the outcome of disease in young patients with breast cancer than standard systems based on clinical and histologic criteria.

Gene expression profiling predicts clinical outcome of breast cancer

Breast cancer patients with the same stage of disease can have markedly different treatment responses and overall outcome. The strongest predictors for metastases (for example, lymph node status and histological grade) fail to classify accurately breast tumours according to their clinical behaviour. Chemotherapy or hormonal therapy reduces the risk of distant metastases by approximately one-third; however, 70-80% of patients receiving this treatment would have survived without it. None of the signatures of breast cancer gene expression reported to date allow for patient-tailored therapy strategies. Here we used DNA microarray analysis on primary breast tumours of 117 young patients, and applied supervised classification to identify a gene expression signature strongly predictive of a short interval to distant metastases ('poor prognosis' signature) in patients without tumour cells in local lymph nodes at diagnosis (lymph node negative). In addition, we established a signature that identifies tumours of BRCA1 carriers. The poor prognosis signature consists of genes regulating cell cycle, invasion, metastasis and angiogenesis. This gene expression profile will outperform all currently used clinical parameters in predicting disease outcome. Our findings provide a strategy to select patients who would benefit from adjuvant therapy.

Expression profiling using microarrays fabricated by an ink-jet oligonucleotide synthesizer

We describe a flexible system for gene expression profiling using arrays of tens of thousands of oligonucleotides synthesized in situ by an ink-jet printing method employing standard phosphoramidite chemistry. We have characterized the dependence of hybridization specificity and sensitivity on parameters including oligonucleotide length, hybridization stringency, sequence identity, sample abundance, and sample preparation method. We find that 60-mer oligonucleotides reliably detect transcript ratios at one copy per cell in complex biological samples, and that ink-jet arrays are compatible with several different sample amplification and labeling techniques. Furthermore, results using only a single carefully selected oligonucleotide per gene correlate closely with those obtained using complementary DNA (cDNA) arrays. Most of the genes for which measurements differ are members of gene families that can only be distinguished by oligonucleotides. Because different oligonucleotide sequences can be specified for each array, we anticipate that ink-jet oligonucleotide array technology will be useful in a wide variety of DNA microarray applications.

Observation of high-energy neutrinos using Cerenkov detectors embedded deep in Antarctic ice

Neutrinos are elementary particles that carry no electric charge and have little mass. As they interact only weakly with other particles, they can penetrate enormous amounts of matter, and therefore have the potential to directly convey astrophysical information from the edge of the Universe and from deep inside the most cataclysmic high-energy regions. The neutrino's great penetrating power, however, also makes this particle difficult to detect. Underground detectors have observed low-energy neutrinos from the Sun and a nearby supernova, as well as neutrinos generated in the Earth's atmosphere. But the very low fluxes of high-energy neutrinos from cosmic sources can be observed only by much larger, expandable detectors in, for example, deep water or ice. Here we report the detection of upwardly propagating atmospheric neutrinos by the ice-based Antarctic muon and neutrino detector array (AMANDA). These results establish a technology with which to build a kilometre-scale neutrino observatory necessary for astrophysical observations.

Experimental annotation of the human genome using microarray technology

The most important product of the sequencing of a genome is a complete, accurate catalogue of genes and their products, primarily messenger RNA transcripts and their cognate proteins. Such a catalogue cannot be constructed by computational annotation alone; it requires experimental validation on a genome scale. Using 'exon' and 'tiling' arrays fabricated by ink-jet oligonucleotide synthesis, we devised an experimental approach to validate and refine computational gene predictions and define full-length transcripts on the basis of co-regulated expression of their exons. These methods can provide more accurate gene numbers and allow the detection of mRNA splice variants and identification of the tissue- and disease-specific conditions under which genes are expressed. We apply our technique to chromosome 22q under 69 experimental condition pairs, and to the entire human genome under two experimental conditions. We discuss implications for more comprehensive, consistent and reliable genome annotation, more efficient, full-length complementary DNA cloning strategies and application to complex diseases.

Functional discovery via a compendium of expression profiles

Ascertaining the impact of uncharacterized perturbations on the cell is a fundamental problem in biology. Here, we describe how a single assay can be used to monitor hundreds of different cellular functions simultaneously. We constructed a reference database or "compendium" of expression profiles corresponding to 300 diverse mutations and chemical treatments in S. cerevisiae, and we show that the cellular pathways affected can be determined by pattern matching, even among very subtle profiles. The utility of this approach is validated by examining profiles caused by deletions of uncharacterized genes: we identify and experimentally confirm that eight uncharacterized open reading frames encode proteins required for sterol metabolism, cell wall function, mitochondrial respiration, or protein synthesis. We also show that the compendium can be used to characterize pharmacological perturbations by identifying a novel target of the commonly used drug dyclonine.

Signaling and circuitry of multiple MAPK pathways revealed by a matrix of global gene expression profiles

Genome-wide transcript profiling was used to monitor signal transduction during yeast pheromone response. Genetic manipulations allowed analysis of changes in gene expression underlying pheromone signaling, cell cycle control, and polarized morphogenesis. A two-dimensional hierarchical clustered matrix, covering 383 of the most highly regulated genes, was constructed from 46 diverse experimental conditions. Diagnostic subsets of coexpressed genes reflected signaling activity, cross talk, and overlap of multiple mitogen-activated protein kinase (MAPK) pathways. Analysis of the profiles specified by two different MAPKs-Fus3p and Kss1p-revealed functional overlap of the filamentous growth and mating responses. Global transcript analysis reflects biological responses associated with the activation and perturbation of signal transduction pathways.

Weight loss prior to clinical AIDS as a predictor of survival. Multicenter AIDS Cohort Study Investigators

In this analysis the aim was to determine the independent effect of moderate to severe weight loss prior to an AIDS diagnosis on survival after AIDS. The study was conducted as part of the Multicenter AIDS Cohort Study (MACS), a longitudinal study of HIV-1-seropositive gay or bisexual men. Measured weight and self-reported weight loss data were collected semiannually from 1984 through 1993. The study population included 962 HIV-1-seropositive men who developed clinical AIDS during the follow-up period. Median survival after AIDS was significantly lower for men with measured weight loss of > or = 4.5 kg 3-9 months and 3-15 months prior to AIDS, or who had lost > 10% of their baseline body weight compared with men with less weight loss or weight gain. Men with self-reported unintentional weight loss of > or = 4.5 kg 3-9 months prior to AIDS had significantly poorer survival (median = 1.05 years vs. 1.48 years; p = 0.0001) compared with men not reporting weight loss. After adjusting for potential confounding factors, men in the high measured weight loss group 3-9 months prior to AIDS still had significantly poorer survival [relative hazard (RH) = 1.36; p = 0.02]. Similar trends were seen for the two longer intervals prior to AIDS (RH = 1.38, p = 0.01; and RH = 1.50, p = 0.02, respectively). Men who self-reported weight loss > or = 4.5 kg 3-9 months prior to AIDS also had significantly poorer survival after AIDS (RH = 1.43; p = 0.002) in multivariate analysis.(ABSTRACT TRUNCATED AT 250 WORDS)