Nonlethal deleterious mutation-induced stress accelerates bacterial aging.

Random mutagenesis, including when it leads to loss of gene function, is a key mechanism enabling microorganisms' long-term adaptation to new environments. However, loss-of-function mutations are often deleterious, triggering, in turn, cellular stress and complex homeostatic stress responses, called "allostasis," to promote cell survival. Here, we characterize the differential impacts of 65 nonlethal, deleterious single-gene deletions on Escherichia coli growth in three different growth environments. Further assessments of select mutants, namely, those bearing single adenosine triphosphate (ATP) synthase subunit deletions, reveal that mutants display reorganized transcriptome profiles that reflect both the environment and the specific gene deletion. We also find that ATP synthase α-subunit deleted (ΔatpA) cells exhibit elevated metabolic rates while having slower growth compared to wild-type (wt) E. coli cells. At the single-cell level, compared to wt cells, individual ΔatpA cells display near normal proliferation profiles but enter a postreplicative state earlier and exhibit a distinct senescence phenotype. These results highlight the complex interplay between genomic diversity, adaptation, and stress response and uncover an "aging cost" to individual bacterial cells for maintaining population-level resilience to environmental and genetic stress; they also suggest potential bacteriostatic antibiotic targets and -as select human genetic diseases display highly similar phenotypes, - a bacterial origin of some human diseases.

HMG-CoA reductase degrader, SR-12813, counteracts statin-induced upregulation of HMG-CoA reductase and augments the anticancer effect of atorvastatin.

Statins are cholesterol-lowering drugs that have exhibited potential as cancer therapeutic agents. However, as some cancer cells are resistant to statins, broadening an anticancer spectrum of statins is desirable. The upregulated expression of the statin target enzyme, 3-hydroxy-3-methyl-glutaryl-coenzyme A (HMG-CoA) reductase (HMGCR), in statin-treated cancer cells is a well-known mechanism of statin resistance, which can be counteracted by the downregulation of HMGCR gene expression, or degradation of the HMGCR protein. However, the mechanism by which HMGCR degradation influences the anticancer effects of statins remain unreported. We tested the effect of the HMGCR degrader compound SR-12813 at a concentration that did not affect the growth of eight diverse tumor cell lines. Combined treatment with atorvastatin and a low concentration of SR-12813 led to lowering of increased HMGCR expression, and augmented the cytostatic effect of atorvastatin in both statin-resistant and -sensitive cancer cells compared with that of atorvastatin treatment alone. Dual-targeting of HMGCR using statins and SR-12813 (or similar compounds) could provide an improved anticancer therapeutic approach.

Clinicopathologic and Molecular Characterization of Anorectal Neuroendocrine Carcinomas Reveals Human Papillomavirus, p53, and c-Myc as Alternative Mechanisms of Carcinogenesis.

Poorly differentiated neuroendocrine carcinomas (NECs) are rare malignant neoplasms with aggressive behavior. The diagnosis remains challenging due to ever-changing terminologies and morphologic overlaps with other disease entities. Herein, we seek to better define anorectal NECs by high-risk human papillomavirus (HPV) status and molecular profiling. Fourteen cases, including 3 men and 11 women with a median age of 63 years, were included. High-risk HPV RNA in situ hybridization was diffusely positive (+) in 7 cases, focal rarely positive (+/-) in 2 cases, and completely negative (-) in 5 cases. By morphology, all HPV(-) NECs were large-cell type, 3 mixed with a tubular adenoma/dysplasia or invasive adenocarcinoma. HPV-related (+ or +/-) NECs were mostly small-cell type, 3 mixed with squamous dysplasia and/or squamous cell carcinoma. Immunohistochemically, all NECs were positive for at least 2 neuroendocrine markers. The HPV(-) NECs were also positive for CDX2, whereas all HPV-related NECs were negative or only focally positive for CDX2, p40, and p63. Overexpression of p53 was found in 3 HPV(-) and 2 HPV(+/-) NECs but not in any HPV(+) NECs. Molecular analysis revealed MYC gene amplification in 4 cases: 2 HPV(-), 1 HPV(+/-), and 1 HPV(+). This was confirmed by fluorescence in situ hybridization in all but 1 HPV(-) NEC, which showed polysomy 8 but no true MYC amplification. Interestingly, only 2 of the 4 MYC amplification-bearing cases, both p53 normal/wild-type, expressed c-Myc protein by immunohistochemistry. The other 2 cases, both p53 overexpressed, did not show c-Myc expression despite true MYC amplification. Our study demonstrates that anorectal NECs arise in HPV-dependent or -independent pathways, with heterogeneous expression of other lineage markers and different molecular signatures. Expressions of p53 and c-Myc proteins appear to be mutually exclusive regardless of HPV status, likely mediating alternative mechanisms of NEC carcinogenesis.

Adjustment in tumbling rates improves bacterial chemotaxis on obstacle-laden terrains.

The mechanisms of bacterial chemotaxis have been extensively studied for several decades, but how the physical environment influences the collective migration of bacterial cells remains less understood. Previous models of bacterial chemotaxis have suggested that the movement of migrating bacteria across obstacle-laden terrains may be slower compared with terrains without them. Here, we show experimentally that the size or density of evenly spaced obstacles do not alter the average exit rate of Escherichia coli cells from microchambers in response to external attractants, a function that is dependent on intact cell-cell communication. We also show, both by analyzing a revised theoretical model and by experimentally following single cells, that the reduced exit time in the presence of obstacles is a consequence of reduced tumbling frequency that is adjusted by the E. coli cells in response to the topology of their environment. These findings imply operational short-term memory of bacteria while moving through complex environments in response to chemotactic stimuli and motivate improved algorithms for self-autonomous robotic swarms.

Rhapsody: predicting the pathogenicity of human missense variants.

MOTIVATION: The biological effects of human missense variants have been studied experimentally for decades but predicting their effects in clinical molecular diagnostics remains challenging. Available computational tools are usually based on the analysis of sequence conservation and structural properties of the mutant protein. We recently introduced a new machine learning method that demonstrated for the first time the significance of protein dynamics in determining the pathogenicity of missense variants. RESULTS: Here, we present a new interface (Rhapsody) that enables fully automated assessment of pathogenicity, incorporating both sequence coevolution data and structure- and dynamics-based features. Benchmarked against a dataset of about 20 000 annotated variants, the methodology is shown to outperform well-established and/or advanced prediction tools. We illustrate the utility of Rhapsody by in silico saturation mutagenesis studies of human H-Ras, phosphatase and tensin homolog and thiopurine S-methyltransferase. AVAILABILITY AND IMPLEMENTATION: The new tool is available both as an online webserver at http://rhapsody.csb.pitt.edu and as an open-source Python package (GitHub repository: https://github.com/prody/rhapsody; PyPI package installation: pip install prody-rhapsody). Links to additional resources, tutorials and package documentation are provided in the 'Python package' section of the website. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Statin drugs to reduce breast cancer recurrence and mortality.

Epidemiologic studies have, variably, shown the concomitant use of statin drugs to be beneficial to cancer outcomes. Statin drugs have been FDA approved for three decades for the treatment of high cholesterol and atherosclerotic coronary artery disease and are widely used. This has engendered studies as to their influence on concomitant diseases, including cancers. In this context, statin use has been correlated, variably, with a decrease in deaths from breast cancer. However, there is no extant model for this effect, and the extent of efficacy is open to question.The overarching goal of this article is to communicate to the reader of the potential of statins to reduce breast cancer progression and mortality. This is the use as a secondary prevention measure, and not as a therapy to directly counter active cancer. First, salient aspects of statin pharmacology, as relates to cardiovascular disease, will be discussed. Second, the basic and clinical research studies that investigate statin usage in breast cancer will be presented. Additionally, statin effects in other cancer types will be included for context. Finally, proposals for future basic and clinical research studies to determine the role of statins in breast cancer management will be presented.

Statins attenuate outgrowth of breast cancer metastases.

BACKGROUND: Metastasis in breast cancer foreshadows mortality, as clinically evident disease is aggressive and generally chemoresistant. Disseminated breast cancer cells often enter a period of dormancy for years to decades before they emerge as detectable cancers. Harboring of these dormant cells is not individually predictable, and available information suggests that these micrometastatic foci cannot be effectively targeted by existing therapies. As such, long-term, relatively non-toxic interventions that prevent metastatic outgrowth would be an advance in treatment. Epidemiological studies have found that statins reduce breast cancer specific mortality but not the incidence of primary cancer. However, the means by which statins reduce mortality without affecting primary tumor development remains unclear. METHODS: We examine statin efficacy against two breast cancer cell lines in models of breast cancer metastasis: a 2D in vitro co-culture model of breast cancer cell interaction with the liver, a 3D ex vivo microphysiological system model of breast cancer metastasis, and two independent mouse models of spontaneous breast cancer metastasis to the lung and liver, respectively. RESULTS: We demonstrate that statins can directly affect the proliferation of breast cancer cells, specifically at the metastatic site. In a 2D co-culture model of breast cancer cell interaction with the liver, we demonstrate that atorvastatin can directly suppress proliferation of mesenchymal but not epithelial breast cancer cells. Further, in an ex vivo 3D liver microphysiological system of breast cancer metastasis, we found that atorvastatin can block stimulated emergence of dormant breast cancer cells. In two independent models of spontaneous breast cancer metastasis to the liver and to the lung, we find that statins significantly reduce proliferation of the metastatic but not primary tumor cells. CONCLUSIONS: As statins can block metastatic tumor outgrowth, they should be considered for use as long-term adjuvant drugs to delay clinical emergence and decrease mortality in breast cancer patients.

Biomarker identification for statin sensitivity of cancer cell lines.

Statins are potent cholesterol reducing drugs that have been shown to reduce tumor cell proliferation in vitro and tumor growth in animal models. Moreover, retrospective human cohort studies demonstrated decreased cancer-specific mortality in patients taking statins. We previously implicated membrane E-cadherin expression as both a marker and mechanism for resistance to atorvastatin-mediated growth suppression of cancer cells; however, a transcriptome-profile-based biomarker signature for statin sensitivity has not yet been reported. Here, we utilized transcriptome data from fourteen NCI-60 cancer cell lines and their statin dose-response data to produce gene expression signatures that identify statin sensitive and resistant cell lines. We experimentally confirmed the validity of the identified biomarker signature in an independent set of cell lines and extended this signature to generate a proposed statin-sensitive subset of tumors listed in the TCGA database. Finally, we predicted drugs that would synergize with statins and found several predicted combination therapies to be experimentally confirmed. The combined bioinformatics-experimental approach described here can be used to generate an initial biomarker signature for anticancer drug therapy.

BalestraWeb: efficient online evaluation of drug-target interactions.

SUMMARY: BalestraWeb is an online server that allows users to instantly make predictions about the potential occurrence of interactions between any given drug-target pair, or predict the most likely interaction partners of any drug or target listed in the DrugBank. It also permits users to identify most similar drugs or most similar targets based on their interaction patterns. Outputs help to develop hypotheses about drug repurposing as well as potential side effects. AVAILABILITY AND IMPLEMENTATION: BalestraWeb is accessible at http://balestra.csb.pitt.edu/. The tool is built using a probabilistic matrix factorization method and DrugBank v3, and the latent variable models are trained using the GraphLab collaborative filtering toolkit. The server is implemented using Python, Flask, NumPy and SciPy.

Resolving Discrepancies in Idylla BRAF Mutational Assay Results Using Targeted Next-Generation Sequencing.

BRAF mutation identification is important for the diagnosis and treatment of several tumor types, both solid and hematologic. Rapid identification of BRAF mutations is required to determine eligibility for targeted BRAF inhibitor therapy. The Idylla BRAF mutation assay is a rapid, multiplex allele-specific PCR test designed to detect the most common oncogenic BRAF V600 mutations in formalin-fixed paraffin-embedded (FFPE) tissue samples. Here, we describe the validation of the Idylla BRAF mutation assay in our laboratory. During routine clinical practice, we noticed cases in which BRAF V600 mutations were identified with unusual amplification curves, with three cases displaying a delayed amplification within a double amplification pattern and two false-positive calls. We therefore initiated a quality improvement effort to systematically and retrospectively evaluate next-generation sequencing (NGS)-tested cases with BRAF mutations identified within five amino acids of BRAF codon V600 and did not identify additional false-positive cases. We hypothesize that late amplification in a double amplification pattern may represent non-specific amplification, whereas cases displaying single delayed amplification curves may stem from the presence of either non-V600 variants, very low-level V600 variants, cytosine deamination artifacts, and/or non-specific amplification by an allele-specific PCR primer. Regardless, we recommend that Idylla BRAF cases with non-classical amplification curves undergo reflex NGS testing. These findings are likely relevant for other Idylla assays interrogating hotspot mutations in genes such as EGFR, IDH1/2, KRAS, and NRAS.

Repurposing of the Cardiovascular Drug Statin for the Treatment of Cancers: Efficacy of Statin-Dipyridamole Combination Treatment in Melanoma Cell Lines.

Metastatic melanoma has a very poor prognosis. Statins, 3-hydroxy-3-methyl-glutaryl-coenzyme A reductase (HMGCR) inhibitors, are cholesterol-lowering agents with a potential for cancer treatment. The inhibition of HMGCR by statins, however, induces feedback, which paradoxically upregulates HMGCR expression via sterol regulatory element-binding protein-2 (SREBP2). Dipyridamole, an antiplatelet agent, is known to inhibit SREBP2 upregulation. We aimed to demonstrate the efficacy of statin-dipyridamole combination treatment in both human and spontaneously occurring canine melanoma cell lines. The half maximal inhibitory concentration (IC50) of atorvastatin showed a 68-92% reduction when combined with dipyridamole, compared with that of atorvastatin alone. In some melanoma cell lines, cell proliferation was suppressed to almost zero by the combination treatment (≥3 µM atorvastatin). Finally, the BRAF inhibitor, vemurafenib, further potentiated the effects of the combined statin-dipyridamole treatment in BRAF V600E mutation-bearing human melanoma cell lines. In conclusion, the inexpensive and frequently prescribed statin-dipyridamole combination therapy may lead to new developments in the treatment of melanoma and may potentiate the effects of vemurafenib for the targeted therapy of BRAF V600E-mutation bearing melanoma patients. The concordance between the data from canine and human melanoma cell lines reinforces this possibility.

Matching experiments across species using expression values and textual information.

MOTIVATION: With the vast increase in the number of gene expression datasets deposited in public databases, novel techniques are required to analyze and mine this wealth of data. Similar to the way BLAST enables cross-species comparison of sequence data, tools that enable cross-species expression comparison will allow us to better utilize these datasets: cross-species expression comparison enables us to address questions in evolution and development, and further allows the identification of disease-related genes and pathways that play similar roles in humans and model organisms. Unlike sequence, which is static, expression data changes over time and under different conditions. Thus, a prerequisite for performing cross-species analysis is the ability to match experiments across species. RESULTS: To enable better cross-species comparisons, we developed methods for automatically identifying pairs of similar expression datasets across species. Our method uses a co-training algorithm to combine a model of expression similarity with a model of the text which accompanies the expression experiments. The co-training method outperforms previous methods based on expression similarity alone. Using expert analysis, we show that the new matches identified by our method indeed capture biological similarities across species. We then use the matched expression pairs between human and mouse to recover known and novel cycling genes as well as to identify genes with possible involvement in diabetes. By providing the ability to identify novel candidate genes in model organisms, our method opens the door to new models for studying diseases. AVAILABILITY: Source code and supplementary information is available at: www.andrew.cmu.edu/user/aaronwis/cotrain12.

Predicting drug-target interactions using probabilistic matrix factorization.

Quantitative analysis of known drug-target interactions emerged in recent years as a useful approach for drug repurposing and assessing side effects. In the present study, we present a method that uses probabilistic matrix factorization (PMF) for this purpose, which is particularly useful for analyzing large interaction networks. DrugBank drugs clustered based on PMF latent variables show phenotypic similarity even in the absence of 3D shape similarity. Benchmarking computations show that the method outperforms those recently introduced provided that the input data set of known interactions is sufficiently large--which is the case for enzymes and ion channels, but not for G-protein coupled receptors (GPCRs) and nuclear receptors. Runs performed on DrugBank after hiding 70% of known interactions show that, on average, 88 of the top 100 predictions hit the hidden interactions. De novo predictions permit us to identify new potential interactions. Drug-target pairs implicated in neurobiological disorders are overrepresented among de novo predictions.

Efficient Lung Cancer Molecular Diagnostics by Combining Next Generation Sequencing with Reflex Idylla Genefusion Assay Testing.

Molecular diagnostics for lung cancer is a well-established standard of care, but how to use the available diagnostic tools for optimal and cost-effective patient care remains unresolved. Here, we show that DNA-only, small gene next-generation sequencing (sNGS) panels (<50 genes) combined with ultra-rapid reflex testing for common fusion transcripts using the Idylla Genefusion assay provide a cost-effective and sufficiently comprehensive testing modality for the majority of lung cancer cases. We also demonstrate the need for additional reflex testing capability on larger DNA and fusion panels for a small subset of lung cancers bearing rare single-nucleotide variants, indels and fusion transcripts and secondary, post-treatment resistance mutations. A similar testing workflow could be adopted for other solid tumor types for which extensive gene/fusion variant profiles are available both in the treatment-naïve and post-therapy settings.

Expression of housekeeping genes varies depending on mevalonate pathway inhibition in cancer cells.

Statins have anticancer effects and may be used as anticancer agents via drug repositioning. In reverse transcription-quantitative polymerase chain reaction (RT-qPCR) assays, the internal reference gene must not be affected by any experimental conditions. As statins exert a wide range of effects on cells by inhibiting the mevalonate pathway, it is possible that statin treatment might alter the expression of housekeeping genes used as internal reference genes, thereby misleading the assessment of obtained gene expression data. Here, we evaluated the expression stability of internal reference genes in atorvastatin-treated cancer cell lines. We treated both statin-sensitive and statin-resistant cancer cell lines with atorvastatin at seven different concentrations and performed RT-qPCR on 15 housekeeping genes whose expression stability was then assessed using five different algorithms. In both statin-sensitive and statin-resistant cancer cell lines, atorvastatin affected the expression of certain internal reference genes in a dose-dependent and cancer cell line-dependent manner; therefore, caution should be exercised when comparing target gene expression between cells. Our findings emphasize the importance of the validation of internal reference genes in gene expression analyses in drug treatment-based cancer research.

Alterations in the omics profiles in mevalonate pathway-inhibited cancer cells.

AIMS: Statins, cholesterol-lowering drugs, are potential therapeutic agents for inhibiting cancer proliferation. However, the mechanisms that mediate the effects of statins, the homeostatic responses of tumor cells to statin therapy, and the modes underlying the antitumor effects of statins remain unclear. MAIN METHODS: To uncover the effects of statins on cancer cells in vitro, we performed transcriptome and metabolome analyses on atorvastatin-treated statin-resistant and statin-sensitive lung cancer cells. KEY FINDINGS: The results of Gene Ontology terms and pathway enrichment analyses showed that after 24 h of atorvastatin treatment, the expression of cell cycle- and DNA replication-related genes was significantly decreased in the statin-sensitive cancer cells. The results of metabolome analysis showed that the components of polyamine metabolism and purine metabolism, glycolysis, and pentose phosphate pathway were decreased in the statin-sensitive cancer cells. SIGNIFICANCE: Differences in cellular properties between statin-sensitive and statin-resistant cancer cells revealed additional candidates for therapeutic targets in statin-treated cancer cells and suggested that inhibiting these metabolic pathways could improve efficacy. In conclusion, combining statins with inhibitors of polyamine metabolism (cell proliferation and protein translation), purine metabolism (DNA synthesis), glycolytic system (energy production), and pentose phosphate pathway (antioxidant stress) might enhance the anticancer effects of statins.

Clonal cytopenia of undetermined significance (CCUS)-associated reversion of donor-derived, transient αß T-cell large granular clonal lymphocytosis, emerging post-transplant in a patient with a history of γδ T-cell large granular lymphocytic leukemia.

Autologous and allogeneic hematopoietic stem cell transplantation (HSCT) has revolutionized the therapy of hematolymphoid malignancies. Yet, how to best detect or predict the emergence of HSCT-related complications remain unresolved. Here, we describe a case of donor-derived, transient Alpha Beta (αß) T-cell large granular clonal lymphocytosis and cytopenia that emerged post-HSCT in a patient with a history of gamma delta (γδ) T-cell large granular lymphocytic leukemia (T-LGLL). Clonal unrelatedness of post-transplant T-LGL lymphocytosis to the patient's pretransplant T-LGLL was first identified by T-cell receptor (TCR) PCR showing different sized fragments of rearranged gamma chains, in addition to shift from γδ to αß TCR expression by flow cytometry analyses. Donor-derivation of the patient's post-transplant clonal lymphocytosis was confirmed by serial chimerism analyses of recipient's blood specimens demonstrating 100% donor DNA. Moreover, oncogenic DNMT3A and RUNX1 mutations were detected by next-generation sequencing (NGS) only in post-transplant specimens. Intriguingly, despite continued increase in DNMT3A and RUNX1 mutation load, the patient's clonal lymphocytosis and anemia eventually largely resolved; yet, the observed mutation profile with persistent thrombocytopenia indicated secondary clonal cytopenia of undetermined significance (CCUS) in the absence of overt morphologic evidence of myeloid neoplasm in the marrow. This case illustrates the utility of longitudinal chimerism analysis and NGS testing combined with flow cytometric immunophenotyping to evaluate emerging donor-derived hematolymphoid processes and to properly interpret partial functional engraftment. It may also support the notion that driver mutation-induced microenvironmental changes may paradoxically contribute to reestablishing tissue homeostasis.

Utility of Select Gene Mutation Detection in Tumors by the Idylla Rapid Multiplex PCR Platform in Comparison to Next-Generation Sequencing.

Testing of tumors by next generation sequencing (NGS) is impacted by relatively long turnaround times and a need for highly trained personnel. Recently, Idylla oncology assays were introduced to test for BRAF, EGFR, KRAS, and NRAS common hotspot mutations that do not require specialized trained personnel. Moreover, the interpretation of results is fully automated, with rapid turnaround time. Though Idylla testing and NGS have been shown to have high concordance in identifying EGFR, BRAF, KRAS, and NRAS hotspot mutations, there is limited experience on optimal ways the Idylla system can be used in routine practice. We retrospectively evaluated all cases with EGFR, BRAF, KRAS, or NRAS mutations identified in clinical specimens sequenced on two different NGS panels at the University of Rochester Medical Center (URMC) molecular diagnostics laboratory between July 2020 and July 2021 and assessed if these mutations would be detected by the Idylla cartridges if used. We found that the Idylla system could accurately identify Tier 1 or 2 actionable genomic alterations in select associated disease pathologies if used. Yet, in a minority of cases, we would have been unable to detect NGS-identified pathogenic mutations due to their absence on the Idylla panels. We derived algorithmic practice guidelines for the use of the Idylla cartridges. Overall, Idylla molecular testing could be implemented either as a first-line standalone diagnostic tool in select indications or for orthogonal confirmation of uncertain results.

Incidental discovery of acute myeloid leukemia during liquid biopsy of a lung cancer patient.

Liquid biopsy is considered an alternative to standard next-generation sequencing (NGS) of solid tumor samples when biopsy tissue is inadequate for testing or when testing of a peripheral blood sample is preferred. A common assumption of liquid biopsies is that the NGS data obtained on circulating cell-free DNA is a high-fidelity reflection of what would be found by solid tumor testing. Here, we describe a case that challenges this widely held assumption. A patient diagnosed with lung carcinoma showed pathogenic IDH1 and TP53 mutations by liquid biopsy NGS at an outside laboratory. Subsequent in-house NGS of a metastatic lymph node fine-needle aspiration (FNA) sample revealed two pathogenic EGFR mutations. Morphologic and immunophenotypic assessment of the patient's blood sample identified acute myeloid leukemia, with in-house NGS confirming and identifying pathogenic IDH1, TP53, and BCOR mutations, respectively. This case, together with a few similar reports, demonstrates that caution is needed when interpreting liquid biopsy NGS results, especially if they are inconsistent with the presumptive diagnosis. Our case suggests that routine parallel sequencing of peripheral white blood cells would substantially increase the fidelity of the obtained liquid biopsy results.

Cross-species queries of large gene expression databases.

MOTIVATION: Expression databases, including the Gene Expression Omnibus and ArrayExpress, have experienced significant growth over the past decade and now hold hundreds of thousands of arrays from multiple species. Since most drugs are initially tested on model organisms, the ability to compare expression experiments across species may help identify pathways that are activated in a similar way in humans and other organisms. However, while several methods exist for finding co-expressed genes in the same species as a query gene, looking at co-expression of homologs or arbitrary genes in other species is challenging. Unlike sequence, which is static, expression is dynamic and changes between tissues, conditions and time. Thus, to carry out cross-species analysis using these databases, we need methods that can match experiments in one species with experiments in another species. RESULTS: To facilitate queries in large databases, we developed a new method for comparing expression experiments from different species. We define a distance metric between the ranking of orthologous genes in the two species. We show how to solve an optimization problem for learning the parameters of this function using a training dataset of known similar expression experiments pairs. The function we learn outperforms previous methods and simpler rank comparison methods that have been used in the past for single species analysis. We used our method to compare millions of array pairs from mouse and human expression experiments. The resulting matches can be used to find functionally related genes, to hypothesize about biological response mechanisms and to highlight conditions and diseases that are activating similar pathways in both species. AVAILABILITY: Supporting methods, results and a Matlab implementation are available from http://sb.cs.cmu.edu/ExpQ/.