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The resonant cavity imaging biosensor (RCIB) is an optical technique for detecting molecular binding interactions label free at many locations in parallel that employs an optical resonant cavity for high sensitivity. Near-infrared light centered at 1512.5 nm couples resonantly through a Fabry-Perot cavity constructed from dielectric reflectors (Si/SiO2), one of which serves as the binding surface. As the wavelength is swept using a tunable laser, a near-infrared digital camera monitors cavity transmittance at each pixel. A wavelength shift in the local resonant response of the optical cavity indicates binding. Positioning the sensing surface with respect to the standing wave pattern of the electric field within the cavity controls the sensitivity with which the presence of bound molecules is detected. Transmitted intensity at thousands of pixel locations is recorded simultaneously in a 10 s, 5 nm scan. An initial proof-of-principle setup has been constructed. A test sample was fabricated with 25,100-mum wide square features, each with a different density of 1-mum square depressions etched 12 nm into the SiO2 surface. The average depth of each etched region was found with 0.05 nm rms precision. In a second test, avidin, bound selectively to biotin conjugated bovine serum albumin, was detected. [ABSTRACT FROM PUBLISHER]

Gene expression is modulated by transcription factors (TFs), which are proteins that generally bind to DNA adjacent to coding regions and initiate transcription. Each target gene can be regulated by more than one TF, and each TF can regulate many targets. For a complete molecular understanding of transcriptional regulation, researchers must first associate each TF with the set of genes that it regulates. Here we present a summary of completed work on the ability to associate 104 TFs with their binding sites using support vector machines (SVMs), which are classification algorithms based in statistical learning theory. We use several types of genomic datasets to train classifiers in order to predict TF binding in the yeast genome. We consider motif matches, subsequence counts, motif conservation, functional annotation, and expression profiles. A simple weighting scheme varies the contribution of each type of genomic data when building a final SVM classifier, which we evaluate using known binding sites published in the literature and in online databases. The SVM algorithm works best when all datasets are combined, producing 73% coverage of known interactions, with a prediction accuracy of almost 0.9. We discuss new ideas and preliminary work for improving SVM classification of biological data. [ABSTRACT FROM AUTHOR]

An abundance of data, including decades of greenhouse gas (GHG) emission rates, atmospheric concentrations, and global average temperatures, is sufficient to allow a strictly empirical evaluation of the U.S. plan for controlling GHGs. This article presents an analysis, based solely on such data, that shows that the difference between atmospheric GHG levels that will be reached if current trends continue, and levels that would be achieved if the goals of the plan are met-even with worldwide implementation-is inconsequential. Further, the expected globally averaged temperature differences are well within measurement error. The results lend additional support to the argument that any mitigation strategy must include drawdown of atmospheric GHGs. Equally important, a particular drawdown strategy, agrigenomics, offers the opportunity for a revolutionary trifecta: climate change mitigation, food security, and medical advances., (Copyright © 2024 Cold Spring Harbor Laboratory Press; all rights reserved.)

Microbes commonly organize into communities consisting of hundreds of species involved in complex interactions with each other. 16S ribosomal RNA (16S rRNA) amplicon profiling provides snapshots that reveal the phylogenies and abundance profiles of these microbial communities. These snapshots, when collected from multiple samples, can reveal the co-occurrence of microbes, providing a glimpse into the network of associations in these communities. However, the inference of networks from 16S data involves numerous steps, each requiring specific tools and parameter choices. Moreover, the extent to which these steps affect the final network is still unclear. In this study, we perform a meticulous analysis of each step of a pipeline that can convert 16S sequencing data into a network of microbial associations. Through this process, we map how different choices of algorithms and parameters affect the co-occurrence network and identify the steps that contribute substantially to the variance. We further determine the tools and parameters that generate robust co-occurrence networks and develop consensus network algorithms based on benchmarks with mock and synthetic data sets. The Microbial Co-occurrence Network Explorer, or MiCoNE (available at https://github.com/segrelab/MiCoNE) follows these default tools and parameters and can help explore the outcome of these combinations of choices on the inferred networks. We envisage that this pipeline could be used for integrating multiple data sets and generating comparative analyses and consensus networks that can guide our understanding of microbial community assembly in different biomes. IMPORTANCE Mapping the interrelationships between different species in a microbial community is important for understanding and controlling their structure and function. The surge in the high-throughput sequencing of microbial communities has led to the creation of thousands of data sets containing information about microbial abundances. These abundances can be transformed into co-occurrence networks, providing a glimpse into the associations within microbiomes. However, processing these data sets to obtain co-occurrence information relies on several complex steps, each of which involves numerous choices of tools and corresponding parameters. These multiple options pose questions about the robustness and uniqueness of the inferred networks. In this study, we address this workflow and provide a systematic analysis of how these choices of tools affect the final network and guidelines on appropriate tool selection for a particular data set. We also develop a consensus network algorithm that helps generate more robust co-occurrence networks based on benchmark synthetic data sets., Competing Interests: The authors declare no conflict of interest.

The long atmospheric residence time of CO 2 creates an urgent need to add atmospheric carbon drawdown to CO 2 regulatory strategies. Synthetic and systems biology (SSB), which enables manipulation of cellular phenotypes, offers a powerful approach to amplifying and adding new possibilities to current land management practices aimed at reducing atmospheric carbon. The participants (in attendance: Christina Agapakis, George Annas, Adam Arkin, George Church, Robert Cook-Deegan, Charles DeLisi, Dan Drell, Sheldon Glashow, Steve Hamburg, Henry Jacoby, Henry Kelly, Mark Kon, Todd Kuiken, Mary Lidstrom, Mike MacCracken, June Medford, Jerry Melillo, Ron Milo, Pilar Ossorio, Ari Patrinos, Keith Paustian, Kristala Jones Prather, Kent Redford, David Resnik, John Reilly, Richard J. Roberts, Daniel Segre, Susan Solomon, Elizabeth Strychalski, Chris Voigt, Dominic Woolf, Stan Wullschleger, and Xiaohan Yang) identified a range of possibilities by which SSB might help reduce greenhouse gas concentrations and which might also contribute to environmental sustainability and adaptation. These include, among other possibilities, engineering plants to convert CO 2 produced by respiration into a stable carbonate, designing plants with an increased root-to-shoot ratio, and creating plants with the ability to self-fertilize. A number of serious ecological and societal challenges must, however, be confronted and resolved before any such application can be fully assessed, realized, and deployed., Competing Interests: The following authors have competing interests: GA (consultant for a DARPA contract awarded to Massachusetts General Hospital, 2017-2021, “Controlling and Countering Gene Drives in Mosquitoes”); GC (http://arep.med.harvard.edu/gmc/tech.html). All other authors have no competing interest., (Copyright © 2020 Charles DeLisi et al.)

There is growing agreement that the aim of United Nations Framework Convention on Climate Change, which is to avoid dangerous anthropogenic interference with the climate system, is not likely to be met without inclusion of methods to physically remove atmospheric carbon. A number of approaches have been suggested, but the community appears to be silent on the potential of one of the most revolutionary technologies of the current century, systems and synthetic biology (SSB). The potential of SSB to modulate the fast carbon cycle, and thereby mitigate climate change is in itself enormous, but if the history of genomics is any measure, it is also reasonable to expect sizeable economic returns on any investment. More generally, the approach to climate control has been badly unbalanced. The last three decades have seen intense international attention to emission control, with no parallel plan to test, scale and implement carbon removal technologies, including attention to their economic, legal and ethical implications. REVIEWERS: This article was reviewed by Richard Roberts, Aristides Patrinos, and Eugene Koonin, all of whom were nominated by Itai Yanai. For the full reviews, please go to the Reviewers' comments section.

Background: The high cost and the long time required to bring drugs into commerce is driving efforts to repurpose FDA approved drugs-to find new uses for which they weren't intended, and to thereby reduce the overall cost of commercialization, and shorten the lag between drug discovery and availability. We report on the development, testing and application of a promising new approach to repositioning., Methods: Our approach is based on mining a human functional linkage network for inversely correlated modules of drug and disease gene targets. The method takes account of multiple information sources, including gene mutation, gene expression, and functional connectivity and proximity of within module genes., Results: The method was used to identify candidates for treating breast and prostate cancer. We found that (i) the recall rate for FDA approved drugs for breast (prostate) cancer is 20/20 (10/11), while the rates for drugs in clinical trials were 131/154 and 82/106; (ii) the ROC/AUC performance substantially exceeds that of comparable methods; (iii) preliminary in vitro studies indicate that 5/5 candidates have therapeutic indices superior to that of Doxorubicin in MCF7 and SUM149 cancer cell lines. We briefly discuss the biological plausibility of the candidates at a molecular level in the context of the biological processes that they mediate., Conclusions: Our method appears to offer promise for the identification of multi-targeted drug candidates that can correct aberrant cellular functions. In particular the computational performance exceeded that of other CMap-based methods, and in vitro experiments indicate that 5/5 candidates have therapeutic indices superior to that of Doxorubicin in MCF7 and SUM149 cancer cell lines. The approach has the potential to provide a more efficient drug discovery pipeline.

The complexity of metabolic networks in microbial communities poses an unresolved visualization and interpretation challenge. We address this challenge in the newly expanded version of a software tool for the analysis of biological networks, VisANT 5.0. We focus in particular on facilitating the visual exploration of metabolic interaction between microbes in a community, e.g. as predicted by COMETS (Computation of Microbial Ecosystems in Time and Space), a dynamic stoichiometric modeling framework. Using VisANT's unique metagraph implementation, we show how one can use VisANT 5.0 to explore different time-dependent ecosystem-level metabolic networks. In particular, we analyze the metabolic interaction network between two bacteria previously shown to display an obligate cross-feeding interdependency. In addition, we illustrate how a putative minimal gut microbiome community could be represented in our framework, making it possible to highlight interactions across multiple coexisting species. We envisage that the "symbiotic layout" of VisANT can be employed as a general tool for the analysis of metabolism in complex microbial communities as well as heterogeneous human tissues. VisANT is freely available at: http://visant.bu.edu and COMETS at http://comets.bu.edu.

Adjuvant therapy following breast cancer surgery generally consists of either a course of chemotherapy, if the cancer lacks hormone receptors, or a course of hormonal therapy, otherwise. Here, we report a correlation between adjuvant strategy and mutated pathway patterns. In particular, we find that for breast cancer patients, pathways enriched in nonsynonymous mutations in the chemotherapy group are distinct from those of the hormonal therapy group. We apply a recently developed method that identifies collaborative pathway groups for hormone and chemotherapy patients. A collaborative group of pathways is one in which each member is altered in the same-generally large-number of samples. In particular, we find the following: (i) a chemotherapy group consisting of three pathways and a hormone therapy group consisting of 20, the members of the two groups being mutually exclusive; (ii) each group is highly enriched in breast cancer drivers; and (iii) the pathway groups are correlates of subtype-based therapeutic recommendations. These results suggest that patient profiling using these pathway groups can potentially enable the development of personalized treatment plans that may be more accurate and specific than those currently available., (©2015 American Association for Cancer Research.)

The number of mutated genes in cancer cells is far larger than the number of mutations that drive cancer. The difficulty this creates for identifying relevant alterations has stimulated the development of various computational approaches to distinguishing drivers from bystanders. We develop and apply an ensemble classifier (EC) machine learning method, which integrates 10 classifiers that are publically available, and apply it to breast and ovarian cancer. In particular we find the following: (1) Using both standard and non-standard metrics, EC almost always outperforms single method classifiers, often by wide margins. (2) Of the 50 highest ranked genes for breast (ovarian) cancer, 34 (30) are associated with other cancers in either the OMIM, CGC or NCG database (P < 10(-22)). (3) Another 10, for both breast and ovarian cancer, have been identified by GWAS studies. (4) Several of the remaining genes--including a protein kinase that regulates the Fra-1 transcription factor which is overexpressed in ER negative breast cancer cells; and Fyn, which is overexpressed in pancreatic and prostate cancer, among others--are biologically plausible. Biological implications are briefly discussed. Source codes and detailed results are available at http://www.visantnet.org/misi/driver&#95;integration.zip.

A shift of the delicate balance between apoptosis and survival-inducing signals determines the fate of neurons during the development of the central nervous system and its homeostasis throughout adulthood. Both pathways, promoting or protecting from apoptosis, trigger a transcriptional program. We conducted whole-genome expression profiling to decipher the transcriptional regulatory elements controlling the apoptotic/survival switch in cerebellar granule neurons following the induction of apoptosis by serum and potassium deprivation or their rescue by either insulin-like growth factor-1 (Igf1) or pituitary adenylyl cyclase-activating polypeptide (Pacap). Although depending on different upstream signaling pathways, the survival effects of Igf1 and Pacap converged into common transcriptional cascades, thus suggesting the existence of a general transcriptional program underlying neuronal survival.

With the rapid accumulation of our knowledge on diseases, disease-related genes and drug targets, network-based analysis plays an increasingly important role in systems biology, systems pharmacology and translational science. The new release of VisANT aims to provide new functions to facilitate the convenient network analysis of diseases, therapies, genes and drugs. With improved understanding of the mechanisms of complex diseases and drug actions through network analysis, novel drug methods (e.g., drug repositioning, multi-target drug and combination therapy) can be designed. More specifically, the new update includes (i) integrated search and navigation of disease and drug hierarchies; (ii) integrated disease-gene, therapy-drug and drug-target association to aid the network construction and filtering; (iii) annotation of genes/drugs using disease/therapy information; (iv) prediction of associated diseases/therapies for a given set of genes/drugs using enrichment analysis; (v) network transformation to support construction of versatile network of drugs, genes, diseases and therapies; (vi) enhanced user interface using docking windows to allow easy customization of node and edge properties with build-in legend node to distinguish different node type. VisANT is freely available at: http://visant.bu.edu.

A host of data on genetic variation from the Human Genome and International HapMap projects, and advances in high-throughput genotyping technologies, have made genome-wide association (GWA) studies technically feasible. GWA studies help in the discovery and quantification of the genetic components of disease risks, many of which have not been unveiled before and have opened a new avenue to understanding disease, treatment, and prevention. This chapter presents an overview of GWA, an important tool for discovering regions of the genome that harbor common genetic variants to confer susceptibility for various diseases or health outcomes in the post-Human Genome Project era. A tutorial on how to conduct a GWA study and some practical challenges specifically related to the GWA design is presented, followed by a detailed GWA case study involving the identification of loci associated with glioma as an example and an illustration of current technologies.

Competing Interests: The authors have declared that no competing interests exist.

We demonstrate an accurate, quantitative, and label-free optical technology for high-throughput studies of receptor-ligand interactions, and apply it to TATA binding protein (TBP) interactions with oligonucleotides. We present a simple method to prepare single-stranded and double-stranded DNA microarrays with comparable surface density, ensuring an accurate comparison of TBP activity with both types of DNA. In particular, we find that TBP binds tightly to single-stranded DNA, especially to stretches of polythymine (poly-T), as well as to the traditional TATA box. We further investigate the correlation of TBP activity with various lengths of DNA and find that the number of TBPs bound to DNA increases >7-fold as the oligomer length increases from 9 to 40. Finally, we perform a full human genome analysis and discover that 35.5% of human promoters have poly-T stretches. In summary, we report, for the first time to our knowledge, the activity of TBP with poly-T stretches by presenting an elegant stepwise analysis of multiple techniques: discovery by a novel quantitative detection of microarrays, confirmation by a traditional gel electrophoresis, and a full genome prediction with computational analyses., (Copyright © 2012 Biophysical Society. Published by Elsevier Inc. All rights reserved.)

Background: Molecular markers based on gene expression profiles have been used in experimental and clinical settings to distinguish cancerous tumors in stage, grade, survival time, metastasis, and drug sensitivity. However, most significant gene markers are unstable (not reproducible) among data sets. We introduce a standardized method for representing cancer markers as 2-level hierarchical feature vectors, with a basic gene level as well as a second level of (more stable) pathway markers, for the purpose of discriminating cancer subtypes. This extends standard gene expression arrays with new pathway-level activation features obtained directly from off-the-shelf gene set enrichment algorithms such as GSEA. Such so-called pathway-based expression arrays are significantly more reproducible across datasets. Such reproducibility will be important for clinical usefulness of genomic markers, and augment currently accepted cancer classification protocols., Results: The present method produced more stable (reproducible) pathway-based markers for discriminating breast cancer metastasis and ovarian cancer survival time. Between two datasets for breast cancer metastasis, the intersection of standard significant gene biomarkers totaled 7.47% of selected genes, compared to 17.65% using pathway-based markers; the corresponding percentages for ovarian cancer datasets were 20.65% and 33.33% respectively. Three pathways, consisting of Type&#95;1&#95;diabetes mellitus, Cytokine-cytokine&#95;receptor&#95;interaction and Hedgehog&#95;signaling (all previously implicated in cancer), are enriched in both the ovarian long survival and breast non-metastasis groups. In addition, integrating pathway and gene information, we identified five (ID4, ANXA4, CXCL9, MYLK, FBXL7) and six (SQLE, E2F1, PTTG1, TSTA3, BUB1B, MAD2L1) known cancer genes significant for ovarian and breast cancer respectively., Conclusions: Standardizing the analysis of genomic data in the process of cancer staging, classification and analysis is important as it has implications for both pre-clinical as well as clinical studies. The paradigm of diagnosis and prediction using pathway-based biomarkers as features can be an important part of the process of biomarker-based cancer analysis, and the resulting canonical (clinically reproducible) biomarkers can be important in standardizing genomic data. We expect that identification of such canonical biomarkers will improve clinical utility of high-throughput datasets for diagnostic and prognostic applications.

A central goal of biology is understanding and describing the molecular basis of plasticity: the sets of genes that are combinatorially selected by exogenous and endogenous environmental changes, and the relations among the genes. The most viable current approach to this problem consists of determining whether sets of genes are connected by some common theme, e.g. genes from the same pathway are overrepresented among those whose differential expression in response to a perturbation is most pronounced. There are many approaches to this problem, and the results they produce show a fair amount of dispersion, but they all fall within a common framework consisting of a few basic components. We critically review these components, suggest best practices for carrying out each step, and propose a voting method for meeting the challenge of assessing different methods on a large number of experimental data sets in the absence of a gold standard.

Background: Identification of active causal regulators is a crucial problem in understanding mechanism of diseases or finding drug targets. Methods that infer causal regulators directly from primary data have been proposed and successfully validated in some cases. These methods necessarily require very large sample sizes or a mix of different data types. Recent studies have shown that prior biological knowledge can successfully boost a method's ability to find regulators., Results: We present a simple data-driven method, Correlation Set Analysis (CSA), for comprehensively detecting active regulators in disease populations by integrating co-expression analysis and a specific type of literature-derived causal relationships. Instead of investigating the co-expression level between regulators and their regulatees, we focus on coherence of regulatees of a regulator. Using simulated datasets we show that our method performs very well at recovering even weak regulatory relationships with a low false discovery rate. Using three separate real biological datasets we were able to recover well known and as yet undescribed, active regulators for each disease population. The results are represented as a rank-ordered list of regulators, and reveals both single and higher-order regulatory relationships., Conclusions: CSA is an intuitive data-driven way of selecting directed perturbation experiments that are relevant to a disease population of interest and represent a starting point for further investigation. Our findings demonstrate that combining co-expression analysis on regulatee sets with a literature-derived network can successfully identify causal regulators and help develop possible hypothesis to explain disease progression.

The National Center for Integrative and Biomedical Informatics (NCIBI) is one of the eight NCBCs. NCIBI supports information access and data analysis for biomedical researchers, enabling them to build computational and knowledge models of biological systems to address the Driving Biological Problems (DBPs). The NCIBI DBPs have included prostate cancer progression, organ-specific complications of type 1 and 2 diabetes, bipolar disorder, and metabolic analysis of obesity syndrome. Collaborating with these and other partners, NCIBI has developed a series of software tools for exploratory analysis, concept visualization, and literature searches, as well as core database and web services resources. Many of our training and outreach initiatives have been in collaboration with the Research Centers at Minority Institutions (RCMI), integrating NCIBI and RCMI faculty and students, culminating each year in an annual workshop. Our future directions include focusing on the TranSMART data sharing and analysis initiative.

The cost and time to develop a drug continues to be a major barrier to widespread distribution of medication. Although the genomic revolution appears to have had little impact on this problem, and might even have exacerbated it because of the flood of additional and usually ineffective leads, the emergence of high throughput resources promises the possibility of rapid, reliable and systematic identification of approved drugs for originally unintended uses. In this paper we develop and apply a method for identifying such repositioned drug candidates against breast cancer, myelogenous leukemia and prostate cancer by looking for inverse correlations between the most perturbed gene expression levels in human cancer tissue and the most perturbed expression levels induced by bioactive compounds. The method uses variable gene signatures to identify bioactive compounds that modulate a given disease. This is in contrast to previous methods that use small and fixed signatures. This strategy is based on the observation that diseases stem from failed/modified cellular functions, irrespective of the particular genes that contribute to the function, i.e., this strategy targets the functional signatures for a given cancer. This function-based strategy broadens the search space for the effective drugs with an impressive hit rate. Among the 79, 94 and 88 candidate drugs for breast cancer, myelogenous leukemia and prostate cancer, 32%, 13% and 17% respectively are either FDA-approved/in-clinical-trial drugs, or drugs with suggestive literature evidences, with an FDR of 0.01. These findings indicate that the method presented here could lead to a substantial increase in efficiency in drug discovery and development, and has potential application for the personalized medicine.

Background: Glioblastoma multiforme (GBM) tends to occur between the ages of 45 and 70. This relatively early onset and its poor prognosis make the impact of GBM on public health far greater than would be suggested by its relatively low frequency. Tissue and blood samples have now been collected for a number of populations, and predisposing alleles have been sought by several different genome-wide association (GWA) studies. The Cancer Genome Atlas (TCGA) at NIH has also collected a considerable amount of data. Because of the low concordance between the results obtained using different populations, only 14 predisposing single nucleotide polymorphism (SNP) candidates in five genomic regions have been replicated in two or more studies. The purpose of this paper is to present an improved approach to biomarker identification., Methods: Association analysis was performed with control of population stratifications using the EIGENSTRAT package, under the null hypothesis of "no association between GBM and control SNP genotypes," based on an additive inheritance model. Genes that are strongly correlated with identified SNPs were determined by linkage disequilibrium (LD) or expression quantitative trait locus (eQTL) analysis. A new approach that combines meta-analysis and pathway enrichment analysis identified additional genes., Results: (i) A meta-analysis of SNP data from TCGA and the Adult Glioma Study identifies 12 predisposing SNP candidates, seven of which are reported for the first time. These SNPs fall in five genomic regions (5p15.33, 9p21.3, 1p21.2, 3q26.2 and 7p15.3), three of which have not been previously reported. (ii) 25 genes are strongly correlated with these 12 SNPs, eight of which are known to be cancer-associated. (iii) The relative risk for GBM is highest for risk allele combinations on chromosomes 1 and 9. (iv) A combined meta-analysis/pathway analysis identified an additional four genes. All of these have been identified as cancer-related, but have not been previously associated with glioma. (v) Some SNPs that do not occur reproducibly across populations are in reproducible (invariant) pathways, suggesting that they affect the same biological process, and that population discordance can be partially resolved by evaluating processes rather than genes., Conclusion: We have uncovered 29 glioma-associated gene candidates; 12 of them known to be cancer related (p = 1. 4 × 10-6), providing additional statistical support for the relevance of the new candidates. This additional information on risk loci is potentially important for identifying Caucasian individuals at risk for glioma, and for assessing relative risk.

COMBREX (http://combrex.bu.edu) is a project to increase the speed of the functional annotation of new bacterial and archaeal genomes. It consists of a database of functional predictions produced by computational biologists and a mechanism for experimental biochemists to bid for the validation of those predictions. Small grants are available to support successful bids.

Objectives: To identify a robust panel of microRNA signatures that can classify tumor from normal kidney using microRNA expression levels. Mounting evidence suggests that microRNAs are key players in essential cellular processes and that their expression pattern can serve as diagnostic biomarkers for cancerous tissues., Methods: We selected 28 clear-cell type human renal cell carcinoma (ccRCC), samples from patient-matched specimens to perform high-throughput, quantitative real-time polymerase chain reaction analysis of microRNA expression levels. The data were subjected to rigorous statistical analyses and hierarchical clustering to produce a discrete set of microRNAs that can robustly distinguish ccRCC from their patient-matched normal kidney tissue samples with high confidence., Results: Thirty-five microRNAs were found that can robustly distinguish ccRCC from their patient-matched normal kidney tissue samples with high confidence. Among this set of 35 signature microRNAs, 26 were found to be consistently downregulated and 9 consistently upregulated in ccRCC relative to normal kidney samples. Two microRNAs, namely, MiR-155 and miR-21, commonly found to be upregulated in other cancers, and miR-210, induced by hypoxia, were also identified as overexpressed in ccRCC in our study. MicroRNAs identified as downregulated in our study can be correlated to common chromosome deletions in ccRCC., Conclusions: Our analysis is a comprehensive, statistically relevant study that identifies the microRNAs dysregulated in ccRCC, which can serve as the basis of molecular markers for diagnosis., (Copyright 2010 Elsevier Inc. All rights reserved.)

A novel method is proposed for direct detection of DNA hybridization on microarrays. Optical interferometry is used for label-free sensing of biomolecular accumulation on glass surfaces, enabling dynamic detection of interactions. Capabilities of the presented method are demonstrated by high-throughput sensing of solid-phase hybridization of oligonucleotides. Hybridization of surface immobilized probes with 20 base pair-long target oligonucleotides was detected by comparing the label-free microarray images taken before and after hybridization. Through dynamic data acquisition during denaturation by washing the sample with low ionic concentration buffer, melting of duplexes with a single-nucleotide mismatch was distinguished from perfectly matching duplexes with high confidence interval (>97%). The presented technique is simple, robust, and accurate, and eliminates the need of using labels or secondary reagents to monitor the oligonucleotide hybridization., ((c) 2010 Elsevier B.V. All rights reserved.)

We develop a general method to identify gene networks from pair-wise correlations between genes in a microarray data set and apply it to a public prostate cancer gene expression data from 69 primary prostate tumors. We define the degree of a node as the number of genes significantly associated with the node and identify hub genes as those with the highest degree. The correlation network was pruned using transcription factor binding information in VisANT (http://visant.bu.edu/) as a biological filter. The reliability of hub genes was determined using a strict permutation test. Separate networks for normal prostate samples, and prostate cancer samples from African Americans (AA) and European Americans (EA) were generated and compared. We found that the same hubs control disease progression in AA and EA networks. Combining AA and EA samples, we generated networks for low low (<7) and high (≥7) Gleason grade tumors. A comparison of their major hubs with those of the network for normal samples identified two types of changes associated with disease: (i) Some hub genes increased their degree in the tumor network compared to their degree in the normal network, suggesting that these genes are associated with gain of regulatory control in cancer (e.g. possible turning on of oncogenes). (ii) Some hubs reduced their degree in the tumor network compared to their degree in the normal network, suggesting that these genes are associated with loss of regulatory control in cancer (e.g. possible loss of tumor suppressor genes). A striking result was that for both AA and EA tumor samples, STAT5a, CEBPB and EGR1 are major hubs that gain neighbors compared to the normal prostate network. Conversely, HIF-lα is a major hub that loses connections in the prostate cancer network compared to the normal prostate network. We also find that the degree of these hubs changes progressively from normal to low grade to high grade disease, suggesting that these hubs are master regulators of prostate cancer and marks disease progression. STAT5a was identified as a central hub, with ~120 neighbors in the prostate cancer network and only 81 neighbors in the normal prostate network. Of the 120 neighbors of STAT5a, 57 are known cancer related genes, known to be involved in functional pathways associated with tumorigenesis. Our method is general and can easily be extended to identify and study networks associated with any two phenotypes.

One of the important challenges to post-genomic biology is relating observed phenotypic alterations to the underlying collective alterations in genes. Current inferential methods, however, invariably omit large bodies of information on the relationships between genes. We present a method that takes account of such information - expressed in terms of the topology of a correlation network - and we apply the method in the context of current procedures for gene set enrichment analysis.

Surprising correlations between human disease phenotypes are emerging. Recent work now reveals startling phenotype connections between species, which could provide new disease models.

Despite its wide usage in biological databases and applications, the role of the gene ontology (GO) in network analysis is usually limited to functional annotation of genes or gene sets with auxiliary information on correlations ignored. Here, we report on new capabilities of VisANT--an integrative software platform for the visualization, mining, analysis and modeling of the biological networks--which extend the application of GO in network visualization, analysis and inference. The new VisANT functions can be classified into three categories. (i) Visualization: a new tree-based browser allows visualization of GO hierarchies. GO terms can be easily dropped into the network to group genes annotated under the term, thereby integrating the hierarchical ontology with the network. This facilitates multi-scale visualization and analysis. (ii) Flexible annotation schema: in addition to conventional methods for annotating network nodes with the most specific functional descriptions available, VisANT also provides functions to annotate genes at any customized level of abstraction. (iii) Finding over-represented GO terms and expression-enriched GO modules: two new algorithms have been implemented as VisANT plugins. One detects over-represented GO annotations in any given sub-network and the other finds the GO categories that are enriched in a specified phenotype or perturbed dataset. Both algorithms take account of network topology (i.e. correlations between genes based on various sources of evidence). VisANT is freely available at http://visant.bu.edu.

We integrate 16 genomic features to construct an evidence-weighted functional-linkage network comprising 21,657 human genes. The functional-linkage network is used to prioritize candidate genes for 110 diseases, and to reliably disclose hidden associations between disease pairs having dissimilar phenotypes, such as hypercholesterolemia and Alzheimer's disease. Many of these disease-disease associations are supported by epidemiology, but with no previous genetic basis. Such associations can drive novel hypotheses on molecular mechanisms of diseases and therapies.

Purpose: We identified significantly hypermethylated genes in clear cell renal cell carcinoma., Materials and Methods: We previously identified a set of under expressed genes in renal cell carcinoma tissue through transcriptional profiling and a robust computational screen. We selected 19 of these genes for hypermethylation analysis using a rigorous search for the best candidate regions, considering CpG islands and transcription factor binding sites. The genes were analyzed for hypermethylation in the DNA of 38 matched clear cell renal cell carcinoma and normal samples using matrix assisted laser desorption ionization time-of-flight mass spectrometry. The significance of hypermethylation was assessed using 3 statistical tests. We validated the down-regulation of significantly hypermethylated genes at the RNA and protein levels in a separate set of patients using reverse transcriptase-polymerase chain reaction, immunohistochemistry and Western blots., Results: We found 7 significantly hypermethylated regions from 6 down-regulated genes, including SFRP1, which was previously shown to be hypermethylated in renal cell carcinoma and other cancer types., Conclusions: To our knowledge we report for the first time that another 5 genes (SCNN1B, SYT6, DACH1, and the tumor suppressors TFAP2A and MT1G) are hypermethylated in renal cell carcinoma. Robust computational screens and the high throughput methylation assay resulted in an enriched set of novel genes that are epigenetically altered in clear cell renal cell carcinoma. Overall the detection of hypermethylation in these highly down-regulated genes suggests that assaying for their methylation using cells from urine or blood could provide the basis for a viable diagnostic test.