Search

Objective Visceral white adipose tissue (WAT) expansion and macrophage accumulation are associated with metabolic dysfunction. Visceral WAT typically shows greater macrophage infiltration. Preadipocytes show varying proinflammatory expression profiles among WAT depots. The objective was to examine the secretomes and chemoattractive properties of preadipocytes from visceral and subcutaneous WAT. Methods A label-free quantitative proteomics approach was applied to study secretomes of subcutaneous and omental preadipocytes from obese subjects. Enzyme-linked immunosorbent assays and chemotaxis assays were used to confirm proinflammatory chemokine secretion between depots. Results Preadipocyte secretomes showed greater variation between depots than did intracellular protein expression. Chemokines were the most differentially secreted proteins. Omental preadipocytes induced chemoattraction of macrophages and monocytes. Neutralizing antibodies to the identified chemokines reduced macrophage/monocyte chemoattraction. Subcutaneous preadipocytes treated with interleukin-6 (IL-6) resembled omental preadipocytes in terms of chemokine secretion and macrophage/monocyte chemoattraction. Janus-activated kinase (JAK1/2) protein expression, which transduces IL-6 signaling, was higher in omental than subcutaneous preadipocytes and WAT. Inhibiting JAK in omental preadipocytes decreased chemokine secretion and macrophage/monocyte chemoattraction to levels closer to that observed in subcutaneous preadipocytes. Conclusions Secretomes of omental and subcutaneous preadipocytes are distinct, with the former inducing more macrophage/monocyte chemoattraction, in part through IL-6/JAK-mediated signaling.

Early detection of patients with elevated risk of developing diabetes mellitus is critical to the improved prevention and overall clinical management of these patients. We aim to apply association rule mining to electronic medical records (EMR) to discover sets of risk factors and their corresponding subpopulations that represent patients at particularly high risk of developing diabetes. Given the high dimensionality of EMRs, association rule mining generates a very large set of rules which we need to summarize for easy clinical use. We reviewed four association rule set summarization techniques and conducted a comparative evaluation to provide guidance regarding their applicability, strengths and weaknesses. We proposed extensions to incorporate risk of diabetes into the process of finding an optimal summary. We evaluated these modified techniques on a real-world prediabetic patient cohort. We found that all four methods produced summaries that described subpopulations at high risk of diabetes with each method having its clear strength. For our purpose, our extension to the Buttom-Up Summarization (BUS) algorithm produced the most suitable summary. The subpopulations identified by this summary covered most high-risk patients, had low overlap and were at very high risk of diabetes.

Introduction Pulmonary carcinoid tumors account for approximately 5% of all lung malignancies in adults, and comprise 30% of all carcinoid tumors. There are limited reagents available to study these rare tumors, and consequently no major advances have been made for patient treatment. We report the generation and characterization of human pulmonary carcinoid tumor cell lines to study underlying biology, and to provide models for testing novel chemotherapeutic agents. Methods Tissue was harvested from three patients with primary pulmonary typical carcinoid tumors undergoing surgical resection. The tumor was dissociated and plated onto dishes in culture media. The established cell lines were characterized by immunohistochemistry, Western blotting, and cell proliferation assays. Tumorigenicity was confirmed by soft agar growth and the ability to form tumors in a mouse xenograft model. Exome and RNA sequencing of patient tumor samples and cell lines was performed using standard protocols. Results Three typical carcinoid tumor lines grew as adherent monolayers in vitro, expressed neuroendocrine markers consistent with the primary tumor, and formed colonies in soft agar. A single cell line produced lung tumors in nude mice after intravenous injection. Exome and RNA sequencing of this cell line showed lineage relationship with the primary tumor, and demonstrated mutations in a number of genes related to neuronal differentiation. Conclusion Three human pulmonary typical carcinoid tumor cell lines have been generated and characterized as a tool for studying the biology and novel treatment approaches for these rare tumors.

The current 'isolate, inactivate, inject' vaccine development strategy has served the field of vaccinology well, and such empirical vaccine candidate development has even led to the eradication of smallpox. However, such an approach suffers from limitations, and as an empirical approach, does not fully utilize our knowledge of immunology and genetics. A more complete understanding of the biological processes culminating in disease resistance is needed. The advent of high-dimensional assay technology and 'systems biology' along with a vaccinomics approach [1,2•] is spawning a new era in the science of vaccine development. Here we review recent developments in systems biology and strategies for applying this approach and its resulting data to expand our knowledge base and drive directed development of new vaccines. We also provide applied examples and point out new directions for the field in order to illustrate the power of systems biology.

We introduce a simple, broadly applicable method for obtaining estimates of nucleotide diversity θ from genomic shotgun sequencing data. The method takes into account the special nature of these data: random sampling of genomic segments from one or more individuals and a relatively high error rate for individual reads. Applying this method to data from the Celera human genome sequencing and SNP discovery project, we obtain estimates of nucleotide diversity in windows spanning the human genome and show that the diversity to divergence ratio is reduced in regions of low recombination. Furthermore, we show that the elevated diversity in telomeric regions is mainly due to elevated mutation rates and not due to decreased levels of background selection. However, we find indications that telomeres as well as centromeres experience greater impact from natural selection than intrachromosomal regions. Finally, we identify a number of genomic regions with increased or reduced diversity compared with the local level of human–chimpanzee divergence and the local recombination rate.

We developed a real-time, visual analytics tool for clinical decision support. The system expands the "recall of past experience" approach that a provider (physician) uses to formulate a course of action for a given patient. By utilizing Big-Data techniques, we enable the provider to recall all similar patients from an institution's electronic medical record (EMR) repository, to explore "what-if" scenarios, and to collect these evidence-based cohorts for future statistical validation and pattern mining.

A highly automated RT-PCR-based approach has been established to validate novel human gene predictions with no prior experimental evidence of mRNA splicing (ab initio predictions). Ab initio gene predictions were selected for high-throughput validation using predicted protein classification, sequence similarity to other genomes, colocalization with an MPSS tag, or microarray expression. Initial microarray prioritization followed by RT-PCR validation was the most efficient combination, resulting in approximately 35% of the ab initio predictions being validated by RT-PCR. Of the 7252 novel genes that were prioritized and processed, 796 constituted real transcripts. In addition, high-throughput RACE successfully extended the 5′ and/or 3′ ends of >60% of RT-PCR-validated genes. Reevaluation of these transcripts produced 574 novel transcripts using RefSeq as a reference. RT-PCR sequencing in combination with RACE on ab initio gene predictions could be used to define the transcriptome across all species.

Accurate annotated assemblies of the mouse and human genomes enable a detailed comparison of the organization and evolution of the two genomes. We have completed several assemblies of both the mouse, with and without public data, and human genomes. Analysis of these assemblies suggests the mouse genome is about 10% smaller than the human genome primarily because of a difference in the content of repetitive DNA between the two genomes. More than 300,000 positions in these two genomes can be aligned with one another based on short segments of sequence similarity. These conserved segments significantly enhance the resolution of the resultant comparative maps and can be used to divide the genomes into regions of conserved-shared synteny. The genes found in such regions are highly conserved as is their relative order and orientation. Comparison of the human and mouse genome is expected to be key to deciphering the important biological information encoded in the mammalian genome. A prerequisite to comparing complex genomes such as those of mouse and human is the availability of annotated assemblies of both genomes that are comparable in quality and completeness. Since February 2001, we have assembled, annotated and delivered to our subscribers two versions of the human genome and two versions of the mouse genome. A third assembly of the human genome is being completed and will be delivered by fall of 2002. These annotated assemblies provide the starting materials for the genome-wide comparisons of the mouse and human reported here. We will begin with a description of the first Celera whole genome assembly of the mouse to provide a general basis of the quality and completeness of these data and then will report the results of a preliminary comparison between these two genomes.

Primate-specific segmental duplications are considered important in human disease and evolution. The inability to distinguish between allelic and duplication sequence overlap has hampered their characterization as well as assembly and annotation of our genome. We developed a method whereby each public sequence is analyzed at the clone level for overrepresentation within a whole-genome shotgun sequence. This test has the ability to detect duplications larger than 15 kilobases irrespective of copy number, location, or high sequence similarity. We mapped 169 large regions flanked by highly similar duplications. Twenty-four of these hot spots of genomic instability have been associated with genetic disease. Our analysis indicates a highly nonrandom chromosomal and genic distribution of recent segmental duplications, with a likely role in expanding protein diversity.

The Celera Discovery System™ (CDS) is a web-accessible research workbench for mining genomic and related biological information. Users have access to the human and mouse genome sequences with annotation presented in summary form in BioMolecule Reports for genes, transcripts and proteins. Over 40 additional databases are available, including sequence, mapping, mutation, genetic variation, mRNA expression, protein structure, motif and classification data. Data are accessible by browsing reports, through a variety of interactive graphical viewers, and by advanced query capability provided by the LION SRS™ search engine. A growing number of sequence analysis tools are available, including sequence similarity, pattern searching, multiple sequence alignment and Hidden Markov Model search. A user workspace keeps track of queries and analyses. CDS is widely used by the academic research community and requires a subscription for access. The system and academic pricing information are available at http://cds.celera.com.

Lung adenocarcinomas from never smokers account for approximately 15 to 20% of all lung cancers and these tumors often carry genetic alterations that are responsive to targeted therapy. Here we examined mutation status in 10 oncogenes among 89 lung adenocarcinomas from never smokers. We also screened for oncogene fusion transcripts in 20 of the 89 tumors by RNA-Seq. In total, 62 tumors had mutations in at least one of the 10 oncogenes, including EGFR (49 cases, 55%), K-ras (5 cases, 6%), BRAF (4 cases, 5%), PIK3CA (3 cases, 3%) and ERBB2 (4 cases, 5%). In addition to ALK fusions identified by IHC/FISH in four cases, two previously known fusions involving EZR- ROS1 and KIF5B-RET were identified by RNA-Seq as well as a third novel fusion transcript that was formed between exons 1–9 of SND1 and exons 2 to 3′ end of BRAF. This in-frame fusion was observed in 3/89 tested tumors and 2/64 additional never smoker lung adenocarcinoma samples. Ectopic expression of SND1-BRAF in H1299 cells increased phosphorylation levels of MEK/ERK, cell proliferation and spheroid formation compared to parental mock-transfected control. Jointly, our results suggest a potential role of the novel BRAF fusion in lung cancer development and therapy.

Over the past decade, the use of semantic databases has served as the basis for storing and analyzing complex, heterogeneous, and irregular data. While there are similarities with traditional relational database systems, semantic data stores provide a rich platform for conducting non-traditional analyses of data. In support of new graph analytic algorithms and specialized graph analytic hardware, we have developed a large semi-synthetic, semantically rich dataset. The construction of this dataset mimics the real-world scenario of using relational databases as the basis for semantic data construction. In order to achieve real-world variable distributions and variable dependencies, data.gov data was used as the basis for developing an approach to build arbitrarily large semi-synthetic datasets. The intent of the semi-synthetic dataset is to serve as a testbed for new semantic graph analyses and computational software/hardware platforms. The construction process and basic data characterization is described. All code related to the data collection, consolidation, and augmentation are available for distribution.

We have developed a large semi-synthetic, semantically rich dataset, modeled after the medical record of a large medical institution. Using the highly diverse data.gov data repository and a multivariate data augmentation strategy, we can generate arbitrarily large semi-synthetic datasets which can be used to test new algorithms and computational platforms. The construction process and basic data characterization are described. The databases, as well as code for data collection, consolidation, and augmentation are available for distribution.

Type-2 Diabetes Mellitus is a growing epidemic that often leads to severe complications. Effective preventive measures exist and identifying patients at high risk of diabetes is a major health-care need. The use of association rule mining (ARM) is advantageous, as it was specifically developed to identify associations between risk factors in an interpretable form. Unfortunately, traditional ARM is not directly applicable to survival outcomes and it lacks the ability to compensate for confounders and to incorporate dosage effects. In this work, we propose Survival Association Rule (SAR) Mining, which addresses these shortcomings. We demonstrate on a real diabetes data set that SARs are naturally more interpretable than the traditional association rules, and predictive models built on top of these rules are very competitive relative to state of the art survival models and substantially outperform the most widely used diabetes index, the Framingham score.

A 2.91-billion base pair (bp) consensus sequence of the euchromatic portion of the human genome was generated by the whole-genome shotgun sequencing method. The 14.8-billion bp DNA sequence was generated over 9 months from 27,271,853 high-quality sequence reads (5.11-fold coverage of the genome) from both ends of plasmid clones made from the DNA of five individuals. Two assembly strategies—a whole-genome assembly and a regional chromosome assembly—were used, each combining sequence data from Celera and the publicly funded genome effort. The public data were shredded into 550-bp segments to create a 2.9-fold coverage of those genome regions that had been sequenced, without including biases inherent in the cloning and assembly procedure used by the publicly funded group. This brought the effective coverage in the assemblies to eightfold, reducing the number and size of gaps in the final assembly over what would be obtained with 5.11-fold coverage. The two assembly strategies yielded very similar results that largely agree with independent mapping data. The assemblies effectively cover the euchromatic regions of the human chromosomes. More than 90% of the genome is in scaffold assemblies of 100,000 bp or more, and 25% of the genome is in scaffolds of 10 million bp or larger. Analysis of the genome sequence revealed 26,588 protein-encoding transcripts for which there was strong corroborating evidence and an additional ∼12,000 computationally derived genes with mouse matches or other weak supporting evidence. Although gene-dense clusters are obvious, almost half the genes are dispersed in low G+C sequence separated by large tracts of apparently noncoding sequence. Only 1.1% of the genome is spanned by exons, whereas 24% is in introns, with 75% of the genome being intergenic DNA. Duplications of segmental blocks, ranging in size up to chromosomal lengths, are abundant throughout the genome and reveal a complex evolutionary history. Comparative genomic analysis indicates vertebrate expansions of genes associated with neuronal function, with tissue-specific developmental regulation, and with the hemostasis and immune systems. DNA sequence comparisons between the consensus sequence and publicly funded genome data provided locations of 2.1 million single-nucleotide polymorphisms (SNPs). A random pair of human haploid genomes differed at a rate of 1 bp per 1250 on average, but there was marked heterogeneity in the level of polymorphism across the genome. Less than 1% of all SNPs resulted in variation in proteins, but the task of determining which SNPs have functional consequences remains an open challenge.

Alzheimer's disease (AD) is associated with progressive cognitive decline leading to dementia. The atrophy/loss of brain structure as seen on Magnetic Resonance Imaging (MRI) is strongly correlated with the severity of the cognitive impairment in AD. In this paper, we set out to find associations between predefined regions of the brain (regions of interest; ROIs) and the severity of the disease. Specifically, we use these associations to address two important issues in AD: (i) typical versus atypical atrophy patterns and (ii) the origin and direction of progression of atrophy, which is currently under debate.We observed that each AD-related ROI is associated with a wide range of severity and that the difference between ROIs is merely a difference in severity distribution. To model differences between the severity distribution of a subpopulation (with significant atrophy in certain ROIs) and the severity distribution of the entire population, we developed the concept of Distributional Association Rules. Using the Distributional Association Rules, we clustered ROIs into disease subsystems. We define a disease subsystem as a contiguous set of ROIs that are collectively implicated in AD. AD is known to be heterogeneous in the sense that multiple sets of ROIs may be related to the disease in a population. We proposed an enhancement to the association rule mining where the algorithm only discovers association rules with ROIs that form an approximately contiguous volume. Next, we applied these association rules to infer the direction of disease progression based on the support measures of the association rules. We also developed a novel statistical test to determine the statistical significance of the discovered direction.We evaluated the proposed method on the Mayo Clinic Alzheimer's Disease Research Center (ADRC) prospective patient cohorts. The key achievements of the methodology is that it accurately identified larger disease subsystems implicated in typical and atypical AD and it successfully mapped the directions of disease progression.The wealth of data available in Radiology gives rise to opportunities for applying this methodology to map out the trajectory of several other diseases, e.g. other neuro-degenerative diseases and cancers, most notably, breast cancer. The applicability of this method is not limited to image data, as associating predictors with severity provides valuable information in most areas of medicine as well as other industries.

Associative classification is a predictive modeling technique that constructs a classifier based on class association rules (also known as predictive association rules; PARs). PARs are association rules where the consequence of the rule is a class label. Associative classification has gained substantial research attention because it successfully joins the benefits of association rule mining with classification. These benefits include the inherent ability of association rule mining to extract high-order interactions among the predictors--an ability that many modern classifiers lack--and also the natural interpretability of the individual PARs.Associative classification is not without its caveats. Association rule mining often discovers a combinatorially large number of association rules, eroding the interpretability of the rule set. Extensive effort has been directed towards developing interestingness measures, which filter (predictive) association rules after they have been generated. These interestingness measures, albeit very successful at selecting interesting rules, lack two features that are highly valuable in the context of classification. First, only few of the interestingness measures are rooted in a statistical model. Given the distinction between a training and a test data set in the classification setting, the ability to make statistical inferences about the performance of the predictive classification rules on the test set is highly desirable. Second, the unfiltered set of predictive assocation rules (PARs) are often redundant, we can prove that certain PARs will not be used to construct a classification model given the presence of other PARs.In this paper, we propose a simple statistical model towards making inferences on the test set about the various performance metrics of predictive association rules. We also derive three filtering criteria based on hypothesis testing, which are very selective (reduce the number of PARs to be considered by the classifier by several orders of magnitude), yet do not effect the performance of the classification adversely. In the case, where the classification model is constructed as a logistic model on top of the PARs, we can mathematically prove, that the filtering criteria do not significantly effect the classifier's performance. We also demonstrate empirically on three publicly available data sets that the vast reduction in the number of PARs indeed did not come at the cost of reducing the predictive performance.

Designing effective and accurate tools for identifying the functional and structural elements in a genome remains at the frontier of genome annotation owing to incompleteness and inaccuracy of the data, limitations in the computational models, and shifting paradigms in genomics, such as alternative splicing. We present a methodology for the automated annotation of genes and their alternatively spliced mRNA transcripts based on existing cDNA and protein sequence evidence from the same species or projected from a related species using syntenic mapping information. At the core of the method is the splice graph, a compact representation of a gene, its exons, introns, and alternatively spliced isoforms. The putative transcripts are enumerated from the graph and assigned confidence scores based on the strength of sequence evidence, and a subset of the high-scoring candidates are selected and promoted into the annotation. The method is highly selective, eliminating the unlikely candidates while retaining 98% of the high-quality mRNA evidence in well-formed transcripts, and produces annotation that is measurably more accurate than some evidence-based gene sets. The process is fast, accurate, and fully automated, and combines the traditionally distinct gene annotation and alternative splicing detection processes in a comprehensive and systematic way, thus considerably aiding in the ensuing manual curation efforts.

The high degree of similarity between the mouse and human genomes is demonstrated through analysis of the sequence of mouse chromosome 16 (Mmu 16), which was obtained as part of a whole-genome shotgun assembly of the mouse genome. The mouse genome is about 10% smaller than the human genome, owing to a lower repetitive DNA content. Comparison of the structure and protein-coding potential of Mmu 16 with that of the homologous segments of the human genome identifies regions of conserved synteny with human chromosomes (Hsa) 3, 8, 12, 16, 21, and 22. Gene content and order are highly conserved between Mmu 16 and the syntenic blocks of the human genome. Of the 731 predicted genes on Mmu 16, 509 align with orthologs on the corresponding portions of the human genome, 44 are likely paralogous to these genes, and 164 genes have homologs elsewhere in the human genome; there are 14 genes for which we could find no human counterpart.

The fly Drosophila melanogaster is one of the most intensively studied organisms in biology and serves as a model system for the investigation of many developmental and cellular processes common to higher eukaryotes, including humans. We have determined the nucleotide sequence of nearly all of the ∼120-megabase euchromatic portion of the Drosophila genome using a whole-genome shotgun sequencing strategy supported by extensive clone-based sequence and a high-quality bacterial artificial chromosome physical map. Efforts are under way to close the remaining gaps; however, the sequence is of sufficient accuracy and contiguity to be declared substantially complete and to support an initial analysis of genome structure and preliminary gene annotation and interpretation. The genome encodes ∼13,600 genes, somewhat fewer than the smaller Caenorhabditis elegans genome, but with comparable functional diversity.

A comparative analysis of the genomes of Drosophila melanogaster , Caenorhabditis elegans , and Saccharomyces cerevisiae —and the proteins they are predicted to encode—was undertaken in the context of cellular, developmental, and evolutionary processes. The nonredundant protein sets of flies and worms are similar in size and are only twice that of yeast, but different gene families are expanded in each genome, and the multidomain proteins and signaling pathways of the fly and worm are far more complex than those of yeast. The fly has orthologs to 177 of the 289 human disease genes examined and provides the foundation for rapid analysis of some of the basic processes involved in human disease.

AS BIOINFORMATICIANS, we think of biological data in terms of an experiment and as a snapshot of some biological process. As such, we store and process large quantity of this data, and we seek to preserve the “sanctity of this data in perpetuity.” However, our users, the biologists, never look back at old data once they have reconciled the data by an appropriate biological model. In truth, data in biology become extinct and replaced by an understanding of the biological processes that generated them. The data of interest to our biologists are always the newer data that expand upon or challenge that understanding. This generation and expiration of data, that is, a life cycle, runs counter to the bioinformatician’s philosophy of data existence and permanence. What is this life cycle, and why is it important? To answer that, we need to follow the lifetime of a typical piece of biological datum. It is first born from an experiment conducted by a researcher. That experiment, in turn, is based on a hypothesis generated from a model for some biological processes. This data is stored, processed, retrieved many times during the course of research. It is accumulated until, collectively, they confirm or refute the hypothesis. However, once they achieved their purpose, their particular contributions will be integrated into the biological models that spawned them, that is, “replaced” by a better understanding of biology. Afterwards, the data is forgotten as the researcher progresses to the next hypothesis and the next experiment. If the generation, utilization, and expiration of biological data happen in a controlled, predictable, or designed fashion, we might have call this a “life cycle.” However, these events in the real world often reflect the Darwinian competition of scientific thought itself, that is, only the fittest survives. In this case, only the most perplexing data stay to challenge the next generation of models, while the rest unceremoniously become extinct as they are absorbed by refinements of the scientific process. Without recognition of this fact, long-running bioinformatics systems will suffer the same fate, as they are clogged with extinct data, they ultimately will be replaced by newer systems without such baggage. How do bioinformatics systems avoid the fate of extinction? Because of the inherent nature of the data, the data must be stored and manipulated with its context of the experiment, the hypothesis, and the model. As data, experiments, hypotheses, and models are reconciled, they are updated, archived or removed from the system appropriately. This is easier said than done. This expansion of information about a piece of data increases the complexity of our bioinformatics systems (databases, algorithms, UIs). Consequently, we don’t consistently capture this information: sometimes partially, sometimes implicitly, and sometimes not at all. Without complete background information, we are unable to reconcile and remove data from our systems, despite that much of it have become obsolete in the minds of our users through the course of scientific achievements. One might counter that, in a “hypotheses from data mining” paradigm, the experiments and the data are considered permanent so that new hypothesis can be continuously mined from the increasing stockpile of data. However, experimental data will change as the scientific process improves: newer instrumentation, techniques, and procedures expand and refine the experimental data. The newer and “cleaner” versions should deprecate older copies, but we rarely do that. In part, the replicated experiment is not really the same