Temporal reproducibility of IgG and IgM autoantibodies in serum from healthy women.

Autoantibodies are present in healthy individuals and altered in chronic diseases. We used repeated samples collected from participants in the NYU Women's Health Study to assess autoantibody reproducibility and repertoire stability over a one-year period using the HuProt array. We included two samples collected one year apart from each of 46 healthy women (92 samples). We also included eight blinded replicate samples to assess laboratory reproducibility. A total of 21,211 IgG and IgM autoantibodies were interrogated. Of those, 86% of IgG (n = 18,303) and 34% of IgM (n = 7,242) autoantibodies showed adequate lab reproducibility (coefficient of variation [CV] < 20%). Intraclass correlation coefficients (ICCs) were estimated to assess temporal reproducibility. A high proportion of both IgG and IgM autoantibodies with CV < 20% (76% and 98%, respectively) showed excellent temporal reproducibility (ICC > 0.8). Temporal reproducibility was lower after using quantile normalization suggesting that batch variability was not an important source of error, and that normalization removed some informative biological information. To our knowledge this study is the largest in terms of sample size and autoantibody numbers to assess autoantibody reproducibility in healthy women. The results suggest that for many autoantibodies a single measurement may be used to rank individuals in studies of autoantibodies as etiologic markers of disease.

Merging and scoring molecular interactions utilising existing community standards: tools, use-cases and a case study.

The evidence that two molecules interact in a living cell is often inferred from multiple different experiments. Experimental data is captured in multiple repositories, but there is no simple way to assess the evidence of an interaction occurring in a cellular environment. Merging and scoring of data are commonly required operations after querying for the details of specific molecular interactions, to remove redundancy and assess the strength of accompanying experimental evidence. We have developed both a merging algorithm and a scoring system for molecular interactions based on the proteomics standard initiative-molecular interaction standards. In this manuscript, we introduce these two algorithms and provide community access to the tool suite, describe examples of how these tools are useful to selectively present molecular interaction data and demonstrate a case where the algorithms were successfully used to identify a systematic error in an existing dataset.

Genome sequence of the plant pathogen and biotechnology agent Agrobacterium tumefaciens C58.

Agrobacterium tumefaciens is a plant pathogen capable of transferring a defined segment of DNA to a host plant, generating a gall tumor. Replacing the transferred tumor-inducing genes with exogenous DNA allows the introduction of any desired gene into the plant. Thus, A. tumefaciens has been critical for the development of modern plant genetics and agricultural biotechnology. Here we describe the genome of A. tumefaciens strain C58, which has an unusual structure consisting of one circular and one linear chromosome. We discuss genome architecture and evolution and additional genes potentially involved in virulence and metabolic parasitism of host plants.

Community dynamics: what happens when we rerun the tape?

The role of historical events, such as disturbances, in producing alternative developmental outcomes in forest structure has long been debated. Diversity in the assemblages of coexisting species is one measure of alternative outcomes of succession. The intermediate disturbance hypothesis proposes that a moderate disturbance level produces the highest levels of species diversity. Here, we use an agent-based model of forest development under a gradient of lightning strike frequency to analyse long-term dynamics of species coexistence in a multi-species forest. The configurations of species that result from disturbance dynamics reflect the interactions between life-history characteristics of the species and disturbance characteristics. Model results suggest that low levels of disturbance lead to highly ordered landscapes which exclude fire and are captured by late successional species. High levels of disturbance lead to oscillation between domination by early successional species and large disturbances. At intermediate levels of disturbance, the forest displays the broadest array of developmental pathways, highest entropy as measured by Shannon's index of diversity, and critical slowing near steady states. Long transients at intermediate regimes may reflect the working out of closely balanced constraints of competition between species with varying strategic adaptations to disturbance. Intermediate disturbance levels also result in the greatest number of alternative diversity configurations as outcomes of succession, reflecting an unpredictable and nonequilibrium forest dynamic.

Cluster analysis and data visualization of large-scale gene expression data.

The discovery of any new gene requires an analysis of the expression context for that gene. Now that the cDNA and genomic sequencing projects are progressing at such a rapid rate, high throughput gene expression screening approaches are beginning to appear to take advantage of that data. We present a strategy for the analysis for large-scale quantitative gene expression measurement data from time course experiments. Our approach takes advantage of cluster analysis and graphical visualization methods to reveal correlated patterns of gene expression from time series data. The coherence of these patterns suggests an order that conforms to a notion of shared pathways and control processes that can be experimentally verified.