Differential rhythmicity: detecting altered rhythmicity in biological data.

MOTIVATION: Biological rhythms, such as rhythms in gene expression controlled by the cell cycle or the circadian clock, are important in cell physiology. A common type of experiment compares rhythmicity in tissues or cells either kept under different conditions or having different genotypes. Such investigations provide insights into underlying mechanisms as well as functions of rhythms. RESULTS: We present and benchmark a set of statistical and computational methods for this type of analysis, here termed differential rhythmicity analysis. The methods detect alterations in rhythm amplitude, phase and signal to noise ratio in one set of measurements compared to another. Using these methods, we compared circadian rhythms in liver mRNA expression in mice held under two different lighting conditions: constant darkness and light-dark cycles, respectively. This analysis revealed widespread and reproducible amplitude increases in mice kept in light-dark cycles. Further analysis of the subset of differentially rhythmic transcripts implied the immune system in mediating ambient light-dark cycles to rhythmic transcriptional activities. The methods are suitable for genome- or proteome-wide studies, and provide rigorous P values against well-defined null hypotheses. AVAILABILITY AND IMPLEMENTATION: The methods were implemented as the accompanying R software package DODR, available on CRAN. CONTACT: pal-olof.westermark@charite.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Rhythmic degradation explains and unifies circadian transcriptome and proteome data.

The rich mammalian cellular circadian output affects thousands of genes in many cell types and has been the subject of genome-wide transcriptome and proteome studies. The results have been enigmatic because transcript peak abundances do not always follow the peaks of gene-expression activity in time. We posited that circadian degradation of mRNAs and proteins plays a pivotal role in setting their peak times. To establish guiding principles, we derived a theoretical framework that fully describes the amplitudes and phases of biomolecules with circadian half-lives. We were able to explain the circadian transcriptome and proteome studies with the same unifying theory, including cases in which transcripts or proteins appeared before the onset of increased production rates. Furthermore, we estimate that 30% of the circadian transcripts in mouse liver and Drosophila heads are affected by rhythmic posttranscriptional regulation.

Detecting rhythms in time series with RAIN.

A fundamental problem in research on biological rhythms is that of detecting and assessing the significance of rhythms in large sets of data. Classic methods based on Fourier theory are often hampered by the complex and unpredictable characteristics of experimental and biological noise. Robust nonparametric methods are available but are limited to specific wave forms. We present RAIN, a robust nonparametric method for the detection of rhythms of prespecified periods in biological data that can detect arbitrary wave forms. When applied to measurements of the circadian transcriptome and proteome of mouse liver, the sets of transcripts and proteins with rhythmic abundances were significantly expanded due to the increased detection power, when we controlled for false discovery. Validation against independent data confirmed the quality of these results. The large expansion of the circadian mouse liver transcriptomes and proteomes reflected the prevalence of nonsymmetric wave forms and led to new conclusions about function. RAIN was implemented as a freely available software package for R/Bioconductor and is presently also available as a web interface.

GABA networks destabilize genetic oscillations in the circadian pacemaker.

Systems of coupled oscillators abound in nature. How they establish stable phase relationships under diverse conditions is fundamentally important. The mammalian suprachiasmatic nucleus (SCN) is a self-sustained, synchronized network of circadian oscillators that coordinates daily rhythms in physiology and behavior. To elucidate the underlying topology and signaling mechanisms that modulate circadian synchrony, we discriminated the firing of hundreds of SCN neurons continuously over days. Using an analysis method to identify functional interactions between neurons based on changes in their firing, we characterized a GABAergic network comprised of fast, excitatory, and inhibitory connections that is both stable over days and changes in strength with time of day. By monitoring PERIOD2 protein expression, we provide the first evidence that these millisecond-level interactions actively oppose circadian synchrony and inject jitter into daily rhythms. These results provide a mechanism by which circadian oscillators can tune their phase relationships under different environmental conditions.

Receptor-transporter interactions of canonical ATP-binding cassette import systems in prokaryotes.

ATP-binding cassette (ABC) transport systems mediate the translocation of solutes across biological membranes at the expense of ATP. They share a common modular architecture comprising two pore-forming transmembrane domains and two nucleotide binding domains. In prokaryotes, ABC transporters are involved in the uptake of a large variety of chemicals, including nutrients, osmoprotectants and signal molecules. In pathogenic bacteria, some ABC importers are virulence factors. Canonical ABC import systems require an additional component, a substrate-specific receptor or binding protein for function. Interaction of the liganded receptor with extracytoplasmic loop regions of the transmembrane domains initiate the transport cycle. In this review we summarize the current knowledge on receptor-transporter interplay provided by crystal structures as well as by biochemical and biophysical means. In particular, we focus on the maltose/maltodextrin transporter of enterobacteria and the transporters for positively charged amino acids from the thermophile Geobacillus stearothermophilus and Salmonella enterica serovar Typhimurium.

FragmentStore--a comprehensive database of fragments linking metabolites, toxic molecules and drugs.

Consideration of biomolecules in terms of their molecular building blocks provides valuable new information regarding their synthesis, degradation and similarity. Here, we present the FragmentStore, a resource for the comparison of fragments found in metabolites, drugs or toxic compounds. Starting from 13,000 metabolites, 16,000 drugs and 2200 toxic compounds we generated 35,000 different building blocks (fragments), which are not only relevant to their biosynthesis and degradation but also provide important information regarding side-effects and toxicity. The FragmentStore provides a variety of search options such as 2D structure, molecular weight, rotatable bonds, etc. Various analysis tools have been implemented including the calculation of amino acid preferences of fragments' binding sites, classification of fragments based on the enzyme classification class of the enzyme(s) they bind to and small molecule library generation via a fragment-assembler tool. Using the FragmentStore, it is now possible to identify the common fragments of different classes of molecules and generate hypotheses about the effects of such intersections. For instance, the co-occurrence of fragments in different drugs may indicate similar targets and possible off-target interactions whereas the co-occurrence of fragments in a drug and a toxic compound/metabolite could be indicative of side-effects. The database is publicly available at: http://bioinformatics.charite.de/fragment_store.

SuperSite: dictionary of metabolite and drug binding sites in proteins.

The increasing structural information about target-bound compounds provide a rich basis to study the binding mechanisms of metabolites and drugs. SuperSite is a database, which combines the structural information with various tools for the analysis of molecular recognition. The main data is made up of 8000 metabolites including 1300 drugs, bound to about 290,000 different receptor binding sites. The analysis tools include features, like the highlighting of evolutionary conserved receptor residues, the marking of putative binding pockets and the superpositioning of different binding sites of the same ligand. User-defined compounds can be edited or uploaded and will be superimposed with the most similar co-crystallized ligand. The user can examine all results online with the molecule viewer Jmol. An implemented search algorithm allows the screening of uploaded proteins, in order to detect potential drug binding sites, which are similar to known binding pockets. The huge data set of target-bound compounds in combination with the provided analysis tools allow to inspect the characteristics of molecular recognition, especially for drug target interactions. SuperSite is publicly available at: http://bioinformatics.charite.de/supersite.