Recognition schemes for protein-nucleic acid interactions

The molecular forces involved in protein-nucleic acid interaction are electrostatic, stacking and hydrogen-bonding. These interactions have a certain amount of specificity due to the directional nature of such interactions and the spatial contributions of the steric effects of different substituent groups. Quantum chemical calculations on these interactions have been reported which clearly bring out such features. While the binding energies for electrostatic interactions are an order of magnitude higher, the differences in interaction energies for structures stabilised by hydrogen-bonding and stacking are relatively small. Thus, the molecular interactions alone cannot explain the highly specific nature of binding observed in certain segments of proteins and nucleic acids. It is therefore logical to assume that the sequence dependent three dimensional structures of these molecules help to place the functional groups in the correct geometry for a favourable interaction between the two molecules. We have carried out 2D-FT nuclear magnetic resonance studies on the oligonucleotide d-GGATCCGGATCC. This oligonucleotide sequence has two binding sites for the restriction enzyme Bam H1. Our studies indicate that the conformation of this DNA fragment is predominantly B-type except near the binding sites where the ribose ring prefers a3E conformation. This interesting finding raises the general question about the presence of specificity in the inherent backbone structures of proteins and nucleic acids as opposed to specific intermolecular interactions which may induce conformational changes to facilitate such binding

SMRT-AgRenSeq-d in potato (Solanum tuberosum) as a method to identify candidates for the nematode resistance Gpa5

Potato is the third most important food crop in the world. Diverse pathogens threaten sustainable crop production but can be controlled, in many cases, through the deployment of disease resistance genes belonging to the family of nucleotide-binding, leucine-rich-repeat (NLR) genes. To identify effective disease resistance genes in established varieties, we have successfully established SMRT-AgRenSeq in tetraploid potatoes and have further enhanced the methodology by including dRenSeq in an approach that we term SMR-AgRenSeq-d. The inclusion of dRenSeq enables the filtering of candidates after the association analysis by establishing a presence/absence matrix across resistant and susceptible varieties that is translated into an F1 score. Using a SMRT-RenSeq-based sequence representation of the NLRome from the cultivar Innovator, SMRT-AgRenSeq-d analyses reliably identified the late blight resistance benchmark genes Rpi-R1, Rpi-R2-like, Rpi-R3a, and Rpi-R3b in a panel of 117 varieties with variable phenotype penetrations. All benchmark genes were identified with an F1 score of 1, which indicates absolute linkage in the panel. This method also identified nine strong candidates for Gpa5 that controls the potato cyst nematode (PCN) species Globodera pallida (pathotypes Pa2/3). Assuming that NLRs are involved in controlling many types of resistances, SMRT-AgRenSeq-d can readily be applied to diverse crops and pathogen systems.</p

SMRT-AgRenSeq-d in potato (Solanum tuberosum) as a method to identify candidates for the nematode resistance Gpa5

Potato is the third most important food crop in the world. Diverse pathogens threaten sustainable crop production but can be controlled, in many cases, through the deployment of disease resistance genes belonging to the family of nucleotide-binding, leucine-rich-repeat (NLR) genes. To identify effective disease resistance genes in established varieties, we have successfully established SMRT-AgRenSeq in tetraploid potatoes and have further enhanced the methodology by including dRenSeq in an approach that we term SMR-AgRenSeq-d. The inclusion of dRenSeq enables the filtering of candidates after the association analysis by establishing a presence/absence matrix across resistant and susceptible varieties that is translated into an F1 score. Using a SMRT-RenSeq-based sequence representation of the NLRome from the cultivar Innovator, SMRT-AgRenSeq-d analyses reliably identified the late blight resistance benchmark genes Rpi-R1, Rpi-R2-like, Rpi-R3a, and Rpi-R3b in a panel of 117 varieties with variable phenotype penetrations. All benchmark genes were identified with an F1 score of 1, which indicates absolute linkage in the panel. This method also identified nine strong candidates for Gpa5 that controls the potato cyst nematode (PCN) species Globodera pallida (pathotypes Pa2/3). Assuming that NLRs are involved in controlling many types of resistances, SMRT-AgRenSeq-d can readily be applied to diverse crops and pathogen systems.</p

Identifying microRNA/mRNA dysregulations in ovarian cancer

Abstract Background MicroRNAs are a class of noncoding RNA molecules that co-regulate the expression of multiple genes via mRNA transcript degradation or translation inhibition. Since they often target entire pathways, they may be better drug targets than genes or proteins. MicroRNAs are known to be dysregulated in many tumours and associated with aggressive or poor prognosis phenotypes. Since they regulate mRNA in a tissue specific manner, their functional mRNA targets are poorly understood. In previous work, we developed a method to identify direct mRNA targets of microRNA using patient matched microRNA/mRNA expression data using an anti-correlation signature. This method, applied to clear cell Renal Cell Carcinoma (ccRCC), revealed many new regulatory pathways compromised in ccRCC. In the present paper, we apply this method to identify dysregulated microRNA/mRNA mechanisms in ovarian cancer using data from The Cancer Genome Atlas (TCGA). Methods TCGA Microarray data was normalized and samples whose class labels (tumour or normal) were ambiguous with respect to consensus ensemble K-Means clustering were removed. Significantly anti-correlated and correlated genes/microRNA differentially expressed between tumour and normal samples were identified. TargetScan was used to identify gene targets of microRNA. Results We identified novel microRNA/mRNA mechanisms in ovarian cancer. For example, the expression level of RAD51AP1 was found to be strongly anti-correlated with the expression of hsa-miR-140-3p, which was significantly down-regulated in the tumour samples. The anti-correlation signature was present separately in the tumour and normal samples, suggesting a direct causal dysregulation of RAD51AP1 by hsa-miR-140-3p in the ovary. Other pairs of potentially biological relevance include: hsa-miR-145/E2F3, hsa-miR-139-5p/TOP2A, and hsa-miR-133a/GCLC. We also identified sets of positively correlated microRNA/mRNA pairs that are most likely result from indirect regulatory mechanisms. Conclusions Our findings identify novel microRNA/mRNA relationships that can be verified experimentally. We identify both generic microRNA/mRNA regulation mechanisms in the ovary as well as specific microRNA/mRNA controls which are turned on or off in ovarian tumours. Our results suggest that the disease process uses specific mechanisms which may be significant for their utility as early detection biomarkers or in the development of microRNA therapies in treating ovarian cancers. The positively correlated microRNA/mRNA pairs suggest the existence of novel regulatory mechanisms that proceed via intermediate states (indirect regulation) in ovarian tumorigenesis.</p

The Kepler Science Data Processing Pipeline Source Code Road Map

We give an overview of the operational concepts and architecture of the Kepler Science Processing Pipeline. Designed, developed, operated, and maintained by the Kepler Science Operations Center (SOC) at NASA Ames Research Center, the Science Processing Pipeline is a central element of the Kepler Ground Data System. The SOC consists of an office at Ames Research Center, software development and operations departments, and a data center which hosts the computers required to perform data analysis. The SOC's charter is to analyze stellar photometric data from the Kepler spacecraft and report results to the Kepler Science Office for further analysis. We describe how this is accomplished via the Kepler Science Processing Pipeline, including, the software algorithms. We present the high-performance, parallel computing software modules of the pipeline that perform transit photometry, pixel-level calibration, systematic error correction, attitude determination, stellar target management, and instrument characterization

Changes in timber haul emissions in the context of shifting forest management and infrastructure

<p>Abstract</p> <p>Background</p> <p>Although significant amounts of carbon may be stored in harvested wood products, the extraction of that carbon from the forest generally entails combustion of fossil fuels. The transport of timber from the forest to primary milling facilities may in particular create emissions that reduce the net sequestration value of product carbon storage. However, attempts to quantify the effects of transport on the net effects of forest management typically use relatively sparse survey data to determine transportation emission factors. We developed an approach for systematically determining transport emissions using: 1) -remotely sensed maps to estimate the spatial distribution of harvests, and 2) - industry data to determine landscape-level harvest volumes as well as the location and processing totals of individual mills. These data support spatial network analysis that can produce estimates of fossil carbon released in timber transport.</p> <p>Results</p> <p>Transport-related emissions, evaluated as a fraction of transported wood carbon at 4 points in time on a landscape in western Montana (USA), rose from 0.5% in 1988 to 1.7% in 2004 as local mills closed and spatial patterns of harvest shifted due to decreased logging on federal lands.</p> <p>Conclusion</p> <p>The apparent sensitivity of transport emissions to harvest and infrastructure patterns suggests that timber haul is a dynamic component of forest carbon management that bears further study both across regions and over time. The monitoring approach used here, which draws only from widely available monitoring data, could readily be adapted to provide current and historical estimates of transport emissions in a consistent way across large areas.</p

High-Resolution Description of Antibody Heavy-Chain Repertoires in Humans

Antibodies' protective, pathological, and therapeutic properties result from their considerable diversity. This diversity is almost limitless in potential, but actual diversity is still poorly understood. Here we use deep sequencing to characterize the diversity of the heavy-chain CDR3 region, the most important contributor to antibody binding specificity, and the constituent V, D, and J segments that comprise it. We find that, during the stepwise D-J and then V-DJ recombination events, the choice of D and J segments exert some bias on each other; however, we find the choice of the V segment is essentially independent of both. V, D, and J segments are utilized with different frequencies, resulting in a highly skewed representation of VDJ combinations in the repertoire. Nevertheless, the pattern of segment usage was almost identical between two different individuals. The pattern of V, D, and J segment usage and recombination was insufficient to explain overlap that was observed between the two individuals' CDR3 repertoires. Finally, we find that while there are a near-infinite number of heavy-chain CDR3s in principle, there are about 3–9 million in the blood of an adult human being

Cholinergic Activation of M2 Receptors Leads to Context-Dependent Modulation of Feedforward Inhibition in the Visual Thalamus

The temporal dynamics of inhibition within a neural network is a crucial determinant of information processing. Here, the authors describe in the visual thalamus how neuromodulation governs the magnitude and time course of inhibition in an input-dependent way

Localization of type 1 diabetes susceptibility to the MHC class I genes HLA-B and HLA-A

The major histocompatibility complex (MHC) on chromosome 6 is associated with susceptibility to more common diseases than any other region of the human genome, including almost all disorders classified as autoimmune. In type 1 diabetes the major genetic susceptibility determinants have been mapped to the MHC class II genes HLA-DQB1 and HLA-DRB1 (refs 1-3), but these genes cannot completely explain the association between type 1 diabetes and the MHC region. Owing to the region's extreme gene density, the multiplicity of disease-associated alleles, strong associations between alleles, limited genotyping capability, and inadequate statistical approaches and sample sizes, which, and how many, loci within the MHC determine susceptibility remains unclear. Here, in several large type 1 diabetes data sets, we analyse a combined total of 1,729 polymorphisms, and apply statistical methods - recursive partitioning and regression - to pinpoint disease susceptibility to the MHC class I genes HLA-B and HLA-A (risk ratios >1.5; Pcombined = 2.01 × 10-19 and 2.35 × 10-13, respectively) in addition to the established associations of the MHC class II genes. Other loci with smaller and/or rarer effects might also be involved, but to find these, future searches must take into account both the HLA class II and class I genes and use even larger samples. Taken together with previous studies, we conclude that MHC-class-I-mediated events, principally involving HLA-B*39, contribute to the aetiology of type 1 diabetes. ©2007 Nature Publishing Group