A compilation of soybean ESTs: generation and analysis.

Whole-genome sequencing is fundamental to understanding the genetic composition of an organism. Given the size and complexity of the soybean genome, an alternative approach is targeted random-gene sequencing, which provides an immediate and productive method of gene discovery. In this study, more than 120000 soybean expressed sequence tags (ESTs) generated from more than 50 cDNA libraries were evaluated. These ESTs coalesced into 16928 contigs and 17336 singletons. On average, each contig was composed of 6 ESTs and spanned 788 bases. The average sequence length submitted to dbEST was 414 bases. Using only those libraries generating more than 800 ESTs each and only those contigs with 10 or more ESTs each, correlated patterns of gene expression among libraries and genes were discerned. Two-dimensional qualitative representations of contig and library similarities were generated based on expression profiles. Genes with similar expression patterns and, potentially, similar functions were identified. These studies provide a rich source of publicly available gene sequences as well as valuable insight into the structure, function, and evolution of a model crop legume genome.     

Soybean genomic survey: BAC-end sequences near RFLP and SSR markers.

We are building a framework physical infrastructure across the soybean genome by using SSR (simple sequence repeat) and RFLP (restriction fragment length polymorphism) markers to identify BACs (bacterial artificial chromosomes) from two soybean BAC libraries. The libraries were prepared from two genotypes, each digested with a different restriction enzyme. The BACs identified by each marker were grouped into contigs. We have obtained BAC- end sequence from BACs within each contig. The sequences were analyzed by the University of Minnesota Center for Computational Genomics and Bioinformatics using BLAST algorithms to search nucleotide and protein databases. The SSR-identified BACs had a higher percentage of significant BLAST hits than did the RFLP-identified BACs. This difference was due to a higher percentage of hits to repetitive-type sequences for the SSR-identified BACs that was offset in part, however, by a somewhat larger proportion of RFLP-identified significant hits with similarity to experimentally defined genes and soybean ESTs (expressed sequence tags). These genes represented a wide range of metabolic functions. In these analyses, only repetitive sequences from SSR-identified contigs appeared to be clustered. The BAC-end sequences also allowed us to identify microsynteny between soybean and the model plants Arabidopsis thaliana and Medicago truncatula. This map-based approach to genome sampling provides a means of assaying soybean genome structure and organization.     

Unexpected patterns of segregation distortion at a selfish supergene in the fire ant <Emphasis Type="Italic">Solenopsis invicta</Emphasis>

Background

The Sb supergene in the fire ant Solenopsis invicta determines the form of colony social organization, with colonies whose inhabitants bear the element containing multiple reproductive queens and colonies lacking it containing only a single queen. Several features of this supergene — including suppressed recombination, presence of deleterious mutations, association with a large centromere, and “green-beard” behavior — suggest that it may be a selfish genetic element that engages in transmission ratio distortion (TRD), defined as significant departures in progeny allele frequencies from Mendelian inheritance ratios. We tested this possibility by surveying segregation ratios in embryo progenies of 101 queens of the “polygyne” social form (3512 embryos) using three supergene-linked markers and twelve markers outside the supergene.

Results

Significant departures from Mendelian ratios were observed at the supergene loci in 3–5 times more progenies than expected in the absence of TRD and than found, on average, among non-supergene loci. Also, supergene loci displayed the greatest mean deviations from Mendelian ratios among all study loci, although these typically were modest. A surprising feature of the observed inter-progeny variation in TRD was that significant deviations involved not only excesses of supergene alleles but also similarly frequent excesses of the alternate alleles on the homologous chromosome. As expected given the common occurrence of such “drive reversal” in this system, alleles associated with the supergene gain no consistent transmission advantage over their alternate alleles at the population level. Finally, we observed low levels of recombination and incomplete gametic disequilibrium across the supergene, including between adjacent markers within a single inversion.

Conclusions

Our data confirm the prediction that the Sb supergene is a selfish genetic element capable of biasing its own transmission during reproduction, yet counterselection for suppressor loci evidently has produced an evolutionary stalemate in TRD between the variant homologous haplotypes on the “social chromosome”. Evidence implicates prezygotic segregation distortion as responsible for the TRD we document, with “true” meiotic drive the most likely mechanism. Low levels of recombination and incomplete gametic disequilibrium across the supergene suggest that selection does not preserve a single uniform supergene haplotype responsible for inducing polygyny.

     

A machine‐learning approach for extending classical wildlife resource selection analyses

Resource selection functions (RSFs) are tremendously valuable for ecologists and resource managers because they quantify spatial patterns in resource utilization by wildlife, thereby facilitating identification of critical habitat areas and characterizing specific habitat features that are selected or avoided. RSFs discriminate between known‐use resource units (e.g., telemetry locations) and available (or randomly selected) resource units based on an array of environmental features, and in their standard form are performed using logistic regression. As generalized linear models, standard RSFs have some notable limitations, such as difficulties in accommodating nonlinear (e.g., humped or threshold) relationships and complex interactions. Increasingly, ecologists are using flexible machine‐learning methods (e.g., random forests, neural networks) to overcome these limitations. Herein, we investigate the seasonal resource selection patterns of mule deer (Odocoileus hemionus) by comparing a logistic regression framework with random forest (RF), a popular machine‐learning algorithm. Random forest (RF) models detected nonlinear relationships (e.g., optimal ranges for slope and elevation) and complex interactions which would have been very challenging to discover and characterize using standard model‐based approaches. Compared with standard RSF models, RF models exhibited improved predictive skill, provided novel insights about resource selection patterns of mule deer, and, when projected across a relevant geographic space, manifested notable differences in predicted habitat suitability. We recommend that wildlife researchers harness the strengths of machine‐learning tools like RF in addition to “classical” tools (e.g., mixed‐effects logistic regression) for evaluating resource selection, especially in cases where extensive telemetry data sets are available.     

Physiological consequences of oxygen-dependent chloride binding to hemoglobin.

The PO(2)-dependent binding of chloride to Hb decreases the Cl(-) concentration of the red blood cell (RBC) intracellular fluid in venous blood to approximately 1-3 mmol/l less than that in arterial blood. This change is physiologically important because 1) Cl(-) is a negative heterotropic allosteric effector of Hb that competes for binding sites with 2,3-bisphosphoglycerate and CO(2) and decreases oxyhemoglobin affinity in several species; 2) it may help reconcile several longstanding problems with measured values of the Donnan ratios for Cl(-), HCO, and H(+) across the RBC membrane that are used to calculate total CO(2) carriage, ion flux rates, and membrane potentials; 3) it is a factor in the change in the dissociation constant for the combined nonvolatile weak acids of Hb associated with the Haldane effect; and 4) it diminishes the decrease in strong ion difference in the RBC intracellular fluid that would otherwise occur from the chloride shift and prevent the known increase of HCO concentration in that compartment.