Analyses of the evolution and function of voltage-gated sodium channel proteins (Navs) have largely been limited to mutations from individual people with diagnosed neuromuscular disease. This project investigates the carboxyl-terminus of the Nav paralog (locus scn4aa 3’) that is preferentially expressed in electric organs of Neotropical weakly-electric fishes (Order Gymnotiformes). As a model system, I used the genus Gymnotus, a diverse clade of fishes that produce species-specific electric organ discharges (EODs). I clarified evolutionary relationships among Gymnotus species using mitochondrial (cytochrome b, and 16S ribosome) and nuclear (rag2, and scn4aa) gene sequences (3739 nucleotide positions from 28 Gymnotus species). I analyzed the molecular evolution of scn4aa 3’, and detected evidence for positive selection at eight amino acid sites in seven Gymnotus lineages. These eight amino acid sites are located in motifs that may be important for modulation of EOD frequencies.

Analyses of the evolution and function of voltage-gated sodium channel proteins (Navs) have largely been limited to mutations from individual people with diagnosed neuromuscular disease. This project investigates the carboxyl-terminus of the Nav paralog (locus scn4aa 3’) that is preferentially expressed in electric organs of Neotropical weakly-electric fishes (Order Gymnotiformes). As a model system, I used the genus Gymnotus, a diverse clade of fishes that produce species-specific electric organ discharges (EODs). I clarified evolutionary relationships among Gymnotus species using mitochondrial (cytochrome b, and 16S ribosome) and nuclear (rag2, and scn4aa) gene sequences (3739 nucleotide positions from 28 Gymnotus species). I analyzed the molecular evolution of scn4aa 3’, and detected evidence for positive selection at eight amino acid sites in seven Gymnotus lineages. These eight amino acid sites are located in motifs that may be important for modulation of EOD frequencies.

Poa L., is known as a highly diverse cosmopolitan genus with taxonomic difficulties that includes unknown species and species with uncertain affinities mainly in West Asia and North Africa. Poa also exhibits a close relationship with two West Asian genera, Eremopoa Roshev. and Oreopoa H. Scholz & Parolly. This study was conducted to: 1) fill the gap of information on the affinities between Poa species with an emphasis on West Asian Poa; 2) revise and evaluate the accuracy of traditional infrageneric classification of West Asian Poa; and 3) clarify the relationship between Poa and two allied genera of Poaceae Barnhart, Eremopoa and Oreopoa. DNA molecular evidence from present phylogenetic analyses of West Asian species of Poa, Eremopoa and Oreopoa, resulted in some great findings as follow: I) Poa caucasica Trin., which is currently assigned to subsection Nivicolae of section Poa from subgenus Poa resolved as a unique new distinct lineage within Poa. II), New treatments are suggested for Poa densa Troitsky, Poa masenderana Freyn & Sint., Poa cenisia All., Poa psychrophila Boiss. & Heldr. and Poa lipskyi. III) Three unclassified species of Poa pseudobulbosa, Poa diversifolia and Poa aitchisonii are assigned here to subgenus Poa and supersection Poa. IV), The present molecular evidence supports inclusion of Eremopoa in Poa and confirms reduction of Eremopoa to a level of subgenus of Poa. V) Present phylogenetic analyses also indicate that monotypic genus Oreopoa H. Scholz & Parolly is part of Poa. These findings require an urgent modification in subgeneric and sectional classification of the genus Poa.

Stem cells differentiate through an organized hierarchy of intermediate cell types to terminally differentiated cell types. This process is largely guided by master transcriptional regulators, but it also depends on the expression of many other types of genes. The discrete cell types in the differentiation hierarchy are often identified based on the expression or non-expression of certain marker genes. Historically, these have often been various cell-surface proteins, which are fairly easy to assay biochemically but are not necessarily causative of the cell type, in the sense of being master transcriptional regulators. This raises important questions about how gene expression across the whole genome controls or reflects cell state, and in particular, differentiation hierarchies. Traditional approaches to understanding gene expression patterns across multiple conditions, such as principal components analysis or K-means clustering, can group cell types based on gene expression, but they do so without knowledge of the differentiation hierarchy. Hierarchical clustering and maximization of parsimony can organize the cell types into a tree, but in general this tree is different from the differentiation hierarchy. Using hematopoietic differentiation as an example, we demonstrate how many genes other than marker genes are able to discriminate between different branches of the differentiation tree by proposing two models for detecting genes that are up-regulated or down-regulated in distinct lineages. We then propose a novel approach to solving the following problem: Given the differentiation hierarchy and gene expression data at each node, construct a weighted Euclidean distance metric such that the minimum spanning tree with respect to that metric is precisely the given differentiation hierarchy. We provide a set of linear constraints that are provably sufficient for the desired construction and a linear programming framework to identify sparse sets of weights, effectively identifying genes that are most relevant for discriminating different parts of the tree. We apply our method to microarray gene expression data describing 38 cell types in the hematopoiesis hierarchy, constructing a sparse weighted Euclidean metric that uses just 175 genes. These 175 genes are different than the marker genes that were used to identify the 38 cell types, hence offering a novel alternative way of discriminating different branches of the tree. A DAVID functional annotation analysis shows that the 175 genes reflect major processes and pathways active in different parts of the tree. However, we find that there are many alternative sets of weights that satisfy the linear constraints. Thus, in the style of random-forest training, we also construct metrics based on random subsets of the genes and compare them to the metric of 175 genes. Our results show that the 175 genes frequently appear in the random metrics, implicating their significance from an empirical point of view as well. Finally, we show how our linear programming method is able to identify columns that were selected to build minimum spanning trees on the nodes of random variable-size matrices.