ASU Scholarship Showcase

Linnorm is a novel normalization and transformation method for the analysis of single cell RNA sequencing (scRNA-seq) data. Linnorm is developed to remove technical noises and simultaneously preserve biological variations in scRNA-seq data, such that existing statistical methods can be improved. Using real scRNA-seq data, we compared Linnorm with existing normalization methods, including NODES, SAMstrt, SCnorm, scran, DESeq and TMM. Linnorm shows advantages in speed, technical noise removal and preservation of cell heterogeneity, which can improve existing methods in the discovery of novel subtypes, pseudo-temporal ordering of cells, clustering analysis, etc. Linnorm also performs better than existing DEG analysis methods, including BASiCS, NODES, SAMstrt, Seurat and DESeq2, in false positive rate control and accuracy.

Plasmodium vivax is the most prevalent malarial species in South America and exerts a substantial burden on the populations it affects. The control and eventual elimination of P. vivax are global health priorities. Genomic research contributes to this objective by improving our understanding of the biology of P. vivax and through the development of new genetic markers that can be used to monitor efforts to reduce malaria transmission. Here we analyze whole-genome data from eight field samples from a region in Cordóba, Colombia where malaria is endemic. We find considerable genetic diversity within this population, a result that contrasts with earlier studies suggesting that P. vivax had limited diversity in the Americas. We also identify a selective sweep around a substitution known to confer resistance to sulphadoxine-pyrimethamine (SP). This is the first observation of a selective sweep for SP resistance in this species. These results indicate that P. vivax has been exposed to SP pressure even when the drug is not in use as a first line treatment for patients afflicted by this parasite. We identify multiple non-synonymous substitutions in three other genes known to be involved with drug resistance in Plasmodium species. Finally, we found extensive microsatellite polymorphisms. Using this information we developed 18 polymorphic and easy to score microsatellite loci that can be used in epidemiological investigations in South America.

Two distinct monocyte (Mo)/macrophage (Mp) subsets (Ly6C^low and Ly6C^hi) orchestrate cardiac recovery process following myocardial infarction (MI). Prostaglandin (PG) E₂ is involved in the Mo/Mp-mediated inflammatory response, however, the role of its receptors in Mos/Mps in cardiac healing remains to be determined. Here we show that pharmacological inhibition or gene ablation of the Ep3 receptor in mice suppresses accumulation of Ly6C^low Mos/Mps in infarcted hearts. Ep3 deletion in Mos/Mps markedly attenuates healing after MI by reducing neovascularization in peri-infarct zones. Ep3 deficiency diminishes CX3C chemokine receptor 1 (CX3CR1) expression and vascular endothelial growth factor (VEGF) secretion in Mos/Mps by suppressing TGFβ1 signaling and subsequently inhibits Ly6C^low Mos/Mps migration and angiogenesis. Targeted overexpression of Ep3 receptors in Mos/Mps improves wound healing by enhancing angiogenesis. Thus, the PGE₂/Ep3 axis promotes cardiac healing after MI by activating reparative Ly6C^low Mos/Mps, indicating that Ep3 receptor activation may be a promising therapeutic target for acute MI.

Inbreeding in hermaphroditic plants can occur through two different mechanisms: biparental inbreeding, when a plant mates with a related individual, or self-fertilization, when a plant mates with itself. To avoid inbreeding, many hermaphroditic plants have evolved self-incompatibility (SI) systems which prevent or limit self-fertilization. One particular SI system—homomorphic SI—can also reduce biparental inbreeding. Homomorphic SI is found in many angiosperm species, and it is often assumed that the additional benefit of reduced biparental inbreeding may be a factor in the success of this SI system. To test this assumption, we developed a spatially-explicit, individual-based simulation of plant populations that displayed three different types of homomorphic SI. We measured the total level of inbreeding avoidance by comparing each population to a self-compatible population (NSI), and we measured biparental inbreeding avoidance by comparing to a population of self-incompatible plants that were free to mate with any other individual (PSI).

Because biparental inbreeding is more common when offspring dispersal is limited, we examined the levels of biparental inbreeding over a range of dispersal distances. We also tested whether the introduction of inbreeding depression affected the level of biparental inbreeding avoidance. We found that there was a statistically significant decrease in autozygosity in each of the homomorphic SI populations compared to the PSI population and, as expected, this was more pronounced when seed and pollen dispersal was limited. However, levels of homozygosity and inbreeding depression were not reduced. At low dispersal, homomorphic SI populations also suffered reduced female fecundity and had smaller census population sizes. Overall, our simulations showed that the homomorphic SI systems had little impact on the amount of biparental inbreeding in the population especially when compared to the overall reduction in inbreeding compared to the NSI population. With further study, this observation may have important consequences for research into the origin and evolution of homomorphic self-incompatibility systems.

Modeling of transcriptional regulatory networks (TRNs) has been increasingly used to dissect the nature of gene regulation. Inference of regulatory relationships among transcription factors (TFs) and genes, especially among multiple TFs, is still challenging. In this study, we introduced an integrative method, LogicTRN, to decode TF–TF interactions that form TF logics in regulating target genes. By combining cis-regulatory logics and transcriptional kinetics into one single model framework, LogicTRN can naturally integrate dynamic gene expression data and TF-DNA-binding signals in order to identify the TF logics and to reconstruct the underlying TRNs. We evaluated the newly developed methodology using simulation, comparison and application studies, and the results not only show their consistence with existing knowledge, but also demonstrate its ability to accurately reconstruct TRNs in biological complex systems.

Under models of isolation-by-distance, population structure is determined by the probability of identity-by-descent between pairs of genes according to the geographic distance between them. Well established analytical results indicate that the relationship between geographical and genetic distance depends mostly on the neighborhood size of the population which represents a standardized measure of gene flow. To test this prediction, we model local dispersal of haploid individuals on a two-dimensional landscape using seven dispersal kernels: Rayleigh, exponential, half-normal, triangular, gamma, Lomax and Pareto. When neighborhood size is held constant, the distributions produce similar patterns of isolation-by-distance, confirming predictions. Considering this, we propose that the triangular distribution is the appropriate null distribution for isolation-by-distance studies. Under the triangular distribution, dispersal is uniform over the neighborhood area which suggests that the common description of neighborhood size as a measure of an effective, local panmictic population is valid for popular families of dispersal distributions. We further show how to draw random variables from the triangular distribution efficiently and argue that it should be utilized in other studies in which computational efficiency is important.

In vitro models that mimic in vivo host-pathogen interactions are needed to evaluate candidate drugs that inhibit bacterial virulence traits. We established a new approach to study Pseudomonas aeruginosa biofilm susceptibility on biotic surfaces, using a three-dimensional (3-D) lung epithelial cell model. P. aeruginosa formed antibiotic resistant biofilms on 3-D cells without affecting cell viability. The biofilm-inhibitory activity of antibiotics and/or the anti-biofilm peptide DJK-5 were evaluated on 3-D cells compared to a plastic surface, in medium with and without fetal bovine serum (FBS). In both media, aminoglycosides were more efficacious in the 3-D cell model. In serum-free medium, most antibiotics (except polymyxins) showed enhanced efficacy when 3-D cells were present. In medium with FBS, colistin was less efficacious in the 3-D cell model. DJK-5 exerted potent inhibition of P. aeruginosa association with both substrates, only in serum-free medium. DJK-5 showed stronger inhibitory activity against P. aeruginosa associated with plastic compared to 3-D cells. The combined addition of tobramycin and DJK-5 exhibited more potent ability to inhibit P. aeruginosa association with both substrates. In conclusion, lung epithelial cells influence the efficacy of most antimicrobials against P. aeruginosa biofilm formation, which in turn depends on the presence or absence of FBS.

Accumulating data from genome-wide association studies (GWAS) have provided a collection of novel candidate genes associated with complex diseases, such as atherosclerosis. We identified an atherosclerosis-associated single-nucleotide polymorphism (SNP) located in the intron of the long noncoding RNA (lncRNA) LINC00305 by searching the GWAS database. Although the function of LINC00305 is unknown, we found that LINC00305 expression is enriched in atherosclerotic plaques and monocytes. Overexpression of LINC00305 promoted the expression of inflammation-associated genes in THP-1 cells and reduced the expression of contractile markers in co-cultured human aortic smooth muscle cells (HASMCs). We showed that overexpression of LINC00305 activated nuclear factor-kappa beta (NF-κB) and that inhibition of NF-κB abolished LINC00305-mediated activation of cytokine expression. Mechanistically, LINC00305 interacted with lipocalin-1 interacting membrane receptor (LIMR), enhanced the interaction of LIMR and aryl-hydrocarbon receptor repressor (AHRR), and promoted protein expression as well as nuclear localization of AHRR. Moreover, LINC00305 activated NF-κB exclusively in the presence of LIMR and AHRR. In light of these findings, we propose that LINC00305 promotes monocyte inflammation by facilitating LIMR and AHRR cooperation and the AHRR activation, which eventually activates NF-κB, thereby inducing HASMC phenotype switching.

This study presents the first global transcriptional profiling and phenotypic characterization of the major human opportunistic fungal pathogen, Candida albicans, grown in spaceflight conditions. Microarray analysis revealed that C. albicans subjected to short-term spaceflight culture differentially regulated 452 genes compared to synchronous ground controls, which represented 8.3% of the analyzed ORFs. Spaceflight-cultured C. albicans–induced genes involved in cell aggregation (similar to flocculation), which was validated by microscopic and flow cytometry analysis. We also observed enhanced random budding of spaceflight-cultured cells as opposed to bipolar budding patterns for ground samples, in accordance with the gene expression data. Furthermore, genes involved in antifungal agent and stress resistance were differentially regulated in spaceflight, including induction of ABC transporters and members of the major facilitator family, downregulation of ergosterol-encoding genes, and upregulation of genes involved in oxidative stress resistance.

Finally, downregulation of genes involved in actin cytoskeleton was observed. Interestingly, the transcriptional regulator Cap1 and over 30% of the Cap1 regulon was differentially expressed in spaceflight-cultured C. albicans. A potential role for Cap1 in the spaceflight response of C. albicans is suggested, as this regulator is involved in random budding, cell aggregation, and oxidative stress resistance; all related to observed spaceflight-associated changes of C. albicans. While culture of C. albicans in microgravity potentiates a global change in gene expression that could induce a virulence-related phenotype, no increased virulence in a murine intraperitoneal (i.p.) infection model was observed under the conditions of this study. Collectively, our data represent an important basis for the assessment of the risk that commensal flora could play during human spaceflight missions. Furthermore, since the low fluid-shear environment of microgravity is relevant to physical forces encountered by pathogens during the infection process, insights gained from this study could identify novel infectious disease mechanisms, with downstream benefits for the general public.

In this study, we present a novel methodology to infer indel parameters from multiple sequence alignments (MSAs) based on simulations. Our algorithm searches for the set of evolutionary parameters describing indel dynamics which best fits a given input MSA. In each step of the search, we use parametric bootstraps and the Mahalanobis distance to estimate how well a proposed set of parameters fits input data. Using simulations, we demonstrate that our methodology can accurately infer the indel parameters for a large variety of plausible settings. Moreover, using our methodology, we show that indel parameters substantially vary between three genomic data sets: Mammals, bacteria, and retroviruses. Finally, we demonstrate how our methodology can be used to simulate MSAs based on indel parameters inferred from real data sets.