Each year, more than 25,000 people succumb to liver cancer in the United States, and this neoplasm represents the second cause of cancer‐related death globally. R‐spondins (RSPOs) are secreted regulators of Wnt signaling that function in development and promote tissue stem cell renewal. In cancer, RSPOs 2 and 3 are oncogenes first identified by insertional mutagenesis screens in tumors induced by mouse mammary tumor virus and by transposon mutagenesis in the colonic epithelium of rodents. RSPO2 has been reported to be activated by chromosomal rearrangements in colorectal cancer and overexpressed in a subset of hepatocellular carcinoma. Using human liver tumor gene expression data, we first discovered that a subset of liver cancers were characterized by high levels of RSPO2 in contrast to low levels in adjacent nontumor tissue. To determine if RSPOs are capable of inducing liver tumors, we used an in vivo model from which we found that overexpression of RSPO2 in the liver promoted Wnt signaling, hepatomegaly, and enhanced liver tumor formation when combined with loss of transformation‐related protein 53 (Trp53). Moreover, the Hippo/yes‐associated protein (Yap) pathway has been implicated in many human cancers, influencing cell survival. Histologic and gene expression studies showed activation of Wnt/β‐catenin and Hippo/Yap pathways following RSPO2 overexpression. We demonstrate that knockdown of Yap1 leads to reduced tumor penetrance following RSPO2 overexpression in the context of loss of Trp53. Conclusion: RSPO2 overexpression leads to tumor formation in the mouse liver in a Hippo/Yap‐dependent manner. Overall, our results suggest a role for Yap in the initiation and progression of liver tumors and uncover a novel pathway activated in RSPO2‐induced malignancies. We show that RSPO2 promotes liver tumor formation in vivo and in vitro and that RSPO2's oncogenic activity requires Hippo/Yap activation in hepatocytes. Both RSPO2 and YAP1 are suggested to represent novel druggable targets in Wnt‐driven tumors of the liver.

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As alternative fuel research continues it is important to educate students as to be an active part of the scientific community. By integrating a new and interesting research area, such as algae biofuels, with a basic concept it is possible to have more student involvement and interaction. Here we use interactive worksheets combining basic knowledge of algae, DNA, and our basic concept: phylogenetic trees. The worksheets are supported by laboratory experiments where the students extract algal DNA, amplify a specific region, obtain the sequence and then build a phylogenetic tree with their obtained sequence and other sequences provided to identify the algal species. The laboratory module is being implemented at three different universities in Nebraska: Creighton University, University of Nebraska‐Kearney, and University of Nebraska‐Lincoln. The laboratory module is set up to cover multiple skills and education requirements so that it may fit in to a multitude of classes. Currently it is being implemented in a microbiology lab course, biochemistry lab course, and cellular biology lab course. This research is funded by: NSF EPSCoR‐ 1004–094

The push for an efficient biofuel is at an all time high all around the world. Lately, a consideration for an algal biofuel has made algae a prime candidate for research. However, the most efficient way to obtain a known culture is to receive it form a culture collection. Or is it? Our research has shown that the genus Chlorella has some issues according to the DNA of the organisms compared to the taxonomy of the culture collections. To show the differences of what the cultures are compared what they are said to be, the pure culture was isolated, its DNA was extracted, PCR amplified, and sent to sequencing. Using the MEGA 5 program in association with the (Internal Transcribed Spacer) ITS2 database, the sequence is annotated. Each element (18S, ITS1, 5.8S, and ITS2) at this point is individually aligned. The elements are then concatenated, and a maximum likelihood tree is run on the MEGA 5 program. This produces a tree that species in the Chlorella genus will group together in an obvious way with acceptable bootstrap values. With this tree, it will be obvious which strains belong to a given species, and which do not. The source of support for this project comes from NSF 25‐6230‐0130‐009

In order to develop a further understanding of the evolutionary relationships between different Chlorella algal strains and related species, methods of algal DNA extraction, PCR, sequencing, and analysis were tailored to our project. With the goal of understanding algal relationships and an intent of affecting algal biofuel production, DNA from multiple regions of algal cells was targeted. Ribosomal DNA including the 18S, ITS1, 5.8S, ITS2, and 28S regions; Chloroplastic DNA (the RuBisCO large subunit coding region); Genomic DNA (the RuBisCO small subunit coding region); and Mitochondrial DNA (Cytochrome C Oxidase subunit I coding region) were picked to be analyzed from each of our algal strains. The comparison of the phylogenetic trees is then being used to determine relationship grouping patterns with increasingly robust trees as more sequencing data is obtained. Further analysis and sequencing of the four regions should give a better understanding of relationships and help to determine the proximity of certain chlorella‐like algal species to one another. Furthermore, extended distance between two strains of the same species provides ground for questioning the nomenclature of certain strains with comparison to their actual genetic composition. Funding provided by NSF‐EPSCoR: 1004–094.

Using the 18S (partial), ITS1 (Internal Transcribed Spacer 1), 5.8S, ITS2 (Internal Transcribed Spacer 2) regions of the genomic SSU (small subunit) DNA, we are able to use basic bioinformatics tools to create an effective phylogenetic tree. Sanger sequencing produces a 1.2–1.5 kb region for each individual species that is annotated into four elements, (partial 18S, ITS1, 5.8S, and ITS2) and is formatted for stand‐alone MAFFT v6.903b (Multiple Alignment using Fast Fourier Transform). The multiple alignment is then carried out on each element and concatenated to remove the potential overlap between the elements. Stand‐alone PhyML v3.0 (Phylogenetic estimation using Maximum Likelihood) is then used on the multiple alignment data, and a tree can be viewed using FigTree v1.3.1 (a phylogenetic tree editor and viewer program). The results of this method gives well‐defined clades that are now being used in genera and even species level determination. Using our simplistic method is an effective way to engaging the problem of green algae identification.