The ability of mouse embryonic stem cells (mESCs) to self-renew or differentiate into various cell lineages is regulated by signaling pathways and a core pluripotency transcriptional network (PTN) comprising Nanog, Oct4, and Sox2. The Wnt/β-catenin pathway promotes pluripotency by alleviating T cell factor TCF3-mediated repression of the PTN. However, it has remained unclear how β-catenin’s function as a transcriptional activator with TCF1 influences mESC fate. Here, we show that TCF1-mediated transcription is up-regulated in differentiating mESCs and that chemical inhibition of β-catenin/TCF1 interaction improves long-term self-renewal and enhances functional pluripotency. Genetic loss of TCF1 inhibited differentiation by delaying exit from pluripotency and conferred a transcriptional profile strikingly reminiscent of self-renewing mESCs with high Nanog expression. Together, our data suggest that β-catenin’s function in regulating mESCs is highly context specific and that its interaction with TCF1 promotes differentiation, further highlighting the need for understanding how its individual protein–protein interactions drive stem cell fate.

Dysregulation of the Wnt pathway leading to accumulation of β-catenin (CTNNB1) is a hallmark of colorectal cancer (CRC). Nuclear CTNNB1 acts as a transcriptional coactivator with TCF/LEF transcription factors, promoting expression of a broad set of target genes, some of which promote tumor growth. However, it remains poorly understood how CTNNB1 interacts with different transcription factors in different contexts to promote different outcomes. While some CTNNB1 target genes are oncogenic, others regulate differentiation. Here, we found that TCF7L1, a Wnt pathway repressor, buffers CTNNB1/TCF target gene expression to promote CRC growth. Loss of TCF7L1 impaired growth and colony formation of HCT116 CRC cells and reduced tumor growth in a mouse xenograft model. We identified a group of CTNNB1/TCF target genes that are activated in the absence of TCF7L1, including EPHB3, a marker of Paneth cell differentiation that has also been implicated as a tumor suppressor in CRC. Knockdown of EPHB3 partially restores growth and normal cell cycle progression of TCF7L1-Null cells. These findings suggest that while CTNNB1 accumulation is critical for CRC progression, activation of specific Wnt target genes in certain contexts may in fact inhibit tumor growth.

Premise of the study:Chloroplast microsatellite loci were characterized from transcriptomes of Artocarpus altilis (breadfruit) and A. camansi (breadnut). They were tested in A. odoratissimus (terap) and A. altilis and evaluated in silico for two congeners.Methods and Results:Fifteen simple sequence repeats (SSRs) were identified in chloroplast sequences from four Artocarpus transcriptome assemblies. The markers were evaluated using capillary electrophoresis in A. odoratissimus (105 accessions) and A. altilis (73). They were also evaluated in silico in A. altilis (10), A. camansi (6), and A. altilis × A. mariannensis (7) transcriptomes. All loci were polymorphic in at least one species, with all 15 polymorphic in A. camansi. Per species, average alleles per locus ranged between 2.2 and 2.5. Three loci had evidence of fragment-length homoplasy.Conclusions:These markers will complement existing nuclear markers by enabling confident identification of maternal and clone lines, which are often important in vegetatively propagated crops such as breadfruit.

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141 Background: Colorectal cancer (CRC) is the second leading cause of cancer-related deaths, and survival can be improved if early, suspect imaging features on CT of the abdomen and pelvis (CTAP) can be routinely identified. At present, up to 40% of these features are undiagnosed on routine CTAP, but this can be improved with a second observer. In this study, we developed a deep ensemble learning method for detecting CRC on CTAP to determine if increasing agreement between ensemble models can decrease the false positives detected by artificial intelligence (AI) second-observer. Methods: 2D U-Net convolutional neural network (CNN) containing 31 million trainable parameters was trained with 58 CRC CT images from Banner MD Anderson (AZ) and MD Anderson Cancer Center (TX) (51 used for training and 7 for validation) and 59 normal CT scans from Banner MD Anderson Cancer Center. 20 of the 25 CRC cases from public domain data (The Cancer Genome Atlas) were used to evaluate the performance of the models. The CRC was segmented using ITK-SNAP open-source software (v. 3.8). To apply the deep ensemble approach, five CNN models were trained independently with random initialization using the same U-Net architect and the same training data. Given a testing CT scan, each of the five trained CNN models was applied to produce tumor segmentation for the testing CT scan. The tumor segmentation results produced by the trained CNN models were then fused using a simple majority voting rule to produce consensus tumor segmentation results. The segmentation was analyzed by the percentage of correct detection, the number of false positives per case, and the Dice similarity coefficient (DSC). If parts of the CRC were flagged by AI, then it was considered correct. A detection was considered false positive if the marked lesion did not overlap with any CRC; contiguous false positives across different slices of CT image were considered a single false positive. DSC measures the quality of the segmentation by measuring the overlap between the ground-truth and AI detected lesion. Results: Our results showed that increasing the agreement between the 5 models dramatically decreases the number of false positives per CT at the expense of slight decrease in accuracy and DSC. This is described in the table. Conclusions: Our results show that AI-based second observer can potentially detect CRC on routine CTAP. Although the initial result yields high false positives per case, ensemble voting is an effective method for decreasing the false positives with a slight decrease in accuracy. This technique can be further improved for eventual clinical application.[Table: see text]