Somatostatin receptor expression in parathyroid neoplasms

Introduction: Parathyroid carcinoma represents a rare cause of primary hyperparathyroidism. Distinguishing carcinoma from the benign tumors underlying primary hyperparathyroidism remains challenging. The diagnostic criteria for parathyroid carcinoma are local and/or metastatic spreading. Atypical parathyroid adenomas share other histological features with carcinomas but lack invasive growth. Somatostatin receptors are commonly expressed in different neuro endocrine tumors, but whether this also holds for parathyroid tumors remains unknown. Aim: Our aim is to examine the immunohistochemical expression of somatostatin receptor 1-5 in parathyroid typical adenomas, atypical adenomas and carcinomas. Methods: We used a tissue microarray construct from a nationwide cohort of parathyroid carcinomas (n = 32), age- and gender-matched typical parathyroid adenomas (n = 72) and atypical parathyroid adenomas (n = 27) for immunohistochemistry of somatostatin receptor subtypes 1-5. We separately assessed cytoplasmic, membrane and nuclear expression and also investigated the associations with histological, biochemical and clinical characteristics. Results: All parathyroid tumor subgroups expressed somatostatin receptors, although membrane expression appeared negligible. Except for somatostatin receptor 1, expression patterns differed between the three tumor types. Adenomas exhibited the weakest and carcinomas the strongest expression of somatostatin receptor 2, 3, 4 and 5. We observed the largest difference for cytoplasmic somatostatin receptor 5 expression. Conclusions: Parathyroid adenomas, atypical adenomas and carcinomas all express somatostatin receptor subtypes 1-5. Somatostatin receptor 5 may serve as a potential tumor marker for malignancy. Studies exploring the role of somatostatin receptor imaging and receptor-specific therapies in patients with parathyroid car cinomas are needed.Peer reviewe

Mouse Model of Alagille Syndrome and Mechanisms of Jagged1 Missense Mutations.

BACKGROUND & AIMS: Alagille syndrome is a genetic disorder characterized by cholestasis, ocular abnormalities, characteristic facial features, heart defects, and vertebral malformations. Most cases are associated with mutations in JAGGED1 (JAG1), which encodes a Notch ligand, although it is not clear how these contribute to disease development. We aimed to develop a mouse model of Alagille syndrome to elucidate these mechanisms. METHODS: Mice with a missense mutation (H268Q) in Jag1 (Jag1+/Ndr mice) were outbred to a C3H/C57bl6 background to generate a mouse model for Alagille syndrome (Jag1Ndr/Ndr mice). Liver tissues were collected at different timepoints during development, analyzed by histology, and liver organoids were cultured and analyzed. We performed transcriptome analysis of Jag1Ndr/Ndr livers and livers from patients with Alagille syndrome, cross-referenced to the Human Protein Atlas, to identify commonly dysregulated pathways and biliary markers. We used species-specific transcriptome separation and ligand-receptor interaction assays to measure Notch signaling and the ability of JAG1Ndr to bind or activate Notch receptors. We studied signaling of JAG1 and JAG1Ndr via NOTCH 1, NOTCH2, and NOTCH3 and resulting gene expression patterns in parental and NOTCH1-expressing C2C12 cell lines. RESULTS: Jag1Ndr/Ndr mice had many features of Alagille syndrome, including eye, heart, and liver defects. Bile duct differentiation, morphogenesis, and function were dysregulated in newborn Jag1Ndr/Ndr mice, with aberrations in cholangiocyte polarity, but these defects improved in adult mice. Jag1Ndr/Ndr liver organoids collapsed in culture, indicating structural instability. Whole-transcriptome sequence analyses of liver tissues from mice and patients with Alagille syndrome identified dysregulated genes encoding proteins enriched at the apical side of cholangiocytes, including CFTR and SLC5A1, as well as reduced expression of IGF1. Exposure of Notch-expressing cells to JAG1Ndr, compared with JAG1, led to hypomorphic Notch signaling, based on transcriptome analysis. JAG1-expressing cells, but not JAG1Ndr-expressing cells, bound soluble Notch1 extracellular domain, quantified by flow cytometry. However, JAG1 and JAG1Ndr cells each bound NOTCH2, and signaling from NOTCH2 signaling was reduced but not completely inhibited, in response to JAG1Ndr compared with JAG1. CONCLUSIONS: In mice, expression of a missense mutant of Jag1 (Jag1Ndr) disrupts bile duct development and recapitulates Alagille syndrome phenotypes in heart, eye, and craniofacial dysmorphology. JAG1Ndr does not bind NOTCH1, but binds NOTCH2, and elicits hypomorphic signaling. This mouse model can be used to study other features of Alagille syndrome and organ development

Regulation of the vertebrate transcriptome in development and disease

In the last decade we have seen a tremendous development in the omics area (genomics, transcriptomics, proteomics etc.), making high throughput methods increasingly costeffective and available. The development in RNA-sequencing technology now enables us to sequence whole transcriptomes of hundreds or even thousands of samples or single cells simultaneously in only a few days. With the ability to quickly create millions of reads for thousands of genes in thousands of samples comes a computational challenge of how to make sense of the data. Due to the use of short sequencing reads, duplicate genes, biased base composition and repetitive regions in the genome, reads might not be uniquely assignable to a single gene. This problem can be solved either by computationally assigning multi-mapping reads to the most likely position, or excluding these reads and normalizing gene expression for the uniquely mappable positions in a gene. In paper I, we describe a software application for efficiently finding and storing the mappability data for every position in the genome, for subsequent use in normalization of RNA-seq data. When the first drafts of the human genome were published in 2001, it became clear that the majority of our DNA does not consist of protein-coding genes. Since then, a multitude of new functional non-coding RNA species have been discovered, but also transcription of seemingly non-functional RNA from open chromatin regions, such as promoter upstream transcripts (PROMPTs). In paper II, we decipher the physical interactions between the exosome complex, the NEXT complex and the cap-binding complex, and the role each complex has in targeting PROMPTs for degradation. In early embryonic development, having a mechanism for starting different developmental programs in a different set of cells is essential for multi-cellular organisms to develop. In the African clawed frog, Xenopus laevis, this mechanism involves sorting maternal RNA to different hemispheres of the oocyte, which will later be inherited asymmetrically to the cells in the developing embryo. The zygotic expression starts only after 12 cell divisions, and at the early stages the maternal RNA control the development. In paper III, we use de novo transcriptome assembly to get a good annotation of X. laevis in the absence of a fully assembled genome. We then use single cell RNA-sequencing to study the RNA sorting and search for sorting motifs in the 2-16 cell stage embryo. An advantage of full length RNA-sequencing is the possibility to study alternative splicing alongside expression estimates. Spinal muscular atrophy (SMA) is genetic disease, characterized by progressive loss of somatic motor neurons. The disease is an effect of the loss of the SMN1 gene, which is only partly compensated for by the orthologous SMN2 gene since it is less efficient in producing full-length SMN protein. SMN is involved in spliceosome assembly, and even though it is ubiquitously expressed it specifically affects a subgroup of somatic motor neurons. In paper IV, we try to elucidate why some motor neurons are resistant and other vulnerable in the disease, by looking at both gene expression and splicing differences in a mouse model of SMA

Effects of uniqueness normalization on expression level.

<p>(<b>A</b>) Histogram showing how uniqueness compensation using MULTo affects the RPKM values at different read lengths. The x-axis show the difference in gene expression between uniqueness compensated and uncompensated expression levels. (<b>B</b>) RPKM values for FTH1 before and after uniqueness normalization. (<b>C</b>) Read coverage and uniqueness profile across FTH1 for 25 nt reads. Uniqueness density was calculated as the proportion unique reads aligning to each genomic coordinate.</p

Comparison of uniqueness compensation methods for RNA-Seq.

<p>Scatter plots showing how gene expression values (RPKM) are affected by uniqueness compensation for transcripts as a function of increasing proportion unique positions. (<b>A</b>) Uniqueness compensation with MULTo corrected transcript lengths are close to optimal compensation line (y = 1/x) (<b>B</b>) ERANGE uniqueness compensation. (<b>C</b>) Cufflinks uniqueness compensation. (<b>D,E</b>) MA-plots between MUL and ERANGE uniqueness compensation (D) and between MUL and cufflinks uniqueness compensation (E) showing how gene expression differences correlate to the gene expression average. Short transcripts were colored red.</p

Efficient and Comprehensive Representation of Uniqueness for Next-Generation Sequencing by Minimum Unique Length Analyses

<div><p>As next generation sequencing technologies are getting more efficient and less expensive, RNA-Seq is becoming a widely used technique for transcriptome studies. Computational analysis of RNA-Seq data often starts with the mapping of millions of short reads back to the genome or transcriptome, a process in which some reads are found to map equally well to multiple genomic locations (multimapping reads). We have developed the <u>M</u>inimum <u>U</u>nique <u>L</u>ength <u>To</u>ol (MULTo), a framework for efficient and comprehensive representation of mappability information, through identification of the shortest possible length required for each genomic coordinate to become unique in the genome and transcriptome. Using the minimum unique length information, we have compared different uniqueness compensation approaches for transcript expression level quantification and demonstrate that the best compensation is achieved by discarding multimapping reads and correctly adjusting gene model lengths. We have also explored uniqueness within specific regions of the mouse genome and enhancer mapping experiments. Finally, by making MULTo available to the community we hope to facilitate the use of uniqueness compensation in RNA-Seq analysis and to eliminate the need to make additional mappability files.</p> </div

Schematic illustration MULTo file generation.

<p>(<b>A</b>) We defined the minimum unique length (MUL) of a genomic coordinate as the length of the shortest starting oligonucleotide at that coordinate that is needed to be unique. To find the MUL value, Fasta files with artificial “reads” of different lengths were iteratively created from whole chromosome fasta files and mapped to the genome using bowtie. When the minimum length needed for uniqueness was found, this value was stored in a binary file. In this example, position 3000091 was unique at 33 base pairs but not at 32, i.e. we have a MUL value of 33. (<b>B</b>) Exemplifying that MUL values can be retrieved from arbitrary regions in just a few lines of code.</p

Uniqueness in the transcriptome.

<p>(<b>A, B</b>) We calculated the proportion of unique positions for each transcript, both for single reads and paired-end fragments (mean 500 nt), and then plotted how many transcripts have a certain proportion of unique positions. The y-axis represents the proportion of all transcripts that satisfies the given condition. (<b>A</b>) Gene-level uniqueness of all RefSeq transcripts. (<b>B</b>) Transcript-level uniqueness for all transcripts from multi-isoform genes. (<b>C</b>) Positional plot of the uniqueness proportion across all coding transcripts. We calculated the number of reads of a specific length that passes through each position, and determined what proportion of these were unique. Since transcripts differ in length, we binned positions together so that each region (upstream, downstream, coding sequence, 5′ and 3′UTR) had the same number of bins for each transcript. The x-axis represents coordinate bins across transcripts.</p