Potential roles of FGF5 as a candidate therapeutic target in prostate cancer

Fibroblast growth factor (FGF) is a secreted ligand that is widely expressed in embryonic tissues but its expression decreases with age. In the developing prostate, FGF5 has been proposed to interact with the Hedgehog (Hh) signaling pathway to guide mitogenic processes. In the adult prostate, the FGF/FGFR signaling axis has been implicated in prostate carcinogenesis, but focused studies on FGF5 functions in the prostate are limited. Functional studies completed in other cancer models point towards FGF5 overexpression as an oncogenic driver associated with stemness, metastatic potential, proliferative capacity, and increased tumor grade. In this review, we explore the significance of FGF5 as a therapeutic target in prostate cancer (PCa) and other malignancies; and we introduce a potential route of investigation to link FGF5 to benign prostatic hyperplasia (BPH). PCa and BPH are two primary contributors to the disease burden of the aging male population and have severe implications on quality of life, psychological wellbeing, and survival. The development of new FGF5 inhibitors could potentially alleviate the health burden of PCa and BPH in the aging male population.

Single-cell RNA sequencing of neurofibromas reveals a tumor microenvironment favorable for neural regeneration and immune suppression in a neurofibromatosis type 1 porcine model

Neurofibromatosis Type 1 (NF1) is one of the most common genetically inherited disorders that affects 1 in 3000 children annually. Clinical manifestations vary widely but nearly always include the development of cutaneous, plexiform and diffuse neurofibromas that are managed over many years. Recent single-cell transcriptomics profiling efforts of neurofibromas have begun to reveal cell signaling processes. However, the cell signaling networks in mature, non-cutaneous neurofibromas remain unexplored. Here, we present insights into the cellular composition and signaling within mature neurofibromas, contrasting with normal adjacent tissue, in a porcine model of NF1 using single-cell RNA sequencing (scRNA-seq) analysis and histopathological characterization. These neurofibromas exhibited classic diffuse-type histologic morphology and expected patterns of S100, SOX10, GFAP, and CD34 immunohistochemistry. The porcine mature neurofibromas closely resemble human neurofibromas histologically and contain all known cellular components of their human counterparts. The scRNA-seq confirmed the presence of all expected cell types within these neurofibromas and identified novel populations of fibroblasts and immune cells, which may contribute to the tumor microenvironment by suppressing inflammation, promoting M2 macrophage polarization, increasing fibrosis, and driving the proliferation of Schwann cells. Notably, we identified tumor-associated IDO1 +/CD274+ (PD-L1) + dendritic cells, which represent the first such observation in any NF1 animal model and suggest the role of the upregulation of immune checkpoints in mature neurofibromas. Finally, we observed that cell types in the tumor microenvironment are poised to promote immune evasion, extracellular matrix reconstruction, and nerve regeneration.

Assessment of Mosaicism and Detection of Cryptic Alleles in CRISPR/Cas9-Engineered Neurofibromatosis Type 1 and TP53 Mutant Porcine Models Reveals Overlooked Challenges in Precision Modeling of Human Diseases

Genome editing in pigs has been made efficient, practical, and economically viable by the CRISPR/Cas9 platform, representing a promising new era in translational modeling of human disease for research and preclinical development of therapies and devices. Porcine embryo microinjection provides a universally available, efficient option over somatic-cell nuclear transfer, but requires that critical considerations be made in genotypic validation of the models that routinely go unaddressed. Accurate validation of genotypes is especially important when modeling genetic disorders, such as neurofibromatosis type 1 (NF1) that exhibits complex genotype–phenotypic relationships. NF1, an autosomal dominant disorder, is particularly hard to model as it manifests very differently across patients, and even within families, with over 3,000 disease-associated mutations of the neurofibromin 1 (NF1) gene identified. The precise nature of the mutations plays a role in the complex phenotypic presentation of the disorder that includes benign and malignant peripheral and central nervous system tumors, a variety of motor deficits and debilitating cognitive impairments and musculoskeletal, cardiovascular, and gastrointestinal disorders. NF1 can also often involve mutations in passenger genes such as TP53. In this manuscript, we describe the creation of three novel porcine models of NF1 and a model additionally harboring a mutation in TP53 by embryo microinjection of CRISPR/Cas9. We present the challenges encountered in validation of genotypes and the methodological strategies developed to counter the hurdles. We present simple options for quantifying level of mosaicism: a quantitative method (targeted amplicon sequencing) for small edits such as SNPs and indels and a semiquantitative method (competitive PCR) for large edits. Characterization of mosaicism allowed for strategic selection of founder pigs for rapid, economical expansion of genetically defined lines. We also present commonly observed unexpected DNA repair products (i.e., structural variants or cryptic alleles) that are refractory to PCR amplification and thus evade detection. We present the use of copy number variance assays to overcome hurdles in detecting cryptic alleles. The report provides a framework for genotypic validation of porcine models created by embryo microinjection and the expansion of lines in an efficient manner.

Integration of the ImageJ Ecosystem in the KNIME Analytics Platform

Open-source software tools are often used for analysis of scientific image data due to their flexibility and transparency in dealing with rapidly evolving imaging technologies. The complex nature of image analysis problems frequently requires many tools to be used in conjunction, including image processing and analysis, data processing, machine learning and deep learning, statistical analysis of the results, visualization, correlation to heterogeneous but related data, and more. However, the development, and therefore application, of these computational tools is impeded by a lack of integration across platforms. Integration of tools goes beyond convenience, as it is impractical for one tool to anticipate and accommodate the current and future needs of every user. This problem is emphasized in the field of bioimage analysis, where various rapidly emerging methods are quickly being adopted by researchers. ImageJ is a popular open-source image analysis platform, with contributions from a global community resulting in hundreds of specialized routines for a wide array of scientific tasks. ImageJ's strength lies in its accessibility and extensibility, allowing researchers to easily improve the software to solve their image analysis tasks. However, ImageJ is not designed for development of complex end-to-end image analysis workflows. Scientists are often forced to create highly specialized and hard-to-reproduce scripts to orchestrate individual software fragments and cover the entire life-cycle of an analysis of an image dataset. KNIME Analytics Platform, a user-friendly data integration, analysis, and exploration workflow system, was designed to handle huge amounts of heterogeneous data in a platform-agnostic, computing environment and has been successful in meeting complex end-to-end demands in several communities, such as cheminformatics and mass spectrometry. Similar needs within the bioimage analysis community led to the creation of the KNIME Image Processing extension which integrates ImageJ into KNIME Analytics Platform, enabling researchers to develop reproducible and scalable workflows, integrating a diverse range of analysis tools. Here we present how users and developers alike can leverage the ImageJ ecosystem via the KNIME Image Processing extension to provide robust and extensible image analysis within KNIME workflows. We illustrate the benefits of this integration with examples, as well as representative scientific use cases.

MRI-based method for lower urinary tract dysfunction in adult male mice

Benign prostatic hyperplasia (BPH) develops in the majority of men as they age. As a result, lower urinary tract symptoms (LUTS) often develop, which significantly decrease quality of life. One model of studying BPH/LUTS in mice is to use a hormone-induced model of lower urinary tract dysfunction (LUTD), but current methods for studying endpoints require multiple analysis techniques that contribute to an overall lengthy process. However, developments in magnetic resonance imaging (MRI) have opened the door for more accurate and time efficient methods. The purpose of this study was to demonstrate the capabilities of MRI for the analysis of LUTD in mice. To do this, whole and partial urogenital tracts were extracted from mice and imaged on a 9.4 Tesla MRI system. Additionally, a device was designed and fabricated to aid in the imaging of up to 100 mouse urogenital tracts in a single imaging session. Images were processed for both qualitative representation of MRI resolution capabilities and quantitative measurements of urogenital tract components. Even the smallest anatomical structures of the urogenital tracts were resolved and quantified, including the ureters, urethra, ductus deferens, and fine nodules and textures on the seminal vesicles, bladder, and prostatic lobes. The visual representations and urogenital component quantifications demonstrated in this study may be of value in lesion detection, diagnosis, and LUTS symptom progression tracking.

Prostate cancer xenografts and hormone induced prostate carcinogenesis

Despite the advancement of transgenic and gene knockout animal models in the prostate cancer research, there is still a need for utilizing xenograft models. Xenografts can be grown in multiple sites/organs within immunocompromised animals such as mice and rats. Although prostate xenografts have been derived from many species, human cells and tissues are the most commonly used due to their potential clinical significance. Xenograft models that progress from one state or stage to another are commonly used to address important scientific questions including malignant transformation, metastatic spread, and castration resistance. Utilization of xenografts are commonly being used to assess the biology and genetics of prostate cancer, as well as, for therapeutic benefit. In addition to models for the study of prostate cancer, xenografts are also utilized as a tool in precision medicine where patient derived xenografts (PDX) can be grown in multiple animals and assessed for therapeutic efficacy. The popularity of such xenograft models and PDXs have led to availability of these resources through public and commercial institutions. In this review, we describe both traditional and emerging models of prostate cancer and their potential uses. Further development of current models and introduction of new models will likely provide new insights and better understanding of prostatic carcinogenesis and progression.

Distinct capacity for differentiation to inner ear cell types by progenitor cells of the cochlea and vestibular organs

Disorders of hearing and balance are most commonly associated with damage to cochlear and vestibular hair cells or neurons. Although these cells are not capable of spontaneous regeneration, progenitor cells in the hearing and balance organs of the neonatal mammalian inner ear have the capacity to generate new hair cells after damage. To investigate whether these cells are restricted in their differentiation capacity, we assessed the phenotypes of differentiated progenitor cells isolated from three compartments of the mouse inner ear - the vestibular and cochlear sensory epithelia and the spiral ganglion - by measuring electrophysiological properties and gene expression. Lgr5⁺ progenitor cells from the sensory epithelia gave rise to hair cell-like cells, but not neurons or glial cells. Newly created hair cell-like cells had hair bundle proteins, synaptic proteins and membrane proteins characteristic of the compartment of origin. PLP1⁺ glial cells from the spiral ganglion were identified as neural progenitors, which gave rise to neurons, astrocytes and oligodendrocytes, but not hair cells. Thus, distinct progenitor populations from the neonatal inner ear differentiate to cell types associated with their organ of origin.

A secretory leukocyte protease inhibitor variant with improved activity against lung infection

Secretory leukocyte protease inhibitor (SLPI) is an important respiratory tract host defense protein, which is proteolytically inactivated by excessive neutrophil elastase (NE) during chronic Pseudomonas infection in the cystic fibrosis (CF) lung. We generated two putative NE-resistant variants of SLPI by site-directed mutagenesis, SLPI-A16G and SLPI-S15G-A16G, with a view to improving SLPI's proteolytic stability. Both variants showed enhanced resistance to degradation in the presence of excess NE as well as CF patient sputum compared with SLPI-wild type (SLPI-WT). The ability of both variants to bind bacterial lipopolysaccharides and interact with nuclear factor-κB DNA binding sites was also preserved. Finally, we demonstrate increased anti-inflammatory activity of the SLPI-A16G protein compared with SLPI-WT in a murine model of pulmonary Pseudomonas infection. This study demonstrates the increased stability of these SLPI variants compared with SLPI-WT and their therapeutic potential as a putative anti-inflammatory treatment for CF lung disease.