Investigating the genetic determination of duration-of-fertility trait in breeding hens.

The duration-of-fertility (DF), which was defined as the number of days when breeding hens lay fertile eggs following copulation or artificial insemination (AI), is an important economic trait in chick production when it has strong effects on fertile egg output and production costs. Little is known about the underlying genes and molecular markers related to DF trait to date. Here, we measured the DF of 701 Chinese Jinghong hens and 408 Jingfen hens. The DF showed high individual variability and potential for genetic improvement. Then, 192 Jinghong breeding hens were provided for a genome-wide association study, 27 SNPs respectively located in three genomic linkage regions (GGA1:41Kb; GGA3:39Kb and GGA8:39Kb) were suggested to be significantly associated with DF. Particularly, 6 of these 27 SNPs were further verified to be associated with DF in the 701 Jinghong and 408 Jingfen hens using PCR-RFLP genotyping method. These 27 SNPs were also mapped to 7 genes according to their genomic position. Furtherly, 5 of these 7 genes were tested using qPCR. Results show that the CYP2D6, WBP2NL, ESR1 and TGFBR3 mRNA expression levels of hens with long DF were significantly higher than the hens with short DF (P < 0.05). Overall, findings in our research provide new insight into the genetic basis of duration-of-fertility in breeding hens while providing new clues for further functional validation on the DF-related genetic regulation mechanism and improvement of DF through chicken breeding.

Proteomics analysis of G protein-coupled receptor kinase 4-inhibited cellular growth of HEK293 cells.

G protein-coupled receptor kinases (GRKs) are involved in a wide range of cellular physiology and pathological activities by specifically phosphorylating activated G protein-coupled receptors (GPCRs) to terminate GPCR signaling, or through regulating non-GPCR substrates. We recently reported that overexpression of GRK4 halts cell proliferation and induces cellular senescent phenotype in HEK293 cells. In this study, a quantitative proteomic assay was performed to analyze the protein profiles between HEK293 cells expressing and not expressing GRK4. Results revealed 39 upregulated and 59 downregulated differently expressed proteins (DEPs) in a total of 4124 identified proteins. Gene ontology (GO) annotation and functional enrichment revealed that the DEPs were related to metabolic processes regulated by the binding of these RNA/proteins under the biological processes. The Kyoto Encyclopedia of Gene and Genomes (KEGG) analysis showed pathways of cell development, division, proliferation, apoptosis, aging, autophagy, cell death and cell cycle progression are involved in. Immunoblotting validation of expression of six key target proteins, CALM1, STAT3, CDK1, CDK6, TOP2A, and GRK4, which speculatively maintain abnormal activity in the above pathways, was consistent with the results of proteomics analysis. Lastly, a biological phenotype assay confirmed that GRK4 promoted HEK293 cell growth blockage and G1/0 arrest. Taken together, this study identified some novel molecules that involve in GRK4 signaling and provided valuable information for further studying the mechanisms underlying GRK4-induced proliferative inhibition. SIGNIFICANCE: A quantitative proteomic assay was performed in HEK293 cells expressing and not expressing GRK4 39 upregulated and 59 downregulated differently expressed proteins (DEPs)were identified. DEPs involved in pathways of cell development, division, proliferation, apoptosis, aging, autophagy, cell death and cell cycle progression. Biological phenotype assay confirmed that GRK4 prompted HEK293 cell growth blockage and G1/0 arrest.

G protein-coupled receptor kinase 4-induced cellular senescence and its senescence-associated gene expression profiling.

Senescent cells have lost their capacity for proliferation and manifest as irreversibly in cell cycle arrest. Many membrane receptors, including G protein-coupled receptors (GPCRs), initiate a variety of intracellular signaling cascades modulating cell division and potentially play roles in triggering cellular senescence response. GPCR kinases (GRKs) belong to a family of serine/threonine kinases. Although their role in homologous desensitization of activated GPCRs is well established, the involvement of the kinases in cell proliferation is still largely unknown. In this study, we isolated GRK4-GFP expressing HEK293 cells by fluorescence-activated cell sorting (FACS) and found that the ectopic expression of GRK4 halted cell proliferation. Cells expressing GRK4 (GRK4(+)) demonstrated cell cycle G1/G0 phase arrest, accompanied with significant increase of senescence-associated-ß-galactosidase (SA-ß-Gal) activity. Expression profiling analysis of 78 senescence-related genes by qRT-PCR showed a total of 17 genes significantly changed in GRK4(+) cells (≥ 2 fold, p < 0.05). Among these, 9 genes - AKT1, p16INK4, p27KIP1, p19INK4, IGFBP3, MAPK14, PLAU, THBS1, TP73 - were up-regulated, while 8 genes, Cyclin A2, Cyclin D1, CDK2, CDK6, ETS1, NBN, RB1, SIRT1, were down-regulated. The increase in cyclin-dependent kinase inhibitors (p16, p27) and p38 MAPK proteins (MAPK14) was validated by immunoblotting. Neither p53 nor p21Waf1/Cip1 protein was detectable, suggesting no p53 activation in the HEK293 cells. These results unveil a novel function of GRK4 on triggering a p53-independent cellular senescence, which involves an intricate signaling network.

Deformable image registration for tissues with large displacements.

Image registration for internal organs and soft tissues is considered extremely challenging due to organ shifts and tissue deformation caused by patients' movements such as respiration and repositioning. In our previous work, we proposed a fast registration method for deformable tissues with small rotations. We extend our method to deformable registration of soft tissues with large displacements. We analyzed the deformation field of the liver by decomposing the deformation into shift, rotation, and pure deformation components and concluded that in many clinical cases, the liver deformation contains large rotations and small deformations. This analysis justified the use of linear elastic theory in our image registration method. We also proposed a region-based neuro-fuzzy transformation model to seamlessly stitch together local affine and local rigid models in different regions. We have performed the experiments on a liver MRI image set and showed the effectiveness of the proposed registration method. We have also compared the performance of the proposed method with the previous method on tissues with large rotations and showed that the proposed method outperformed the previous method when dealing with the combination of pure deformation and large rotations. Validation results show that we can achieve a target registration error of [Formula: see text] and an average centerline distance error of [Formula: see text]. The proposed technique has the potential to significantly improve registration capabilities and the quality of intraoperative image guidance. To the best of our knowledge, this is the first time that the complex displacement of the liver is explicitly separated into local pure deformation and rigid motion.

Automatic error correction using adaptive weighting for vessel-based deformable image registration.

In this paper, we extend our previous work on deformable image registration to inhomogenous tissues. Inhomogenous tissues include the tissues with embedded tumors, which is common in clinical applications. It is a very challenging task since the registration method that works for homogenous tissues may not work well with inhomogenous tissues. The maximum error normally occurs in the regions with tumors and often exceeds the acceptable error threshold. In this paper, we propose a new error correction method with adaptive weighting to reduce the maximum registration error. Our previous fast deformable registration method is used in the inner loop. We have also proposed a new evaluation metric average error of deformation field (AEDF) to evaluate the registration accuracy in regions between vessels and bifurcation points. We have validated the proposed method using liver MR images from human subjects. AEDF results show that the proposed method can greatly reduce the maximum registration errors when compared with the previous method with no adaptive weighting. The proposed method has the potential to be used in clinical applications to reduce registration errors in regions with tumors.

Endoscopy-MR Image Fusion for Image Guided Procedures.

Minimally invasive endoscope based abdominal procedures provide potential advantages over conventional open surgery such as reduced trauma, shorter hospital stay, and quick recovery. One major limitation of using this technique is the narrow view of the endoscope and the lack of proper 3D context of the surgical site. In this paper, we propose a rapid and accurate method to align intraoperative stereo endoscopic images of the surgical site with preoperative Magnetic Resonance (MR) images. Gridline light pattern is projected on the surgical site to facilitate the registration. The purpose of this surface-based registration is to provide 3D context of the surgical site to the endoscopic view. We have validated the proposed method on a liver phantom and achieved the surface registration error of 0.76 ± 0.11 mm.

Extraction of liver vessel centerlines under guidance of patient-specific models.

Fast extraction of blood vessels of abdominal organs is still a challenging task especially in intra-procedural treatments due to large tissue deformation. In this study, we propose a novel joint vessel extraction and registration framework. This vessel extraction technique is under the guidance of prior knowledge patient specific models. The proposed technique automatically provides correspondence between extracted vessels and pre-procedural vessels, which is important for image guidance such as labeled vessels from pre-procedural models, improves the quality of disease diagnosis using multiple images and follow-up, and provides important information for nonrigid image registration. Another key component in our framework is to dynamically update mapped pre-procedural models by rapidly registering the patient model to the current image based on strain energy, point marks and 3D extracted vessels currently available. We have demonstrated the effectiveness of our technique in extraction of vessels from liver MR images. Validation shows a extraction error of 3.99 mm. This technique has the potential to significantly improve the quality of intra-procedural image guidance, diagnosis of disease and treatment planning.

3D curve constrained deformable registration using a neuro-fuzzy transformation model.

Image registration of abdominal organs and soft tissues is considered daunting due to large organ shift and tissue deformation caused by patient motion, respiration, etc. In this study, we propose a novel neuro-fuzzy deformable registration technique that is constrained by 3D curves of vessel centerlines and point marks while minimizing strain energy. We present an analytical global optimal solution in the case when 3D curves, strain energy and point marks are considered, which will provide fast and robust deformable match for internal structures such as blood vessels, and significantly reduce the chance to get trapped in local minima. We have demonstrated the effectiveness of our deformable technique in registering liver MR images. Validation shows a target registration error of 1.98 mm and an average centerline distance error of 1.65 mm. This technique has the potential to significantly improve registration capability and the quality of intra-operative image guidance.

Rapid dynamic image registration of the beating heart for diagnosis and surgical navigation.

Dynamic cardiac magnetic resonance imaging (MR) and computed tomography (CT) provide cardiologists and cardiac surgeons with high-quality 4-D images for diagnosis and therapy, yet the effective use of these high-quality anatomical models remains a challenge. Ultrasound (US) is a flexible imaging tool, but the US images produced are often difficult to interpret unless they are placed within their proper 3-D anatomical context. The ability to correlate real-time 3-D US volumes (RT3D US) with dynamic MR/CT images would offer a significant contribution to improve the quality of cardiac procedures. In this paper, we present a rapid two-step method for registering RT3D US to high-quality dynamic 3-D MR/CT images of the beating heart. This technique overcomes some major limitations of image registration (such as the correct registration result not necessarily occurring at the maximum of the mutual information (MI) metric) using the MI metric. We demonstrate the effectiveness of our method in a dynamic heart phantom (DHP) study and a human subject study. The achieved mean target registration error of CT+US images in the phantom study is 2.59 mm. Validation using human MR/US volumes shows a target registration error of 1.76 mm. We anticipate that this technique will substantially improve the quality of cardiac diagnosis and therapies.

Dynamic 2D ultrasound and 3D CT image registration of the beating heart.

Two-dimensional ultrasound (US) is widely used in minimally invasive cardiac procedures due to its convenience of use and noninvasive nature. However, the low quality of US images often limits their utility as a means for guiding procedures, since it is often difficult to relate the images to their anatomical context. To improve the interpretability of the US images while maintaining US as a flexible anatomical and functional real-time imaging modality, we describe a multimodality image navigation system that integrates 2D US images with their 3D context by registering them to high quality preoperative models based on magnetic resonance imaging (MRI) or computed tomography (CT) images. The mapping from such a model to the patient is completed using spatial and temporal registrations. Spatial registration is performed by a two-step rapid registration method that first approximately aligns the two images as a starting point to an automatic registration procedure. Temporal alignment is performed with the aid of electrocardiograph (ECG) signals and a latency compensation method. Registration accuracy is measured by calculating the TRE. Results show that the error between the US and preoperative images of a beating heart phantom is 1.7 +/-0.4 mm, with a similar performance being observed in in vivo animal experiments.

Registration of 3-D CT and 2-D flat images of mouse via affine transformation.

It is difficult to directly coregister the 3-D fluorescence molecular tomography (FMT) image of a small tumor in a mouse whose maximal diameter is only a few millimeters with a larger CT image of the entire animal that spans about 10 cm. This paper proposes a new method to register 2-D flat and 3-D CT image first to facilitate the registration between small 3-D FMT images and large 3-D CT images. A novel algorithm combining differential evolution and improved simplex method for the registration between the 2-D flat and 3-D CT images is introduced and validated with simulated images and real images of mice. The visualization of the alignment of the 3-D FMT and CT image through 2-D registration shows promising results.

Dynamic 3D ultrasound and MR image registration of the beating heart.

Real-time three-dimensional ultrasound (RT3D US) is an ideal imaging modality for the diagnosis of cardiac disease. RT3D US is a flexible, inexpensive, non-invasive tool that provides important diagnostic information related to cardiac function. Unfortunately, RT3D US suffers from inherent shortcomings, such as low signal-to-noise ratio and limited field of view, producing images that are difficult to interpret. Multi-modal dynamic cardiac image registration is a well-recognized approach that compensates for these deficiencies while retaining the advantages of RT3D US imaging. The clinical application of multi-modal image registration methods is difficult, and there are a number of implementation issues to be resolved. In this work, we present a method for the rapid registration of RT3D US images of the beating heart to high-resolution magnetic resonance (MR) images. This method was validated using a volunteer image set. Validation results demonstrate that this approach can achieve rapid registration of images of the beating heart with fiducial landmark and registration errors of 1.25 +/- 0.63 and 1.76 mm respectively. This technique can potentially be used to improve the diagnosis of cardiac disease by augmenting RT3D US images with high-resolution MR images and to facilitate intra-operative image fusion for minimally invasive cardio-thoracic surgical navigation.