Identification of 11p14.1-p15.3 deletion probably associated with short stature, relative macrocephaly, and delayed closure of the fontanelles

NELL1 membranous nephropathy: clinical associations provide mechanistic clues

Neural epidermal growth factor-like 1 (NELL1) membranous nephropathy (MN) is notable for its segmental deposit distribution, IgG1 dominant deposits, and comparatively high rate of spontaneous remission. It has been associated with a variety of exposures and secondary conditions, specifically use of thiol-containing medications - including lipoic acid, bucillamine, and tiopronin - as well as traditional indigenous medications (TIM) particularly those with high mercury content, and non-steroid anti-inflammatory drugs (NSAIDs). Malignancies, graft vs. host disease (GVHD), infection, and autoimmune conditions have also been associated with NELL1 MN. Herein, we provide a detailed summary of the clinicopathologic features of NELL1 and associations with underlying conditions, with a focus on treatment and outcomes. Rare cases of dual NELL1 and phospholipase A2 receptor (PLA2R) positive MN are reviewed. Genome-wide association study of NELL1, role of NELL1 in other physiologic and pathologic processes, and connection between NELL1 MN and malignancy with relevance of NELL1 tumor staining are examined. Finally, relationships and potential disease mechanisms of thiol- and mercury- associated NELL1 MN are discussed.

Comparative population genomic analysis uncovers novel genomic footprints and genes associated with small body size in Chinese pony

Background

Body size is considered as one of the most fundamental properties of an organism. Due to intensive breeding and artificial selection throughout the domestication history, horses exhibit striking variations for heights at withers and body sizes. Debao pony (DBP), a famous Chinese horse, is known for its small body size and lives in Guangxi mountains of southern China. In this study, we employed comparative population genomics to study the genetic basis underlying the small body size of DBP breed based on the whole genome sequencing data. To detect genomic signatures of positive selection, we applied three methods based on population comparison, fixation index (F_ST), cross population composite likelihood ratio (XP-CLR) and nucleotide diversity (θπ), and further analyzed the results to find genomic regions under selection for body size-related traits.

Results

A number of protein-coding genes in windows with the top 1% values of F_ST (367 genes), XP-CLR (681 genes), and log₂ (θπ ratio) (332 genes) were identified. The most significant signal of positive selection was mapped to the NELL1 gene, probably underlies the body size and development traits, and may also have been selected for short stature in the DBP population. In addition, some other loci on different chromosomes were identified to be potentially involved in the development of body size.

Conclusions

Results of our study identified some positively selected genes across the horse genome, which are possibly involved in body size traits. These novel candidate genes may be useful targets for clarifying our understanding of the molecular basis of body size and as such they should be of great interest for future research into the genetic architecture of relevant traits in horse breeding program.

Inactivation of Nell-1 in Chondrocytes Significantly Impedes Appendicular Skeletogenesis

NELL-1, an osteoinductive protein, has been shown to regulate skeletal ossification. Interestingly, an interstitial 11p14.1-p15.3 deletion involving the Nell-1 gene was recently reported in a patient with short stature and delayed fontanelle closure. Here we sought to define the role of Nell-1 in endochondral ossification by investigating Nell-1-specific inactivation in Col2α1-expressing cell lineages. Nell-1flox/flox ; Col2α1-Cre+ (Nell-1Col2α1 KO) mice were generated for comprehensive analysis. Nell-1Col2α1 KO mice were born alive but displayed subtle femoral length shortening. At 1 and 3 months postpartum, Nell-1 inactivation resulted in dwarfism and premature osteoporotic phenotypes. Specifically, Nell-1Col2α1 KO femurs and tibias exhibited significantly reduced length, bone mineral density (BMD), bone volume per tissue volume (BV/TV), trabecular number/thickness, cortical volume/thickness/density, and increased trabecular separation. The decreased bone formation rate revealed by dynamic histomorphometry was associated with altered numbers and/or function of osteoblasts and osteoclasts. Furthermore, longitudinal observations by in vivo micro-CT showed delayed and reduced mineralization at secondary ossification centers in mutants. Histologically, reduced staining intensities of Safranin O, Col-2, Col-10, and fewer BrdU-positive chondrocytes were observed in thinner Nell-1Col2α1 KO epiphyseal plates along with altered distribution and weaker expression level of Ihh, Patched-1, PTHrP, and PTHrP receptor. Primary Nell-1Col2α1 KO chondrocytes also exhibited decreased proliferation and differentiation, and its downregulated expression of the Ihh-PTHrP signaling molecules can be partially rescued by exogenous Nell-1 protein. Moreover, intranuclear Gli-1 protein and gene expression of the Gli-1 downstream target genes, Hip-1 and N-Myc, were also significantly decreased with Nell-1 inactivation. Notably, the rescue effects were diminished/reduced with application of Ihh signaling inhibitors, cyclopamine or GANT61. Taken together, these findings suggest that Nell-1 is a pivotal modulator of epiphyseal homeostasis and endochondral ossification. The cumulative chondrocyte-specific Nell-1 inactivation significantly impedes appendicular skeletogenesis resulting in dwarfism and premature osteoporosis through inhibiting Ihh signaling and predominantly altering the Ihh-PTHrP feedback loop. © 2018 American Society for Bone and Mineral Research.

Neurexin Superfamily Cell Membrane Receptor Contactin-Associated Protein Like-4 (Cntnap4) Is Involved in Neural EGFL-Like 1 (Nell-1)-Responsive Osteogenesis

Contactin-associated protein-like 4 (Cntnap4) is a member of the neurexin superfamily of transmembrane molecules that have critical functions in neuronal cell communication. Cntnap4 knockout mice display decreased presynaptic gamma-aminobutyric acid (GABA) and increased dopamine release that is associated with severe, highly penetrant, repetitive, and perseverative movements commonly found in human autism spectrum disorder patients. However, no known function of Cntnap4 has been revealed besides the nervous system. Meanwhile, secretory protein neural EGFL-like 1 (Nell-1) is known to exert potent osteogenic effects in multiple small and large animal models without the off-target effects commonly found with bone morphogenetic protein 2. In this study, while searching for a Nell-1-specific cell surface receptor during osteogenesis, we identified and validated a ligand/receptor-like interaction between Nell-1 and Cntnap4 by demonstrating: 1) Nell-1 and Cntnap4 colocalization on the surface of osteogenic-committed cells; 2) high-affinity interaction between Nell-1 and Cntnap4; 3) abrogation of Nell-1-responsive Wnt and MAPK signaling transduction, as well as osteogenic effects, via Cntnap4 knockdown; and 4) replication of calvarial cleidocranial dysplasias-like defects observed in Nell-1-deficient mice in Wnt1-Cre-mediated Cntnap4-knockout transgenic mice. In aggregate, these findings indicate that Cntnap4 plays a critical role in Nell-1-responsive osteogenesis. Further, this is the first functional annotation for Cntnap4 in the musculoskeletal system. Intriguingly, Nell-1 and Cntnap4 also colocalize on the surface of human hippocampal interneurons, implicating Nell-1 as a potential novel ligand for Cntnap4 in the nervous system. This unexpected characterization of the ligand/receptor-like interaction between Nell-1 and Cntnap4 indicates a novel biological functional axis for Nell-1 and Cntnap4 in osteogenesis and, potentially, in neural development and function. © 2018 American Society for Bone and Mineral Research.

A Deep Learning Approach for Predicting Antidepressant Response in Major Depression Using Clinical and Genetic Biomarkers

In the wake of recent advances in scientific research, personalized medicine using deep learning techniques represents a new paradigm. In this work, our goal was to establish deep learning models which distinguish responders from non-responders, and also to predict possible antidepressant treatment outcomes in major depressive disorder (MDD). To uncover relationships between the responsiveness of antidepressant treatment and biomarkers, we developed a deep learning prediction approach resulting from the analysis of genetic and clinical factors such as single nucleotide polymorphisms (SNPs), age, sex, baseline Hamilton Rating Scale for Depression score, depressive episodes, marital status, and suicide attempt status of MDD patients. The cohort consisted of 455 patients who were treated with selective serotonin reuptake inhibitors (treatment-response rate = 61.0%; remission rate = 33.0%). By using the SNP dataset that was original to a genome-wide association study, we selected 10 SNPs (including ABCA13 rs4917029, BNIP3 rs9419139, CACNA1E rs704329, EXOC4 rs6978272, GRIN2B rs7954376, LHFPL3 rs4352778, NELL1 rs2139423, NUAK1 rs2956406, PREX1 rs4810894, and SLIT3 rs139863958) which were associated with antidepressant treatment response. Furthermore, we pinpointed 10 SNPs (including ARNTL rs11022778, CAMK1D rs2724812, GABRB3 rs12904459, GRM8 rs35864549, NAALADL2 rs9878985, NCALD rs483986, PLA2G4A rs12046378, PROK2 rs73103153, RBFOX1 rs17134927, and ZNF536 rs77554113) in relation to remission. Then, we employed multilayer feedforward neural networks (MFNNs) containing 1-3 hidden layers and compared MFNN models with logistic regression models. Our analysis results revealed that the MFNN model with 2 hidden layers (area under the receiver operating characteristic curve (AUC) = 0.8228 ± 0.0571; sensitivity = 0.7546 ± 0.0619; specificity = 0.6922 ± 0.0765) performed maximally among predictive models to infer the complex relationship between antidepressant treatment response and biomarkers. In addition, the MFNN model with 3 hidden layers (AUC = 0.8060 ± 0.0722; sensitivity = 0.7732 ± 0.0583; specificity = 0.6623 ± 0.0853) achieved best among predictive models to predict remission. Our study indicates that the deep MFNN framework may provide a suitable method to establish a tool for distinguishing treatment responders from non-responders prior to antidepressant therapy.