Clinical Phenotypes Associated to Engrailed 2 Gene Alterations in a Series of Neuropediatric Patients

Tactile sensory processing deficits in genetic mouse models of autism spectrum disorder

Altered sensory processing is a common feature in autism spectrum disorder (ASD), as recognized in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). Although altered responses to tactile stimuli are observed in over 60% of individuals with ASD, the neurobiological basis of this phenomenon is poorly understood. ASD has a strong genetic component and genetic mouse models can provide valuable insights into the mechanisms underlying tactile abnormalities in ASD. This review critically addresses recent findings regarding tactile processing deficits found in mouse models of ASD, with a focus on behavioral, anatomical, and functional alterations. Particular attention was given to cellular and circuit-level functional alterations, both in the peripheral and central nervous systems, with the objective of highlighting possible convergence mechanisms across models. By elucidating the impact of mutations in ASD candidate genes on somatosensory circuits and correlating them with behavioral phenotypes, this review significantly advances our understanding of tactile deficits in ASD. Such insights not only broaden our comprehension but also pave the way for future therapeutic interventions.

PAFAH1B3 is a KLF9 target gene that promotes proliferation and metastasis in pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal human malignancies. Uncontrolled cell proliferation, invasion and migration of pancreatic cancer cells are the fundamental causes of death in PDAC patients. Our previous studies showed that KLF9 inhibits the proliferation, invasion and migration of pancreatic cancer cells. However, the underlying mechanisms are not fully understood. In this study, we found that platelet-activating factor acetylhydrolase IB3 (PAFAH1B3) is highly expressed in pancreatic cancer tissues and cells. In vitro and in vivo studies showed that overexpression of PAFAH1B3 promoted the proliferation and invasion of pancreatic cancer cells, while downregulation of PAFAH1B3 inhibited these processes. We found that KLF9 expression is negatively correlated with PAFAH1B3 expression in pancreatic cancer tissues and cells. Western blotting revealed that KLF9 negatively regulates the expression of PAFAH1B3 in pancreatic cancer tissues and cells. Rescue experiments showed that overexpression of PAFAH1B3 could partially attenuate the suppression of pancreatic cancer cell proliferation, invasion and migration induced by KLF9 overexpression. Finally, chromatin immunoprecipitation (ChIP) and dual-luciferase reporter assays were carried out, and the results showed that KLF9 directly binds to the promoter of PAFAH1B3 and inhibits its transcriptional activity. In conclusion, our study indicated that KLF9 can inhibit the proliferation, invasion, migration and metastasis of pancreatic cancer cells by inhibiting PAFAH1B3.

Gene editing in monogenic autism spectrum disorder: animal models and gene therapies

Autism spectrum disorder (ASD) is a lifelong neurodevelopmental disease, and its diagnosis is dependent on behavioral manifestation, such as impaired reciprocal social interactions, stereotyped repetitive behaviors, as well as restricted interests. However, ASD etiology has eluded researchers to date. In the past decades, based on strong genetic evidence including mutations in a single gene, gene editing technology has become an essential tool for exploring the pathogenetic mechanisms of ASD via constructing genetically modified animal models which validates the casual relationship between genetic risk factors and the development of ASD, thus contributing to developing ideal candidates for gene therapies. The present review discusses the progress in gene editing techniques and genetic research, animal models established by gene editing, as well as gene therapies in ASD. Future research should focus on improving the validity of animal models, and reliable DNA diagnostics and accurate prediction of the functional effects of the mutation will likely be equally crucial for the safe application of gene therapies.

Joint dimension reduction and clustering analysis of single-cell RNA-seq and spatial transcriptomics data

Dimension reduction and (spatial) clustering is usually performed sequentially; however, the low-dimensional embeddings estimated in the dimension-reduction step may not be relevant to the class labels inferred in the clustering step. We therefore developed a computation method, Dimension-Reduction Spatial-Clustering (DR-SC), that can simultaneously perform dimension reduction and (spatial) clustering within a unified framework. Joint analysis by DR-SC produces accurate (spatial) clustering results and ensures the effective extraction of biologically informative low-dimensional features. DR-SC is applicable to spatial clustering in spatial transcriptomics that characterizes the spatial organization of the tissue by segregating it into multiple tissue structures. Here, DR-SC relies on a latent hidden Markov random field model to encourage the spatial smoothness of the detected spatial cluster boundaries. Underlying DR-SC is an efficient expectation-maximization algorithm based on an iterative conditional mode. As such, DR-SC is scalable to large sample sizes and can optimize the spatial smoothness parameter in a data-driven manner. With comprehensive simulations and real data applications, we show that DR-SC outperforms existing clustering and spatial clustering methods: it extracts more biologically relevant features than conventional dimension reduction methods, improves clustering performance, and offers improved trajectory inference and visualization for downstream trajectory inference analyses.

Prostate Cancer Biomarkers: From diagnosis to prognosis and precision-guided therapeutics

Prostate cancer (PCa) is one of the most commonly diagnosed malignancies and among the leading causes of cancer-related death worldwide. It is a highly heterogeneous disease, ranging from remarkably slow progression or inertia to highly aggressive and fatal disease. As therapeutic decision-making, clinical trial design and outcome highly depend on the appropriate stratification of patients to risk groups, it is imperative to differentiate between benign versus more aggressive states. The incorporation of clinically valuable prognostic and predictive biomarkers is also potentially amenable in this process, in the timely prevention of metastatic disease and in the decision for therapy selection. This review summarizes the progress that has so far been made in the identification of the genomic events that can be used for the classification, prediction and prognostication of PCa, and as major targets for clinical intervention. We include an extensive list of emerging biomarkers for which there is enough preclinical evidence to suggest that they may constitute crucial targets for achieving significant advances in the management of the disease. Finally, we highlight the main challenges that are associated with the identification of clinically significant PCa biomarkers and recommend possible ways to overcome such limitations.

Engrailed-2 is a cell autonomous regulator of neurogenesis in cultured hippocampal neural stem cells

Abnormalities in genes that regulate early brain development are known risk factors for neurodevelopmental disorders. Engrailed-2 (En2) is a homeodomain transcription factor with established roles in cerebellar patterning. En2 is highly expressed in the developing mid-hindbrain region, and En2 knockout (KO) mice exhibit major deficits in mid-hindbrain structures. However, En2 is also expressed in forebrain regions including the hippocampus, but its function is unknown. Previous studies have shown that the hippocampus of En2-KO mice exhibits reductions in its volume and cell numbers due to aberrant neurogenesis. Aberrant neurogenesis is due, in part, to noncell autonomous effects, specifically, reductions of innervating norepinephrine fibers from the locus coeruleus. In this study, we investigate possible cell autonomous roles of En2 in hippocampal neurogenesis. We examine proliferation, survival, and differentiation using cultures of hippocampal neurospheres of P7 wild-type (WT) and En2-KO hippocampal neural progenitor cells (NPCs). At 7 days, En2-KO neurospheres were larger on average than WT spheres and exhibited 2.5-fold greater proliferation and 2-fold increase in apoptotic cells, similar to in vivo KO phenotype. Further, En2-KO cultures exhibited 40% less cells with neurite projections, suggesting decreased differentiation. Lastly, reestablishing En2 expression in En2-KO NPCs rescued excess proliferation. These results indicate that En2 functions in hippocampal NPCs by inhibiting proliferation and promoting survival and differentiation in a cell autonomous manner. More broadly, this study suggests that En2 impacts brain structure and function in diverse regions outside of the mid-hindbrain.

Advances in cerebral palsy biomarkers

Cerebral palsy (CP), defined as a group of nonprogressive disorders of movement and posture, is the most common cause of severe neurodisability in children. The prevalence of CP is the same across the globe, affecting approximately 17 million people worldwide. Cerebral Palsy is an umbrella term used to describe the disease due to its inherent heterogeneity. For instance, CP has multiple (1) causes; (2) clinical types; (3) patterns of neuropathology on brain imaging and (4) it's associated with several developmental pathologies such as intellectual disability, autism, epilepsy, and visual impairment. Understanding its physiopathology is crucial to developing protective strategies. Despite its importance, there is still insufficient progress in the areas of CP prediction, early diagnosis, treatment, and prevention. Herein we describe the current risk factors and biomarkers used for the diagnosis and prediction of CP. With the advancement in biomarker discovery, we predict that our understanding of the etiopathophysiology of CP will also increase, lending to more opportunities for developing novel treatments and prognosis.

Integrated Transcriptome Analyses Revealed Key Target Genes in Mouse Models of Autism

Genetic mutations are the major pathogenic factor of Autism Spectrum Disorder (ASD). In recent years, more and more ASD risk genes have been revealed, among which there are a group of transcriptional regulators. Considering the similarity of the core clinical phenotypes, it is possible that these different factors may regulate the expression levels of certain key targets. Identification of these targets could facilitate the understanding of the etiology and developing of novel diagnostic and therapeutic methods. Therefore, we performed integrated transcriptome analyses of RNA-Seq and microarray data in multiple ASD mouse models and identified a number of common downstream genes in various brain regions, many of which are related to the structure and function of the synapse components or drug addiction. We then established protein-protein interaction networks of the overlapped targets and isolated the hub genes by 11 algorithms based on the topological structure of the networks, including Sdc4, Vegfa, and Cp in the Cortex-Adult subgroup, Gria1 in the Cortex-Juvenile subgroup, and Kdr, S1pr1, Ubc, Grm2, Grin2b, Nrxn1, Pdyn, Grin3a, Itgam, Grin2a, Gabra2, and Camk4 in the Hippocampus-Adult subgroup, many of which have been associated with ASD in previous studies. Finally, we cross compared our results with human brain transcriptional data sets and verified several key candidates, which may play important role in the pathology process of ASD, including SDC4, CP, S1PR1, UBC, PDYN, GRIN2A, GABRA2, and CAMK4. In summary, by integrated bioinformatics analysis, we have identified a series of potentially important molecules for future ASD research. Autism Res 2020, 13: 352-368. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Abnormal transcriptional regulation accounts for a significant portion of Autism Spectrum Disorder. In this study, we performed transcriptome analyses of mouse models to identify common downstream targets of transcriptional regulators involved in ASD. We identified several recurrent target genes that are close related to the common pathological process of ASD, including SDC4, CP, S1PR1, UBC, PDYN, GRM2, NRXN1, GRIN3A, ITGAM, GRIN2A, GABRA2, and CAMK4. These results provide potentially important targets for understanding the molecular mechanism of ASD.

Functional Outcomes of Cerebellar Malformations

The cerebellum is well-established as a primary center for controlling sensorimotor functions. However, recent experiments have demonstrated additional roles for the cerebellum in higher-order cognitive functions such as language, emotion, reward, social behavior, and working memory. Based on the diversity of behaviors that it can influence, it is therefore not surprising that cerebellar dysfunction is linked to motor diseases such as ataxia, dystonia, tremor, and Parkinson's disease as well to non-motor disorders including autism spectrum disorders (ASD), schizophrenia, depression, and anxiety. Regardless of the condition, there is a growing consensus that developmental disturbances of the cerebellum may be a central culprit in triggering a number of distinct pathophysiological processes. Here, we consider how cerebellar malformations and neuronal circuit wiring impact brain function and behavior during development. We use the cerebellum as a model to discuss the expanding view that local integrated brain circuits function within the context of distributed global networks to communicate the computations that drive complex behavior. We highlight growing concerns that neurological and neuropsychiatric diseases with severe behavioral outcomes originate from developmental insults to the cerebellum.