Transcriptome-(phospho)proteome characterization of brain of a germline model of cytoplasmic-predominant Pten expression with autism-like phenotypes

AbstractPTEN has a strong Mendelian association with autism spectrum disorder (ASD), representing a special case in autism’s complex genetic architecture. Animal modeling for constitutional Pten mutation creates an opportunity to study how disruption of Pten affects neurobiology and glean potential insight into ASD pathogenesis. Subsequently, we comprehensively characterized the neural (phospho)proteome of Ptenm3m4/m3m4 mice, which exhibits cytoplasmic-predominant Pten expression, by applying mass spectrometry technology to their brains at two-weeks- (P14) and six-weeks-of-age (P40). The differentially expressed/phosphorylated proteins were subjected to gene enrichment, pathway, and network analyses to assess the affected biology. We identified numerous differentially expressed/phosphorylated proteins, finding greater dysregulation at P40 consistent with prior transcriptomic data. The affected pathways were largely related to PTEN function or neurological processes, while scant direct overlap was found across datasets. Network analysis pointed to ASD risk genes like Pten and Psd-95 as major regulatory hubs, suggesting they likely contribute to initiation or maintenance of cellular and perhaps organismal phenotypes related to ASD.

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Transcriptome-(Phospho)proteome Characterization of the Brain of a Constitutional Model of Cytoplasmic-predominant Pten Expression With Autism-Like Phenotypes

10.21203/rs.3.rs-111823/v1 ◽

2020 ◽

Author(s):

Stetson Thacker ◽

Charis Eng

Keyword(s):

Biological Networks ◽

Autism Spectrum ◽

Pten Expression ◽

Differentially Expressed ◽

Future Research ◽

Wildtype Mouse ◽

Network Analyses ◽

Phosphorylated Proteins ◽

Mass Spectrometry Technology ◽

The Brain

Abstract BackgroundPTEN, a well-studied tumor suppressor, has one of the strongest Mendelian associations with autism spectrum disorder (ASD), representing a special case in autism’s complex genetic architecture. Animal modeling for constitutional Pten mutation creates an opportunity to study how disruption of Pten affects neurobiology, providing insights that may be generalizable or at least inform our understanding of ASD. Although the neural transcriptome has been well characterized in Pten models, little has been done concerning the proteome and phosphoproteome. This is a critical gap in knowledge given that these –omic landscapes are more proximal to the actively observed biology than the transcriptome.MethodsWe sought to comprehensively characterize the neural proteome and phosphoproteome of the Ptenm3m4/m3m4 mouse, which exhibits cytoplasmic-predominant Pten expression. Proteomic and phosphoproteomic scans of Ptenm3m4/m3m4 and wildtype mouse brain at two-weeks- (P14) and six-weeks-of-age (P40) were performed using liquid chromatography with tandem mass spectrometry technology. Following quantification of differentially expressed/phosphorylated proteins, we performed gene overlap, gene enrichment, pathway, and network analyses to identify the similarity across the various datasets and understand the affected biological landscape.ResultsWe identified numerous differentially expressed/phosphorylated proteins, finding that dysregulation was greater at P40, consistent with the prior neural transcriptome data. We found the affected biological pathways were largely related to PTEN function, neurological processes, or neuroinflammation. Although we found minimal overlap among differentially expressed transcriptome-proteome-phosphoproteome molecules between P14 and P40 brains, there was congruence amongst the affected pathways. Importantly, network analysis identified Pten and Psd-95 as predominant regulatory nodes in the proteome and phosphoproteome, respectively. Moreover, we found overlap between our differentially expressed/phosphorylated proteins and known ASD risk genes.ConclusionsDifferential expression/phosphorylation revealed by transcriptome-proteome/phosphoproteome analyses of a germline Pten mutation model point to ASD risk genes like Pten and Psd-95 as major hubs in the protein networks, highlighting their important regulatory influence. Our observations here suggest Pten and Psd-95, known interactors in biological networks in the brain, are critical to either initiation or maintenance of cellular and perhaps organismal phenotypes related to ASD. Future research should explore rescuing Pten and Psd-95 function in attempts to ameliorate neurological pathologies and behavioral abnormalities.

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BrainEXP-NPD: a database of transcriptomic profiles of human brains of six neuropsychiatric disorders

10.1101/2021.05.30.446363 ◽

2021 ◽

Author(s):

Cuihua Xia ◽

Teng Ma ◽

Chuan Jiao ◽

Chao Chen ◽

Chunyu Liu

Keyword(s):

Bipolar Disorder ◽

Differential Expression ◽

Differentially Expressed Genes ◽

Neuropsychiatric Disorders ◽

Brain Regions ◽

Autism Spectrum ◽

Differentially Expressed ◽

Sequencing Data ◽

Network Analyses ◽

Gene Differential Expression

Background: Spatio-temporal gene expression has been widely used to study gene functions and biological mechanisms in diseases. Numerous microarray and RNA sequencing data focusing on brain transcriptomes in neuropsychiatric disorders have accumulated. However, their consistency, reproducibility has not been properly evaluated. Except for a few psychiatric disorders, like schizophrenia, bipolar disorder and autism, most have not been compared to each other for cross-disorder comparisons. Methods: We organized 48 human brain transcriptome datasets from six sources. The original brain donors include patients with schizophrenia (SCZ, N=427), bipolar disorder (BD, N=312), major depressive disorder (MDD, N=219), autism spectrum disorder (ASD, N=53), Alzheimer's disease (AD, N=765), Parkinson's disease (PD, N=163) as well as controls as unaffected by such disorders (CTRL, N=6,378), making it a total of 8,317 samples. Raw data included multiple brain regions of both sexes, with ages ranging from embryonic to seniors. After standardization, quality control, filtering and removal of known and unknown covariates, we performed comprehensive meta- and mega- analyses, including gene differential expression and gene co-expression network. Results: A total of 6922, 3011, 2703, 4389, 3507, 4279 significantly differentially expressed genes (FDR q < 0.05) were detected in the comparisons of 6 brain regions of SCZ-CTRL, 5 brain regions of BD-CTRL, 6 brain regions of MDD-CTRL, 4 brain regions of ASD-CTRL, 7 brain regions of AD-CTRL, and 6 brain regions of PD-CTRL, respectively. Most differentially expressed genes were brain region-specific and disease-specific. SCZ and BD have a maximal transcriptome similarity in striatum (ρ=0.42) among the four brain regions, as measured by Spearman's correlation of differential expression log2 FC values. SCZ and MDD have a maximal transcriptome similarity in hippocampus (ρ=0.30) among the five brain regions. BD and MDD have a maximal transcriptome similarity in frontal cortex (ρ=0.45) among the five brain regions. Other disease pairs have a less transcriptome similarity (ρ<0.1) in all brain regions. PD is negatively correlated with SCZ, BD, and MDD in cerebellum and striatum. We also performed coexpression network analyses for different disorders and controls separately. We developed a database named BrainEXP-NPD (http://brainexpnpd.org:8088/BrainEXPNPD/), to provide a user-friendly web interface for accessing the data, and analytical results of meta- and mega-analyses, including gene differential expression and gene co-expression networks between cases and controls on different brain regions, sexes and age groups. Discussion: BrainEXP-NPD compiled the largest collection of brain transcriptomic data of major neuropsychiatric disorders and presented lists of differentially expressed genes and coexpression modules in multiple brain regions of six major disorders.

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Genome-Wide Identification and Characterization of Long Noncoding RNAs Involved in Chinese Wheat Mosaic Virus Infection of Nicotiana benthamiana

Biology ◽

10.3390/biology10030232 ◽

2021 ◽

Vol 10 (3) ◽

pp. 232

Author(s):

Weiran Zheng ◽

Haichao Hu ◽

Qisen Lu ◽

Peng Jin ◽

Linna Cai ◽

...

Keyword(s):

Virus Infection ◽

Mosaic Virus ◽

Nicotiana Benthamiana ◽

Noncoding Rnas ◽

Long Noncoding Rnas ◽

Differentially Expressed ◽

Genome Wide ◽

Chinese Wheat

Recent studies have shown that a large number of long noncoding RNAs (lncRNAs) can regulate various biological processes in animals and plants. Although lncRNAs have been identified in many plants, they have not been reported in the model plant Nicotiana benthamiana. Particularly, the role of lncRNAs in plant virus infection remains unknown. In this study, we identified lncRNAs in N. benthamiana response to Chinese wheat mosaic virus (CWMV) infection by RNA sequencing. A total of 1175 lncRNAs, including 65 differentially expressed lncRNAs, were identified during CWMV infection. We then analyzed the functions of some of these differentially expressed lncRNAs. Interestingly, one differentially expressed lncRNA, XLOC_006393, was found to participate in CWMV infection as a precursor to microRNAs in N. benthamiana. These results suggest that lncRNAs play an important role in the regulatory network of N. benthamiana in response to CWMV infection.

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“Guilt by association” is not competitive with genetic association for identifying autism risk genes

Scientific Reports ◽

10.1038/s41598-021-95321-y ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Margot Gunning ◽

Paul Pavlidis

Keyword(s):

Machine Learning ◽

Genetic Association ◽

Gene Networks ◽

Rare Variants ◽

Association Studies ◽

Genetic Disorders ◽

Autism Spectrum ◽

Biological Data ◽

Disease Genes ◽

Risk Genes

AbstractDiscovering genes involved in complex human genetic disorders is a major challenge. Many have suggested that machine learning (ML) algorithms using gene networks can be used to supplement traditional genetic association-based approaches to predict or prioritize disease genes. However, questions have been raised about the utility of ML methods for this type of task due to biases within the data, and poor real-world performance. Using autism spectrum disorder (ASD) as a test case, we sought to investigate the question: can machine learning aid in the discovery of disease genes? We collected 13 published ASD gene prioritization studies and evaluated their performance using known and novel high-confidence ASD genes. We also investigated their biases towards generic gene annotations, like number of association publications. We found that ML methods which do not incorporate genetics information have limited utility for prioritization of ASD risk genes. These studies perform at a comparable level to generic measures of likelihood for the involvement of genes in any condition, and do not out-perform genetic association studies. Future efforts to discover disease genes should be focused on developing and validating statistical models for genetic association, specifically for association between rare variants and disease, rather than developing complex machine learning methods using complex heterogeneous biological data with unknown reliability.

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Microtubule Dynamics Plays a Vital Role in Plant Adaptation and Tolerance to Salt Stress

International Journal of Molecular Sciences ◽

10.3390/ijms22115957 ◽

2021 ◽

Vol 22 (11) ◽

pp. 5957

Author(s):

Hyun Jin Chun ◽

Dongwon Baek ◽

Byung Jun Jin ◽

Hyun Min Cho ◽

Mi Suk Park ◽

...

Keyword(s):

Gene Expression ◽

Cell Wall ◽

Salt Stress ◽

Differentially Expressed ◽

Differentially Expressed Proteins ◽

Related Gene ◽

Plant Adaptation ◽

Salt Stress Response ◽

Network Analyses ◽

Cell Wall Biogenesis

Although recent studies suggest that the plant cytoskeleton is associated with plant stress responses, such as salt, cold, and drought, the molecular mechanism underlying microtubule function in plant salt stress response remains unclear. We performed a comparative proteomic analysis between control suspension-cultured cells (A0) and salt-adapted cells (A120) established from Arabidopsis root callus to investigate plant adaptation mechanisms to long-term salt stress. We identified 50 differentially expressed proteins (45 up- and 5 down-regulated proteins) in A120 cells compared with A0 cells. Gene ontology enrichment and protein network analyses indicated that differentially expressed proteins in A120 cells were strongly associated with cell structure-associated clusters, including cytoskeleton and cell wall biogenesis. Gene expression analysis revealed that expressions of cytoskeleton-related genes, such as FBA8, TUB3, TUB4, TUB7, TUB9, and ACT7, and a cell wall biogenesis-related gene, CCoAOMT1, were induced in salt-adapted A120 cells. Moreover, the loss-of-function mutant of Arabidopsis TUB9 gene, tub9, showed a hypersensitive phenotype to salt stress. Consistent overexpression of Arabidopsis TUB9 gene in rice transgenic plants enhanced tolerance to salt stress. Our results suggest that microtubules play crucial roles in plant adaptation and tolerance to salt stress. The modulation of microtubule-related gene expression can be an effective strategy for developing salt-tolerant crops.

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Emerging behavioral and neuroimaging biomarkers for early and accurate characterization of autism spectrum disorders: a systematic review

Translational Psychiatry ◽

10.1038/s41398-020-01178-6 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Chandrakanta S. Hiremath ◽

Kommu John Vijay Sagar ◽

B. K. Yamini ◽

Akhila S. Girimaji ◽

Raghavendra Kumar ◽

...

Keyword(s):

Early Identification ◽

Longitudinal Research ◽

Pathological Condition ◽

Neurodevelopmental Disorder ◽

Behavioral Assessment ◽

Autism Spectrum ◽

Quantitative Mri ◽

Assessment Tools ◽

Identification And Characterization

AbstractThe possibility of early treatment and a better outcome is the direct product of early identification and characterization of any pathological condition. Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by impairment in social communication, restricted, and repetitive patterns of behavior. In recent times, various tools and methods have been developed for the early identification and characterization of ASD features as early as 6 months of age. Thorough and exhaustive research has been done to identify biomarkers in ASD using noninvasive neuroimaging and various molecular methods. By employing advanced assessment tools such as MRI and behavioral assessment methods for accurate characterization of the ASD features and may facilitate pre-emptive interventional and targeted therapy programs. However, the application of advanced quantitative MRI methods is still confined to investigational/laboratory settings, and the clinical implication of these imaging methods in personalized medicine is still in infancy. Longitudinal research studies in neurodevelopmental disorders are the need of the hour for accurate characterization of brain–behavioral changes that could be monitored over a period of time. These findings would be more reliable and consistent with translating into the clinics. This review article aims to focus on the recent advancement of early biomarkers for the characterization of ASD features at a younger age using behavioral and quantitative MRI methods.

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