Biology subjects

Pramod, A. B.

Publications and source records attributed to Pramod, A. B..

4 recordsLinked to original sources

bioRxiv2020

Cortical Organoids Model Early Brain Development Disrupted by 16p11.2 Copy Number Variants in Autism

Reciprocal deletion and duplication of 16p11.2 region is the most common copy number variation (CNV) associated with Autism Spectrum Disorders. We generated cortical organoids from skin fibroblasts of patients with 16p11.2 CNV to investigate impacted neurodevelopmental processes. We show that organoid size recapitulates macrocephaly and microcephaly phenotypes observed in the patients with 16p11.2 deletions and duplications. The CNV has mirror-opposite effect on neuronal maturation, proliferation, and synapse number, in concordance with its effect on brain growth in humans. We demonstrate that 16p11.2 CNV alters the ratio of neurons to neural progenitors in organoids during early neurogenesis, with excess of neurons and depletion of neural progenitors observed in deletions, and mirror phenotypes in duplications. Transcriptomic and proteomic profiling revealed multiple dysregulated pathways, including defects in neuron migration. Inhibition of activity of the small GTPase RhoA rescued migration deficits. This study provides insights into potential neurobiological mechanisms behind the 16p11.2 CNV during neocortical development.

neuroscience↗

bioRxiv2020

Isoform transcriptome of developing human brain provides new insights into autism risk variants

Alternative splicing plays important role in brain development, however its global contribution to human neurodevelopmental diseases (NDD) has not been fully investigated. Here, we examined the relationships between full-length splicing isoforms expression in the brain and de novo loss-of-function mutations identified in the patients with NDDs. We analyzed the full-length isoform transcriptome of the developing human brain and observed differentially expressed isoforms and isoform co-expression modules undetectable by gene-level analyses. These isoforms were enriched in loss-of-function mutations and microexons, co-expressed with a unique set of partners, and had higher prenatal expression. We experimentally tested the impact of splice site mutations in five NDD risk genes, including SCN2A, DYRK1A and BTRC, and demonstrated exon skipping. Furthermore, our results suggest that the splice site mutation in BTRC reduces translational efficiency, likely impacting Wnt signaling through impaired degradation of {beta}-catenin. We propose that functional effect of mutations associated with human diseases should be investigated at the isoform-rather than the gene-level resolution. HighlightsO_LIDifferential isoform expression analysis of the human brain transcriptome reveals neurodevelopmental processes and pathways undetectable by differential gene expression analyses. C_LIO_LISplicing isoforms impacted by neurodevelopmental disease (NDD) risk mutations exhibit higher prenatal expression, are enriched in microexons and are involved in neuronal-related functions. C_LIO_LIIsoform co-expression network analysis identifies modules with splicing and synaptic functions that are enriched in NDD mutations. C_LIO_LISplice site mutations impacting NDD risk genes cause exon skipping and produce novel isoforms with altered biological properties. C_LIO_LIFunctional impact of mutations should be investigated at the full-length isoform-level rather than the gene-level resolution C_LI

neuroscience↗

bioRxiv2020

Autism-linked Cullin3 germline haploinsufficiency impacts cytoskeletal dynamics and cortical neurogenesis through RhoA signaling

E3-ubiquitin ligase Cullin3 (Cul3) is a high confidence risk gene for Autism Spectrum Disorder (ASD) and Developmental Delay (DD). To investigate how Cul3 mutations impact brain development, we generated haploinsufficient Cul3 mouse model using CRISPR/Cas9 genome engineering. Cul3 mutant mice exhibited social and cognitive deficits and hyperactive behavior. Brain MRI found decreased volume of cortical regions and changes in many other brain regions of Cul3 mutant mice starting from early postnatal development. Spatiotemporal transcriptomic and proteomic profiling of the brain implicated neurogenesis and cytoskeletal defects as key drivers of Cul3 functional impact. Specifically, dendritic growth, filamentous actin puncta, and spontaneous network activity were reduced in Cul3 mutant mice. Inhibition of small GTPase RhoA, a molecular substrate of Cul3 ligase, rescued dendrite length and network activity phenotypes. Our study identified neuronal cytoskeleton and Rho signaling as primary targets of Cul3 mutation during early brain development.

neuroscience↗

bioRxiv2018

PRESTO, a new tool for integrating large-scale -omics data and discovering disease-specific signatures

BackgroundCohesive visualization and interpretation of hyperdimensional, large-scale -omics data is an ongoing challenge, particularly for biologists and clinicians involved in current highly complex sequencing studies. Multivariate studies are often better suited towards non-linear network analysis than differential expression testing. Here, we present PRESTO, a PREdictive Stochastic neighbor embedding Tool for Omics, which allows unsupervised dimensionality reduction of multivariate data matrices with thousands of subjects or conditions. PRESTO is intuitively integrated into an interactive user interface that helps to visualize the multidimensional patterns in genome-wide transcriptomic data from basic science and clinical studies.\n\nResultsPRESTO was tested with multiple input omics platforms, including microarray and proteomics from both mouse and human clinical datasets. PRESTO can analyze up to tens of thousands of genes and shows no increase in processing time with a large number of samples or patients. In complex datasets, such as those with multiple time points, several patient groups, or diverse mouse strains, PRESTO outperformed conventional methods. Core co-expressed gene networks were intuitively grouped in clusters, or gates, after dimensionality reduction and remained consistent across users. Networks were identified and assigned to physiological and pathological functions that cannot be gleaned from conventional bioinformatics analyses. PRESTO detected gene networks from the natural variations among mouse macrophages and human blood leukocytes. We applied PRESTO to clinical transcriptomic and proteomic data from large patient cohorts and detected disease-defining signatures in antibody-mediated kidney transplant rejection, renal cell carcinoma, and relapsing acute myeloid leukemia (AML). In AML, PRESTO confirmed a previously described gene signature and found a new signature of 10 genes that is highly predictive of patient outcome.\n\nConclusionsPRESTO offers an important integration of powerful bioinformatics tools with an interactive user interface that increases data analysis accessibility beyond bioinformaticians and coders. Here, we show that PRESTO out performs conventional methods, such as DE analysis, in multi-dimensional datasets and can identify biologically relevant co-expression gene networks. In paired samples or time points, co-expression networks could be compared for insight into longitudinal regulatory mechanisms. Additionally, PRESTO identified disease-specific signatures in clinical datasets with highly significant diagnostic and prognostic potential.

bioinformatics↗