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Yousaf, A.

Publications and source records attributed to Yousaf, A..

2 recordsLinked to original sources

SPECC1L-deficient palate mesenchyme cells show speed and directionality defect

Clefts of the lip and/or palate (CL/P) are common anomalies that occur in 1/800 live births. Pathogenic SPECC1L variants identified in patients with rare atypical clefts and syndromic CL/P suggest the gene plays a primary role in face and palate development. We have generated Specc1l gene-trap (Specc1lcGT) and truncation (Specc1l{Delta}C510) alleles that cause embryonic or perinatal lethality, respectively. Specc1lcGT/{Delta}C510 compound mutants show delayed and abnormal palatal shelf elevation at E14.5. By E15.5, the mutant shelves do elevate and fuse, however, the palatal rugae form abnormally. Palatogenesis requires extensive mesenchymal remodeling, especially during palatal shelf elevation. We posit that this remodeling involves collective movement of neural crest-derived palatal mesenchyme cells. Live time-lapse microscopy was performed to visualize in vitro wound-repair assays with wildtype and SPECC1L-deficient primary mouse embryonic palatal mesenchyme (MEPM) cells. SPECC1L-deficient MEPM cells consistently showed delayed closure in wound-repair assays. To evaluate which features of cellular movement were responsible, we performed automated particle image velocimetry (PIV) and manual cell tracking. The analyses revealed that both cell speed and directionality are disrupted in SPECC1L-deficient cells compared to controls. To determine if primary MEPM cells can move collectively, we assayed stream formation, which is a hallmark of collective movement. Indeed, MEPM cultures displayed correlated movement of neighboring cells. Importantly, correlation length was reduced in SPECC1L-deficient cultures, consistent with a role for SPECC1L in collective migration. Furthermore, we demonstrated that activation of the PI3K-AKT pathway with the 740Y-P small molecule can rescue the wound-closure delay in SPECC1L-deficient MEPM cells. Cell tracking analyses showed that this rescue was due to both increased speed and improved directionality. Altogether, our data showed a novel role for SPECC1L in guided movement through modulation of PI3K-AKT signaling.

cell biology↗

Mapping the genetics of neuropsychological traits to the molecular network of the human brain using a data integrative approach

MotivationComplex neuropsychiatric conditions including autism spectrum disorders are among the most heritable neurodevelopmental disorders with distinct profiles of neuropsychological traits. A variety of genetic factors modulate these traits (phenotypes) underlying clinical diagnoses. To explore the associations between genetic factors and phenotypes, genome-wide association studies are broadly applied. Stringent quality checks and thorough downstream analyses for in-depth interpretation of the associations are an indispensable prerequisite. However, in the area of neuropsychology there is no framework existing, which besides performing association studies also affiliates genetic variants at the brain and gene network level within a single framework.\n\nResultsWe present a novel bioinformatics approach in the field of neuropsychology that integrates current state-of-the-art tools, algorithms and brain transcriptome data to elaborate the association of phenotype and genotype data. The integration of transcriptome data gives an advantage over the existing pipelines by directly translating genetic associations to brain regions and developmental patterns. Based on our data integrative approach, we identify genetic variants associated with Intelligence Quotient (IQ) in an autism cohort and found their respective genes to be expressed in specific brain areas.\n\nConclusionOur data integrative approach revealed that IQ is related to early down-regulated and late up-regulated gene modules implicated in frontal cortex and striatum, respectively. Besides identifying new gene associations with IQ we also provide a proof of concept, as several of the identified genes in our analysis are candidate genes related to intelligence in autism, intellectual disability, and Alzheimers disease. The framework provides a complete extensive analysis starting from a phenotypic trait data to its association at specific brain areas at vulnerable time points within a timespan of four days.\n\nAvailability and ImplementationOur framework is implemented in R and Python. It is available as an in-house script, which can be provided on demand.\n\nContactafsheen.yousaf@kgu.de

bioinformatics↗