Pathogenic convergence of CNVs in genes functionally associated to a severe neuromotor developmental delay syndrome

Mapping combinatorial expression of non-clustered protocadherins in the developing brain identifies novel PCDH19-mediated cell adhesion properties

Non-clustered protocadherins (ncPcdhs) are adhesive molecules with spatio-temporally regulated overlapping expression in the developing nervous system. Although their unique role in neurogenesis has been widely studied, their combinatorial role in brain physiology and pathology is poorly understood. Using probabilistic cell typing by in situ sequencing, we demonstrate combinatorial inter- and intra-familial expression of ncPcdhs in the developing mouse cortex and hippocampus, at single-cell resolution. We discovered the combinatorial expression of Protocadherin-19 (Pcdh19), a protein involved in PCDH19-clustering epilepsy, with Pcdh1, Pcdh9 or Cadherin 13 (Cdh13) in excitatory neurons. Using aggregation assays, we demonstrate a code-specific adhesion function of PCDH19; mosaic PCDH19 absence in PCDH19+9 and PCDH19 + CDH13, but not in PCDH19+1 codes, alters cell-cell interaction. Interestingly, we found that PCDH19 as a dominant protein in two heterophilic adhesion codes could promote trans-interaction between them. In addition, we discovered increased CDH13-mediated cell adhesion in the presence of PCDH19, suggesting a potential role of PCDH19 as an adhesion mediator of CDH13. Finally, we demonstrated novel cis-interactions between PCDH19 and PCDH1, PCDH9 and CDH13. These observations suggest that there is a unique combinatorial code with a cell- and region-specific characteristic where a single molecule defines the heterophilic cell-cell adhesion properties of each code.

Clinical Characteristics Suggestive of a Genetic Cause in Cerebral Palsy: A Systematic Review

BACKGROUND

Cerebral palsy (CP) is a clinical diagnosis and was long categorized as an acquired disorder, but more and more genetic etiologies are being identified. This review aims to identify the clinical characteristics that are associated with genetic CP to aid clinicians in selecting candidates for genetic testing.

METHODS

The PubMed database was systematically searched to identify genes associated with CP. The clinical characteristics accompanying these genetic forms of CP were compared with published data of large CP populations resulting in the identification of potential indicators of genetic CP.

RESULLTS

Of 1930 articles retrieved, 134 were included. In these, 55 CP genes (described in two or more cases, n = 272) and 79 candidate genes (described in only one case) were reported. The most frequently CP-associated genes were PLP1 (21 cases), ARG1 (17 cases), and CTNNB1 (13 cases). Dyskinesia and the absence of spasticity were identified as strong potential indicators of genetic CP. Presence of intellectual disability, no preterm birth, and no unilateral distribution of symptoms were classified as moderate genetic indicators.

CONCLUSIONS

Genetic causes of CP are increasingly identified. The clinical characteristics associated with genetic CP can aid clinicians regarding to which individual with CP to offer genetic testing. The identified potential genetic indicators need to be validated in large CP cohorts but can provide the first step toward a diagnostic algorithm for genetic CP.

Identification of SNPs and Candidate Genes for Milk Production Ability in Yorkshire Pigs

Sow milk production ability is an important limiting factor impacting suboptimal growth and the survival of piglets. Through pig genetic improvement, litter sizes have been increased. Larger litters need more suckling mammary glands, which results in increased milk from the lactating sow. Hence, there is much significance to exploring sow lactation performance. For milk production ability, it is not practical to directly measure the milk yield, we used litter weight gain (LWG) throughout sow lactation as an indicator. In this study, we estimated the heritability of LWG, namely, 0.18 ± 0.07. We then performed a GWAS, and detected seven significant SNPs, namely, Sus scrofa Chromosome (SSC) 2: ASGA0010040 (p = 7.73E-11); SSC2:MARC0029355 (p = 1.30E-08), SSC6: WU_10.2_6_65751151 (p = 1.32E-10), SSC7: MARC0058875 (p = 4.99E-09), SSC10: WU_10.2_10_49571394 (p = 6.79E-08), SSC11: M1GA0014659 (p = 1.19E-07), and SSC15: MARC0042106 (p = 1.16E-07). We performed the distribution of phenotypes corresponding to the genotypes of seven significant SNPs and showed that ASGA0010040, MARC0029355, MARC0058875, WU_10.2_10_49571394, M1GA0014659, and MARC0042106 had extreme phenotypic values that corresponded to the homozygous genotypes, while the intermediate values corresponded to the heterozygous genotypes. We screened for flanking regions ± 200 kb nearby the seven significant SNPs, and identified 38 genes in total. Among them, 28 of the candidates were involved in lactose metabolism, colostrum immunity, milk protein, and milk fat by functional enrichment analysis. Through the combined analysis between 28 candidate genes and transcriptome data of the sow mammary gland, we found nine commons (ANO3, MUC15, DISP3, FBXO6, CLCN6, HLA-DRA, SLA-DRB1, SLA-DQB1, and SLA-DQA1). Furthermore, by comparing the chromosome positions of the candidate genes with the quantitative trait locus (QTLs) as previously reported, a total of 17 genes were found to be within 0.86–94.02 Mb of the reported QTLs for sow milk production ability, in which, NAV2 was found to be located with 0.86 Mb of the QTL region ssc2: 40936355. In conclusion, we identified seven significant SNPs located on SSC2, 6, 7, 10, 11, and 15, and propose 28 candidate genes for the ability to produce milk in Yorkshire pigs, 10 of which were key candidates.