Correction of Hair Shaft Defects through Allele-Specific Silencing of Mutant Krt75

Generation and integration of hair follicle-primed spheroids in bioengineered skin constructs

Skin is a complex organ made up of different cell layers, appendages, connective tissues, and immune repertoires. These different components interact extensively to maintain the overall functions of the integumentary system. In particular, appendages such as hair follicles critically contribute to the skin's function in thermoregulation, sensory perception, and homeostatic regeneration. Despite a strong need for better skin regenerative therapeutics, efforts to bio-engineer highly functional appendage-containing human reconstituted skinin vitrohave not yielded much success. Here, we report methods in generating and incorporating hair follicle-primed heterotypic spheroids into epidermal-dermal skin constructs that induced invaginating outgrowths with follicle-like organization and lineage gene expression. By co-culturing epithelial keratinocytes (KCs) with dermal papilla (DP) cells in low attachment plates, we established the media and culture conditions that best supported the viability, signalling and remodelling of the cell aggregates to form 3D KC-DP spheroids with the expression of both DP inductiveness and hair follicle lineage genes. We show that long-term growth and maturation of KC cells in these spheroids was supported by incorporation into epidermal-dermal constructs but not in scaffold-less media. When cultured, the bio-fabricated constructs developed invaginations from the integrated spheroids with follicle-forming potential. The generation of these constructs is a step towards the development of functional hair-bearing skin mimetics.

Overexpression of MYB in the Skin Induces Alopecia and Epidermal Hyperplasia

Skin homeostasis is controlled by a complex interplay between tightly regulated transcription factors and signaling pathways. MYB is a transcription factor expressed in hair follicle progenitor cells and found overexpressed in adnexal skin tumors. However, the biological consequences of deregulated MYB expression in the skin remain poorly understood. To address this, we generated transgenic mice that overexpress MYB in epidermal and follicular keratinocytes. These mice exhibited a normal hair coat after birth but gradually developed alopecia, accompanied by altered follicular differentiation, disrupted hair cycle, and a marked depletion of hair follicle stem cells. Additionally, transgenic mice developed massive epidermal hyperplasia and hyperkeratosis. Global expression profiling not only confirmed that the skin of these mice exhibited transcriptomic features of alopecia and epidermal differentiation, but also revealed features of psoriasis and the inflammatory response. The latter was further confirmed by the increased T-cell infiltration found in the skin of transgenic mice. Overall, these results suggest that tight regulation of MYB expression in the skin is critical to maintain skin homeostasis.

Cancer therapeutic targeting using mutant-p53-specific siRNAs

Mutations in Tp53 compromise therapeutic response, due either to the dominant-negative effect over the functional wild-type allele; or as a result of the survival advantage conferred by mutant p53 to which cancer cells become addicted. Thus, targeting mutant p53 represents an effective therapeutic strategy to treat over half of all cancers. We have therefore generated a series of small-interfering-RNAs, capable of targeting four p53 hot-spot mutants which represent ~20% of all p53 mutations. These mutant–p53-specific siRNAs (MupSi) are highly specific in silencing the expression of the intended mutants without affecting wild-type p53. Functionally, these MupSis induce cell death by abrogating both the addiction to mutant p53 and the dominant-negative effect; and retard tumor growth in xenografts when administered in a therapeutic setting. These data together demonstrate the possibility of targeting mutant p53 specifically to improve clinical outcome.

ASPsiRNA: A Resource of ASP-siRNAs Having Therapeutic Potential for Human Genetic Disorders and Algorithm for Prediction of Their Inhibitory Efficacy

Allele-specific siRNAs (ASP-siRNAs) have emerged as promising therapeutic molecules owing to their selectivity to inhibit the mutant allele or associated single-nucleotide polymorphisms (SNPs) sparing the expression of the wild-type counterpart. Thus, a dedicated bioinformatics platform encompassing updated ASP-siRNAs and an algorithm for the prediction of their inhibitory efficacy will be helpful in tackling currently intractable genetic disorders. In the present study, we have developed the ASPsiRNA resource (http://crdd.osdd.net/servers/aspsirna/) covering three components viz (i) ASPsiDb, (ii) ASPsiPred, and (iii) analysis tools like ASP-siOffTar. ASPsiDb is a manually curated database harboring 4543 (including 422 chemically modified) ASP-siRNAs targeting 78 unique genes involved in 51 different diseases. It furnishes comprehensive information from experimental studies on ASP-siRNAs along with multidimensional genetic and clinical information for numerous mutations. ASPsiPred is a two-layered algorithm to predict efficacy of ASP-siRNAs for fully complementary mutant (Eff^mut) and wild-type allele (Eff^wild) with one mismatch by ASPsiPred^SVM and ASPsiPred^matrix, respectively. In ASPsiPred^SVM, 922 unique ASP-siRNAs with experimentally validated quantitative Eff^mut were used. During 10-fold cross-validation (10nCV) employing various sequence features on the training/testing dataset (T737), the best predictive model achieved a maximum Pearson’s correlation coefficient (PCC) of 0.71. Further, the accuracy of the classifier to predict Eff^mut against novel genes was assessed by leave one target out cross-validation approach (LOTOCV). ASPsiPred^matrix was constructed from rule-based studies describing the effect of single siRNA:mRNA mismatches on the efficacy at 19 different locations of siRNA. Thus, ASPsiRNA encompasses the first database, prediction algorithm, and off-target analysis tool that is expected to accelerate research in the field of RNAi-based therapeutics for human genetic diseases.