Abstract
Among genodermatoses, trichothiodystrophies (TTDs) are a rare genetically heterogeneous group of syndromic conditions, presenting with skin, hair, and nail abnormalities. An extra-cutaneous involvement (craniofacial district and neurodevelopment) can be also a part of the clinical picture. The presence of photosensitivity describes three forms of TTDs: MIM#601675 (TTD1), MIM#616390 (TTD2) and MIM#616395 (TTD3), that are caused by variants afflicting some components of the DNA Nucleotide Excision Repair (NER) complex and with more marked clinical consequences. In the present research, 24 frontal images of paediatric patients with photosensitive TTDs suitable for facial analysis through the next-generation phenotyping (NGP) technology were obtained from the medical literature. The pictures were compared to age and sex-matched to unaffected controls using 2 distinct deep-learning algorithms: DeepGestalt and GestaltMatcher (Face2Gene, FDNA Inc., USA). To give further support to the observed results, a careful clinical revision was undertaken for each facial feature in paediatric patients with TTD1 or TTD2 or TTD3. Interestingly, a distinctive facial phenotype emerged by the NGP analysis delineating a specific craniofacial dysmorphic spectrum. In addition, we tabulated every single detail within the observed cohort. The novelty of the present research includes the facial characterization in children with the photosensitive types of TTDs through the 2 different algorithms. This result can become additional criteria for early diagnosis, and for subsequent targeted molecular investigations as well as a possible tailored multidisciplinary personalized management.
This is a preview of subscription content, access via your institution
Access options
Subscribe to this journal
Receive 12 print issues and online access
$259.00 per year
only $21.58 per issue
Rent or buy this article
Get just this article for as long as you need it
$39.95
Prices may be subject to local taxes which are calculated during checkout

Data availability
Data regarding this study are available from the corresponding author upon reasonable request.
References
Price VH, Odom RB, Ward WH, Jones FT. Trichothiodystrophy: Sulfur-deficient brittle hair as a marker for a neuroectodermal symptom complex. Arch Derm. 1980;116:1375–84.
Abagge KT, Haupenthal F, Felber GY, Raskin S. PIBIDS syndrome in two Brazilian siblings. BMJ Case Rep. 2018;11:e223744.
Belloni Fortina A, Alaibac M, Piaserico S, Peserico A. PIBI(D)S: clinical and molecular characterization of a new case. J Eur Acad Dermatol Venereol. 2001;15:65–9.
Botta E, Nardo T, Orioli D, Guglielmino R, Ricotti R, Bondanza S, et al. Genotype-phenotype relationships in trichothiodystrophy patients with novel splicing mutations in the XPD gene. Hum Mutat. 2009;30:438–45.
Boyle J, Ueda T, Oh KS, Imoto K, Tamura D, Jagdeo J, et al. Persistence of repair proteins at unrepaired DNA damage distinguishes diseases with ERCC2 (XPD) mutations: cancer-prone xeroderma pigmentosum vs. non-cancer-prone trichothiodystrophy. Hum Mutat. 2008;29:1194–208.
Brooks BP, Thompson AH, Clayton JA, Chan CC, Tamura D, Zein WM, et al. Ocular manifestations of trichothiodystrophy. Ophthalmology. 2011;118:2335–42.
Chen E, Cleaver JE, Weber CA, Packman S, Barkovich AJ, Koch TK, et al. Trichothiodystrophy: clinical spectrum, central nervous system imaging, and biochemical characterization of two siblings. J Invest Dermatol. 1994;103:154S–8S.
Farmaki E, Nedelkopoulou N, Delli F, Sarafidis K, Zafeiriou DI. Brittle hair, photosensitivity, brain hypomyelination and immunodeficiency: clues to trichothiodystrophy. Indian J Pediatr. 2017;84:89–90.
Gruber R, Zschocke A, Zellner H, Schmuth M. Successful treatment of trichothiodystrophy with dupilumab. Clin Exp Dermatol. 2021;46:1381–3.
Happle R, Traupe H, Gröbe H, Bonsmann G. The Tay syndrome (congenital ichthyosis with trichothiodystrophy). Eur J Pediatr. 1984;141:147–52.
Itin PH, Sarasin A, Pittelkow MR. Trichothiodystrophy: update on the sulfur-deficient brittle hair syndromes. J Am Acad Dermatol. 2001;44:891–920.
Kleijer WJ, Beemer FA, Boom BW. Intermittent hair loss in a child with PIBI(D)S syndrome and trichothiodystrophy with defective DNA repair-xeroderma pigmentosum group D. Am J Med Genet. 1994;52:227–30.
Savarirayan R, Gardner RJ, Sinclair RD, McDowell M, Cleaver JE. Rough skin, brittle hair, and photosensitivity: a mild phenotypic variant of trichothiodystrophy. J Med Genet. 2000;37:312–4.
Stefanini M, Lagomarsini P, Arlett CF, Marinoni S, Borrone C, Crovato F, et al. Xeroderma pigmentosum (complementation group D) mutation is present in patients affected by trichothiodystrophy with photosensitivity. Hum Genet. 1986;74:107–12.
Takayama K, Danks DM, Salazar EP, Cleaver JE, Weber CA. DNA repair characteristics and mutations in the ERCC2 DNA repair and transcription gene in a trichothiodystrophy patient. Hum Mutat. 1997;9:519–25.
Tay CH. Ichthyosiform erythroderma, hair shaft abnormalities, and mental and growth retardation. A new recessive disorder. Arch Dermatol. 1971;104:4–13.
Veres K, Nagy N, Háromszéki B, Solymosi Á, Vass V, Széll M, et al. The first reported case of trichothiodystrophy in hungary: a young male patient with mutations in the ERCC2 Gene. Acta Dermatovenerol Croat. 2018;26:169–72.
Zhou X, Khan SG, Tamura D, Patronas NJ, Zein WM, Brooks BP, et al. Brittle hair, developmental delay, neurologic abnormalities, and photosensitivity in a 4-year-old girl. J Am Acad Dermatol. 2010;63:323–8.
Zhou X, Khan SG, Tamura D, Ueda T, Boyle J, Compe E, et al. Abnormal XPD-induced nuclear receptor transactivation in DNA repair disorders: trichothiodystrophy and xeroderma pigmentosum. Eur J Hum Genet. 2013;21:831–7.
Liehr T, Acquarola N, Pyle K, St-Pierre S, Rinholm M, Bar O, et al. Next generation phenotyping in Emanuel and Pallister-Killian syndrome using computer-aided facial dysmorphology analysis of 2D photos. Clin Genet. 2018;93:378–81.
Mak BC, Sanchez Russo R, Gambello MJ, Fleischer N, Black ED, Leslie E, et al. Craniofacial features of 3q29 deletion syndrome: application of next-generation phenotyping technology. Am J Med Genet A. 2021;185:2094–101.
Pantel JT, Hajjir N, Danyel M, Elsner J, Abad-Perez AT, Hansen P, et al. Efficiency of computer-aided facial phenotyping (DeepGestalt) in individuals with and without a genetic syndrome: diagnostic accuracy study. J Med Internet Res. 2020;22:e19263.
Daykin E, Fleischer N, Abdelwahab M, Hassib N, Schiffmann R, Ryan E, et al. Investigation of a dysmorphic facial phenotype in patients with Gaucher disease types 2 and 3. Mol Genet Metab. 2021;134:274–80.
Gurovich Y, Hanani Y, Bar O, Nadav G, Fleischer N, Gelbman D, et al. Identifying facial phenotypes of genetic disorders using deep learning. Nat Med. 2019;25:60–4.
Sharma A, Vans E, Shigemizu D, Boroevich KA, Tsunoda T. DeepInsight: a methodology to transform a non-image data to an image for convolution neural network architecture. Sci Rep. 2019;9:11399.
Hsieh TC, Bar-Haim A, Moosa S, Ehmke N, Gripp KW, Pantel JT, et al. GestaltMatcher facilitates rare disease matching using facial phenotype descriptors. Nat Genet. 2022;54:349–57.
Tekendo-Ngongang C, Owosela B, Fleischer N, Addissie YA, Malonga B, Badoe E, et al. Rubinstein-Taybi syndrome in diverse populations. Am J Med Genet A. 2020;182:2939–50.
Acknowledgements
The authors thank Nicole Fleischer and Carolina Alves, for their technical support in performing the DeepGestalt and GestaltMatcher experiments.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Ethics approval
This study was performed according to the Helsinki declaration. Approval from an internal review board was not necessary since the study is based on literature revision.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Pascolini, G., Gaudioso, F., Baldi, M. et al. Facial clues to the photosensitive trichothiodystrophy phenotype in childhood. J Hum Genet 68, 437–443 (2023). https://doi.org/10.1038/s10038-023-01134-4
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s10038-023-01134-4