Journal of Clinical Pediatric Dentistry. 2024; 48(6): 1-11. doi: 10.22514/jocpd.2024.122
Review

Research progress of proteomics in congenital craniofacial anomalies

Shujie Hou1,2,3, Xuqin Liang4, Yuhua Jiao2,5, Boxi Yan6, Kangying Liu4, Hongmei Lin7, Yi Ding4, Huimei Huang8, Jieni Zhang1,3,*,, Huaxiang Zhao2,5,*,

1Department of Orthodontics, Peking University School and Hospital of Stomatology, 100081 Beijing, China

2Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi’an Jiaotong University, 710004 Xi’an, Shaanxi, China

3National Center of Stomatology, National Clinical Research Center for Oral Diseases, National Engineering Laboratory for Digital and Material Technology of Stomatology, Beijing Key Laboratory for Digital Stomatology, Research Center of Engineering and Technology for Computerized Dentistry Ministry of Health, NMPA Key Laboratory for Dental Materials, 100081 Beijing, China

4Department of Physiology and Pathophysiology, School of Basic Medical Sciences, Xi’an Jiaotong University, 710061 Xi’an, Shaanxi, China

5Department of Orthodontics, College of Stomatology, Xi’an Jiaotong University, 710004 Xi’an, Shaanxi, China

6Second Clinical Division, Peking University School and Hospital of Stomatology, 100020 Beijing, China

7Department of Pediatrics, The Second Affiliated Hospital of Xi’an Jiaotong University, 710004 Xi’an, Shaanxi, China

8Department of Nephrology, Xi’an Children’s Hospital, The Affiliated Children’s Hospital of Xi’an Jiaotong University, 710003 Xi’an, Shaanxi, China

*Corresponding Author(s):sd88383511@126.com (Jieni Zhang); huaxiangzhao@xjtu.edu.cn (Huaxiang Zhao)

History Submitted: 29 November 2023 | Accepted: 21 March 2024 | Published: 03 November 2024
Copyright:  ©2024  The Author(s). Published by MRE Press.
This is an open access article under the CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/).

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Abstract

Congenital craniofacial anomalies (CFAs) are among the most common birth defects, significantly affecting the appearance, oral function and mental health of patients. These anomalies are etiologically complex, involving genetics, environmental factors and gene-environment interactions. While genetic studies have identified numerous potential causal genes/risk loci for CFAs, the pathogenic mechanisms still largely remain elusive. Proteomics, the large-scale analysis of proteins, offers a comprehensive view of disease pathogenesis and their systemic effects. During the past two decades, the application of proteomics in CFA research has uncovered many biomarkers for early diagnosis and shed light on underlying mechanisms driving these anomalies. Here, we review the advancements and contributions of proteomics to congenital CFA research, outlining technological advances, novel findings from human body fluid proteomics, and integrative multi-omics approaches.

Keywords:Craniofacial anomaly;Proteomics;Mass spectrometry;Human body fluid proteomics;Research progress
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Cite this article

Shujie Hou, Xuqin Liang, Yuhua Jiao, Boxi Yan, Kangying Liu, Hongmei Lin, Yi Ding, Huimei Huang, Jieni Zhang, Huaxiang Zhao. Research progress of proteomics in congenital craniofacial anomalies. Journal of Clinical Pediatric Dentistry. 2024; 48(6): 1-11. doi: 10.22514/jocpd.2024.122

1. Introduction

Congenital craniofacial anomalies (CFAs) account for over one-third of all congenital birth defects [1], causing considerable neonatal morbidity and mortality. Affecting the head and face of patients, CFAs impact not only the patient’s physical appearance, but also their orofacial function and mental health, thereby raising the financial burden and reducing the quality of life for both patients and their families [2]. The etiologies of CFAs are complex, including genetics, environmental factors and gene-environment interactions [3].

Over the past two decades, omics technologies, such as genomics [4, 5], transcriptomics [6, 7] and proteomics [8], have enriched CFA research. While genome-wide association studies (GWAS) and whole-exome sequencing (WES) have spotlighted numerous potential causal genes/risk loci [9, 10, 11, 12], much remains to be uncovered about CFAs, from its etiology to overall health impacts.

Proteins, as the primary effectors of genomic information, provide cells with their structure and drive most functions of cells [13]. Positioned downstream of the genome and transcriptome, the proteome can capture the cumulative effects of multiple upstream factors, offering a comprehensive view into disease pathogenesis [14]. Furthermore, proteomics could unveil systemic health impacts from diseases, such as the nutritional deficiencies related to CFAs [15]. Thus, delving into proteomics for CFAs not only elucidates their etiologies but also broadens our grasp on their overall health implications. In the present review, we discuss the pivotal roles and advancements of proteomics in congenital CFA research, touching on aspects like technological advances, novel findings from human body fluid proteomics, and multi-omics approaches (Fig. 1).

Advancements and contributions of proteomics in congenital craniofacial anomaly research. This figure illustrates recent advancements and contributions of proteomics to the field of congenital craniofacial anomaly (CFA) research. We focus on the novel findings gained from the proteomic analysis of human body fluids such as plasma/serum, saliva and amniotic fluid. The evolution of proteomics technology from 2-D gel electrophoresis to quantitative proteomics and multi-omics approaches has deepened our understanding of CFA etiology, facilitated more precise prenatal molecular diagnosis, and provided insights into CFA’s effects on general health. In the future, the application of single-cell proteomics and spatiotemporal proteomics can promise to further revolutionize CFA research, offering more sophisticated tools for in-depth study. 2-DE: two-dimensional gel electrophoresis; MALDI-TOF-MS: matrix-assisted laser desorption-time of flight mass spectrometry; iTRAQ: isobaric tags for relative and absolute quantitation; TMT: tandem mass tags.

Fig. 1.Advancements and contributions of proteomics in congenital craniofacial anomaly research. This figure illustrates recent advancements and contributions of proteomics to the field of congenital craniofacial anomaly (CFA) research. We focus on the novel findings gained from the proteomic analysis of human body fluids such as plasma/serum, saliva and amniotic fluid. The evolution of proteomics technology from 2-D gel electrophoresis to quantitative proteomics and multi-omics approaches has deepened our understanding of CFA etiology, facilitated more precise prenatal molecular diagnosis, and provided insights into CFA’s effects on general health. In the future, the application of single-cell proteomics and spatiotemporal proteomics can promise to further revolutionize CFA research, offering more sophisticated tools for in-depth study. 2-DE: two-dimensional gel electrophoresis; MALDI-TOF-MS: matrix-assisted laser desorption-time of flight mass spectrometry; iTRAQ: isobaric tags for relative and absolute quantitation; TMT: tandem mass tags.

2. Overview of craniofacial anomalies: classifications, etiologies and omics research

CFAs can affect the head, face, or both, encompassing malformations like cranial, ocular, nasal and orofacial defects [16, 17]. These anomalies not only hinder vital functions like breathing and feeding, potentially leading to infant mortality, but also negatively impact speech, hearing, mental health and social integration [18, 19]. Moreover, certain CFAs may increase the risk of complications, such as obstructive sleep apnea syndrome (OSAS) from micrognathia, leading to long-term health issues [20].

CFAs can occur alone (non-syndromic CFAs) or alongside other congenital anomalies like neural, cardiac or skeletal defects (syndromic CFAs) [21]. Syndromic CFAs can be categorized into craniosynostoses and cleft syndromes [22]. Craniosynostosis involves the premature fusion of one or more cranial sutures, which can limit skull growth, alter its features, and eventually lead to skull base asymmetries [23]. As for cleft syndromes, prevalent ones include Van der Woude, Pierre-Robin, Treacher-Collins and Nager syndrome [24, 25]. Non-syndromic CFAs most commonly manifest as non-syndromic orofacial clefts (NSOFCs), which include cleft lip only, cleft palate only and cleft lip and palate [26]. NSOFCs are a diverse group of disorders that affect the lips and oral cavity. Their occurrence ranges from 1/700 to 1/1000 live births globally, varying by ethnicity and geography [27, 28]. Another notable CFA is craniofacial microsomia (also known as first and second branchial arch anomalies or lateral facial dysplasia). These malformations affect about 1 in 5500 to 26,000 newborns globally, and can lead to facial asymmetry due to deformities in the facial skeleton and soft tissue [29].

Syndromic CFAs are primarily Mendelian monogenic disorders that have been extensively studied over the past years. With genomic technology advancements, numerous genes and variants causing syndromic CFAs have been identified, and confirmed using animal models [30, 31]. Non-syndromic CFAs often show phenotype variability and non-Mendelian inheritance patterns, which are thought to be complex diseases influenced by both genetic and environmental factors [32]. Techniques like GWAS and next-generation sequencing (NGS) have identified at least 45 risk loci [33, 34] and various candidate genes for NSOFCs [4, 12, 35]. Transcriptomics, meanwhile, is a popular tool in CFA research, helping to uncover mechanisms by analyzing transcriptomic shifts during normal and abnormal tissue development [6, 36].

3. Proteomics to study craniofacial anomalies

Proteomics is the comprehensive analysis of proteins on a large scale, significantly enhancing our understanding of physiological and pathological processes in the post-genomic era [37]. Through proteomics, researchers can identify biomarkers across various diseases at protein levels, delve into post-translational modifications, and investigate protein-protein interactions using mass spectrometry (MS) [38, 39]. Many studies have applied MS-based proteomics to congenital CFAs, shedding light on their etiology and underlying mechanisms.

3.1 Proteomics technology applications to CFAs: from 2-D gel electrophoresis to quantitative proteomics

Two-dimensional gel electrophoresis (2-DE) is a well-established method for protein separation, allowing simultaneous analysis of multiple samples [40]. Using 2-DE in combination with matrix assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF-MS), Xingang Yuan et al. [41] identified a potential link between peroxiredoxin1 (PRX1) and 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD)-induced cleft palate in mice. However, 2-DE faces challenges in accurately separating and quantifying proteins, sometimes leading to loss of certain proteins [42]. To address these limitations, shotgun proteomic technology was introduced to CFA research. This approach can identify hundreds of proteins at once, making it an invaluable tool for comparative proteomics due to its enhanced efficiency and sensitivity [43]. Sosa-Acosta et al. [44] utilized shotgun proteomics to uncover dysregulated extracellular matrix (ECM)-related proteins modulating the microcephalic phenotype. As technological advancements continue, various quantitative proteomic methods have emerged in CFA research. Broadly speaking, these can be classed into two categories: label-free quantification and mass tag [45, 46]. Mauricio Quiñones-Vega et al. [47] utilized label-free quantification proteomics to suggest that higher expression of integrins in microcephaly might be associated with high internalization of Zika virus (ZIKV). Turning to mass tag quantitative proteomics, isobaric tags for relative and absolute quantitation (iTRAQ) and tandem mass tags (TMT) are two prominent techniques at present. For instance, Chen Wang et al. [48] combined iTRAQ with liquid chromatography tandem-MS (LC-MS/MS) to uncover that 14-3-3σ and annexin A1 (ANXA1) are pivotal in cleft palate formation, while we [49] deployed TMT-labeled quantitative MS to discern unique protein profiles in NSOFC patient serum. In sum, the advancement of proteomics equips researchers with increasingly efficient, sensitive and precise techniques to investigate CFAs.

3.2 Human body fluid proteomics for craniofacial anomalies

3.2.1 Plasma/serum

Blood, an indispensable sample for clinical tests, holds great potential for detecting disease biomarkers. The plasma proteome, one of the most complex proteomes in the human body, comprises secreted proteins from various organs and tissues, positioning it as an ideal matrix for comprehensive protein biomarker discover [49]. For early disease surveillance of congenital CFAs, maternal blood is preferred over methods like amniocentesis or chorionic villus sampling due to its non-invasive nature [50].

Srinivasa R. Nagalla et al. [51] analyzed maternal serum samples from the first and second trimesters to discover potential serum biomarkers linked to Down’s syndrome, one of the most prevalent gross chromosomal abnormalities in live births. They employed a suite of complementary proteomic techniques, including fluorescence 2-DE, 2-dimensional liquid chromatography-chromatofocusing (2D-CF), multidimensional protein identification technology, and MALDI-TOF-MS peptide profiling. Their research identified 19 proteins specific to the first trimester, 16 specific to the second, and 10 that were differentially present in both trimesters, paving the way for developing a novel Down’s syndrome screening test.

The prenatal diagnosis of NSOFCs is challenging due to the ultrasonographic examination limitations: the reported detection rate of NSOFCs is only 65%, with over half of cleft palates going undetected [52]. Consequently, there’s growing interest in using maternal serum for the prenatal diagnosis of NSOFCs. In a study by Xinhuan Wang et al. [53], iTRAQ-based MS were employed to investigate the differences in maternal serum protein profiles. They compared 20 pregnant women with NSOFC fetuses to 20 with healthy fetuses, later confirming their findings through multiple reaction monitoring-MS and enzyme linked immunosorbent assay (ELISA). This work suggested that apolipoproteins A (APOA), haptoglobin (HPT) and c-reactive protein (CRP) proteins as potential serum biomarkers for NSOFC prenatal diagnosis, introducing a novel proteomics-based prenatal diagnostic approach for NSOFCs.

In addition to the application in prenatal diagnosis of CFAs, serum proteomics can reflect the physiological state of patients, helping us understanding the etiology of CFAs and their impact on general health. In a previous study [15], we utilized the shotgun approach to examine the plasma proteome of 13 children with NSOFCs and 10 healthy controls. Our findings indicated that reduced levels of retinol-binding protein 4 (RBP4) and vitamin A were associated with newborns with NSOFCs, suggesting a potential need for early vitamin A supplementation. In another study of ours [49], we employed a TMT-labeled quantitative MS technique to study the serum proteomics of NSOFC patients, indicating that the levels of sex hormone binding globulin (SHBG), periostin sapiens (POSTN), osteomodulin (OMD) and aggrecan core protein (ACAN) were significantly higher in the NSOFC group compared to the healthy control group. This work suggested that NSOFCs may influence the expression of collagen-related and bone-associated proteins. Table 1 provides a summary of recent studies on CFAs based on plasma/serum proteomics.

Table 1.Summary of recent studies on CFAs based on plasma/serum proteomics, saliva proteomics and amniotic fluid proteomics.
StudySample typeParticipantsObjectiveMethod of proteomicsResultsSubsequent verificationRef.
Hou et al. [49] (2023)Supernatant serum5 NSOFC patients and 5 healthy controlsAnalyze the proteomic changes in postnatal patients with NSOFCTMT-labeled quantitative MSNSOFC patient serum exhibited distinct protein profiles relative to healthy controls, and NSOFCs might change the expression of collagen-related and bone-related proteinsPRMPMID: 34170602
Wang et al. [53] (2022)Maternal serum20 pregnant women with NSOFC fetuses and 20 pregnant women with healthy fetusesIdentify specific biomarkers in maternal serum for predicting NSOFC prenatallyiTRAQ-based MS; MRM-MSAPOA, HPT and CRP proteins are potential serum biomarkers for prenatal diagnosis of NSOFCELISAPMID: 34951699
Zhao et al. [4] (2022)Maternal serum10 non-pregnant women, 10 pregnant women with healthy fetuses, and 10 pregnant women with Down’s syndrome fetusesIdentify Down’s syndrome biomarkers in maternal serumiTRAQ11 proteins had significantly different serum levels between the Down’s syndrome fetus group and healthy fetuses’ groupNPPMID: 31793358
Zhang et al. [15] (2014)Serum13 children with NSCLP and 10 control children, aged 2 to 3.5 yearsIdentify and evaluate the differential expression of serum protein profiles in NSCLP children and unaffected babiesShotgun proteomicsReduced levels of RBP4 and vitamin A were related to newborns with NSCLPWB; HPLCPMID: 24695672
Yu et al. [54] (2012)Maternal serum6 pregnancies with fetuses affected by Down’s syndrome and 6 pregnancies with normal fetusesIdentify novel proteins for Down syndrome screening2-DE; MS29 proteins were identified in maternal serum obtained from pregnancies with fetuses affected by Down’s syndromeELISAPMID: 22678011
Nagalla et al. [51] (2007)Maternal serum56 women with Down’s syndrome fetuses and 56 control women with non-Down’s syndrome fetusesIdentify potential serum biomarkers to detect Down’s syndrome2D-DIGE; 2D-CF; MudPIT; MALDI-TOF-MS peptide profiling28 and 26 proteins were differentially present in first- and second-trimester samples, respectivelyNPPMID: 17373838
Zuanazzi et al. [55] (2017)SalivaA 25-y-old woman clinically diagnosed with Zika fever in the first trimester and her dizygotic twins: a female with microcephaly and a male without microcephalyExplored the potential biomarkers to diagnose Zika virus related microcephalyLC-ESI-MS/MSThe virus may have entered the oral cavity through the salivary glands, leading to an infection that persists into the postnatal periodRT-PCRPMID: 28825520
Szabo et al. [56] (2012)Saliva31 cleft lip and palate patients and a control group with 20 healthy volunteersEvaluate protein composition differences between samples with cleft lip and palate and healthy controlsMALDI-TOF/TOF-MSThe presence of cleft lip and palate stimulated the expression of several proteins, included AP-3, DMKN, NID1-precursor, TGF-β3 and ZRANB2NPPMID: 21504360
Acosta et al. [44] (2022)Amniotic fluid2 pregnant women who were positive for ZIKV and bearing non-microcephalic fetuses, two pregnant women who were positive for ZIKV and bearing microcephalic fetusesIdentify and understand the biological processes affected by CZS developmentShotgun proteomicThe microcephalic phenotypes are modulated by a down-regulation of the ECM proteins and the impairment of the innate immune system processesNPPMID: 34676661
Cho et al. [57] (2010)Amniotic fluid10 chromosomal normal and 10 Down syndrome-affected pregnancies in the second trimester of pregnancyDiscover novel biomarkers for Down syndrome2D-LC; MS/MS; LTQ-Orbitrap mass spectrometer, spectral countingAPP and TNC-C showed a 2-fold increase in Down Syndrome-amniotic fluid samples compared to controlsELISAPMID: 20459121
Datta et al. [58] (2008)Amniotic fluidC57BL/6J and C57BL/6N mice treated with alcohol (exposed) or saline (control) on day 8 of gestationScreen amniotic fluid for biomarkers that could discriminate between FAS-positive and FAS-negative pregnanciesMALDI-TOF-MS; LC-MS/MS; MudPITAFP deficiency is a biomarker for FAS-positiveNPPMID: 18240165
Tsangaris et al. [59] (2006)Amniotic fluid18 women in the 17th week of pregnancy: 12 pregnancies with normal fetuses and 6 pregnancies with fetuses affected by Down syndromeDiscover novel biomarkers for Down syndrome2-DE, 3 MALDI-TOF-MS, nano-ESI-MS/MSCO1A1, CO3A1, CO5A1 and PGBM are differentially expressed in all pregnancies with DS testedWBPMID: 16847874
Abbreviations: NP: not performed; CZS: congenital Zika syndrome; FAS: fetal alcohol syndrome; NSOFC: non-syndromic orofacial clefts; NSCLP: non-syndromic cleft of the lip and/or palate; PRM: parallel reaction monitoring; TMT: tandem mass tag; 2-DE: two-dimensional electrophoresis; MS: mass spectrometry; MALDI-TOF-MS: matrix-assisted laser desorption-time of flight mass spectrometry; LC-MS/MS: liquid chromatography tandem mass spectrometry; MudPIT: multidimensional protein identification technology; MS/MS: tandem mass spectrometry; 2D-LC: two-dimensional liquid chromatography; 2D-DIGE: fluorescence 2-dimensional gel electrophoresis; 2D-CF: 2-dimensional liquid chromatography-chromatofocusing; LC-ESI-MS/MS: liquid chromatography electrospray Ionization tandem mass spectrometry; HPLC: high-performance liquid chromatography; iTRAQ: isobaric tags for relative and absolute quantitation; MRM: multiple reaction monitoring; ELISA: enzyme linked immunosorbent assay; WB: western blot; ECM: extracellular matrix; AFP: alpha fetoprotein; TF: serotransferrin; A1BG: alpha-1b-glycoprotein; DES: desmin; SERPINA1: alpha-1-antitrypsin; CP: ceruloplasmin; APCS: serum amyloid P-component; APP: amyloid precursor protein; TNC-C: tenascin-C; APOA: apolipoprotein A; HPT: haptoglobin; CRP: c-reactive protein; CO1A1: collagen alpha 1 (I) chain; CO3A1: collagen alpha 1 (III) chain; CO5A1: collagen alpha 1 (V) chain d; PGBM: basement membrane-specific heparin sulfate proteoglycan core protein; RBP4: retinol-binding protein 4; AP-3: adaptor protein-3; DMKN: dermokine; NID1: nidogen 1; TGF-β3: transforming growth factor-β3; ZRANB2: zinc finger ran-binding domain-containing protein 2; LTQ: linear trap quadrupole.

3.2.2 Saliva

Saliva, secreted by salivary glands, whose development is influenced by certain CFAs. Due to this connection with CFAs, saliva serves as an ideal source of biomarkers for various diseases, particularly oral-facial diseases [60]. Saliva collection is less invasive and faster than blood collection. Over the past 20 years, proteomics has extensively studied human saliva, especially with MS [61].

In 2012, Gyula Tamas Szabo et al. [56] employed a top-down strategy to identify potential biomarkers for elucidating the mechanism underlying cleft lip and palate at protein level. By analyzing proteins in the saliva collected from 31 patients with cleft lip and palate, and comparing them with 20 healthy volunteers using MALDI-TOF, they identified several biomarkers, including adaptor protein-3 (AP-3), dermokine (DMKN), nidogen 1 (NID1)-precursor, transforming growth factor-β3 (TGF-β3) and zinc finger ran-binding domain-containing protein 2 (ZRANB2). These proteins might play crucial roles in tissue regeneration and the molecular repair mechanisms in cleft lip and palate patients. Notably, this method not only showed significant disease-associated protein biomarkers, but also provided an accessible diagnostic platform that reduces patient discomfort. Table 1 provides a summary of recent studies on CFAs based on saliva proteomics.

3.2.3 Amniotic fluid

Amniotic fluid is derived from both maternal and fetal tissues, encompassing substances from the placenta, fetal lung secretions, skin, urine and gastric fluid [62]. As amniotic fluid directly reflects the internal environment of the fetus, analyzing it can provide valuable information about the condition of fetal structures [63]. Since the 1950s, when amniotic fluid was first utilized in prenatal diagnosis for fetal conditions [64], it has become a widely used tool.

Chan-Kyung J. Cho et al. [57] explored the proteome of amniotic fluid using 2-dimensional liquid chromatography-chromatofocusing (2D-CF) and MS/MS to analyze samples from both chromosomally normal and Down’s syndrome-affected pregnancies, identifying amyloid precursor protein (APP) and tenascin (TNC) as potential biomarkers. Similarly, George Th. Tsangaris et al. [59] studied amniotic fluid from Down’s syndrome and chromosomally normal fetuses at the 17th week of gestation using 2-DE, matrix-assisted laser desorption/ionization-mass spectrometry (MALDI-MS) and nano-electrospray ionization tandem mass spectrometry (nano-ESI-MS/MS), which indicated that SFRS4 as a unique marker present only in the amniotic fluid of Down’s syndrome fetuses. Fetal Alcohol Syndrome (FAS) is characterized by craniofacial defects, mental retardation and stunted growth [65]. Aiming to discover protein biomarkers in amniotic fluid to facilitate early diagnosis of FAS, Susmita Datta et al. [58] administered alcohol to pregnant mice (the exposed group) and saline to a control group on the 8th day of gestation. On the 17th day, utilizing LC-MS/MS and multidimensional protein identification technology (MudPIT), they identified AFP as a potential biomarker for distinguishing FAS-positive from FAS-negative pregnancies.

ZIKV is associated with multiple birth defects such as microcephaly [66]. This virus can exhibit vertical transmission during pregnancy [55], providing a pathway to investigate the mechanisms underlying ZIKV-induced microcephaly through the analysis of amniotic fluid. Patricia Sosa-Acosta et al. [44] employed a shotgun proteomic approach to examine the amniotic fluid from pregnant women infected with ZIKV. They compared those carrying microcephalic fetuses to those with non-microcephalic fetuses and ZIKA-negative controls. This study suggested that the down-regulation of ECM-related proteins and impairment of innate immune system processes might modulate microcephalic phenotypes. We summarize recent studies on CFAs based on amniotic fluid proteomics in Table 1.

3.3 Multi-omics approaches to investigate craniofacial anomalies

Congenital CFAs are complex disorders. With the advancement of multi-omics, researchers now employ integrated multi-omics approaches to uncover the mechanisms of CFAs [36].

Huanhuan Pang et al. [67] conducted a multi-omics study on ZIKV-infected mouse brains, encompassing transcriptomics, proteomics, phosphoproteomics and metabolomics. They observed a dramatic alteration in NAD+-related metabolic pathways. Furthermore, they inferred a potential link between MAPK and cyclic GMP–protein kinase G signaling with ZIKV-induced microcephaly. In another study, Xin Chen et al. [7] employed RNA microarray and TMT-labeled quantitative proteomics to explore key genes of nonsyndromic microtia. Notably, this research, being the first to integrate transcriptomic and proteomic analyses for nonsyndromic microtia, identified several candidate genes, including laminin β2 (LAMB2), cartilage oligomeric matrix protein (COMP), APOA2, apolipoprotein C-II (APOC2), APOC3 and alpha-2 macroglobulin (A2M). Similarly, Angèle Tingaud‑Sequeira et al. [68] revealed the link between EYA3 gene and the oculo-auriculo-vertebral spectrum (OAVS), which manifests as asymmetric ear anomalies, hemifacial microsomia and ocular and vertebral defects. Through WES, researchers identified variants in eyes absent 3 (EYA3) gene, and using proteomic analyses, unveiled four potential upstream regulators: peroxisome proliferator-activated receptor-gamma coactivator-1beta (PPARGC1B), yes-associated protein 1 (YAP1), nuclear factor erythroid 2 like 2 (NFE2L2) and myelocytomatosis viral oncogene homolog (MYC). We summarize recent studies on CFAs using multi-omics approaches in Table 2.

Table 2.Summary of recent studies on CFAs using multi-omics approaches.
StudySample typeParticipantsObjectiveMethod of multi-omicsResultsSubsequent verificationRef.
Riedhammer et al. [69] (2022)Fibroblasts; blood samples6 individuals with Suleiman-El-Hattab syndrome; zebrafishDescribe the Suleiman-El-Hattab syndrome and explore the consequences of biallelic TASP1 loss-of-function variantsGenomics; proteomicsPossible downstream mechanisms of TASP1 deficiency include perturbed HOX gene expression and dysregulated TFIIA complex causing Suleiman-El-Hattab syndromeWBPMID: 35512351
Chen et al. [7] (2022)Cartilage tissues10 patients with third-degree nonsyndromic microtia and five control subjectsExplored its potential pathogenesis of nonsyndromic microtiaTranscriptomics; proteomicsThe dysregulation of LAMB2, COMP, APOA2, APOC2, APOC3 and A2M, contributing to nonsyndromic microtiaPRMPMID: 35647449
Sequeira et al. [68] (2021)Blood samples124 OAVS patientsIdentify and characterize a new gene associated with OAVSGenomics; proteomicsEYA3 is the gene identified to be associated with OAVSImmunocytochemistry; WB; RT-qPCRPMID: 33475861
Dong et al. [8] (2021)Auricular cartilage samples16 unilateral microtia patients who had undergone ear reconstruction surgeryExplore the pathogenesis of microtiaProteomics; transcriptomics47 genes as molecular markers of microtia progressionNPPMID: 33840765
Pang et al. [67] (2021)Mouse brain samplesICR mice (8–10 weeks old) and their pups after birthExplore the mechanisms of how ZIKV infection cause microcephaly in newbornsTranscriptomics; proteomics; phosphor-proteomics; metabolomicsMAPK and cGMP–protein kinase G signaling may be associated with ZIKV-induced microcephaly, and NAD+ metabolic reprogramming is a pathogenic driverImmunohistochemistry; WBPMID: 34385701
Kato et al. [70] (2021)Blood leucocytesA patient with global developmental delay, who presented macrocephal, characteristic facial features, and cutis marmorataExplore the mechanisms of CUL3-related disordersGenomics; proteomicsA de novo missense variant in CUL3 was identifiedNPPMID: 33130828
Magini et al. [71] (2019)FibroblastsChildren from 12 unrelated families presented with developmental disorder characterized by microcephalyExplore the function of SMPD4 in microcephalyGenomics; proteomicsSMPD4 links homeostasis of membrane sphingolipids to cell fate by regulating the cross-talk between the ER and the outer nuclear envelope, while its loss reveals a pathogenic mechanism in microcephalyFlow cytometry; immunofluorescence; WBPMID: 31495489
Abbreviations: NP: not performed; OAVS: oculo-auriculo-vertebral spectrum; ZIKV: Zika virus; MS: mass spectrometry; PRM: Parallel Reaction Monitoring; TFIIA: transcription initiation factor IIA; ER: endoplasmic reticulum; cGMP: cyclic guanosine monophosphate; MAPK: mitogen-activated protein kinase; NAD+: nicotinamide adenine dinucleotide; GMP: guanosine monophosphate; TASP: taspase 1; HOX: homeobox; WB: western blot; LAMB: laminin beta; COMP: cartilage oligomeric matrix protein; APOA: apolipoprotein A; APOC: apolipoprotein C; A2M: alpha-2 macroglobulin; RT-qPCR: real-time quantitative polymerase chain reaction; ICR: institute of cancer research; CUL: cullin; SMPD4: sphingomyelin phosphodiesterase 4.

Luke Reilly et al. [72] provided a thorough proteo-genomic strategy to uncover potential biomarker candidates for human diseases. The strategy begins with the use of genome sequencing to screening variant sites in the sample. Subsequently, MS technology evaluates the expression levels of proteins, highlighting potential biomarkers/targets. Finally, by integrating both genomic and proteomic data, a deeper understanding of disease mechanisms can be revealed.

3.4 Limitations and suggestions for using proteomics in analyzing predictors of congenital craniofacial anomalies

While mass spectrometry technology has significantly advanced, it’s important to recognize the limitations in studies using mass-spectrometry-based proteomics for understanding the mechanisms and identifying biomarkers of CFAs. Predominantly, these studies utilize a bottom-up approach, analyzing peptides through LC-MS/MS [73]. This method complicates protein identification and obscures the recognition of certain protein forms in the sample, as the connection between peptides and their original proteoforms is lost during digestion [74]. Consequently, reconstructing the proteome’s complexity at the proteoform level from peptides becomes challenging. An alternative top-down approach, which starts with intact proteins, can circumvent some of these issues [75]. However, limitations related to protein abundance and molecular weight restrict its application.

Although this review concentrates on proteomics in CFAs, it’s crucial to acknowledge that no single “omics” technique can entirely elucidate all molecular events leading to disease. Therefore, to understand CFAs comprehensively, it is essential to integrate multi-omics data, including genomics, transcriptomics and proteomics [36, 70]. Such integration allows for evaluating the effects of pathogenic genetic variants at multiple levels, clarifying the molecular and biochemical pathways underlying pathological phenotypes and predicting the occurrence of CFAs.

4. Future perspectives

Proteomics, particularly MS-based proteomics, facilitates large-scale protein analysis, shedding light on the molecular mechanisms and pathological processes of CFAs. However, there are still challenges to overcome, including handling minuscule-size samples, detecting low-abundance proteins with crucial functions, and investigating protein function in specific spatiotemporal contexts [76]. In light of these challenges, emerging proteomics technologies might soon be applied in this field.

Single-cell proteomics offers a detailed understanding into the composition and function of proteins in individual cells [77]. This method can exhibit the nuances of cell-to-cell variability and heterogeneity [78]. Unlike traditional proteomics which often requires larger sample sizes, even small samples suffice for robust analyses due to its sensitivity [79]. Beyond merely identifying proteins within cells, this approach may also be used to investigate protein functions and interactions between cells [80]. Thus, it greatly enriches our understanding of intercellular signaling communication and regulatory networks.

Imaging proteins in situ within whole organs or organisms has posed significant challenges for many years [81]. To address this issue, Harsharan Singh Bhatia et al. [82] introduced a breakthrough solution with their technology termed three-dimensional (3D) imaging of solvent-cleared organs profiled by mass spectrometry (DISCO-MS). This method integrates whole-organ/organism clearing and imaging, deep-learning-based image analysis, robotic tissue extraction and ultra-high-sensitivity MS. DISCO-MS allows researchers to obtain spatial-molecular profiles in various disease models by analyzing fluorescently labeled target regions. Unlike traditional proteomic studies, DISCO-MS preserves the sample’s integrity, offering comprehensive information into the entire sample, including both the structure and function of tissues and organs.

5. Concluding remarks

Proteomics applications have significantly enhanced our understanding of the molecular mechanisms behind CFAs, deepened our comprehension of the overall health impacts from CFAs, and offered new possibilities for early diagnosis. In this review, we present recent advancements in mass-spectrometry-based proteomics for CFA research, emphasizing the importance of body fluid analysis in biomarker discovery. Such analysis holds immense potential for prenatal diagnosis and treatment of CFAs, with the prospect of translating this information into innovative clinical applications in precision medicine and personalized nutrition. We also discuss the importance of integrating multi-omics approaches and offers new perspectives for future research. Looking ahead, we believe that establishing proteomic databases or resources specific to CFAs, akin to the creation of transcriptomics databases for craniofacial development, is essential for the effective identification of biomarkers through proteomics. This approach promises to open new avenues for future research and clinical application in the field.

Abbreviations

NP, not performed; CZS, congenital Zika syndrome; FAS, fetal alcohol syndrome; NSOFC, non-syndromic orofacial clefts; NSCLP, non-syndromic cleft of the lip and/or palate; OAVS, oculo-auriculo-vertebral spectrum; ZIKV, Zika virus; MS, mass spectrometry; PRM, Parallel Reaction Monitoring; TMT, tandem mass tag; 2-DE, two-dimensional electrophoresis; MALDI-TOF-MS, matrix-assisted laser desorption-time of flight mass spectrometry; LC-MS/MS, liquid chromatography tandem mass spectrometry; MudPIT, multidimensional protein identification technology; MS/MS, tandem mass spectrometry; 2D-LC, two-dimensional liquid chromatography; 2D-DIGE, fluorescence 2-dimensional gel electrophoresis; 2D-CF, 2-dimensional liquid chromatography-chromatofocusing; LC-ESI-MS/MS, liquid chromatography electrospray Ionization tandem mass spectrometry; HPLC, high-performance liquid chromatography; iTRAQ, isobaric tags for relative and absolute quantitation; MRM, multiple reaction monitoring; ELISA, enzyme linked immunosorbent assay; WB, western blot; TFIIA, transcription initiation factor IIA; ER, endoplasmic reticulum; cGMP, cyclic guanosine monophosphate; MAPK, mitogen-activated protein kinase; NAD+, nicotinamide adenine dinucleotide; GMP, guanosine monophosphate; BTB, broad-complex/tramtrack/bric-a-brac; ECM, extracellular matrix; AFP, alpha fetoprotein; TF, serotransferrin; A1BG, alpha-1b-glycoprotein; DES, desmin; SERPINA1, alpha-1-antitrypsin; CP, ceruloplasmin; APCS, serum amyloid P-component; APP, amyloid precursor protein; TNC-C, tenascin-C; APOA, apolipoproteins A; HPT, haptoglobin; CRP, c-reactive protein; CO1A1, collagen alpha 1 (I) chain; CO3A1, collagen alpha 1 (III) chain; CO5A1, collagen alpha 1 (V) chain d; PGBM, basement membrane-specific heparin sulfate proteoglycan core protein; PRX1, peroxiredoxin1; TCDD, 2,3,7,8-tetrachlorodibenzo-p-dioxin; ANXA1, annexin A1; RBP4, retinol-binding protein 4; SHBG, sex hormone binding globulin; POSTN, periostin sapiens; OMD, osteomodulin; ACAN, aggrecan core protein; AP-3, adaptor protein-3; DMKN, dermokine; NID1, nidogen 1; TGF-β3, transforming growth factor-β3; ZRANB2, zinc finger ran-binding domain-containing protein 2; LTQ, linear trap quadrupole; APP, amyloid precursor protein; MALDI-MS, matrix-assisted laser desorption/ionization-mass spectrometry; LAMB2, laminin β2; COMP, cartilage oligomeric matrix protein; APOC2, apolipoprotein C-II; A2M, alpha-2 macroglobulin; EYA3, eyes absent 3; PPARGC1B, peroxisome proliferator-activated receptor-gamma coactivator-1beta; YAP1, yes-associated protein 1; NFE2L2, nuclear factor erythroid 2 like 2; MYC, myelocytomatosis viral oncogene homolog; TASP, taspase; HOX, homeobox; RT-qPCR, real-time quantitative polymerase chain reaction; ICR, institute of cancer research; CUL, cullin; SMPD4, sphingomyelin phosphodiesterase 4.

Availability of Data and Materials

The data are contained within this article.

Author contributions

HXZ, JNZ and HMH—designed this review. SJH—collected the data. SJH, JNZ and HXZ—analyzed the data. HXZ, SJH, XQL, YHJ, KYL and HML—wrote the original draft. HXZ, BXY, SJH, YD, JNZ and HMH—revised the manuscript. All authors read and approved the final manuscript.

Ethics approval and consent to participate

Not applicable.

Acknowledgment

We thank Jiuxiang Lin (Peking University) for helping in this work. Fig. 1 was created with BioRender.com.

Funding

This work was supported by National Natural Science Foundation of China (No. 82001030), Hygiene and Health Development Scientific Research Fostering Plan of Haidian District Beijing (No. HP2023-12-509001), and Young Clinical Research Fund of the Chinese Stomatological Association (No. CSA-02022-03).

Conflict of interest

The authors declare no conflict of interest.

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