Abstract:Cranial neural crest cells undergo cellular growth, patterning, and differentiation within the branchial arches to form cartilage and bone, resulting in a precise pattern of skeletal elements forming the craniofacial skeleton. However, it is unclear how cranial neural crest cells are regulated to give rise to the different shapes and sizes of the bone and cartilage. Epigenetic regulators are good candidates to be involved in this regulation, since they can exert both broad as well as precise control on pattern… Show more
“…Incidentally, the penultimate version of this manuscript found Kat2b as a potential novel finding in craniofacial development. An article that was very recently published supports the validity of this finding 69 , which is the first study in the literature to describe the role of Kat2b in craniofacial development. The results of our analysis suggest this gene may also have a more specific role in the fusion of palatal shelves.…”
Section: Discussionsupporting
confidence: 55%
“…Our findings suggest that Kat2b may also have an important, undiscovered role in the fusion of the palatal shelves. Indeed, in a very recent publication Kat2a and Kat2b are suggested to be epigenetic regulators required for craiofacial bone and cartilage growth and differentiation 69 .Figure 3The differential network for the pathway “Signaling by NOTCH1” in the lateral compartment. Tan edges are associations that are stronger in the anterior domain, and red edges are stronger in the posterior domain.…”
The advent of next-generation sequencing has introduced new opportunities in analyzing gene expression data. Research in systems biology has taken advantage of these opportunities by gleaning insights into gene regulatory networks through the analysis of gene association networks. Contrasting networks from different populations can reveal the many different roles genes fill, which can lead to new discoveries in gene function. Pathologies can also arise from aberrations in these gene-gene interactions. Exposing these network irregularities provides a new avenue for understanding and treating diseases. A general framework for integrating known gene regulatory pathways into a differential network analysis between two populations is proposed. The framework importantly allows for any gene-gene association measure to be used, and inference is carried out through permutation testing. A simulation study investigates the performance in identifying differentially connected genes when incorporating known pathways, even if the pathway knowledge is partially inaccurate. Another simulation study compares the general framework with four state-of-the-art methods. Two RNA-seq datasets are analyzed to illustrate the use of this framework in practice. In both examples, the analysis reveals genes and pathways that are known to be biologically significant along with potentially novel findings that may be used to motivate future research.
“…Incidentally, the penultimate version of this manuscript found Kat2b as a potential novel finding in craniofacial development. An article that was very recently published supports the validity of this finding 69 , which is the first study in the literature to describe the role of Kat2b in craniofacial development. The results of our analysis suggest this gene may also have a more specific role in the fusion of palatal shelves.…”
Section: Discussionsupporting
confidence: 55%
“…Our findings suggest that Kat2b may also have an important, undiscovered role in the fusion of the palatal shelves. Indeed, in a very recent publication Kat2a and Kat2b are suggested to be epigenetic regulators required for craiofacial bone and cartilage growth and differentiation 69 .Figure 3The differential network for the pathway “Signaling by NOTCH1” in the lateral compartment. Tan edges are associations that are stronger in the anterior domain, and red edges are stronger in the posterior domain.…”
The advent of next-generation sequencing has introduced new opportunities in analyzing gene expression data. Research in systems biology has taken advantage of these opportunities by gleaning insights into gene regulatory networks through the analysis of gene association networks. Contrasting networks from different populations can reveal the many different roles genes fill, which can lead to new discoveries in gene function. Pathologies can also arise from aberrations in these gene-gene interactions. Exposing these network irregularities provides a new avenue for understanding and treating diseases. A general framework for integrating known gene regulatory pathways into a differential network analysis between two populations is proposed. The framework importantly allows for any gene-gene association measure to be used, and inference is carried out through permutation testing. A simulation study investigates the performance in identifying differentially connected genes when incorporating known pathways, even if the pathway knowledge is partially inaccurate. Another simulation study compares the general framework with four state-of-the-art methods. Two RNA-seq datasets are analyzed to illustrate the use of this framework in practice. In both examples, the analysis reveals genes and pathways that are known to be biologically significant along with potentially novel findings that may be used to motivate future research.
“…PPARD (peroxisome proliferator activated receptor delta) is a critical gene for normal adipose development and lipid homeostasis 43 while ELF2 (E74 like ETS transcription factor 2) plays a key role in the development of lymphocytes 44 . KAT2A (lysine acetyltransferase 2A) is necessary for growth and differentiation of craniofacial cartilage and bone in zebrafish and mice 45 , RARA (retinoic acid receptor alpha) affects the hippocampal development 46 and finally BGLAP is produced by osteoblasts shaping new bones in chickens 47 .…”
Aim of the present study was to investigate whether body weight (BW) in broilers is associated with functional modular genes. To this end, first a GWAS for BW was conducted using 6,598 broilers and the high density SNP array. The next step was to search for positional candidate genes and QTLs within strong LD genomic regions around the significant SNPs. Using all positional candidate genes, a network was then constructed and community structure analysis was performed. Finally, functional enrichment analysis was applied to infer the functional relevance of modular genes. A total number of 645 positional candidate genes were identified in strong LD genomic regions around 11 genome-wide significant markers. 428 of the positional candidate genes were located within growth related QTLs. Community structure analysis detected 5 modules while functional enrichment analysis showed that 52 modular genes participated in developmental processes such as skeletal system development. An additional number of 14 modular genes (
GABRG1, NGF, APOBEC2, STAT5B, STAT3, SMAD4, MED1, CACNB1, SLAIN2, LEMD2, ZC3H18, TMEM132D, FRYL
and
SGCB
) were also identified as related to body weight. Taken together, current results suggested a total number of 66 genes as most plausible functional candidates for the trait examined.
“…The KAT2B gene encodes the p300/CBP-associated factor containing a histone acetyl transferase (HAT) domain, which has also been associated with acetylation of non-histone proteins [ 43 ], such as E2F1, and forms a pro-apoptotic complex with RB and E2F1 [ 44 ]. Moreover, recently, the KAT2B gene has been related to cell growth and differentiation during craniofacial and cartilage development in zebrafish and mouse embryos, which may be mediated by acetylation activity of the encoded protein [ 45 ]. These findings reinforce important levels of gene transcription control related to proliferation and differentiation mediated by E2F transcription factors, as well as their partners, which is in line with the control of cell cycle progression to maintain the function and development of specialized organs and systems that are more numerous at fetal stage, as highlighted in further sections of this article.…”
Since pre- and postnatal development are programmed during early prenatal life, studies addressing the complete transcriptional landscape during organogenesis are needed. Therefore, we aimed to disentangle differentially expressed (DE) genes between fetuses (at 35 days old) and embryos (at 25 days old) through RNA-sequencing analysis using the pig as model. In total, 1705 genes were DE, including the top DE IBSP, COL6A6, HBE1, HBZ, HBB, and NEUROD6 genes, which are associated with developmental transition from embryos to fetuses, such as ossification, skeletal muscle development, extracellular matrix organization, cardiovascular system, erythrocyte differentiation, and neuronal system. In pathway analysis, embryonic development highlighted those mainly related to morphogenic signaling and cell interactions, which are crucial for transcriptional control during the establishment of the main organs in early prenatal development, while pathways related to myogenesis, neuronal development, and cardiac and striated muscle contraction were enriched for fetal development, according to the greater complexity of organs and body structures at this developmental stage. Our findings provide an exploratory and informative transcriptional landscape of pig organogenesis, which might contribute to further studies addressing specific developmental events in pigs and in other mammals.
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