The Emerging Physiological Role of AGMO 10 Years after Its Gene Identification

Sailer, S; Keller, Markus A.; Werner, Ernst R.; Watschinger, Katrin

doi:10.3390/life11020088

Cited by 22 publications

(19 citation statements)

References 103 publications

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“…We then searched for specific hAd7 marker genes that exhibit this same relationship with HOMA-IR, and identified several, including AGMO, ALPK3, FHOD3, and LIN7A (Figure 4f, g). Of note, AGMO (also called TMEM195) has emerged as a candidate locus in T2D GWAS 49,50 .Taken together, our data suggest that hAd7 may have an outsized influence on the risk of T2D, despite representing only ~1% of human adipocytes.…”

Section: Relationships Between Wat Cell Types and Human Diseasementioning

confidence: 66%

A single cell atlas of human and mouse white adipose tissue

Emont

Jacobs

Essene

et al. 2021

Preprint

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White adipose tissue (WAT), once regarded as morphologically and functionally bland, is now recognized to be dynamic, plastic, heterogenous, and involved in a wide array of biological processes including energy homeostasis, glucose and lipid handling, blood pressure control, and host defense1. High fat feeding and other metabolic stressors cause dramatic changes in adipose morphology, physiology, and cellular composition1, and alterations in adiposity are associated with insulin resistance, dyslipidemia, and type 2 diabetes (T2D)2. Here, we provide detailed cellular atlases of human and murine subcutaneous and visceral white fat at single cell resolution across a range of body weight. We identify subpopulations of adipocytes, adipose stem and progenitor cells (ASPCs), vascular, and immune cells and demonstrate commonalities and differences across species and dietary conditions. We link specific cell types to increased risk of metabolic disease, and we provide an initial blueprint for a comprehensive set of interactions between individual cell types in the adipose niche in leanness and obesity. These data comprise an extensive resource for the exploration of genes, traits, and cell types in the function of WAT across species, depots, and nutritional conditions.

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Section: Relationships Between Wat Cell Types and Human Diseasementioning

confidence: 66%

A single cell atlas of human and mouse white adipose tissue

Emont

Jacobs

Essene

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…This revealed that, while in most cases we already had a significant amount of structural data, either experimental (sequence identity > 95%) or through template-based homology (sequence identity > 20%), there are 1,448 proteins with very high structural coverage (PDB sequence identity >20% or pLDDT > 90 for more than 95% of all residues of the protein) where AlphaFold contributes over 50% of the structural data. This is the case of relevant proteins in the biomedical context such as AGMO, an enzyme that has been linked to numerous diseases such as cancer and diabetes [ 30 ], DEGS1, another enzyme with a critical role in lipid metabolism [ 31 ], or PEMT, another lipid metabolism enzyme and was amongst the first genes associated with non-alcoholic fatty liver disease [ 32 ] ( Fig 2E–2G respectively). In all three cases no structural information was known nor any template with enough sequence identity to build a model (sequence identity > 20%).…”

Section: Resultsmentioning

confidence: 99%

The structural coverage of the human proteome before and after AlphaFold

et al. 2022

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The protein structure field is experiencing a revolution. From the increased throughput of techniques to determine experimental structures, to developments such as cryo-EM that allow us to find the structures of large protein complexes or, more recently, the development of artificial intelligence tools, such as AlphaFold, that can predict with high accuracy the folding of proteins for which the availability of homology templates is limited. Here we quantify the effect of the recently released AlphaFold database of protein structural models in our knowledge on human proteins. Our results indicate that our current baseline for structural coverage of 48%, considering experimentally-derived or template-based homology models, elevates up to 76% when including AlphaFold predictions. At the same time the fraction of dark proteome is reduced from 26% to just 10% when AlphaFold models are considered. Furthermore, although the coverage of disease-associated genes and mutations was near complete before AlphaFold release (69% of Clinvar pathogenic mutations and 88% of oncogenic mutations), AlphaFold models still provide an additional coverage of 3% to 13% of these critically important sets of biomedical genes and mutations. Finally, we show how the contribution of AlphaFold models to the structural coverage of non-human organisms, including important pathogenic bacteria, is significantly larger than that of the human proteome. Overall, our results show that the sequence-structure gap of human proteins has almost disappeared, an outstanding success of direct consequences for the knowledge on the human genome and the derived medical applications.

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“…This revealed that, while in most cases we already had a significant amount of structural data, either experimental (sequence identity > 95%) or through template-based homology (sequence identity > 20%), there are 1,448 proteins with very high structural coverage (PDB sequence identity >20% or pLDDT > 90 for more than 95% of all residues of the protein) where AlphaFold contributes over 50% of the structural data. This is the case of relevant proteins in the biomedical context such as AGMO, an enzyme that has been linked to numerous diseases such as cancer and diabetes[30], DEGS1, another enzyme with a critical role in lipid metabolism[31], or PEMT, another lipid metabolism enzyme and was amongst the first genes associated with non-alcoholic fatty liver disease[32] ( Figure 2e-g respectively). In all three cases no structural information was known nor any template with enough sequence identity to build a model (sequence identity > 20%).…”

Section: Resultsmentioning

confidence: 99%

The structural coverage of the human proteome before and after AlphaFold

Porta-Pardo

Ruiz-Serra

Valencia

2021

Preprint

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The protein structure field is experiencing a revolution. From the increased throughput of techniques to determine experimental structures, to developments such as cryo-EM that allow us to find the structures of large protein complexes or, more recently, the development of artificial intelligence tools, such as AlphaFold, that can predict with high accuracy the folding of proteins for which the availability of homology templates is limited. Here we quantify the effect of the recently released AlphaFold database of protein structural models in our knowledge on human proteins. Our results indicate that our current baseline for structural coverage of 47%, considering experimentally-derived or template-based homology models, elevates up to 75% when including AlphaFold predictions, reducing the fraction of dark proteome from 22% to just 7% and the number of proteins without structural information from 4.832 to just 29. Furthermore, although the coverage of disease-associated genes and mutations was near complete before AlphaFold release (70% of ClinVar pathogenic mutations and 74% of oncogenic mutations), AlphaFold models still provide an additional coverage of 2% to 14% of these critically important sets of biomedical genes and mutations. We also provide several examples of disease-associated proteins where AlphaFold provides critical new insights. Overall, our results show that the sequence-structure gap of human proteins has almost disappeared, an outstanding success of direct consequences for the knowledge on the human genome and the derived medical applications.

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The Emerging Physiological Role of AGMO 10 Years after Its Gene Identification

Cited by 22 publications

References 103 publications

A single cell atlas of human and mouse white adipose tissue

A single cell atlas of human and mouse white adipose tissue

The structural coverage of the human proteome before and after AlphaFold

The structural coverage of the human proteome before and after AlphaFold

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