Using human genetics to improve safety assessment of therapeutics

Carss, Keren; Deaton, Aimée M.; Río-Espínola, Alberto Del; Diogo, Dorothée; Fielden, Mark R.; Kulkarni, Diptee; Moggs, Jonathan G.; Newham, Peter; Nelson, Matthew R.; Sistare, Frank D.; Ward, Lucas D.; Yuan, Jing

doi:10.1038/s41573-022-00561-w

Cited by 42 publications

(35 citation statements)

References 237 publications

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“…Identification of therapeutic targets is generally based on a consistent upregulation or downregulation in the expression of target genes. Then, genetic manipulation and/or targeted drug-based screening allow the therapeutic targets to be confirmed, which could in turn help to identify feasible treatment options (9)(10)(11)(12). Therefore, genetic deletion or small molecule-based inhibition of PTPN22, such as with PTPN22-IN-1, would be helpful for the development of therapeutic agents to prevent AAA progression (36).…”

Section: Discussionmentioning

confidence: 99%

“…High-throughput platform-based biomarker identification has been highlighted as a promising approach for the diagnosis and prevention of AAA and other diseases (6)(7)(8). The discovery of therapeutic targets is generally based on a consistent increase or decrease in the expression of target genes in patients and/or experimental models, followed by genetic manipulation and/or targeted drug-based screening, which allow confirmation of the therapeutic targets and subsequent identification of feasible treatment options (9)(10)(11)(12). Integrative bioinformatics and experimental validation are highly efficient tools for identifying biomarkers to accurately predict the occurrence of AAA (6,13).…”

Section: Introductionmentioning

confidence: 99%

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Identification of PTPN22 as a potential genetic biomarker for abdominal aortic aneurysm

Ruan

Gao

Jiang

et al. 2022

Front. Cardiovasc. Med.

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Abdominal aortic aneurysm (AAA) is a severe life-threatening disease that is generally asymptomatic and is diagnosed at a very late stage. The genetic component underpinning AAA is considerable, with an estimated heritability of up to 70%. Therefore, identifying genetic biomarkers for AAA is valuable for predicting high-risk populations. We used integrative bioinformatics and cellular AAA model-based validation to reveal that the gene encoding protein tyrosine phosphatase non-receptor type 22 (PTPN22) may be a potentially useful diagnostic biomarker for AAA. Integrative bioinformatics analyses of clinical specimens showed that PTPN22 expression was consistently upregulated in aortic tissues and peripheral blood mononuclear cells (PBMCs) derived from patients with AAA. Moreover, transcriptomics data revealed that PTPN22 is a potential biomarker for AAA with limited diagnostic value in patients with thoracic aortic aneurysm/dissection. Single-cell RNA sequencing-based findings further highlight PTPN22 expression in aortic immune cells and vascular smooth muscle cells (VSMCs) is consistently upregulated in patients with AAA. A cellular AAA model was eventually employed to verify the increase in PTPN22 expression. Collectively, the results indicate that PTPN22 could be a potentially useful diagnostic biomarker for AAA.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identification of PTPN22 as a potential genetic biomarker for abdominal aortic aneurysm

Ruan

Gao

Jiang

et al. 2022

Front. Cardiovasc. Med.

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“…An additional ion channel of critical importance is the hERG channel [94][95][96] encoded by the gene KCNH2. Mutations and perturbations in this channel can lead to shortening or prolongation of the QT interval 95,[97][98][99] , and drug interactions with this channel can lead to cardiac arrythmia which represents a critical bottleneck surrounding drug discovery and development 100,101 .…”

Section: Developmental Induction Conditions Promote Progressive Elect...mentioning

confidence: 99%

A patterned human heart tube organoid model generated by pluripotent stem cell self-assembly

Volmert

Aguirre

Riggs

et al. 2022

Preprint

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Human pluripotent stem cells can recapitulate significant features of mammalian organ development in vitro, including key aspects of heart development. We hypothesized that the organoids thus created can be made substantially more relevant by mimicking aspects of in utero gestation, leading to higher physiological and anatomical resemblance to their in vivo counterparts. Here, we report steps towards generating developmentally inspired maturation methodologies to differentiate early human heart organoids into patterned heart-tube-like structures in a reproducible and high-throughput fashion by complete self-organization. The maturation strategy consists of the controlled and stepwise exposure to metabolic (glucose, fatty acids) and hormonal signals (T3, IGF-1) as present during early heart development. These conditions elicit important transcriptomic, cellular, morphological, metabolomic, and functional changes over a 10-day period consistent with continuously increasing heart complexity, maturation, and patterning. Our data reveals the emergence of atrial and ventricular cardiomyocyte populations, valvular cells, epicardial cells, proepicardial-derived cells, endothelial cells, stromal cells, conductance cells, and cardiac progenitors, all of them cell types present in the primitive heart tube. Anatomically, the organoids elongate and develop well-differentiated atrial and ventricular chambers with compacted myocardial muscle walls and a proepicardial organ. For the first time in a completely self-organizing heart organoid, we show anterior-posterior patterning due to an endogenous retinoic acid gradient originating at the atrial pole, where proepicardial and atrial populations reside, mimicking the developmental process present within the primitive heart tube. Collectively, these findings highlight the ability of self-organization and developmental maturation strategies to recapitulate human heart development. Our patterned human heart tube model constitutes a powerful in vitro tool for dissecting the role of different cell types and genes in human heart development, as well as disease modeling congenital heart defects, and represents a step forward in creating fully synthetic human hearts.

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“…Computational approaches reflective of human biology could bridge this gap and provide supportive information regarding potential ADE prediction. Hence, there is a strong in interest in computational strategies: ADE prediction has been tackled via statistical methods based on human genetics (Carss et al, 2022;Duffy et al, 2020;Nguyen et al, 2019), chemical structure-based approaches (Liu et al, 2014;Niu & Zhang, 2017;Pauwels et al, 2011;Yamanishi et al, 2012;Zhang et al, 2015;Zhao et al, 2018), approaches using high-throughput literature mining (Deftereos et al, 2011), gene expression data (Cakir et al, 2021;Z. Wang et al, 2016), protein sequences (Takarabe et al, 2012), electronic health records (Vilar et al, 2012) and data from electronic pharmacovigilance systems such as the FDA Adverse Event Reporting System (Schotland et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

MultiGML: Multimodal Graph Machine Learning for Prediction of Adverse Drug Events

Krix

Long

Madan

et al. 2023

Preprint

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Adverse drug events constitute a major challenge for the success of clinical trials. Several computational strategies have been suggested to estimate the risk of adverse drug events in preclinical drug development. While these approaches have demonstrated high utility in practice, they are at the same time limited to specific information sources and thus neglect a wealth of information that is uncovered by fusion of different data sources, including biological protein function, gene expression, chemical compound structure, cell-based imaging, etc. In this work we propose an integrative and explainable Graph Machine Learning approach (MultiGML), which fuses knowledge graphs with multiple further data modalities to predict drug related adverse events. MultiGML demonstrates excellent prediction performance compared to alternative algorithms, including various knowledge graph embedding techniques. MultiGML distinguishes itself from alternative techniques by providing in-depth explanations of model predictions, which point towards biological mechanisms associated with predictions of an adverse drug event.

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Using human genetics to improve safety assessment of therapeutics

Cited by 42 publications

References 237 publications

Identification of PTPN22 as a potential genetic biomarker for abdominal aortic aneurysm

Identification of PTPN22 as a potential genetic biomarker for abdominal aortic aneurysm

A patterned human heart tube organoid model generated by pluripotent stem cell self-assembly

MultiGML: Multimodal Graph Machine Learning for Prediction of Adverse Drug Events

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