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Article: Protein-protein interactions and genetic diseases: The interactome.

Lage, Kasper

2014 Volume 1842, Issue 10, Page(s) 1971–1980

Abstract: Protein-protein interactions mediate essentially all biological processes. Despite the quality of these data being widely questioned a decade ago, the reproducibility of large-scale protein interaction data is now much improved and there is little ... ...

Abstract	Protein-protein interactions mediate essentially all biological processes. Despite the quality of these data being widely questioned a decade ago, the reproducibility of large-scale protein interaction data is now much improved and there is little question that the latest screens are of high quality. Moreover, common data standards and coordinated curation practices between the databases that collect the interactions have made these valuable data available to a wide group of researchers. Here, I will review how protein-protein interactions are measured, collected and quality controlled. I discuss how the architecture of molecular protein networks has informed disease biology, and how these data are now being computationally integrated with the newest genomic technologies, in particular genome-wide association studies and exome-sequencing projects, to improve our understanding of molecular processes perturbed by genetics in human diseases. This article is part of a Special Issue entitled: From Genome to Function.
Language	English
Publishing date	2014-06-02
Publishing country	Netherlands
Document type	Review
ZDB-ID	60-7
ISSN	1879-2596 ; 1879-260X ; 1872-8006 ; 1879-2642 ; 1879-2618 ; 1879-2650 ; 0006-3002 ; 0005-2728 ; 0005-2736 ; 0304-4165 ; 0167-4838 ; 1388-1981 ; 0167-4889 ; 0167-4781 ; 0304-419X ; 1570-9639 ; 0925-4439 ; 1874-9399
ISSN (online)	1879-2596 ; 1879-260X ; 1872-8006 ; 1879-2642 ; 1879-2618 ; 1879-2650
ISSN	0006-3002 ; 0005-2728 ; 0005-2736 ; 0304-4165 ; 0167-4838 ; 1388-1981 ; 0167-4889 ; 0167-4781 ; 0304-419X ; 1570-9639 ; 0925-4439 ; 1874-9399
DOI	10.1016/j.bbadis.2014.05.028
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Article ; Online: Current advancements of modelling schizophrenia using patient-derived induced pluripotent stem cells.

Dubonyte, Ugne / Asenjo-Martinez, Andrea / Werge, Thomas / Lage, Kasper / Kirkeby, Agnete

Acta neuropathologica communications

2022 Volume 10, Issue 1, Page(s) 183

Abstract: Schizophrenia (SZ) is a severe psychiatric disorder, with a prevalence of 1-2% world-wide and substantial health- and social care costs. The pathology is influenced by both genetic and environmental factors, however the underlying cause still remains ... ...

Abstract	Schizophrenia (SZ) is a severe psychiatric disorder, with a prevalence of 1-2% world-wide and substantial health- and social care costs. The pathology is influenced by both genetic and environmental factors, however the underlying cause still remains elusive. SZ has symptoms including delusions, hallucinations, confused thoughts, diminished emotional responses, social withdrawal and anhedonia. The onset of psychosis is usually in late adolescence or early adulthood. Multiple genome-wide association and whole exome sequencing studies have provided extraordinary insights into the genetic variants underlying familial as well as polygenic forms of the disease. Nonetheless, a major limitation in schizophrenia research remains the lack of clinically relevant animal models, which in turn hampers the development of novel effective therapies for the patients. The emergence of human induced pluripotent stem cell (hiPSC) technology has allowed researchers to work with SZ patient-derived neuronal and glial cell types in vitro and to investigate the molecular basis of the disorder in a human neuronal context. In this review, we summarise findings from available studies using hiPSC-based neural models and discuss how these have provided new insights into molecular and cellular pathways of SZ. Further, we highlight different examples of how these models have shown alterations in neurogenesis, neuronal maturation, neuronal connectivity and synaptic impairment as well as mitochondrial dysfunction and dysregulation of miRNAs in SZ patient-derived cultures compared to controls. We discuss the pros and cons of these models and describe the potential of using such models for deciphering the contribution of specific human neural cell types to the development of the disease.
MeSH term(s)	Animals ; Adolescent ; Humans ; Adult ; Induced Pluripotent Stem Cells/metabolism ; Schizophrenia/genetics ; Schizophrenia/metabolism ; Genome-Wide Association Study ; Psychotic Disorders ; Neurons/metabolism
Language	English
Publishing date	2022-12-16
Publishing country	England
Document type	Journal Article ; Review ; Research Support, Non-U.S. Gov't
ZDB-ID	2715589-4
ISSN	2051-5960 ; 2051-5960
ISSN (online)	2051-5960
ISSN	2051-5960
DOI	10.1186/s40478-022-01460-2
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Article ; Online: Prediction of cancer driver genes through network-based moment propagation of mutation scores.

Gumpinger, Anja C / Lage, Kasper / Horn, Heiko / Borgwardt, Karsten

Bioinformatics (Oxford, England)

2020 Volume 36, Issue Suppl_1, Page(s) i508–i515

Abstract: Motivation: Gaining a comprehensive understanding of the genetics underlying cancer development and progression is a central goal of biomedical research. Its accomplishment promises key mechanistic, diagnostic and therapeutic insights. One major step in ...

Abstract	Motivation: Gaining a comprehensive understanding of the genetics underlying cancer development and progression is a central goal of biomedical research. Its accomplishment promises key mechanistic, diagnostic and therapeutic insights. One major step in this direction is the identification of genes that drive the emergence of tumors upon mutation. Recent advances in the field of computational biology have shown the potential of combining genetic summary statistics that represent the mutational burden in genes with biological networks, such as protein-protein interaction networks, to identify cancer driver genes. Those approaches superimpose the summary statistics on the nodes in the network, followed by an unsupervised propagation of the node scores through the network. However, this unsupervised setting does not leverage any knowledge on well-established cancer genes, a potentially valuable resource to improve the identification of novel cancer drivers. Results: We develop a novel node embedding that enables classification of cancer driver genes in a supervised setting. The embedding combines a representation of the mutation score distribution in a node's local neighborhood with network propagation. We leverage the knowledge of well-established cancer driver genes to define a positive class, resulting in a partially labeled dataset, and develop a cross-validation scheme to enable supervised prediction. The proposed node embedding followed by a supervised classification improves the predictive performance compared with baseline methods and yields a set of promising genes that constitute candidates for further biological validation. Availability and implementation: Code available at https://github.com/BorgwardtLab/MoProEmbeddings. Supplementary information: Supplementary data are available at Bioinformatics online.
MeSH term(s)	Computational Biology ; Humans ; Mutation ; Neoplasms/genetics ; Oncogenes ; Protein Interaction Maps
Language	English
Publishing date	2020-07-13
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btaa452
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Article ; Online: Protein interaction networks in the vasculature prioritize genes and pathways underlying coronary artery disease.

Zhu, Qiuyu Martin / Hsu, Yu-Han H / Lassen, Frederik H / MacDonald, Bryan T / Stead, Stephanie / Malolepsza, Edyta / Kim, April / Li, Taibo / Mizoguchi, Taiji / Schenone, Monica / Guzman, Gaelen / Tanenbaum, Benjamin / Fornelos, Nadine / Carr, Steven A / Gupta, Rajat M / Ellinor, Patrick T / Lage, Kasper

Communications biology

2024 Volume 7, Issue 1, Page(s) 87

Abstract: Population-based association studies have identified many genetic risk loci for coronary artery disease (CAD), but it is often unclear how genes within these loci are linked to CAD. Here, we perform interaction proteomics for 11 CAD-risk genes to map ... ...

Abstract	Population-based association studies have identified many genetic risk loci for coronary artery disease (CAD), but it is often unclear how genes within these loci are linked to CAD. Here, we perform interaction proteomics for 11 CAD-risk genes to map their protein-protein interactions (PPIs) in human vascular cells and elucidate their roles in CAD. The resulting PPI networks contain interactions that are outside of known biology in the vasculature and are enriched for genes involved in immunity-related and arterial-wall-specific mechanisms. Several PPI networks derived from smooth muscle cells are significantly enriched for genetic variants associated with CAD and related vascular phenotypes. Furthermore, the networks identify 61 genes that are found in genetic loci associated with risk of CAD, prioritizing them as the causal candidates within these loci. These findings indicate that the PPI networks we have generated are a rich resource for guiding future research into the molecular pathogenesis of CAD.
MeSH term(s)	Humans ; Coronary Artery Disease/genetics ; Protein Interaction Maps ; Gene Regulatory Networks ; Genetic Loci ; Proteomics
Language	English
Publishing date	2024-01-12
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
ISSN	2399-3642
ISSN (online)	2399-3642
DOI	10.1038/s42003-023-05705-1
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Article ; Online: Protein interaction studies in human induced neurons indicate convergent biology underlying autism spectrum disorders.

Pintacuda, Greta / Hsu, Yu-Han H / Tsafou, Kalliopi / Li, Ka Wan / Martín, Jacqueline M / Riseman, Jackson / Biagini, Julia C / Ching, Joshua K T / Mena, Daya / Gonzalez-Lozano, Miguel A / Egri, Shawn B / Jaffe, Jake / Smit, August B / Fornelos, Nadine / Eggan, Kevin C / Lage, Kasper

Cell genomics

2023 Volume 3, Issue 3, Page(s) 100250

Abstract: Autism spectrum disorders (ASDs) have been linked to genes with enriched expression in the brain, but it is unclear how these genes converge into cell-type-specific networks. We built a protein-protein interaction network for 13 ASD-associated genes in ... ...

Abstract	Autism spectrum disorders (ASDs) have been linked to genes with enriched expression in the brain, but it is unclear how these genes converge into cell-type-specific networks. We built a protein-protein interaction network for 13 ASD-associated genes in human excitatory neurons derived from induced pluripotent stem cells (iPSCs). The network contains newly reported interactions and is enriched for genetic and transcriptional perturbations observed in individuals with ASDs. We leveraged the network data to show that the ASD-linked brain-specific isoform of ANK2 is important for its interactions with synaptic proteins and to characterize a PTEN-AKAP8L interaction that influences neuronal growth. The IGF2BP1-3 complex emerged as a convergent point in the network that may regulate a transcriptional circuit of ASD-associated genes. Our findings showcase cell-type-specific interactomes as a framework to complement genetic and transcriptomic data and illustrate how both individual and convergent interactions can lead to biological insights into ASDs.
Language	English
Publishing date	2023-01-24
Publishing country	United States
Document type	Journal Article
ISSN	2666-979X
ISSN (online)	2666-979X
DOI	10.1016/j.xgen.2022.100250
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Article ; Online: Leadership.

Bowdish, Dawn / Desai, Tejal A / DePace, Angela / Haswell, Elizabeth S / Baltrus, David / García, Andrés J / Deans, Tara / Lage, Kasper / Wittkopp, Patricia

Cell systems

2021 Volume 12, Issue 1, Page(s) 1–4

Abstract: We asked group leaders how they foster mutually reinforcing research productivity and psychological safety in their teams. ...

Abstract	We asked group leaders how they foster mutually reinforcing research productivity and psychological safety in their teams.
MeSH term(s)	Biomedical Research ; Leadership ; Set, Psychology
Language	English
Publishing date	2021-01-21
Publishing country	United States
Document type	Journal Article
ZDB-ID	2854138-8
ISSN	2405-4720 ; 2405-4712
ISSN (online)	2405-4720
ISSN	2405-4712
DOI	10.1016/j.cels.2020.12.004
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Article ; Online: Protein interaction networks in the vasculature prioritize genes and pathways underlying coronary artery disease

Qiuyu Martin Zhu / Yu-Han H. Hsu / Frederik H. Lassen / Bryan T. MacDonald / Stephanie Stead / Edyta Malolepsza / April Kim / Taibo Li / Taiji Mizoguchi / Monica Schenone / Gaelen Guzman / Benjamin Tanenbaum / Nadine Fornelos / Steven A. Carr / Rajat M. Gupta / Patrick T. Ellinor / Kasper Lage

Communications Biology, Vol 7, Iss 1, Pp 1-

2024 Volume 15

Abstract: Abstract Population-based association studies have identified many genetic risk loci for coronary artery disease (CAD), but it is often unclear how genes within these loci are linked to CAD. Here, we perform interaction proteomics for 11 CAD-risk genes ... ...

Abstract	Abstract Population-based association studies have identified many genetic risk loci for coronary artery disease (CAD), but it is often unclear how genes within these loci are linked to CAD. Here, we perform interaction proteomics for 11 CAD-risk genes to map their protein-protein interactions (PPIs) in human vascular cells and elucidate their roles in CAD. The resulting PPI networks contain interactions that are outside of known biology in the vasculature and are enriched for genes involved in immunity-related and arterial-wall-specific mechanisms. Several PPI networks derived from smooth muscle cells are significantly enriched for genetic variants associated with CAD and related vascular phenotypes. Furthermore, the networks identify 61 genes that are found in genetic loci associated with risk of CAD, prioritizing them as the causal candidates within these loci. These findings indicate that the PPI networks we have generated are a rich resource for guiding future research into the molecular pathogenesis of CAD.
Keywords	Biology (General) ; QH301-705.5
Language	English
Publishing date	2024-01-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: CSMD1 regulates brain complement activity and circuit development.

Baum, Matthew L / Wilton, Daniel K / Fox, Rachel G / Carey, Alanna / Hsu, Yu-Han H / Hu, Ruilong / Jäntti, Henna J / Fahey, Jaclyn B / Muthukumar, Allie K / Salla, Nikkita / Crotty, William / Scott-Hewitt, Nicole / Bien, Elizabeth / Sabatini, David A / Lanser, Toby B / Frouin, Arnaud / Gergits, Frederick / Håvik, Bjarte / Gialeli, Chrysostomi /

Nacu, Eugene / Lage, Kasper / Blom, Anna M / Eggan, Kevin / McCarroll, Steven A / Johnson, Matthew B / Stevens, Beth

Brain, behavior, and immunity

2024 Volume 119, Page(s) 317–332

Abstract: Complement proteins facilitate synaptic elimination during neurodevelopmental pruning, but neural complement regulation is not well understood. CUB and Sushi Multiple Domains 1 (CSMD1) can regulate complement activity in vitro, is expressed in the brain, ...

Abstract	Complement proteins facilitate synaptic elimination during neurodevelopmental pruning, but neural complement regulation is not well understood. CUB and Sushi Multiple Domains 1 (CSMD1) can regulate complement activity in vitro, is expressed in the brain, and is associated with increased schizophrenia risk. Beyond this, little is known about CSMD1 including whether it regulates complement activity in the brain or otherwise plays a role in neurodevelopment. We used biochemical, immunohistochemical, and proteomic techniques to examine the regional, cellular, and subcellular distribution as well as protein interactions of CSMD1 in the brain. To evaluate whether CSMD1 is involved in complement-mediated synapse elimination, we examined Csmd1-knockout mice and CSMD1-knockout human stem cell-derived neurons. We interrogated synapse and circuit development of the mouse visual thalamus, a process that involves complement pathway activity. We also quantified complement deposition on synapses in mouse visual thalamus and on cultured human neurons. Finally, we assessed uptake of synaptosomes by cultured microglia. We found that CSMD1 is present at synapses and interacts with complement proteins in the brain. Mice lacking Csmd1 displayed increased levels of complement component C3, an increased colocalization of C3 with presynaptic terminals, fewer retinogeniculate synapses, and aberrant segregation of eye-specific retinal inputs to the visual thalamus during the critical period of complement-dependent refinement of this circuit. Loss of CSMD1 in vivo enhanced synaptosome engulfment by microglia in vitro, and this effect was dependent on activity of the microglial complement receptor, CR3. Finally, human stem cell-derived neurons lacking CSMD1 were more vulnerable to complement deposition. These data suggest that CSMD1 can function as a regulator of complement-mediated synapse elimination in the brain during development.
Language	English
Publishing date	2024-03-27
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	639219-2
ISSN	1090-2139 ; 0889-1591
ISSN (online)	1090-2139
ISSN	0889-1591
DOI	10.1016/j.bbi.2024.03.041
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Article ; Online: Genoppi is an open-source software for robust and standardized integration of proteomic and genetic data

Greta Pintacuda / Frederik H. Lassen / Yu-Han H. Hsu / April Kim / Jacqueline M. Martín / Edyta Malolepsza / Justin K. Lim / Nadine Fornelos / Kevin C. Eggan / Kasper Lage

Nature Communications, Vol 12, Iss 1, Pp 1-

2021 Volume 10

Abstract: Genetic variation can impact protein complexes and interaction networks, but reconciling genetic and proteomic information remains challenging. To address this need, the authors develop Genoppi —a computational tool for integrating genetics and cell-type- ...

Abstract	Genetic variation can impact protein complexes and interaction networks, but reconciling genetic and proteomic information remains challenging. To address this need, the authors develop Genoppi —a computational tool for integrating genetics and cell-type-specific proteomics data.
Keywords	Science ; Q
Language	English
Publishing date	2021-05-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
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Article ; Online: Systematic auditing is essential to debiasing machine learning in biology.

Eid, Fatma-Elzahraa / Elmarakeby, Haitham A / Chan, Yujia Alina / Fornelos, Nadine / ElHefnawi, Mahmoud / Van Allen, Eliezer M / Heath, Lenwood S / Lage, Kasper

Communications biology

2021 Volume 4, Issue 1, Page(s) 183

Abstract: Biases in data used to train machine learning (ML) models can inflate their prediction performance and confound our understanding of how and what they learn. Although biases are common in biological data, systematic auditing of ML models to identify and ... ...

Abstract	Biases in data used to train machine learning (ML) models can inflate their prediction performance and confound our understanding of how and what they learn. Although biases are common in biological data, systematic auditing of ML models to identify and eliminate these biases is not a common practice when applying ML in the life sciences. Here we devise a systematic, principled, and general approach to audit ML models in the life sciences. We use this auditing framework to examine biases in three ML applications of therapeutic interest and identify unrecognized biases that hinder the ML process and result in substantially reduced model performance on new datasets. Ultimately, we show that ML models tend to learn primarily from data biases when there is insufficient signal in the data to learn from. We provide detailed protocols, guidelines, and examples of code to enable tailoring of the auditing framework to other biomedical applications.
MeSH term(s)	Animals ; Bias ; Data Mining ; Databases, Protein ; Histocompatibility Antigens/metabolism ; Humans ; Machine Learning ; Pharmaceutical Preparations/chemistry ; Pharmaceutical Preparations/metabolism ; Protein Binding ; Protein Interaction Maps ; Proteins/chemistry ; Proteins/metabolism ; Proteome ; Proteomics ; Reproducibility of Results
Chemical Substances	Histocompatibility Antigens ; Pharmaceutical Preparations ; Proteins ; Proteome
Language	English
Publishing date	2021-02-10
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
ISSN	2399-3642
ISSN (online)	2399-3642
DOI	10.1038/s42003-021-01674-5
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