Genomic and multi-tissue proteomic integration for understanding the biology of disease and other complex traits

Abstract Understanding the tissue-specific genetic architecture of protein levels is instrumental to understand the biology of health and disease. We generated a genomic atlas of protein levels in multiple neurologically relevant tissues (380 brain, 835 cerebrospinal fluid (CSF) and 529 plasma), by profiling thousands of proteins (713 CSF, 931 plasma and 1079 brain) in a large and well-characterized cohort. We identified 274, 127 and 32 protein quantitative loci (pQTL) for CSF, plasma and brain respectively. cis-pQTL were more likely to be shared across tissues but trans-pQTL tend to be tissue-specific. Between 44% to 68.2% of the pQTL do not colocalize with expression, splicing, methylation or histone QTLs, indicating that protein levels have a different genetic architecture to those that regulate gene expression. By combining our pQTL with Mendelian Randomization approaches we identified potential novel biomarkers and drug targets for neurodegenerative diseases including Alzheimer disease and frontotemporal dementia. Here we present the first multi-tissue study yielding hundred of novel pQTLs. This data will be instrumental to identify the functional gene from GWAS signals, identify novel biological protein-protein interactions, identify novel potential biomarkers and drug targets for complex traits.

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Genomic and multi-tissue proteomic integration for understanding the biology of disease and other complex traits

10.1101/2020.06.25.20140277 ◽

2020 ◽

Cited By ~ 1

Author(s):

Chengran Yang ◽

Fabiana G. Farias ◽

Laura Ibanez ◽

Brooke Sadler ◽

Maria Victoria Fernandez ◽

...

Keyword(s):

Gene Expression ◽

Complex Traits ◽

Drug Targets ◽

Genetic Architecture ◽

Genome Wide Association Study ◽

Mendelian Randomization ◽

Protein Levels ◽

Genome Wide ◽

Novel Biomarkers ◽

Potential Drug Targets

AbstractExpression quantitative trait loci (eQTL) mapping has successfully resolved some genome-wide association study (GWAS) loci for complex traits1–6. However, there is a need for implementing additional “omic” approaches to untangle additional loci and provide a biological context for GWAS signals. We generated a detailed landscape of the genomic architecture of protein levels in multiple neurologically relevant tissues (brain, cerebrospinal fluid (CSF) and plasma), by profiling thousands of proteins in a large and well-characterized cohort. We identified 274, 127 and 32 protein quantitative loci (pQTL) for CSF, plasma and brain respectively. We demonstrated that cis-pQTL are more likely to be shared across tissues but trans-pQTL are tissue-specific. Between 78% to 87% of pQTL are not eQTL, indicating that protein levels have a different genetic architecture than gene expression. By combining our pQTL with Mendelian Randomization approaches we identified potential novel biomarkers and drug targets for neurodegenerative diseases including Alzheimer disease and frontotemporal dementia. In the context of personalized medicine, these results highlight the need for implementing additional functional genomic approaches beyond gene expression in order to understand the biology of complex traits, and to identify novel biomarkers and potential drug targets for those traits.

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A Modified TurboID Approach Identifies Tissue-Specific Centriolar Components In C. elegans

10.1101/2021.12.20.473533 ◽

2021 ◽

Author(s):

Elisabeth Holzer ◽

Cornelia Rumpf-Kienzl ◽

Sebastian Falk ◽

Alexander Dammermann

Keyword(s):

Protein Interactions ◽

Affinity Purification ◽

Experimental Models ◽

Protein Protein Interactions ◽

Tissue Specific ◽

C Elegans ◽

Early Embryos ◽

Somatic Tissues ◽

Specific Variation ◽

Labeling Method

Proximity-dependent labeling approaches such as BioID have been a great boon to studies of protein-protein interactions in the context of cytoskeletal structures such as centrosomes which are poorly amenable to traditional biochemical approaches like immunoprecipitation and tandem affinity purification. Yet, these methods have so far not been applied extensively to invertebrate experimental models such as C. elegans given the long labeling times required for the original promiscuous biotin ligase variant BirA*. Here, we show that the recently developed variant TurboID successfully probes the interactomes of both stably associated (SPD-5) and dynamically localized (PLK-1) centrosomal components. We further develop an indirect proximity labeling method employing a GFP nanobody- TurboID fusion, which allows the identification of protein interactors in a tissue-specific manner in the context of the whole animal. Critically, this approach utilizes available endogenous GFP fusions, avoiding the need to generate multiple additional strains for each target protein and the potential complications associated with overexpressing the protein from transgenes. Using this method, we identify homologs of two highly conserved centriolar components, Cep97 and Bld10/Cep135, which are present in various somatic tissues of the worm. Surprisingly, neither protein is expressed in early embryos, likely explaining why these proteins have escaped attention until now. Our work expands the experimental repertoire for C. elegans and opens the door for further studies of tissue-specific variation in centrosome architecture.

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Exploring Protein-Protein Interactions as Drug Targets for Anti-cancer Therapy with In Silico Workflows

Methods in Molecular Biology - Proteomics for Drug Discovery ◽

10.1007/978-1-4939-7201-2_15 ◽

2017 ◽

pp. 221-236 ◽

Cited By ~ 11

Author(s):

Alexander Goncearenco ◽

Minghui Li ◽

Franco L. Simonetti ◽

Benjamin A. Shoemaker ◽

Anna R. Panchenko

Keyword(s):

Cancer Therapy ◽

Protein Interactions ◽

In Silico ◽

Drug Targets ◽

Protein Protein Interactions ◽

Anti Cancer

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An integrated strategy for the discovery of drug targets by the analysis of protein–protein interactions

International Journal of Mass Spectrometry ◽

10.1016/j.ijms.2003.12.039 ◽

2004 ◽

Vol 238 (2) ◽

pp. 119-130 ◽

Cited By ~ 3

Author(s):

John M. Peltier ◽

Srdjan Askovic ◽

Robert R. Becklin ◽

Cindy Lou Chepanoske ◽

Yew-Seng J. Ho ◽

...

Keyword(s):

Protein Interactions ◽

Drug Targets ◽

Protein Protein Interactions ◽

Integrated Strategy

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Direct AKAP-Mediated Protein-Protein Interactions as Potential Drug Targets

Handbook of Experimental Pharmacology - Protein-Protein Interactions as New Drug Targets ◽

10.1007/978-3-540-72843-6_20 ◽

2008 ◽

pp. 483-503 ◽

Cited By ~ 21

Author(s):

C. Hundsrucker ◽

E. Klussmann

Keyword(s):

Protein Interactions ◽

Drug Targets ◽

Potential Drug ◽

Protein Protein Interactions ◽

Potential Drug Targets

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Identification of Drug Targets from Integrated Database of Diabetes Mellitus Genes Using Protein-Protein Interactions

Application of Computational Intelligence to Biology - SpringerBriefs in Applied Sciences and Technology ◽

10.1007/978-981-10-0391-2_8 ◽

2016 ◽

pp. 83-86 ◽

Cited By ~ 1

Author(s):

Duggineni Kalyani ◽

Naresh Babu Muppalaneni ◽

Ch Ambedkar ◽

Kiran Kumar Reddi

Keyword(s):

Diabetes Mellitus ◽

Protein Interactions ◽

Drug Targets ◽

Protein Protein Interactions

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In Silico Recognition of Protein-Protein Interaction

Advanced Data Mining Technologies in Bioinformatics ◽

10.4018/978-1-59140-863-5.ch013 ◽

2011 ◽

pp. 248-268

Author(s):

Byung-Hoon Park ◽

Phuongan Dam ◽

Chongle Pan ◽

Ying Xu ◽

Al Geist ◽

...

Keyword(s):

Protein Interactions ◽

Regulatory Networks ◽

Machine Learning Techniques ◽

Translation Regulation ◽

Future Research ◽

Protein Protein Interactions ◽

Protein Protein Interaction ◽

Protein Levels ◽

Protein Functions ◽

Model Protein

Protein-protein interactions are fundamental to cellular processes. They are responsible for phenomena like DNA replication, gene transcription, protein translation, regulation of metabolic pathways, immunologic recognition, signal transduction, etc. The identification of interacting proteins is therefore an important prerequisite step in understanding their physiological functions. Due to the invaluable importance to various biophysical activities, reliable computational methods to infer protein-protein interactions from either structural or genome sequences are in heavy demand lately. Successful predictions, for instance, will facilitate a drug design process and the reconstruction of metabolic or regulatory networks. In this chapter, we review: (a) high-throughput experimental methods for identification of protein-protein interactions, (b) existing databases of protein-protein interactions, (c) computational approaches to predicting protein-protein interactions at both residue and protein levels, (d) various statistical and machine learning techniques to model protein-protein interactions, and (e) applications of protein-protein interactions in predicting protein functions. We also discuss intrinsic drawbacks of the existing approaches and future research directions.

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Protein Dynamics in F-like Bacterial Conjugation

Biomedicines ◽

10.3390/biomedicines8090362 ◽

2020 ◽

Vol 8 (9) ◽

pp. 362

Author(s):

Nicholas Bragagnolo ◽

Christina Rodriguez ◽

Naveed Samari-Kermani ◽

Alice Fours ◽

Mahboubeh Korouzhdehi ◽

...

Keyword(s):

Antibiotic Resistance ◽

Protein Interactions ◽

Drug Targets ◽

Dynamic Models ◽

Structural Information ◽

Imaging Techniques ◽

Antibiotic Resistance Genes ◽

Protein Protein Interactions ◽

Secretion Systems ◽

Type Iv

Efficient in silico development of novel antibiotics requires high-resolution, dynamic models of drug targets. As conjugation is considered the prominent contributor to the spread of antibiotic resistance genes, targeted drug design to disrupt vital components of conjugative systems has been proposed to lessen the proliferation of bacterial antibiotic resistance. Advancements in structural imaging techniques of large macromolecular complexes has accelerated the discovery of novel protein-protein interactions in bacterial type IV secretion systems (T4SS). The known structural information regarding the F-like T4SS components and complexes has been summarized in the following review, revealing a complex network of protein-protein interactions involving domains with varying degrees of disorder. Structural predictions were performed to provide insight on the dynamicity of proteins within the F plasmid conjugative system that lack structural information.

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Pseudokinase drug intervention: a potentially poisoned chalice

Biochemical Society Transactions ◽

10.1042/bst20130078 ◽

2013 ◽

Vol 41 (4) ◽

pp. 1083-1088 ◽

Cited By ~ 10

Author(s):

Jeroen Claus ◽

Angus J.M. Cameron ◽

Peter J. Parker

Keyword(s):

Protein Interactions ◽

Drug Targets ◽

Binding Pocket ◽

Atp Binding ◽

Potential Drug ◽

Protein Protein Interactions ◽

Drug Intervention ◽

Potential Impact ◽

Potential Drug Targets ◽

Selection Of

Pseudokinases, the catalytically impaired component of the kinome, have recently been found to share more properties with active kinases than previously thought. In many pseudokinases, ATP binding and even some activity is preserved, highlighting these proteins as potential drug targets. In both active kinases and pseudokinases, binding of ATP or drugs in the nucleotide-binding pocket can stabilize specific conformations required for activity and protein–protein interactions. We discuss the implications of locking particular conformations in a selection of (pseudo)kinases and the dual potential impact on the druggability of these proteins.

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