Semantic text mining in early drug discovery for type 2 diabetes

Hansson, Lena; Hansen, Rasmus Borup; Pletscher-Frankild, Sune; Berzins, Rudolfs; Hansen, Daniel Hvidberg; Madsen, Dennis; Christensen, Sten B.; Christiansen, Malene Revsbech; Boulund, Ulrika; Wolf, Xenia Asbæk; Kjærulff, Sonny Kim; Bunt, Martijn van de; Tulin, Søren; Jensen, Thomas Skøt; Wernersson, Rasmus; Jensen, Jan

doi:10.1371/journal.pone.0233956

Cited by 16 publications

(11 citation statements)

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“…The approach used to identify proteins related to T2D was based on a previously described text mining method [ 12 ]. Here, we expanded the biology underpinning T2D by increasing the list of proteins that are either known or postulated to be involved in disease prevention or progression, either directly or through interactions with other known proteins.…”

Section: Methodsmentioning

confidence: 99%

“…Efforts to identify targets for phenotypic screening have benefitted greatly from the increased availability of diverse data types and computational power. For example, as we have previously described [ 12 ], data-driven text mining can score proteins according to relevance to T2D-related biology and identify lists of putative targets at a greater scale than the capacity of current screening approaches. As EndoC-βH1 phenotypic screening efforts so far have been limited to a semi-high-throughput 96-well assay format [ 13 ], there is a need to develop robust, miniaturized phenotypic assays for large-scale screening to enable efficient target discovery and realize the full potential of combined in silico/in vitro target discovery approaches.…”

Section: Introductionmentioning

confidence: 99%

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Large-Scale Functional Genomics Screen to Identify Modulators of Human β-Cell Insulin Secretion

et al. 2022

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Type 2 diabetes (T2D) is a chronic metabolic disorder affecting almost half a billion people worldwide. Impaired function of pancreatic β-cells is both a hallmark of T2D and an underlying factor in the pathophysiology of the disease. Understanding the cellular mechanisms regulating appropriate insulin secretion has been of long-standing interest in the scientific and clinical communities. To identify novel genes regulating insulin secretion we developed a robust arrayed siRNA screen measuring basal, glucose-stimulated, and augmented insulin secretion by EndoC-βH1 cells, a human β-cell line, in a 384-well plate format. We screened 521 candidate genes selected by text mining for relevance to T2D biology and identified 23 positive and 68 negative regulators of insulin secretion. Among these, we validated ghrelin receptor (GHSR), and two genes implicated in endoplasmic reticulum stress, ATF4 and HSPA5. Thus, we have demonstrated the feasibility of using EndoC-βH1 cells for large-scale siRNA screening to identify candidate genes regulating β-cell insulin secretion as potential novel drug targets. Furthermore, this screening format can be adapted to other disease-relevant functional endpoints to enable large-scale screening for targets regulating cellular mechanisms contributing to the progressive loss of functional β-cell mass occurring in T2D.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Large-Scale Functional Genomics Screen to Identify Modulators of Human β-Cell Insulin Secretion

et al. 2022

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“…They mention that these links have been found by combining genetic sequencing approaches, proteomic studies, and metabolomic studies. Hansson et al (2020) described a method to support novel drug discovery related to diabetes. Their focus was to investigate the effects of proteins on different metabolic pathways.…”

Section: Literature Based Discoverymentioning

confidence: 99%

Using Literature Based Discovery to Gain Insights Into the Metabolomic Processes of Cardiac Arrest

Henry

Wijesinghe

Myers

et al. 2021

Front. Res. Metr. Anal.

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In this paper, we describe how we applied LBD techniques to discover lecithin cholesterol acyltransferase (LCAT) as a druggable target for cardiac arrest. We fully describe our process which includes the use of high-throughput metabolomic analysis to identify metabolites significantly related to cardiac arrest, and how we used LBD to gain insights into how these metabolites relate to cardiac arrest. These insights lead to our proposal (for the first time) of LCAT as a druggable target; the effects of which are supported by in vivo studies which were brought forth by this work. Metabolites are the end product of many biochemical pathways within the human body. Observed changes in metabolite levels are indicative of changes in these pathways, and provide valuable insights toward the cause, progression, and treatment of diseases. Following cardiac arrest, we observed changes in metabolite levels pre- and post-resuscitation. We used LBD to help discover diseases implicitly linked via these metabolites of interest. Results of LBD indicated a strong link between Fish Eye disease and cardiac arrest. Since fish eye disease is characterized by an LCAT deficiency, it began an investigation into the effects of LCAT and cardiac arrest survival. In the investigation, we found that decreased LCAT activity may increase cardiac arrest survival rates by increasing ω-3 polyunsaturated fatty acid availability in circulation. We verified the effects of ω-3 polyunsaturated fatty acids on increasing survival rate following cardiac arrest via in vivo with rat models.

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“…Many NLP systems and language resources are available for extracting different types of information from scientific literature: identifying drug names [21], discovering drugs [10], examining the side-effects of drugs [15], extracting biomedical terminology [4,28,31] or events [19,26,6], and extracting wet-lab protocol [22] are some of these examples.…”

Section: Related Workmentioning

confidence: 99%

Analyzing Research Trends in Inorganic Materials Literature Using NLP

Kuniyoshi¹,

Ozawa²,

Misawa³

2021

Preprint

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In the field of inorganic materials science, there is a growing demand to extract knowledge such as physical properties and synthesis processes of materials by machine-reading a large number of papers. This is because materials researchers refer to many papers in order to come up with promising terms of experiments for material synthesis. However, there are only a few systems that can extract material names and their properties. This study proposes a large-scale natural language processing (NLP) pipeline for extracting material names and properties from materials science literature to enable the search and retrieval of results in materials science. Therefore, we propose a label definition for extracting material names and properties and accordingly build a corpus containing 836 annotated paragraphs extracted from 301 papers for training a named entity recognition (NER) model. Experimental results demonstrate the utility of this NER model; it achieves successful extraction with a micro-F1 score of 78.1%. To demonstrate the efficacy of our approach, we present a thorough evaluation on a real-world automatically annotated corpus by applying our trained NER model to 12,895 materials science papers. We analyze the trend in materials science by visualizing the outputs of the NLP pipeline. For example, the countryby-year analysis indicates that in recent years, the number of papers on "MoS2," a material used in perovskite solar cells, has been increasing rapidly in China but decreasing in the United States. Further, according to the conditions-by-year analysis, the processing temperature of the catalyst material "PEDOT:PSS" is shifting below 200 • C, and the number of reports with a processing time exceeding 5 h is increasing slightly.

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Semantic text mining in early drug discovery for type 2 diabetes

Cited by 16 publications

References 50 publications

Large-Scale Functional Genomics Screen to Identify Modulators of Human β-Cell Insulin Secretion

Large-Scale Functional Genomics Screen to Identify Modulators of Human β-Cell Insulin Secretion

Using Literature Based Discovery to Gain Insights Into the Metabolomic Processes of Cardiac Arrest

Analyzing Research Trends in Inorganic Materials Literature Using NLP

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