Using literature-based discovery to identify candidate genes for the interaction between myocardial infarction and depression

Graph embedding-based link prediction for literature-based discovery in Alzheimer's Disease

OBJECTIVE

We explore the framing of literature-based discovery (LBD) as link prediction and graph embedding learning, with Alzheimer's Disease (AD) as our focus disease context. The key link prediction setting of prediction window length is specifically examined in the context of a time-sliced evaluation methodology.

METHODS

We propose a four-stage approach to explore literature-based discovery for Alzheimer's Disease, creating and analyzing a knowledge graph tailored to the AD context, and predicting and evaluating new knowledge based on time-sliced link prediction. The first stage is to collect an AD-specific corpus. The second stage involves constructing an AD knowledge graph with identified AD-specific concepts and relations from the corpus. In the third stage, 20 pairs of training and testing datasets are constructed with the time-slicing methodology. Finally, we infer new knowledge with graph embedding-based link prediction methods. We compare different link prediction methods in this context. The impact of limiting prediction evaluation of LBD models in the context of short-term and longer-term knowledge evolution for Alzheimer's Disease is assessed.

RESULTS

We constructed an AD corpus of over 16 k papers published in 1977-2021, and automatically annotated it with concepts and relations covering 11 AD-specific semantic entity types. The knowledge graph of Alzheimer's Disease derived from this resource consisted of ∼11 k nodes and ∼394 k edges, among which 34% were genotype-phenotype relationships, 57% were genotype-genotype relationships, and 9% were phenotype-phenotype relationships. A Structural Deep Network Embedding (SDNE) model consistently showed the best performance in terms of returning the most confident set of link predictions as time progresses over 20 years. A huge improvement in model performance was observed when changing the link prediction evaluation setting to consider a more distant future, reflecting the time required for knowledge accumulation.

CONCLUSION

Neural network graph-embedding link prediction methods show promise for the literature-based discovery context, although the prediction setting is extremely challenging, with graph densities of less than 1%. Varying prediction window length on the time-sliced evaluation methodology leads to hugely different results and interpretations of LBD studies. Our approach can be generalized to enable knowledge discovery for other diseases.

AVAILABILITY

Code, AD ontology, and data are available at https://github.com/READ-BioMed/readbiomed-lbd.

Shared genetics between classes of obesity and psychiatric disorders: A large-scale genome-wide cross-trait analysis

AIMS

Epidemiological studies demonstrate an association between classes of obesity and psychiatric disorders, although little is known about shared genetics and causality of association. Thus, we aimed to investigate shared genetics and causal link between different classes of obesity and psychiatric disorders.

METHODS

We used genome-wide association study (GWAS) summary data range from 9725 to 500,199 sample sizes of European descent, conducted a large-scale genome-wide cross-trait association study to investigate genetic overlap between the classes of obesity and anorexia nervosa, attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, major depressive disorder, obsessive-compulsive disorder, schizophrenia, anxiety disorders and Tourette syndrome. We conducted transcriptome-wide association study analysis (TWAS) to identified variants regulated gene expression in those related disorders. Finally, pathway enrichment analysis to identified major pathways.

RESULTS

In the combined analysis, we replicated 211 previously reported loci and discovered 58 novel independent loci that were associated with all three classes of obesity and related psychiatric disorders. Functional analysis revealed that the identified variants regulated gene expression in major tissues belonging to exocrine/endocrine, digestive, circulatory, adipose, digestive, respiratory, and nervous systems, such as DCC, NEGR1, INO80E. Mendelian randomization analyses suggested that there may be a two-way or one-way causal relationship between obesity and psychiatric disorders.

CONCLUSION

This large-scale genome-wide cross-trait analysis identified shared genetics and potential causal links between classes of obesity and psychiatric disorders (attention deficit hyperactivity disorder, autism spectrum disorder, anorexia nervosa, major depressive disorder, schizophrenia, and obsessive-compulsive disorder). Such shared genetics suggests potential new biological functions in common among them.

Literature-based discovery approaches for evidence-based healthcare: a systematic review

PURPOSE

Literature-Based Discovery (LBD) is a text mining technique used to generate novel hypotheses from vast amounts of literature sources, by identifying links between concepts from disparate sources. One of the main areas where it has been predominantly applied is the healthcare domain, whereby promising results, in the form of novel hypotheses, have been reported. The purpose of this work was to conduct a systematic literature review of recent publications on LBD in the healthcare domain in order to assess the trends in the approaches used and to identify issues and challenges for such systems.

METHODS

The review was conducted following the principles of the Kitchenham method. The selected studies have been scrutinized and the derived findings have been reported following the PRISMA guidelines.

RESULTS

The review results reveal useful information regarding the application areas, the data sources considered, the approaches used, the performance in terms of accuracy and reliability and future research challenges. The results of this review will be beneficial to LBD researchers and other stakeholders in the healthcare domain, by providing them with useful insights on the approaches to adopt, data sources to consider, evaluation model to use and challenges to reflect on.

CONCLUSION

The synthesis of the results of this work has shed light on recent issues and challenges that drive new LBD models and provides avenues for their application in other diverse areas in the healthcare domain. To the best of our knowledge, no such recent review has been conducted.

Biochemical analysis of ectonucleotidases on primary rat vascular smooth muscle cells and in silico investigation of their role in vascular diseases

Vascular smooth muscle cells (VSMCs) exhibit a high degree of plasticity when they undergo the progression from a normal to a disease condition, which makes them a potential target for evaluating early markers and for the development of new therapies. Purinergic signalling plays a key role in vascular tonus control, ATP being an inductor of vasoconstriction, whereas adenosine mediates a vasodilation effect antagonising the ATP actions. The control of extracellular ATP and adenosine levels is done by ectonucleotidases, which represent a potential target to be evaluated in the progression of cardiovascular diseases. In this study, we analysed the basal activity and expression of the ectonucleotidases in aortic rat VSMCs, and we further performed in silico analysis to determine the expression of those enzymes in conditions that mimicked vascular diseases. Cultured in vitro VSMCs showed a prominent expression of Entpd1 followed by Entpd2 and Nt5e (CD73) and very low levels of Entpd3. Slightly faster AMP hydrolysis was observed when compared to ATP and ADP nucleotides. In silico analysis showed that the ectonucleotidases were modulated after induction of conditions that can lead to vascular diseases such as, hypertensive and hypotensive mice models (Nt5e); exposition to high-fat (Entpd1 and Entpd2) or high-phosphate (Nt5e) diet; mechanical stretch (Entpd1, Entpd2 and Nt5e); and myocardial infarction (Entpd1). Our data show that VSMCs are able to efficiently metabolise the extracellular nucleotides generating adenosine. The modulation of Entpd1, Entdp2 and Nt5e in vascular diseases suggests these ectoenzymes as potential targets or markers to be investigated in future studies.