Gene clustering by Latent Semantic Indexing of MEDLINE abstracts

HOGA1 Suppresses Renal Cell Carcinoma Growth via Inhibiting the Wnt/β-Catenin Signalling Pathway

Changes in hydroxyproline metabolism are reported to promote tumorigenesis. HOGA1 is a useful marker for diagnosing primary hyperoxaluria 3, catalysing the final step of mitochondrial hydroxyproline metabolism from 4-hydroxy-2-oxoglutarate (HOG) to glyoxylate and pyruvate; however, its specific mechanism in RCC remains unclear. This study investigated the role of HOGA1 in the pathogenesis of ccRCC. The results showed that HOGA1 was decreased significantly in tumour tissues, with this low expression associated with a poor prognosis in patients with ccRCC. QTL mapping showed that Hoga1 was cis-regulated. Gene enrichment analyses showed that Hoga1 co-expressed genes were enriched in the Wnt/β-catenin signalling pathway. Furthermore, in vitro and in vivo assays demonstrated that HOGA1 significantly inhibited the proliferation, invasion and migration of renal carcinoma cells via the Wnt/β-catenin-c-Myc/CyclinD1 axis, probably via regulating the level of HOG. In conclusion, this study demonstrates that HOGA1 has a tumour suppressor role by inhibiting the Wnt/β-catenin signalling pathway. This finding provides new insights into the function of HOGA1 in ccRCC.

Exploring genetic mapping and co-expression patterns to illuminate significance of Tbx20 in cardiac biology

The transcription factor Tbx20 is integral to heart development and plays a significant role in various cardiac diseases. Despite its established importance, the regulatory mechanisms and functional significance of Tbx20 remain incompletely understood. To elucidate these mechanisms, we initially conducted eQTL mapping to identify genetic loci associated with Tbx20 expression in heart tissue from BXD mice. Co-expression and enrichment analyses revealed pathways linked to Tbx20, including dilated cardiomyopathy, hypertrophic cardiomyopathy, and FoxO signaling. Additionally, protein-protein interaction studies identified essential cardiac proteins, such as Myl2 and Myl7, along with upstream regulators like Mef2c. To validate our bioinformatic findings, we performed quantitative reverse transcription polymerase chain reaction (qRT-PCR) to assess the relative mRNA expression levels of TBX20 and Mef2c in the heart tissues of BXD mice compared to their parental strains (B6 and D2). Our results demonstrated significant up-regulation of both TBX20 and Mef2c in the BXD group relative to the parental strains. Conversely, both genes were down-regulated in B6, D2, Control, and Treatment groups when compared to BXD mice. These findings confirm the predicted regulatory roles of TBX20 and Mef2c in cardiac development as suggested by our initial analyses.This study not only reinforces the critical role of Tbx20 in cardiac gene regulation but also highlights its potential as a therapeutic target for cardiovascular disorders. Further investigations into Tbx20 and its interactions will enhance our understanding of heart biology and contribute to the development of targeted therapies for heart diseases.

Unraveling the molecular mechanisms of Ace2-mediated post-COVID-19 cognitive dysfunction through systems genetics approach

The dysregulation of Angiotensin-converting enzyme 2 (ACE2) in central nervous system is believed associates with COVID-19 induced cognitive dysfunction. However, the detailed mechanism remains largely unknown. In this study, we performed a comprehensive system genetics analysis on hippocampal ACE2 based on BXD mice panel. Expression quantitative trait loci (eQTLs) mapping showed that Ace2 was strongly trans-regulated, and the elevation of Ace2 expression level was significantly correlated with impaired cognitive functions. Further Gene co-expression analysis showed that Ace2 may be correlated with the membrane proteins in Calcium signaling pathway. Further, qRT-PCR confirmed that SARS-CoV-2 spike S1 protein upregulated ACE2 expression together with eight membrane proteins in Calcium Signaling pathway. Moreover, such elevation can be attenuated by recombinant ACE2. Collectively, our findings revealed a potential mechanism of Ace2 in cognitive dysfunction, which could be beneficial for COVID-19-induced cognitive dysfunction prevention and potential treatment.

Large-scale identification of social and behavioral determinants of health from clinical notes: comparison of Latent Semantic Indexing and Generative Pretrained Transformer (GPT) models

BACKGROUND

Social and behavioral determinants of health (SBDH) are associated with a variety of health and utilization outcomes, yet these factors are not routinely documented in the structured fields of electronic health records (EHR). The objective of this study was to evaluate different machine learning approaches for detection of SBDH from the unstructured clinical notes in the EHR.

METHODS

Latent Semantic Indexing (LSI) was applied to 2,083,180 clinical notes corresponding to 46,146 patients in the MIMIC-III dataset. Using LSI, patients were ranked based on conceptual relevance to a set of keywords (lexicons) pertaining to 15 different SBDH categories. For Generative Pretrained Transformer (GPT) models, API requests were made with a Python script to connect to the OpenAI services in Azure, using gpt-3.5-turbo-1106 and gpt-4-1106-preview models. Prediction of SBDH categories were performed using a logistic regression model that included age, gender, race and SBDH ICD-9 codes.

RESULTS

LSI retrieved patients according to 15 SBDH domains, with an overall average PPV ≥ 83%. Using manually curated gold standard (GS) sets for nine SBDH categories, the macro-F1 score of LSI (0.74) was better than ICD-9 (0.71) and GPT-3.5 (0.54), but lower than GPT-4 (0.80). Due to document size limitations, only a subset of the GS cases could be processed by GPT-3.5 (55.8%) and GPT-4 (94.2%), compared to LSI (100%). Using common GS subsets for nine different SBDH categories, the macro-F1 of ICD-9 combined with either LSI (mean 0.88, 95% CI 0.82-0.93), GPT-3.5 (0.86, 0.82-0.91) or GPT-4 (0.88, 0.83-0.94) was not significantly different. After including age, gender, race and ICD-9 in a logistic regression model, the AUC for prediction of six out of the nine SBDH categories was higher for LSI compared to GPT-4.0.

CONCLUSIONS

These results demonstrate that the LSI approach performs comparable to more recent large language models, such as GPT-3.5 and GPT-4.0, when using the same set of documents. Importantly, LSI is robust, deterministic, and does not have document-size limitations or cost implications, which make it more amenable to real-world applications in health systems.

IGFBPL1 is a master driver of microglia homeostasis and resolution of neuroinflammation in glaucoma and brain tauopathy

Microglia shift toward an inflammatory phenotype during aging that is thought to exacerbate age-related neurodegeneration. The molecular and cellular signals that resolve neuroinflammation post-injury are largely undefined. Here, we exploit systems genetics methods based on the extended BXD murine reference family and identify IGFBPL1 as an upstream cis-regulator of microglia-specific genes to switch off inflammation. IGFBPL1 is expressed by mouse and human microglia, and higher levels of its expression resolve lipopolysaccharide-induced neuroinflammation by resetting the transcriptome signature back to a homeostatic state via IGF1R signaling. Conversely, IGFBPL1 deficiency or selective deletion of IGF1R in microglia shifts these cells to an inflammatory landscape and induces early manifestation of brain tauopathy and retinal neurodegeneration. Therapeutic administration of IGFBPL1 drives pro-homeostatic microglia and prevents glaucomatous neurodegeneration and vision loss in mice. These results identify IGFBPL1 as a master driver of the counter-inflammatory microglial modulator that presents an endogenous resolution of neuroinflammation to prevent neurodegeneration in eye and brain.

Expression Levels of the Tnni3k Gene in the Heart Are Highly Associated with Cardiac and Glucose Metabolism-Related Phenotypes and Functional Pathways

BACKGROUND

Troponin-I interacting kinase encoded by the TNNI3K gene is expressed in nuclei and Z-discs of cardiomyocytes. Mutations in TNNI3K were identified in patients with cardiac conduction diseases, arrhythmias, and cardiomyopathy.

METHODS

We performed cardiac gene expression, whole genome sequencing (WGS), and cardiac function analysis in 40 strains of BXD recombinant inbred mice derived from C57BL/6J (B6) and DBA/2J (D2) strains. Expression quantitative trait loci (eQTLs) mapping and gene enrichment analysis was performed, followed by validation of candidate Tnni3k-regulatory genes.

RESULTS

WGS identified compound splicing and missense T659I Tnni3k variants in the D2 parent and some BXD strains (D allele) and these strains had significantly lower Tnni3k expression than those carrying wild-type Tnni3k (B allele). Expression levels of Tnni3k significantly correlated with multiple cardiac (heart rate, wall thickness, PR duration, and T amplitude) and metabolic (glucose levels and insulin resistance) phenotypes in BXDs. A significant cis-eQTL on chromosome 3 was identified for the regulation of Tnni3k expression. Furthermore, Tnni3k-correlated genes were primarily involved in cardiac and glucose metabolism-related functions and pathways. Genes Nodal, Gnas, Nfkb1, Bmpr2, Bmp7, Smad7, Acvr1b, Acvr2b, Chrd, Tgfb3, Irs1, and Ppp1cb were differentially expressed between the B and D alleles.

CONCLUSIONS

Compound splicing and T659I Tnni3k variants reduce cardiac Tnni3k expression and Tnni3k levels are associated with cardiac and glucose metabolism-related phenotypes.

A Web Application for Biomedical Text Mining of Scientific Literature Associated with Coronavirus-Related Syndromes: Coronavirus Finder

In this study, a web application was developed that comprises scientific literature associated with the Coronaviridae family, specifically for those viruses that are members of the Genus Betacoronavirus, responsible for emerging diseases with a great impact on human health: Middle East Respiratory Syndrome-Related Coronavirus (MERS-CoV) and Severe Acute Respiratory Syndrome-Related Coronavirus (SARS-CoV, SARS-CoV-2). The information compiled on this webserver aims to understand the basics of these viruses' infection, and the nature of their pathogenesis, enabling the identification of molecular and cellular components that may function as potential targets on the design and development of successful treatments for the diseases associated with the Coronaviridae family. Some of the web application's primary functions are searching for keywords within the scientific literature, natural language processing for the extraction of genes and words, the generation and visualization of gene networks associated with viral diseases derived from the analysis of latent semantic space, and cosine similarity measures. Interestingly, our gene association analysis reveals drug targets in understudies, and new targets suggested in the scientific literature to treat coronavirus.

Siamese Neural Networks for Damage Detection and Diagnosis of Jacket-Type Offshore Wind Turbine Platforms

Offshore wind energy is increasingly being realized at deeper ocean depths where jacket foundations are now the greatest choice for dealing with the hostile environment. The structural stability of these undersea constructions is critical. This paper states a methodology to detect and classify damage in a jacket-type support structure for offshore wind turbines. Because of the existence of unknown external disturbances (wind and waves), standard structural health monitoring technologies, such as guided waves, cannot be used directly in this application. Therefore, using vibration-response-only accelerometer measurements, a methodology based on two in-cascade Siamese convolutional neural networks is proposed. The first Siamese network detects the damage (discerns whether the structure is healthy or damaged). Then, in case damage is detected, a second Siamese network determines the damage diagnosis (classifies the type of damage). The main results and claims of the proposed methodology are the following ones: (i) It is solely dependent on accelerometer sensor output vibration data, (ii) it detects damage and classifies the type of damage, (iii) it operates in all wind turbine regions of operation, (iv) it requires less data to train since it is built on Siamese convolutional neural networks, which can learn from very little data compared to standard machine/deep learning algorithms, (v) it is validated in a scaled-down experimental laboratory setup, and (vi) its feasibility is demonstrated as all computed metrics (accuracy, precision, recall, and F1 score) for the obtained results remain above 96%.

Systems genetics analysis defines importance of TMEM43/LUMA for cardiac- and metabolic-related pathways

Broad cellular functions and diseases including muscular dystrophy, arrhythmogenic right ventricular cardiomyopathy (ARVC5) and cancer are associated with transmembrane protein43 (TMEM43/LUMA). The study aimed to investigate biological roles of TMEM43 through genetic regulation, gene pathways and gene networks, candidate interacting genes, and up- or downstream regulators. Cardiac transcriptomes from 40 strains of recombinant inbred BXD mice and two parental strains representing murine genetic reference population (GRP) were applied for genetic correlation, functional enrichment, and coexpression network analysis using systems genetics approach. The results were validated in a newly created knock-in Tmem43-S358L mutation mouse model (Tmem43S358L) that displayed signs of cardiac dysfunction, resembling ARVC5 phenotype seen in humans. We found high Tmem43 levels among BXDs with broad variability in expression. Expression of Tmem43 highly negatively correlated with heart mass and heart rate among BXDs, whereas levels of Tmem43 highly positively correlated with plasma high-density lipoproteins (HDL). Through finding differentially expressed genes (DEGs) between Tmem43S358L mutant and wild-type (Tmem43WT) lines, 18 pathways (out of 42 found in BXDs GRP) that are involved in ARVC, hypertrophic cardiomyopathy, dilated cardiomyopathy, nonalcoholic fatty liver disease, Alzheimer's disease, Parkinson's disease, and Huntington's disease were verified. We further constructed Tmem43-mediated gene network, in which Ctnna1, Adcy6, Gnas, Ndufs6, and Uqcrc2 were significantly altered in Tmem43S358L mice versus Tmem43WT controls. Our study defined the importance of Tmem43 for cardiac- and metabolism-related pathways, suggesting that cardiovascular disease-relevant risk factors may also increase risk of metabolic and neurodegenerative diseases via TMEM43-mediated pathways.

Siamese Neural Networks: An Overview

Similarity has always been a key aspect in computer science and statistics. Any time two element vectors are compared, many different similarity approaches can be used, depending on the final goal of the comparison (Euclidean distance, Pearson correlation coefficient, Spearman's rank correlation coefficient, and others). But if the comparison has to be applied to more complex data samples, with features having different dimensionality and types which might need compression before processing, these measures would be unsuitable. In these cases, a siamese neural network may be the best choice: it consists of two identical artificial neural networks each capable of learning the hidden representation of an input vector. The two neural networks are both feedforward perceptrons, and employ error back-propagation during training; they work parallelly in tandem and compare their outputs at the end, usually through a cosine distance. The output generated by a siamese neural network execution can be considered the semantic similarity between the projected representation of the two input vectors. In this overview we first describe the siamese neural network architecture, and then we outline its main applications in a number of computational fields since its appearance in 1994. Additionally, we list the programming languages, software packages, tutorials, and guides that can be practically used by readers to implement this powerful machine learning model.

The Genetic Dissection of Ace2 Expression Variation in the Heart of Murine Genetic Reference Population

Background: A high inflammatory and cytokine burden that induces vascular inflammation, myocarditis, cardiac arrhythmias, and myocardial injury is associated with a lethal outcome in COVID-19. The SARS-CoV-2 virus utilizes the ACE2 receptor for cell entry in a similar way to SARS-CoV. This study investigates the regulation, gene network, and associated pathways of ACE2 that may be involved in inflammatory and cardiovascular complications of COVID-19. Methods: Cardiovascular traits were determined in the one of the largest mouse genetic reference populations: BXD recombinant inbred strains using blood pressure, electrocardiography, and echocardiography measurements. Expression quantitative trait locus (eQTL) mapping, genetic correlation, and functional enrichment analysis were used to identify Ace2 regulation, gene pathway, and co-expression networks. Results: A wide range of variation was found in expression of Ace2 among the BXD strains. Levels of Ace2 expression are negatively correlated with cardiovascular traits, including systolic and diastolic blood pressure and P wave duration and amplitude. Ace2 co-expressed genes are significantly involved in cardiac- and inflammatory-related pathways. The eQTL mapping revealed that Cyld is a candidate upstream regulator for Ace2. Moreover, the protein-protein interaction (PPI) network analysis inferred several potential key regulators (Cul3, Atf2, Vcp, Jun, Ppp1cc, Npm1, Mapk8, Set, Dlg1, Mapk14, and Hspa1b) for Ace2 co-expressed genes in the heart. Conclusions: Ace2 is associated with blood pressure, atrial morphology, and sinoatrial conduction in BXD mice. Ace2 co-varies with Atf2, Cyld, Jun, Mapk8, and Mapk14 and is enriched in the RAS, TGFβ, TNFα, and p38α signaling pathways, involved in inflammation and cardiac damage. We suggest that all these novel Ace2-associated genes and pathways may be targeted for preventive, diagnostic, and therapeutic purposes in cardiovascular damage in patients with systemic inflammation, including COVID-19 patients.

Evaluation of Sirtuin-3 probe quality and co-expressed genes using literature cohesion

BACKGROUND

Gene co-expression studies can provide important insights into molecular and cellular signaling pathways. The GeneNetwork database is a unique resource for co-expression analysis using data from a variety of tissues across genetically distinct inbred mice. However, extraction of biologically meaningful co-expressed gene sets is challenging due to variability in microarray platforms, probe quality, normalization methods, and confounding biological factors. In this study, we tested whether literature derived functional cohesion could be used as an objective metric in lieu of 'ground truth' to evaluate the quality of probes and microarray datasets.

RESULTS

We examined Sirtuin-3 (Sirt3) co-expressed gene sets extracted from either liver or brain tissues of BXD recombinant inbred mice in the GeneNetwork database. Depending on the microarray platform, there were as many as 26 probes that targeted different regions of Sirt3 primary transcript. Co-expressed gene sets (ranging from 100-1000 genes) associated with each Sirt3 probe were evaluated using the previously developed literature-derived cohesion p-value (LPv) and benchmarked against 'gold standards' derived from proteomic studies or Gene Ontology classifications. We found that the maximal F-measure was obtained at an average window size of 535 genes. Using set size of 500 genes, the Pearson correlations between LPv and F-measure as well as between LPv and mitochondrial gene enrichment p-values were 0.90 and 0.93, respectively. Importantly, we found that the LPv approach can distinguish high quality Sirt3 probes. Analysis of the most functionally cohesive Sirt3 co-expressed gene set revealed core metabolic pathways that were shared between hippocampus and liver as well as distinct pathways which were unique to each tissue. These results are consistent with other studies that suggest Sirt3 is a key metabolic regulator and has distinct functions in energy-producing vs. energy-demanding tissues.

CONCLUSIONS

Our results provide proof-of-concept that literature cohesion analysis is useful for evaluating the quality of probes and microarray datasets, particularly when experimentally derived gold standards are unavailable. Our approach would enable researchers to rapidly identify biologically meaningful co-expressed gene sets and facilitate discovery from high throughput genomic data.

Ethanol's Effect on Coq7 Expression in the Hippocampus of Mice

Coenzyme Q (CoQ) is a well-studied molecule, present in every cell membrane in the body, best known for its roles as a mitochondrial electron transporter and a potent membrane anti-oxidant. Much of the previous work was done in vitro in yeast and more recent work has suggested that CoQ may have additional roles prompting calls for a re-assessment of its role using in vivo systems in mammals. Here we investigated the putative role of Coenzyme Q in ethanol-induced effects in vivo using BXD RI mice. We examined hippocampal expression of Coq7 in saline controls and after an acute ethanol treatment, noting enriched biologic processes and pathways following ethanol administration. We also identified 45 ethanol-related phenotypes that were significantly correlated with Coq7 expression, including six phenotypes related to conditioned taste aversion and ethanol preference. This analysis highlights the need for further investigation of Coq7 and related genes in vivo as well as previously unrecognized roles that it may play in the hippocampus.

β-Arrestin Based Receptor Signaling Paradigms: Potential Therapeutic Targets for Complex Age-Related Disorders

G protein coupled receptors (GPCRs) were first characterized as signal transducers that elicit downstream effects through modulation of guanine (G) nucleotide-binding proteins. The pharmacotherapeutic exploitation of this signaling paradigm has created a drug-based field covering nearly 50% of the current pharmacopeia. Since the groundbreaking discoveries of the late 1990s to the present day, it is now clear however that GPCRs can also generate productive signaling cascades through the modulation of β-arrestin functionality. β-Arrestins were first thought to only regulate receptor desensitization and internalization - exemplified by the action of visual arrestin with respect to rhodopsin desensitization. Nearly 20 years ago, it was found that rather than controlling GPCR signal termination, productive β-arrestin dependent GPCR signaling paradigms were highly dependent on multi-protein complex formation and generated long-lasting cellular effects, in contrast to G protein signaling which is transient and functions through soluble second messenger systems. β-Arrestin signaling was then first shown to activate mitogen activated protein kinase signaling in a G protein-independent manner and eventually initiate protein transcription - thus controlling expression patterns of downstream proteins. While the possibility of developing β-arrestin biased or functionally selective ligands is now being investigated, no additional research has been performed on its possible contextual specificity in treating age-related disorders. The ability of β-arrestin-dependent signaling to control complex and multidimensional protein expression patterns makes this therapeutic strategy feasible, as treating complex age-related disorders will likely require therapeutics that can exert network-level efficacy profiles. It is our understanding that therapeutically targeting G protein-independent effectors such as β-arrestin will aid in the development of precision medicines with tailored efficacy profiles for disease/age-specific contextualities.

Navigating the Functional Landscape of Transcription Factors via Non-Negative Tensor Factorization Analysis of MEDLINE Abstracts

In this study, we developed and evaluated a novel text-mining approach, using non-negative tensor factorization (NTF), to simultaneously extract and functionally annotate transcriptional modules consisting of sets of genes, transcription factors (TFs), and terms from MEDLINE abstracts. A sparse 3-mode term × gene × TF tensor was constructed that contained weighted frequencies of 106,895 terms in 26,781 abstracts shared among 7,695 genes and 994 TFs. The tensor was decomposed into sub-tensors using non-negative tensor factorization (NTF) across 16 different approximation ranks. Dominant entries of each of 2,861 sub-tensors were extracted to form term–gene–TF annotated transcriptional modules (ATMs). More than 94% of the ATMs were found to be enriched in at least one KEGG pathway or GO category, suggesting that the ATMs are functionally relevant. One advantage of this method is that it can discover potentially new gene–TF associations from the literature. Using a set of microarray and ChIP-Seq datasets as gold standard, we show that the precision of our method for predicting gene–TF associations is significantly higher than chance. In addition, we demonstrate that the terms in each ATM can be used to suggest new GO classifications to genes and TFs. Taken together, our results indicate that NTF is useful for simultaneous extraction and functional annotation of transcriptional regulatory networks from unstructured text, as well as for literature based discovery. A web tool called Transcriptional Regulatory Modules Extracted from Literature (TREMEL), available at http://binf1.memphis.edu/tremel, was built to enable browsing and searching of ATMs.

The effect of alcohol on the differential expression of cluster of differentiation 14 gene, associated pathways, and genetic network

Alcohol consumption affects human health in part by compromising the immune system. In this study, we examined the expression of the Cd14 (cluster of differentiation 14) gene, which is involved in the immune system through a proinflammatory cascade. Expression was evaluated in BXD mice treated with saline or acute 1.8 g/kg i.p. ethanol (12.5% v/v). Hippocampal gene expression data were generated to examine differential expression and to perform systems genetics analyses. The Cd14 gene expression showed significant changes among the BXD strains after ethanol treatment, and eQTL mapping revealed that Cd14 is a cis-regulated gene. We also identified eighteen ethanol-related phenotypes correlated with Cd14 expression related to either ethanol responses or ethanol consumption. Pathway analysis was performed to identify possible biological pathways involved in the response to ethanol and Cd14. We also constructed a genetic network for Cd14 using the top 20 correlated genes and present several genes possibly involved in Cd14 and ethanol responses based on differential gene expression. In conclusion, we found Cd14, along with several other genes and pathways, to be involved in ethanol responses in the hippocampus, such as increased susceptibility to lipopolysaccharides and neuroinflammation.

GeneNetwork: A Toolbox for Systems Genetics

The goal of systems genetics is to understand the impact of genetic variation across all levels of biological organization, from mRNAs, proteins, and metabolites, to higher-order physiological and behavioral traits. This approach requires the accumulation and integration of many types of data, and also requires the use of many types of statistical tools to extract relevant patterns of covariation and causal relations as a function of genetics, environment, stage, and treatment. In this protocol we explain how to use the GeneNetwork web service, a powerful and free online resource for systems genetics. We provide workflows and methods to navigate massive multiscalar data sets and we explain how to use an extensive systems genetics toolkit for analysis and synthesis. Finally, we provide two detailed case studies that take advantage of human and mouse cohorts to evaluate linkage between gene variants, addiction, and aging.

Weighted Genetic Risk Scores and Prediction of Weight Gain in Solid Organ Transplant Populations

Background

Polygenic obesity in Solid Organ Transplant (SOT) populations is considered a risk factor for the development of metabolic abnormalities and graft survival. Few studies to date have studied the genetics of weight gain in SOT recipients. We aimed to determine whether weighted genetic risk scores (w-GRS) integrating genetic polymorphisms from GWAS studies (SNP group#1 and SNP group#2) and from Candidate Gene studies (SNP group#3) influence BMI in SOT populations and if they predict ≥10% weight gain (WG) one year after transplantation. To do so, two samples (n_A = 995, n_B = 156) were obtained from naturalistic studies and three w-GRS were constructed and tested for association with BMI over time. Prediction of 10% WG at one year after transplantation was assessed with models containing genetic and clinical factors.

Results

w-GRS were associated with BMI in sample A and B combined (BMI increased by 0.14 and 0.11 units per additional risk allele in SNP group#1 and #2, respectively, p-values<0.008). w-GRS of SNP group#3 showed an effect of 0.01 kg/m² per additional risk allele when combining sample A and B (p-value 0.04). Models with genetic factors performed better than models without in predicting 10% WG at one year after transplantation.

Conclusions

This is the first study in SOT evaluating extensively the association of w-GRS with BMI and the influence of clinical and genetic factors on 10% of WG one year after transplantation, showing the importance of integrating genetic factors in the final model. Genetics of obesity among SOT recipients remains an important issue and can contribute to treatment personalization and prediction of WG after transplantation.

Prioritization, clustering and functional annotation of MicroRNAs using latent semantic indexing of MEDLINE abstracts

Background

The amount of scientific information about MicroRNAs (miRNAs) is growing exponentially, making it difficult for researchers to interpret experimental results. In this study, we present an automated text mining approach using Latent Semantic Indexing (LSI) for prioritization, clustering and functional annotation of miRNAs.

Results

For approximately 900 human miRNAs indexed in miRBase, text documents were created by concatenating titles and abstracts of MEDLINE citations which refer to the miRNAs. The documents were parsed and a weighted term-by-miRNA frequency matrix was created, which was subsequently factorized via singular value decomposition to extract pair-wise cosine values between the term (keyword) and miRNA vectors in reduced rank semantic space. LSI enables derivation of both explicit and implicit associations between entities based on word usage patterns. Using miR2Disease as a gold standard, we found that LSI identified keyword-to-miRNA relationships with high accuracy. In addition, we demonstrate that pair-wise associations between miRNAs can be used to group them into categories which are functionally aligned. Finally, term ranking by querying the LSI space with a group of miRNAs enabled annotation of the clusters with functionally related terms.

Conclusions

LSI modeling of MEDLINE abstracts provides a robust and automated method for miRNA related knowledge discovery. The latest collection of miRNA abstracts and LSI model can be accessed through the web tool miRNA Literature Network (miRLiN) at http://bioinfo.memphis.edu/mirlin.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1223-2) contains supplementary material, which is available to authorized users.

Unsupervised text mining for assessing and augmenting GWAS results

Text mining can assist in the analysis and interpretation of large-scale biomedical data, helping biologists to quickly and cheaply gain confirmation of hypothesized relationships between biological entities. We set this question in the context of genome-wide association studies (GWAS), an actively emerging field that contributed to identify many genes associated with multifactorial diseases. These studies allow to identify groups of genes associated with the same phenotype, but provide no information about the relationships between these genes. Therefore, our objective is to leverage unsupervised text mining techniques using text-based cosine similarity comparisons and clustering applied to candidate and random gene vectors, in order to augment the GWAS results. We propose a generic framework which we used to characterize the relationships between 10 genes reported associated with asthma by a previous GWAS. The results of this experiment showed that the similarities between these 10 genes were significantly stronger than would be expected by chance (one-sided p-value<0.01). The clustering of observed and randomly selected gene also allowed to generate hypotheses about potential functional relationships between these genes and thus contributed to the discovery of new candidate genes for asthma.

The Genetic Architecture of Murine Glutathione Transferases

Glutathione S-transferase (GST) genes play a protective role against oxidative stress and may influence disease risk and drug pharmacokinetics. In this study, massive multiscalar trait profiling across a large population of mice derived from a cross between C57BL/6J (B6) and DBA2/J (D2)—the BXD family—was combined with linkage and bioinformatic analyses to characterize mechanisms controlling GST expression and to identify downstream consequences of this variation. Similar to humans, mice show a wide range in expression of GST family members. Variation in the expression of Gsta4, Gstt2, Gstz1, Gsto1, and Mgst3 is modulated by local expression QTLs (eQTLs) in several tissues. Higher expression of Gsto1 in brain and liver of BXD strains is strongly associated (P < 0.01) with inheritance of the B6 parental allele whereas higher expression of Gsta4 and Mgst3 in brain and liver, and Gstt2 and Gstz1 in brain is strongly associated with inheritance of the D2 parental allele. Allele-specific assays confirmed that expression of Gsto1, Gsta4, and Mgst3 are modulated by sequence variants within or near each gene locus. We exploited this endogenous variation to identify coexpression networks and downstream targets in mouse and human. Through a combined systems genetics approach, we provide new insight into the biological role of naturally occurring variants in GST genes.

Genetic regulation analysis reveals involvement of tumor necrosis factor and alpha-induced protein 3 in stress response in mice

In order to study whether Tnfaip3 is related to stress response and further to find it's genetic regulation, we use C57BL/6J (B6) and DBA/2 (D2) mice to built the model of chronic unpredictable mild stress. RT-PCR, Western blotting and immunohistochemistry were used for studying the expression variation of Tnfaip3 in hippocampus tissue of B6 and D2 mice after being stressed. We found that the expression of Tnfaip3 was more remarkably increased in chronic unpredictable stress models than that in untreated mice (P<0.05). It is indicated that Tnfaip3 might take part in the process of stress response. The expression of Tnfaip3 is regulated by a cis-acting quantitative trait locus (cis-eQTL). We identified 5 genes are controlled by Tnfaip3 and the expression of 64 genes highly associated with Tnfaip3, 9 of those have formerly been participate in stress related pathways. In order to estimate the relationship between Tnfaip3 and its downstream genes or network members, we transfected SH-SY5Y cells with Tnfaip3 siRNA leading to down-regulation of Tnfaip3 mRNA. We confirmed a significant influence of Tnfaip3 depletion on the expression of Tsc22d3, Pex7, Rap2a, Slc2a3, and Gap43. These validated downstream genes and members of Tnfaip3 gene network provide us new insight into the biological mechanisms of Tnfaip3 in chronic unpredictable stress.

Hippocampal Transcriptomic and Proteomic Alterations in the BTBR Mouse Model of Autism Spectrum Disorder

Autism spectrum disorders (ASD) are complex heterogeneous neurodevelopmental disorders of an unclear etiology, and no cure currently exists. Prior studies have demonstrated that the black and tan, brachyury (BTBR) T+ Itpr3tf/J mouse strain displays a behavioral phenotype with ASD-like features. BTBR T+ Itpr3tf/J mice (referred to simply as BTBR) display deficits in social functioning, lack of communication ability, and engagement in stereotyped behavior. Despite extensive behavioral phenotypic characterization, little is known about the genes and proteins responsible for the presentation of the ASD-like phenotype in the BTBR mouse model. In this study, we employed bioinformatics techniques to gain a wide-scale understanding of the transcriptomic and proteomic changes associated with the ASD-like phenotype in BTBR mice. We found a number of genes and proteins to be significantly altered in BTBR mice compared to C57BL/6J (B6) control mice controls such as BDNF, Shank3, and ERK1, which are highly relevant to prior investigations of ASD. Furthermore, we identified distinct functional pathways altered in BTBR mice compared to B6 controls that have been previously shown to be altered in both mouse models of ASD, some human clinical populations, and have been suggested as a possible etiological mechanism of ASD, including “axon guidance” and “regulation of actin cytoskeleton.” In addition, our wide-scale bioinformatics approach also discovered several previously unidentified genes and proteins associated with the ASD phenotype in BTBR mice, such as Caskin1, suggesting that bioinformatics could be an avenue by which novel therapeutic targets for ASD are uncovered. As a result, we believe that informed use of synergistic bioinformatics applications represents an invaluable tool for elucidating the etiology of complex disorders like ASD.

A Bayesian approach for inducing sparsity in generalized linear models with multi-category response

BACKGROUND

The dimension and complexity of high-throughput gene expression data create many challenges for downstream analysis. Several approaches exist to reduce the number of variables with respect to small sample sizes. In this study, we utilized the Generalized Double Pareto (GDP) prior to induce sparsity in a Bayesian Generalized Linear Model (GLM) setting. The approach was evaluated using a publicly available microarray dataset containing 99 samples corresponding to four different prostate cancer subtypes.

RESULTS

A hierarchical Sparse Bayesian GLM using GDP prior (SBGG) was developed to take into account the progressive nature of the response variable. We obtained an average overall classification accuracy between 82.5% and 94%, which was higher than Support Vector Machine, Random Forest or a Sparse Bayesian GLM using double exponential priors. Additionally, SBGG outperforms the other 3 methods in correctly identifying pre-metastatic stages of cancer progression, which can prove extremely valuable for therapeutic and diagnostic purposes. Importantly, using Geneset Cohesion Analysis Tool, we found that the top 100 genes produced by SBGG had an average functional cohesion p-value of 2.0E-4 compared to 0.007 to 0.131 produced by the other methods.

CONCLUSIONS

Using GDP in a Bayesian GLM model applied to cancer progression data results in better subclass prediction. In particular, the method identifies pre-metastatic stages of prostate cancer with substantially better accuracy and produces more functionally relevant gene sets.

Software Suite for Gene and Protein Annotation Prediction and Similarity Search

In the computational biology community, machine learning algorithms are key instruments for many applications, including the prediction of gene-functions based upon the available biomolecular annotations. Additionally, they may also be employed to compute similarity between genes or proteins. Here, we describe and discuss a software suite we developed to implement and make publicly available some of such prediction methods and a computational technique based upon Latent Semantic Indexing (LSI), which leverages both inferred and available annotations to search for semantically similar genes. The suite consists of three components. BioAnnotationPredictor is a computational software module to predict new gene-functions based upon Singular Value Decomposition of available annotations. SimilBio is a Web module that leverages annotations available or predicted by BioAnnotationPredictor to discover similarities between genes via LSI. The suite includes also SemSim, a new Web service built upon these modules to allow accessing them programmatically. We integrated SemSim in the Bio Search Computing framework (http://www.bioinformatics.deib. polimi.it/bio-seco/seco/), where users can exploit the Search Computing technology to run multi-topic complex queries on multiple integrated Web services. Accordingly, researchers may obtain ranked answers involving the computation of the functional similarity between genes in support of biomedical knowledge discovery.

Genetic regulatory network analysis reveals that low density lipoprotein receptor-related protein 11 is involved in stress responses in mice

To study whether Lrp11 is involved in stress response and find its expression regulatory network, the model of stress has been built using C57BL/6J (B6) and DBA/2 (D2) mice. Western blotting, qPCR and immunohistochemistry were used to investigate the expression variation of Lrp11 in amygdala tissue after exposure to stress. We found the quantity of Lrp11 was more obvious in stress models than that in normal mice (P<0.05) which suggests Lrp11 might participate in the process of stress response. The expression of Lrp11 is controlled by a cis-acting quantitative trait locus (cis-eQTL). We identified four genes that are regulated by Lrp11 and the expression of 66 genes highly correlated with Lrp11, seven of which have previously been implicated in stress pathways. To evaluate the relationship between Lrp11 and its downstream genes or network members, we transfected HEK 293T cells and SH-SY5Y cells with Lrp11 siRNA leading to down-regulation of Lrp11mRNA and were able to confirm a significant influence of Lrp11 depletion on the expression of Xpnpep1, Maneal, Pgap1 and Uprt. These validated downstream targets and members of Lrp11 gene network provide new insight into the biological role of Lrp11 and may be an important risk factor in the development of stress.

Discovering hidden connections among diseases, genes and drugs based on microarray expression profiles with negative-term filtering

Microarrays based on gene expression profiles (GEPs) can be tailored specifically for a variety of topics to provide a precise and efficient means with which to discover hidden information. This study proposes a novel means of employing existing GEPs to reveal hidden relationships among diseases, genes, and drugs within a rich biomedical database, PubMed. Unlike the co-occurrence method, which considers only the appearance of keywords, the proposed method also takes into account negative relationships and non-relationships among keywords, the importance of which has been demonstrated in previous studies. Three scenarios were conducted to verify the efficacy of the proposed method. In Scenario 1, disease and drug GEPs (disease: lymphoma cancer, lymph node cancer, and drug: cyclophosphamide) were used to obtain lists of disease- and drug-related genes. Fifteen hidden connections were identified between the diseases and the drug. In Scenario 2, we adopted different diseases and drug GEPs (disease: AML-ALL dataset and drug: Gefitinib) to obtain lists of important diseases and drug-related genes. In this case, ten hidden connections were identified. In Scenario 3, we obtained a list of disease-related genes from the disease-related GEP (liver cancer) and the drug (Capecitabine) on the PharmGKB website, resulting in twenty-two hidden connections. Experimental results demonstrate the efficacy of the proposed method in uncovering hidden connections among diseases, genes, and drugs. Following implementation of the weight function in the proposed method, a large number of the documents obtained in each of the scenarios were judged to be related: 834 of 4028 documents, 789 of 1216 documents, and 1928 of 3791 documents in Scenarios 1, 2, and 3, respectively. The negative-term filtering scheme also uncovered a large number of negative relationships as well as non-relationships among these connections: 97 of 834, 38 of 789, and 202 of 1928 in Scenarios 1, 2, and 3, respectively.

A genome-wide MeSH-based literature mining system predicts implicit gene-to-gene relationships and networks

Background

The large amount of literature in the post-genomics era enables the study of gene interactions and networks using all available articles published for a specific organism. MeSH is a controlled vocabulary of medical and scientific terms that is used by biomedical scientists to manually index articles in the PubMed literature database. We hypothesized that genome-wide gene-MeSH term associations from the PubMed literature database could be used to predict implicit gene-to-gene relationships and networks. While the gene-MeSH associations have been used to detect gene-gene interactions in some studies, different methods have not been well compared, and such a strategy has not been evaluated for a genome-wide literature analysis. Genome-wide literature mining of gene-to-gene interactions allows ranking of the best gene interactions and investigation of comprehensive biological networks at a genome level.

Results

The genome-wide GenoMesh literature mining algorithm was developed by sequentially generating a gene-article matrix, a normalized gene-MeSH term matrix, and a gene-gene matrix. The gene-gene matrix relies on the calculation of pairwise gene dissimilarities based on gene-MeSH relationships. An optimized dissimilarity score was identified from six well-studied functions based on a receiver operating characteristic (ROC) analysis. Based on the studies with well-studied Escherichia coli and less-studied Brucella spp., GenoMesh was found to accurately identify gene functions using weighted MeSH terms, predict gene-gene interactions not reported in the literature, and cluster all the genes studied from an organism using the MeSH-based gene-gene matrix. A web-based GenoMesh literature mining program is also available at: http://genomesh.hegroup.org. GenoMesh also predicts gene interactions and networks among genes associated with specific MeSH terms or user-selected gene lists.

Conclusions

The GenoMesh algorithm and web program provide the first genome-wide, MeSH-based literature mining system that effectively predicts implicit gene-gene interaction relationships and networks in a genome-wide scope.

Gelsius: a literature-based workflow for determining quantitative associations between genes and biological processes

An effective knowledge extraction and quantification methodology from biomedical literature would allow the researcher to organize and analyze the results of high-throughput experiments on microarrays and next-generation sequencing technologies. Despite the large amount of raw information available on the web, a tool able to extract a measure of the correlation between a list of genes and biological processes is not yet available. In this paper, we present Gelsius, a workflow that incorporates biomedical literature to quantify the correlation between genes and terms describing biological processes. To achieve this target, we build different modules focusing on query expansion and document cononicalization. In this way, we reached to improve the measurement of correlation, performed using a latent semantic analysis approach. To the best of our knowledge, this is the first complete tool able to extract a measure of genes-biological processes correlation from literature. We demonstrate the effectiveness of the proposed workflow on six biological processes and a set of genes, by showing that correlation results for known relationships are in accordance with definitions of gene functions provided by NCI Thesaurus. On the other side, the tool is able to propose new candidate relationships for later experimental validation. The tool is available at >http://bioeda1.polito.it:8080/medSearchServlet/.

Textrous!: extracting semantic textual meaning from gene sets

The un-biased and reproducible interpretation of high-content gene sets from large-scale genomic experiments is crucial to the understanding of biological themes, validation of experimental data, and the eventual development of plans for future experimentation. To derive biomedically-relevant information from simple gene lists, a mathematical association to scientific language and meaningful words or sentences is crucial. Unfortunately, existing software for deriving meaningful and easily-appreciable scientific textual ‘tokens’ from large gene sets either rely on controlled vocabularies (Medical Subject Headings, Gene Ontology, BioCarta) or employ Boolean text searching and co-occurrence models that are incapable of detecting indirect links in the literature. As an improvement to existing web-based informatic tools, we have developed Textrous!, a web-based framework for the extraction of biomedical semantic meaning from a given input gene set of arbitrary length. Textrous! employs natural language processing techniques, including latent semantic indexing (LSI), sentence splitting, word tokenization, parts-of-speech tagging, and noun-phrase chunking, to mine MEDLINE abstracts, PubMed Central articles, articles from the Online Mendelian Inheritance in Man (OMIM), and Mammalian Phenotype annotation obtained from Jackson Laboratories. Textrous! has the ability to generate meaningful output data with even very small input datasets, using two different text extraction methodologies (collective and individual) for the selecting, ranking, clustering, and visualization of English words obtained from the user data. Textrous!, therefore, is able to facilitate the output of quantitatively significant and easily appreciable semantic words and phrases linked to both individual gene and batch genomic data.

Expression levels of obesity-related genes are associated with weight change in kidney transplant recipients

Background

The aim of this study was to investigate the association of gene expression profiles in subcutaneous adipose tissue with weight change in kidney transplant recipients and to gain insights into the underlying mechanisms of weight gain.

Methodology/Principal Findings

A secondary data analysis was done on a subgroup (n = 26) of existing clinical and gene expression data from a larger prospective longitudinal study examining factors contributing to weight gain in transplant recipients. Measurements taken included adipose tissue gene expression profiles at time of transplant, baseline and six-month weight, and demographic data. Using multivariate linear regression analysis controlled for race and gender, expression levels of 1553 genes were significantly (p<0.05) associated with weight change. Functional analysis using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes classifications identified metabolic pathways that were enriched in this dataset. Furthermore, GeneIndexer literature mining analysis identified a subset of genes that are highly associated with obesity in the literature and Ingenuity pathway analysis revealed several significant gene networks associated with metabolism and endocrine function. Polymorphisms in several of these genes have previously been linked to obesity.

Conclusions/Significance

We have successfully identified a set of molecular pathways that taken together may provide insights into the mechanisms of weight gain in kidney transplant recipients. Future work will be done to determine how these pathways may contribute to weight gain.

Effective use of latent semantic indexing and computational linguistics in biological and biomedical applications

Text mining is rapidly becoming an essential technique for the annotation and analysis of large biological data sets. Biomedical literature currently increases at a rate of several thousand papers per week, making automated information retrieval methods the only feasible method of managing this expanding corpus. With the increasing prevalence of open-access journals and constant growth of publicly-available repositories of biomedical literature, literature mining has become much more effective with respect to the extraction of biomedically-relevant data. In recent years, text mining of popular databases such as MEDLINE has evolved from basic term-searches to more sophisticated natural language processing techniques, indexing and retrieval methods, structural analysis and integration of literature with associated metadata. In this review, we will focus on Latent Semantic Indexing (LSI), a computational linguistics technique increasingly used for a variety of biological purposes. It is noted for its ability to consistently outperform benchmark Boolean text searches and co-occurrence models at information retrieval and its power to extract indirect relationships within a data set. LSI has been used successfully to formulate new hypotheses, generate novel connections from existing data, and validate empirical data.

Literature aided determination of data quality and statistical significance threshold for gene expression studies

Background

Gene expression data are noisy due to technical and biological variability. Consequently, analysis of gene expression data is complex. Different statistical methods produce distinct sets of genes. In addition, selection of expression p-value (EPv) threshold is somewhat arbitrary. In this study, we aimed to develop novel literature based approaches to integrate functional information in analysis of gene expression data.

Methods

Functional relationships between genes were derived by Latent Semantic Indexing (LSI) of Medline abstracts and used to calculate the function cohesion of gene sets. In this study, literature cohesion was applied in two ways. First, Literature-Based Functional Significance (LBFS) method was developed to calculate a p-value for the cohesion of differentially expressed genes (DEGs) in order to objectively evaluate the overall biological significance of the gene expression experiments. Second, Literature Aided Statistical Significance Threshold (LASST) was developed to determine the appropriate expression p-value threshold for a given experiment.

Results

We tested our methods on three different publicly available datasets. LBFS analysis demonstrated that only two experiments were significantly cohesive. For each experiment, we also compared the LBFS values of DEGs generated by four different statistical methods. We found that some statistical tests produced more functionally cohesive gene sets than others. However, no statistical test was consistently better for all experiments. This reemphasizes that a statistical test must be carefully selected for each expression study. Moreover, LASST analysis demonstrated that the expression p-value thresholds for some experiments were considerably lower (p < 0.02 and 0.01), suggesting that the arbitrary p-values and false discovery rate thresholds that are commonly used in expression studies may not be biologically sound.

Conclusions

We have developed robust and objective literature-based methods to evaluate the biological support for gene expression experiments and to determine the appropriate statistical significance threshold. These methods will assist investigators to more efficiently extract biologically meaningful insights from high throughput gene expression experiments.

Pathogenic mechanisms of neurodegeneration based on the phenotypic expression of progressive forms of immune-mediated neurologic disease

Considering there are no treatments for progressive forms of multiple sclerosis (MS), a comprehensive understanding of the role of neurodegeneration in the pathogenesis of MS should lead to novel therapeutic strategies to treat it. Many studies have implicated viral triggers as a cause of MS, yet no single virus has been exclusively shown to cause MS. Given this, human and animal viral models of MS are used to study its pathogenesis. One example is human T-lymphotropic virus type 1-associated myelopathy/tropical spastic paraparesis (HAM/TSP). Importantly, HAM/TSP is similar clinically, pathologically, and immunologically to progressive MS. Interestingly, both MS and HAM/TSP patients were found to make antibodies to heterogeneous nuclear ribonucleoprotein (hnRNP) A1, an RNA-binding protein overexpressed in neurons. Anti-hnRNP A1 antibodies reduced neuronal firing and caused neurodegeneration in neuronal cell lines, suggesting the autoantibodies are pathogenic. Further, microarray analyses of neurons exposed to anti-hnRNP A1 antibodies revealed novel pathways of neurodegeneration related to alterations of RNA levels of the spinal paraplegia genes (SPGs). Mutations in SPGs cause hereditary spastic paraparesis, genetic disorders clinically indistinguishable from progressive MS and HAM/TSP. Thus, there is a strong association between involvement of SPGs in neurodegeneration and the clinical phenotype of progressive MS and HAM/TSP patients, who commonly develop spastic paraparesis. Taken together, these data begin to clarify mechanisms of neurodegeneration related to the clinical presentation of patients with chronic immune-mediated neurological disease of the central nervous system, which will give insights into the design of novel therapies to treat these neurological diseases.

2,3,7,8-Tetrachlorodibenzo-p-dioxin-mediated production of reactive oxygen species is an essential step in the mechanism of action to accelerate human keratinocyte differentiation

Chloracne is commonly observed in humans exposed to 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD); yet, the mechanism of toxicity is not well understood. Using normal human epidermal keratinocytes, we investigated the mechanism of TCDD-mediated enhancement of epidermal differentiation by integrating functional genomic, metabolomic, and biochemical analyses. TCDD increased the expression of 40% of the genes of the epidermal differentiation complex found on chromosome 1q21 and 75% of the genes required for de novo ceramide biosynthesis. Lipid analysis demonstrated that eight of the nine classes of ceramides were increased by TCDD, altering the ratio of ceramides to free fatty acids. TCDD decreased the expression of the glucose transporter, SLC2A1, and most of the glycolytic transcripts, followed by decreases in glycolytic intermediates, including pyruvate. NADH and Krebs cycle intermediates were decreased, whereas NAD(+) was increased. Mitochondrial glutathione (GSH) reductase activity and the GSH/glutathione disulfide ratio were decreased by TCDD, ultimately leading to mitochondrial dysfunction, characterized by decreased inner mitochondrial membrane potential and ATP production, and increased production of the reactive oxygen species (ROS), hydrogen peroxide. Aryl hydrocarbon receptor (AHR) antagonists blocked the response of many transcripts to TCDD, and the endpoints of decreased ATP production and differentiation, suggesting regulation by the AHR. Cotreatment of cells with chemical antioxidants or the enzyme catalase blocked the TCDD-mediated acceleration of keratinocyte cornified envelope formation, an endpoint of terminal differentiation. Thus, TCDD-mediated ROS production is a critical step in the mechanism of this chemical to accelerate keratinocyte differentiation.

Systems genetics of metabolism: the use of the BXD murine reference panel for multiscalar integration of traits

Metabolic homeostasis is achieved by complex molecular and cellular networks that differ significantly among individuals and are difficult to model with genetically engineered lines of mice optimized to study single gene function. Here, we systematically acquired metabolic phenotypes by using the EUMODIC EMPReSS protocols across a large panel of isogenic but diverse strains of mice (BXD type) to study the genetic control of metabolism. We generated and analyzed 140 classical phenotypes and deposited these in an open-access web service for systems genetics (www.genenetwork.org). Heritability, influence of sex, and genetic modifiers of traits were examined singly and jointly by using quantitative-trait locus (QTL) and expression QTL-mapping methods. Traits and networks were linked to loci encompassing both known variants and novel candidate genes, including alkaline phosphatase (ALPL), here linked to hypophosphatasia. The assembled and curated phenotypes provide key resources and exemplars that can be used to dissect complex metabolic traits and disorders.

Clustering algorithms in biomedical research: a review

Applications of clustering algorithms in biomedical research are ubiquitous, with typical examples including gene expression data analysis, genomic sequence analysis, biomedical document mining, and MRI image analysis. However, due to the diversity of cluster analysis, the differing terminologies, goals, and assumptions underlying different clustering algorithms can be daunting. Thus, determining the right match between clustering algorithms and biomedical applications has become particularly important. This paper is presented to provide biomedical researchers with an overview of the status quo of clustering algorithms, to illustrate examples of biomedical applications based on cluster analysis, and to help biomedical researchers select the most suitable clustering algorithms for their own applications.

EGFR regulation of epidermal barrier function

Keratinocyte terminal differentiation is the process that ultimately forms the epidermal barrier that is essential for mammalian survival. This process is controlled, in part, by signal transduction and gene expression mechanisms, and the epidermal growth factor receptor (EGFR) is known to be an important regulator of multiple epidermal functions. Using microarray analysis of a confluent cell density-induced model of keratinocyte differentiation, we identified 2,676 genes that are regulated by epidermal growth factor (EGF), a ligand of the EGFR. We further discovered, and separately confirmed by functional assays, that EGFR activation abrogates all of the known essential processes of keratinocyte differentiation by 1) decreasing the expression of lipid matrix biosynthetic enzymes, 2) regulating numerous genes forming the cornified envelope, and 3) suppressing the expression of tight junction proteins. In organotypic cultures of skin, EGF acted to impair epidermal barrier integrity, as shown by increased transepidermal water loss. As defective epidermal differentiation and disruption of barrier function are primary features of many human skin diseases, we used bioinformatic analyses to identify genes that are known to be associated with skin diseases. Compared with non-EGF-regulated genes, EGF-regulated genes were significantly enriched for skin disease genes. These results provide a systems-level understanding of the actions of EGFR signaling to inhibit keratinocyte differentiation, providing new insight into the role of EGFR imbalance in skin pathogenesis.

Connecting the dots between PubMed abstracts

Background

There are now a multitude of articles published in a diversity of journals providing information about genes, proteins, pathways, and diseases. Each article investigates subsets of a biological process, but to gain insight into the functioning of a system as a whole, we must integrate information from multiple publications. Particularly, unraveling relationships between extra-cellular inputs and downstream molecular response mechanisms requires integrating conclusions from diverse publications.

Methodology

We present an automated approach to biological knowledge discovery from PubMed abstracts, suitable for “connecting the dots” across the literature. We describe a storytelling algorithm that, given a start and end publication, typically with little or no overlap in content, identifies a chain of intermediate publications from one to the other, such that neighboring publications have significant content similarity. The quality of discovered stories is measured using local criteria such as the size of supporting neighborhoods for each link and the strength of individual links connecting publications, as well as global metrics of dispersion. To ensure that the story stays coherent as it meanders from one publication to another, we demonstrate the design of novel coherence and overlap filters for use as post-processing steps.

Conclusions

We demonstrate the application of our storytelling algorithm to three case studies: i) a many-one study exploring relationships between multiple cellular inputs and a molecule responsible for cell-fate decisions, ii) a many-many study exploring the relationships between multiple cytokines and multiple downstream transcription factors, and iii) a one-to-one study to showcase the ability to recover a cancer related association, viz. the Warburg effect, from past literature. The storytelling pipeline helps narrow down a scientist's focus from several hundreds of thousands of relevant documents to only around a hundred stories. We argue that our approach can serve as a valuable discovery aid for hypothesis generation and connection exploration in large unstructured biological knowledge bases.

Latent Semantic Indexing of PubMed abstracts for identification of transcription factor candidates from microarray derived gene sets

Background

Identification of transcription factors (TFs) responsible for modulation of differentially expressed genes is a key step in deducing gene regulatory pathways. Most current methods identify TFs by searching for presence of DNA binding motifs in the promoter regions of co-regulated genes. However, this strategy may not always be useful as presence of a motif does not necessarily imply a regulatory role. Conversely, motif presence may not be required for a TF to regulate a set of genes. Therefore, it is imperative to include functional (biochemical and molecular) associations, such as those found in the biomedical literature, into algorithms for identification of putative regulatory TFs that might be explicitly or implicitly linked to the genes under investigation.

Results

In this study, we present a Latent Semantic Indexing (LSI) based text mining approach for identification and ranking of putative regulatory TFs from microarray derived differentially expressed genes (DEGs). Two LSI models were built using different term weighting schemes to devise pair-wise similarities between 21,027 mouse genes annotated in the Entrez Gene repository. Amongst these genes, 433 were designated TFs in the TRANSFAC database. The LSI derived TF-to-gene similarities were used to calculate TF literature enrichment p-values and rank the TFs for a given set of genes. We evaluated our approach using five different publicly available microarray datasets focusing on TFs Rel, Stat6, Ddit3, Stat5 and Nfic. In addition, for each of the datasets, we constructed gold standard TFs known to be functionally relevant to the study in question. Receiver Operating Characteristics (ROC) curves showed that the log-entropy LSI model outperformed the tf-normal LSI model and a benchmark co-occurrence based method for four out of five datasets, as well as motif searching approaches, in identifying putative TFs.

Conclusions

Our results suggest that our LSI based text mining approach can complement existing approaches used in systems biology research to decipher gene regulatory networks by providing putative lists of ranked TFs that might be explicitly or implicitly associated with sets of DEGs derived from microarray experiments. In addition, unlike motif searching approaches, LSI based approaches can reveal TFs that may indirectly regulate genes.

A potential link between autoimmunity and neurodegeneration in immune-mediated neurological disease

Multiple sclerosis (MS) patients make antibodies to heterogeneous nuclear ribonuclear protein A1 (hnRNP-A1), a nucleocytoplasmic protein. We hypothesized this autoimmune reaction might contribute to neurodegeneration. Antibodies from MS patients reacted with hnRNP-A1-'M9', its nuclear translocation sequence. Transfection of anti-M9 antibodies into neurons resulted in neuronal injury and changes in transcripts related to hnRNP-A1 function. Importantly, RNA levels for the spinal paraplegia genes (SPGs) decreased. Changes in SPG RNA levels were confirmed in neurons purified from MS brains. Also, we show molecular interactions between spastin (the encoded protein of SPG4) and hnRNP-A1. These data suggest a link between autoimmunity, clinical phenotype and neurodegeneration in MS.

Functional cohesion of gene sets determined by latent semantic indexing of PubMed abstracts

High-throughput genomic technologies enable researchers to identify genes that are co-regulated with respect to specific experimental conditions. Numerous statistical approaches have been developed to identify differentially expressed genes. Because each approach can produce distinct gene sets, it is difficult for biologists to determine which statistical approach yields biologically relevant gene sets and is appropriate for their study. To address this issue, we implemented Latent Semantic Indexing (LSI) to determine the functional coherence of gene sets. An LSI model was built using over 1 million Medline abstracts for over 20,000 mouse and human genes annotated in Entrez Gene. The gene-to-gene LSI-derived similarities were used to calculate a literature cohesion p-value (LPv) for a given gene set using a Fisher's exact test. We tested this method against genes in more than 6,000 functional pathways annotated in Gene Ontology (GO) and found that approximately 75% of gene sets in GO biological process category and 90% of the gene sets in GO molecular function and cellular component categories were functionally cohesive (LPv<0.05). These results indicate that the LPv methodology is both robust and accurate. Application of this method to previously published microarray datasets demonstrated that LPv can be helpful in selecting the appropriate feature extraction methods. To enable real-time calculation of LPv for mouse or human gene sets, we developed a web tool called Gene-set Cohesion Analysis Tool (GCAT). GCAT can complement other gene set enrichment approaches by determining the overall functional cohesion of data sets, taking into account both explicit and implicit gene interactions reported in the biomedical literature.

Literature-aided interpretation of gene expression data with the weighted global test

Most methods for the interpretation of gene expression profiling experiments rely on the categorization of genes, as provided by the Gene Ontology (GO) and pathway databases. Due to the manual curation process, such databases are never up-to-date and tend to be limited in focus and coverage. Automated literature mining tools provide an attractive, alternative approach. We review how they can be employed for the interpretation of gene expression profiling experiments. We illustrate that their comprehensive scope aids the interpretation of data from domains poorly covered by GO or alternative databases, and allows for the linking of gene expression with diseases, drugs, tissues and other types of concepts. A framework for proper statistical evaluation of the associations between gene expression values and literature concepts was lacking and is now implemented in a weighted extension of global test. The weights are the literature association scores and reflect the importance of a gene for the concept of interest. In a direct comparison with classical GO-based gene sets, we show that use of literature-based associations results in the identification of much more specific GO categories. We demonstrate the possibilities for linking of gene expression data to patient survival in breast cancer and the action and metabolism of drugs. Coupling with online literature mining tools ensures transparency and allows further study of the identified associations. Literature mining tools are therefore powerful additions to the toolbox for the interpretation of high-throughput genomics data.

Discovering gene functional relationships using FAUN (Feature Annotation Using Nonnegative matrix factorization)

BACKGROUND

Searching the enormous amount of information available in biomedical literature to extract novel functional relationships among genes remains a challenge in the field of bioinformatics. While numerous (software) tools have been developed to extract and identify gene relationships from biological databases, few effectively deal with extracting new (or implied) gene relationships, a process which is useful in interpretation of discovery-oriented genome-wide experiments.

RESULTS

In this study, we develop a Web-based bioinformatics software environment called FAUN or Feature Annotation Using Nonnegative matrix factorization (NMF) to facilitate both the discovery and classification of functional relationships among genes. Both the computational complexity and parameterization of NMF for processing gene sets are discussed. FAUN is tested on three manually constructed gene document collections. Its utility and performance as a knowledge discovery tool is demonstrated using a set of genes associated with Autism.

CONCLUSIONS

FAUN not only assists researchers to use biomedical literature efficiently, but also provides utilities for knowledge discovery. This Web-based software environment may be useful for the validation and analysis of functional associations in gene subsets identified by high-throughput experiments.