RNA alternative splicing impacts the risk for alcohol use disorder.

Alcohol use disorder (AUD) is a complex genetic disorder characterized by problems arising from excessive alcohol consumption. Identifying functional genetic variations that contribute to risk for AUD is a major goal. Alternative splicing of RNA mediates the flow of genetic information from DNA to gene expression and expands proteome diversity. We asked whether alternative splicing could be a risk factor for AUD. Herein, we used a Mendelian randomization (MR)-based approach to identify skipped exons (the predominant splicing event in brain) that contribute to AUD risk. Genotypes and RNA-seq data from the CommonMind Consortium were used as the training dataset to develop predictive models linking individual genotypes to exon skipping in the prefrontal cortex. We applied these models to data from the Collaborative Studies on Genetics of Alcoholism to examine the association between the imputed cis-regulated splicing outcome and the AUD-related traits. We identified 27 exon skipping events that were predicted to affect AUD risk; six of these were replicated in the Australian Twin-family Study of Alcohol Use Disorder. Their host genes are DRC1, ELOVL7, LINC00665, NSUN4, SRRM2 and TBC1D5. The genes downstream of these splicing events are enriched in neuroimmune pathways. The MR-inferred impacts of the ELOVL7 skipped exon on AUD risk was further supported in four additional large-scale genome-wide association studies. Additionally, this exon contributed to changes of gray matter volumes in multiple brain regions, including the visual cortex known to be involved in AUD. In conclusion, this study provides strong evidence that RNA alternative splicing impacts the susceptibility to AUD and adds new information on AUD-relevant genes and pathways. Our framework is also applicable to other types of splicing events and to other complex genetic disorders.

Colorimetric detection of sequence-specific microRNA based on duplex-specific nuclease-assisted nanoparticle amplification.

Developing simple and rapid methods for sequence-specific microRNA (miRNA) analysis is imperative to the miRNA study and use in clinical diagnosis. We have developed a colorimetric method for miRNA detection based on duplex-specific nuclease (DSN)-assisted signal amplification coupled to the aggregation of gold nanoparticles (AuNPs). The proposed method involves two processes: target-mediated probe digestion by a DSN enzyme and probe-triggered AuNP aggregation as a switch for signal output. The reaction system consists of a rationally designed probe complex formed by two partly complementary DNA probes, and two sets of different oligonucleotide-modified AuNPs with sequences complementary to a DNA probe in the probe complex. In the presence of target miRNA, the probe complex is invaded, resulting in the formation of a miRNA-probe heteroduplex as the substrate of the DSN enzyme, and releasing the other probe to link to the AuNPs. The proposed method allows quantitative detection of miR-122 in the range of 20 pM to 1 nM with a detection limit of ∼16 pM, and shows an excellent ability to discriminate single-base differences. Moreover, the detection assay can be applied to accurately quantify miR-122 in cancerous cell lysates which is in excellent agreement with the results from a commercial miRNA detection kit. This method is simple, cost-effective, highly selective, and free of dye label and separation procedures.

Computational identification of surrogate genes for prostate cancer phases using machine learning and molecular network analysis.

BACKGROUND: Prostate cancer is one of the most common malignant diseases and is characterized by heterogeneity in the clinical course. To date, there are no efficient morphologic features or genomic biomarkers that can characterize the phenotypes of the cancer, especially with regard to metastasis--the most adverse outcome. Searching for effective surrogate genes out of large quantities of gene expression data is a key to cancer phenotyping and/or understanding molecular mechanisms underlying prostate cancer development. RESULTS: Using the maximum relevance minimum redundancy (mRMR) method on microarray data from normal tissues, primary tumors and metastatic tumors, we identifed four genes that can optimally classify samples of different prostate cancer phases. Moreover, we constructed a molecular interaction network with existing bioinformatic resources and co-identifed eight genes on the shortest-paths among the mRMR-identified genes, which are potential co-acting factors of prostate cancer. Functional analyses show that molecular functions involved in cell communication, hormone-receptor mediated signaling, and transcription regulation play important roles in the development of prostate cancer. CONCLUSION: We conclude that the surrogate genes we have selected compose an effective classifier of prostate cancer phases, which corresponds to a minimum characterization of cancer phenotypes on the molecular level. Along with their molecular interaction partners, it is fairly to assume that these genes may have important roles in prostate cancer development; particularly, the un-reported genes may bring new insights for the understanding of the molecular mechanisms. Thus our results may serve as a candidate gene set for further functional studies.

Identifying Genes Associated with Alzheimer's Disease Using Gene-Based Polygenic Risk Score.

BACKGROUND: Except APOE, Alzheimer's disease (AD) associated genes identified in recent large-scale genome-wide association studies (GWAS) had small effects and explained a small portion of heritability. Many AD-associated genes have even smaller effects thereby sub-threshold p-values in large-scale GWAS and remain to be identified. For some AD-associated genes, drug targeting them may have limited efficacies due to their small effect sizes. OBJECTIVE: The purpose of this study is to identify AD-associated genes with sub-threshold p-values and prioritize drugs targeting AD-associated genes that have large efficacies. METHODS: We developed a gene-based polygenic risk score (PRS) to identify AD genes. It was calculated using SNPs located within genes and having the same directions of effects in different study cohorts to exclude cohort-specific findings and false positives. Gene co-expression modules and protein-protein interaction networks were used to identify AD-associated genes that interact with multiple other genes, as drugs targeting them have large efficacies via co-regulation or interactions. RESULTS: Gene-based PRS identified 389 genes with 164 of them not previously reported as AD-associated. These 389 genes explained 56.12% -97.46% SNP heritability; and they were enriched in brain tissues and 164 biological processes, most of which are related to AD and other neurodegenerative diseases. We prioritized 688 drugs targeting 64 genes that were in the same co-expression modules and/or PPI networks. CONCLUSIONS: Gene-based PRS is a cost-effective way to identify AD-associated genes without substantially increasing the sample size. Co-expression modules and PPI networks can be used to identify drugs having large efficacies.

The Role of circRNAs in the Diagnosis of Colorectal Cancer: A Meta-Analysis.

Background: A novel category of non-coding circular RNAs (circRNAs) has been found to be dysregulated in colorectal cancer (CRC) and significantly contribute to its progression. However, the feasibility of using circRNA as a diagnostic biomarker for CRC remains to be elucidated. Herein, we aimed to comprehensively collect and analyze evidence regarding the potential application of circRNAs as diagnostic indicators for CRC. Methods: A comprehensive retrieval of relevant studies dating from January, 2015 to December 2020, was carried out in PubMed, Cochrane Library, and Web of Science. Data regarding the diagnostic accuracy of circRNA for CRC, including sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the curve (AUC), were obtained from the included studies. Quality assessment of diagnostic accuracy studies (QUADAS-2) was used to assess the methodological quality of each study. Statistical analysis was performed using STAT and RevMan software. Results: Eighteen studies, involving a total of 2021 individuals, were included in the present meta-analysis. The specimens examined included tissue, serum, and plasma. The pooled sensitivity, specificity, DOR, PLR, NLR, and AUC, with a 95% confidence interval (CI), of circRNAs in the diagnosis of CRC were 0.78 (0.71-0.83), 0.73 (0.68-0.78), 9.68 (6.76-13.85), 2.92 (2.45-3.50), 0.30 (0.23-0.39), and 0.81 (0.78-0.85), respectively. Subgroup analysis showed that the upregulated circRNAs in the tissue or plasma possessed relatively higher diagnostic values for CRC than the downregulated circRNAs. There was no significant difference between the tissue-derived and non-tissue-derived circRNA subgroups. Conclusion: circRNA may be used as a diagnostic biomarker for CRC because of its relatively high diagnostic accuracy in distinguishing CRC patients from normal controls. Further prospective studies are needed to identify more representative circRNAs as diagnostic markers for CRC.

Circulating miRNAs as Biomarkers for CYP2B6 Enzyme Activity.

The CYP2B6 gene is highly polymorphic and its activity shows wide interindividual variability. However, substantial variability in CYP2B6 activity remains unexplained by the known CYP2B6 genetic variations. Circulating, cell-free micro RNAs (miRNAs) may serve as biomarkers of hepatic enzyme activity. CYP2B6 activity in 72 healthy volunteers was determined using the disposition of efavirenz as a probe drug. Circulating miRNA expression was quantified from baseline plasma samples. A linear model consisting of the effects of miRNA expression, genotype-determined metabolizer status, and demographic information was developed to predict CYP2B6 activity. Expression of 2,510 miRNAs were quantified out of which 7 miRNAs, together with the CYP2B6-genotypic metabolizer status and demographics, was shown to be predictive markers for CYP2B6 activity. The reproducibility of the model was evaluated by cross-validation. The average Pearson's correlation (R) between the predicted and observed maximum plasma concentration (Cmax ) ratios of efavirenz and its metabolite-8-OH efavirenz using the linear model with all features (7 miRNA + metabolizer status + age + sex + race) was 0.6702. Similar results were also observed using area under the curve (AUC) ratios (Pearson correlation's R = 0.6035). Thus, at least 36% (R2 ) of the variability of in vivo CYP2B6 activity was explained using this model. This is a significant improvement over the models using only the genotype-based metabolizer status or the demographic information, which explained only 6% or less of the variability of in vivo CYP2B6 activity. Our results, therefore, demonstrate that circulating plasma miRNAs can be valuable biomarkers for in vivo CYP2B6 activity.

RegSNPs-intron: a computational framework for predicting pathogenic impact of intronic single nucleotide variants.

Single nucleotide variants (SNVs) in intronic regions have yet to be systematically investigated for their disease-causing potential. Using known pathogenic and neutral intronic SNVs (iSNVs) as training data, we develop the RegSNPs-intron algorithm based on a random forest classifier that integrates RNA splicing, protein structure, and evolutionary conservation features. RegSNPs-intron showed excellent performance in evaluating the pathogenic impacts of iSNVs. Using a high-throughput functional reporter assay called ASSET-seq (ASsay for Splicing using ExonTrap and sequencing), we evaluate the impact of RegSNPs-intron predictions on splicing outcome. Together, RegSNPs-intron and ASSET-seq enable effective prioritization of iSNVs for disease pathogenesis.

System modeling reveals the molecular mechanisms of HSC cell cycle alteration mediated by Maff and Egr3 under leukemia.

BACKGROUND: Molecular mechanisms of the functional alteration of hematopoietic stem cells (HSCs) in leukemic environment attract intensive research interests. As known in previous researches, Maff and Egr3 are two important genes having opposite functions on cell cycle; however, they are both highly expressed in HSCs under leukemia. Hence, exploring the molecular mechanisms of how the genes act on cell cycle will help revealing the functional alteration of HSCs. RESULTS: We herein utilize the bioinformatic resources to computationally model the acting mechanisms of Maff and Egr3 on cell cycle. Using the data of functional experiments as reference, molecular acting mechanisms are optimally enumerated through model selection. The results are consolidated by evidences from gene sequence analysis, thus having enhanced the confidence of our pilot findings, which suggest that HSCs possibly undergo a "adaptation - suppression" process in response to the malignant environment of leukemia. CONCLUSION: As a pilot research, our results may provide valuable insights for further experimental studies. Meanwhile, our research method combining computational modeling and data from functional experiments can be worthwhile for knowledge discovery; and it can be generalized and extended to other biological/biomedical studies.

Ultrasensitive, colorimetric detection of microRNAs based on isothermal exponential amplification reaction-assisted gold nanoparticle amplification.

MicroRNAs (miRNAs) play important roles in a wide range of biological processes, and their aberrant expressions are linked to a large number of human diseases and disorders. In this work, we developed a colorimetric method for rapid, ultrasensitive miRNA detection via isothermal exponential amplification reaction (EXPAR)-assisted gold nanoparticle (AuNP) amplification. The sensing probe designed with a tandem phosphorothioate modification in the backbone of the polyadenines at the 5' terminus was employed to directly assemble onto the surface of AuNP with high adsorption affinity. The recognition domain at the 3' terminus of the sensing probe hybridizes with target miRNAs to trigger EXPAR with exponential signal amplification. With the amplification reaction with the action of DNA polymerase, the sensing probe gradually detaches from the AuNP, resulting in the aggregation of bare AuNPs in the high-salt reaction environment due to lack of DNA protection. The presence of AuNP aggregation is conveniently measured by UV-vis spectroscopy. Our proposed method could provide a linear detection range from 50fM to 10nM with a detection limit of ∼46fM within 60min, and also discriminate a single-nucleotide difference between homologous miRNAs.

Digitalization of a non-irradiated acute myeloid leukemia model.

BACKGROUND: Computer-aided, interdisciplinary researches for biomedicine have valuable prospects, as digitalization of experimental subjects provide opportunities for saving the economic costs of researches, as well as promoting the acquisition of knowledge. Acute myeloid leukemia (AML) is intensively studied over long periods of time. Till nowaday, most of the studies primarily focus on the leukemic cells rather than how normal hematopoietic cells are affected by the leukemic environment. Accordingly, the conventional animal models for AML are mostly myeloablated as leukemia can be induced with short latency and complete penetrance. Meanwhile, most previous computational models focus on modeling the leukemic cells but not the multi-tissue leukemic body resided by both leukemic and normal blood cells. Recently, a non-irradiated AML mouse model has been established; therefore, normal hematopoietic cells can be investigated during leukemia development. Experiments based on the non-irradiated animal model have monitored the kinetics of leukemic and (intact) hematopoietic cells in multiple tissues simultaneously; and thus a systematic computational model for the multi-tissue hematopoiesis under leukemia has become possible. RESULTS: In the present work, we adopted the modeling methods in previous works, but aimed to model the tri-tissue (peripheral blood, spleen and bone marrow) dynamics of hematopoiesis under leukemia. The cell kinetics generated from the non-irradiated experimental model were used as the reference data for modeling. All mathematical formulas were systematically enumerated, and model parameters were estimated via numerical optimization. Multiple validations by additional experimental data were then conducted for the established computational model. In the results, we illustrated that the important fact of functional depression of hematopoietic stem/progenitor cells (HSC/HPC) in leukemic bone marrow (BM), which must require additional experiments to be established, could also be inferred from our computation model that utilized only the cell kinetics data as the input. CONCLUSION: The digitalized AML model established in the present work is effective for reconstructing the hematopoiesis under leukemia as well as simulating the hematopoietic response to leukemic cell expansion. Given the validity and efficiency, the model can be of potential utilities in future biomedical studies; additionally, the modeling method itself can be also applied elsewhere.

Enzyme-free detection of sequence-specific microRNAs based on nanoparticle-assisted signal amplification strategy.

Developing direct and convenient methods for microRNAs (miRNAs) analysis is of great significance in understanding biological functions of miRNAs, and early diagnosis of cancers. We have developed a rapid, enzyme-free method for miRNA detection based on nanoparticle-assisted signal amplification coupling fluorescent metal nanoclusters as signal output. The proposed method involves two processes: target miRNA-mediated nanoparticle capture, which consists of magnetic microparticle (MMP) probe and CuO nanoparticle (NP) probe, and nanoparticle-mediated amplification for signal generation, which consists of fluorescent DNA-Cu/Ag nanocluster (NC) and 3-mercaptopropionic acid (MPA). In the presence of target miRNA, MMP probe and NP probe sandwich-capture the target miRNA via their respective complementary sequence. The resultant sandwich complex (MMP probe-miRNA-CuO NP probe) is separated using a magnetic field and further dissolved by acidolysis to turn CuO NP into a great amount of copper (II) ions (Cu(2+)). Cu(2+) could disrupt the interactions between thiol moiety of MPA and the fluorescent Cu/Ag NCs by preferentially reacting with MPA to form a disulfide compound as intermediate. By this way, the fluorescence emission of the DNA-Cu/Ag NCs in the presence of MPA increases upon the increasing concentration of Cu(2+), which is directly proportional to the amount of target miRNA. The proposed method allows quantitative detection of a liver-specific miR-221-5p in the range of 5 pM to 1000 pM with a detection limit of ~0.73 pM, and shows a good ability to discriminate single-base difference. Moreover, the detection assay can be applied to detect miRNA in cancerous cell lysates in excellent agreement with that from a commercial miRNA detection kit.

Motif-Based Text Mining of Microbial Metagenome Redundancy Profiling Data for Disease Classification.

BACKGROUND: Text data of 16S rRNA are informative for classifications of microbiota-associated diseases. However, the raw text data need to be systematically processed so that features for classification can be defined/extracted; moreover, the high-dimension feature spaces generated by the text data also pose an additional difficulty. RESULTS: Here we present a Phylogenetic Tree-Based Motif Finding algorithm (PMF) to analyze 16S rRNA text data. By integrating phylogenetic rules and other statistical indexes for classification, we can effectively reduce the dimension of the large feature spaces generated by the text datasets. Using the retrieved motifs in combination with common classification methods, we can discriminate different samples of both pneumonia and dental caries better than other existing methods. CONCLUSIONS: We extend the phylogenetic approaches to perform supervised learning on microbiota text data to discriminate the pathological states for pneumonia and dental caries. The results have shown that PMF may enhance the efficiency and reliability in analyzing high-dimension text data.

Identification of hepatocellular carcinoma-related genes with a machine learning and network analysis.

Liver cancer is one of the leading causes of cancer mortality worldwide. Hepatocellular carcinoma (HCC) is the main type of liver cancer. We applied a machine learning approach with maximum-relevance-minimum-redundancy (mRMR) algorithm followed by incremental feature selection (IFS) to a set of microarray data generated from 43 tumor and 52 nontumor samples. With the machine learning approach, we identified 117 gene probes that could optimally separate tumor and nontumor samples. These genes not only include known HCC-relevant genes such as MT1X, BMI1, and CAP2, but also include cancer genes that were not found previously to be closely related to HCC, such as TACSTD2. Then, we constructed a molecular interaction network based on the protein-protein interaction (PPI) data from the STRING database and identified 187 genes on the shortest paths among the genes identified with the machine learning approach. Network analysis reveals new potential roles of ubiquitin C in the pathogenesis of HCC. Based on gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, we showed that the identified subnetwork is significantly enriched in biological processes related to cell death. These results bring new insights of understanding the process of HCC.

Quantitative dynamic modelling of the gene regulatory network controlling adipogenesis.

Gene regulatory networks (GRNs) coherently coordinate the expressions of genes and control the behaviors of cellular systems. The complexity in modeling a quantitative GRN usually results from inaccurate parameter estimation, which is mostly due to small sample sizes. For better modeling of GRNs, we have designed a small-sample iterative optimization algorithm (SSIO) to quantitatively model GRNs with nonlinear regulatory relationships. The algorithm utilizes gene expression data as the primary input and it can be applied in case of small-sized samples. Using SSIO, we have quantitatively constructed the dynamic models for the GRNs controlling human and mouse adipogenesis. Compared with two other commonly-used methods, SSIO shows better performance with relatively lower residual errors, and it generates rational predictions on the adipocyte responses to external signals and steady-states. Sensitivity analysis further indicates the validity of our method. Several differences are observed between the GRNs of human and mouse adipocyte differentiations, suggesting the differences in regulatory efficiencies of the transcription factors between the two species. In addition, we use SSIO to quantitatively determine the strengths of the regulatory interactions as well as to optimize regulatory models. The results indicate that SSIO facilitates better investigation and understanding of gene regulatory processes.

A computational study identifies HIV progression-related genes using mRMR and shortest path tracing.

Since statistical relationships between HIV load and CD4+ T cell loss have been demonstrated to be weak, searching for host factors contributing to the pathogenesis of HIV infection becomes a key point for both understanding the disease pathology and developing treatments. We applied Maximum Relevance Minimum Redundancy (mRMR) algorithm to a set of microarray data generated from the CD4+ T cells of viremic non-progressors (VNPs) and rapid progressors (RPs) to identify host factors associated with the different responses to HIV infection. Using mRMR algorithm, 147 gene had been identified. Furthermore, we constructed a weighted molecular interaction network with the existing protein-protein interaction data from STRING database and identified 1331 genes on the shortest-paths among the genes identified with mRMR. Functional analysis shows that the functions relating to apoptosis play important roles during the pathogenesis of HIV infection. These results bring new insights of understanding HIV progression.

Characterizing criticality of proteins by systems dynamics: Escherichia coli central carbon metabolism as a working example.

BACKGROUND: Systems biology calls for studying system-level properties of genes and proteins rather than their individual chemical/biological properties, regarding the bio-molecules as system components. By characterizing how critical the components are to the system and classifying them accordingly, we can study the underlying complex mechanisms, facilitating researches in drug target selection, metabolic engineering, complex disease, etc. Up to date, most studies aiming at this goal are confined to the topology-based or flux-analysis approaches. However, proteins have tertiary structures and specific functions, especially in metabolic systems. Thus topological properties such as connectivity, path length, etc., are not good surrogates for protein properties. Also, the manner of individual sensitivity analysis in most flux-analysis approaches cannot reveal the simultaneous impacts on collateral components as well as the overall impact on the system, thus lacking in system-level perspective. RESULTS: In the present work, we developed a method to directly assess protein system-level properties based on system dynamics and in silico knockouts, regarding to the conceptual term "criticality". Applying the method to E. coli central carbon metabolic system, we found that multiple enzymes including phosphoglycerate kinase, enolase, transketolase-b, etc., had critical roles in the system in terms of both system states and dynamical stability. In contrast, another set of enzymes including glucose-6-phosphate isomerise, pyruvate kinase, phosphoglucomutase, etc., exerted very little influences when deleted. The finding is consistent with experimental characterization of metabolic essentiality and other studies on E. coli gene essentiality and functions. We also found that enzymes could affect distant metabolites or enzymes even greater than a close neighbour and asymmetry in system-level properties of enzymes catalyzing alternative pathways could give rise to local flux compensation. CONCLUSIONS: Our method creates a different angle for evaluating protein criticality to a biological system from the conventional methodologies. Moreover, the method leads to consistent results with experimental references, showing its efficiency in studying protein system-level properties. Besides working on metabolic systems, the application of the method can be extended to other kinds of bio-systems to reveal the constitutive/functional properties of system building blocks.

An improved kinetic model for the acetone-butanol-ethanol pathway of Clostridium acetobutylicum and model-based perturbation analysis.

BACKGROUND: Comprehensive kinetic models of microbial metabolism can enhance the understanding of system dynamics and regulatory mechanisms, which is helpful in optimizing microbial production of industrial chemicals. Clostridium acetobutylicum produces solvents (acetone-butanol-ethanol, ABE) through the ABE pathway. To systematically assess the potential of increased production of solvents, kinetic modeling has been applied to analyze the dynamics of this pathway and make predictive simulations. Up to date, only one kinetic model for C. acetobutylicum supported by experiment has been reported as far as we know. But this model did not integrate the metabolic regulatory effects of transcriptional control and other complex factors. It also left out the information of some key intermediates (e.g. butyryl-phosphate). RESULTS: We have developed an improved kinetic model featured with the incorporation of butyryl-phosphate, inclusion of net effects of complex metabolic regulations, and quantification of endogenous enzyme activity variations caused by these regulations. The simulation results of our model are more consistent with published experimental data than the previous model, especially in terms of reflecting the kinetics of butyryl-phosphate and butyrate. Through parameter perturbation analysis, it was found that butyrate kinase has large and positive influence on butanol production while CoA transferase has negative effect on butanol production, suggesting that butyrate kinase has more efficiency in converting butyrate to butanol than CoA transferase. CONCLUSIONS: Our improved kinetic model of the ABE process has more capacity in approaching real circumstances, providing much more insight in the regulatory mechanisms and potential key points for optimization of solvent productions. Moreover, the modeling strategy can be extended to other biological processes.