Search

Background Atrial fibrillation (AF) increases risk of embolic stroke, and in postoperative patients, increases cost of care. Consequently, ECG screening for AF in high‐risk patients is important but labor‐intensive. Artificial intelligence (AI) may reduce AF detection workload, but AI development presents challenges. Methods and Results We used a novel approach to AI development for AF detection using both surface ECG recordings and atrial epicardial electrograms obtained in postoperative cardiac patients. Atrial electrograms were used only to facilitate establishing true AF for AI development; this permitted the establishment of an AI‐based tool for subsequent AF detection using ECG records alone. A total of 5 million 30‐second epochs from 329 patients were annotated as AF or non‐AF by expert ECG readers for AI training and validation, while 5 million 30‐second epochs from 330 different patients were used for AI testing. AI performance was assessed at the epoch level as well as AF burden at the patient level. AI achieved an area under the receiver operating characteristic curve of 0.932 on validation and 0.953 on testing. At the epoch level, testing results showed means of AF detection sensitivity, specificity, negative predictive value, positive predictive value, and F1 (harmonic mean of positive predictive value and sensitivity) as 0.970, 0.814, 0.976, 0.776, and 0.862, respectively, while the intraclass correlation coefficient for AF burden detection was 0.952. At the patient level, AF burden sensitivity and positive predictivity were 96.2% and 94.5%, respectively. Conclusions Use of both atrial electrograms and surface ECG permitted development of a robust AI‐based approach to postoperative AF recognition and AF burden assessment. This novel tool may enhance detection and management of AF, particularly in patients following operative cardiac surgery.

Abstract Background Childhood adiposity is inversely associated with young adult percent dense breast volume (%DBV) and absolute dense breast volume (ADBV), which could contribute to its protective effect for breast cancer later in life. The objective of this study was to identify metabolites in childhood serum that may mediate the inverse association between childhood adiposity and young adult breast density. Methods Longitudinal data from 182 female participants in the Dietary Intervention Study in Children (DISC) and the DISC 2006 (DISC06) Follow-Up Study were analyzed. Childhood adiposity was assessed by anthropometry at the DISC visit with serum available that occurred closest to menarche and expressed as a body mass index (BMI) z-score. Serum metabolites were measured by untargeted metabolomics using ultra-high-performance liquid chromatography–tandem mass spectrometry. %DBV and ADBV were measured by magnetic resonance imaging at the DISC06 visit when participants were 25–29 years old. Robust mixed effects linear regression was used to identify serum metabolites associated with childhood BMI z-scores and breast density, and the R package mediation was used to quantify mediation. Results Of the 115 metabolites associated with BMI z-scores (FDR

Abstract Background To date, there are no effective treatments for most neurodegenerative diseases. Knowledge graphs can provide comprehensive and semantic representation for heterogeneous data, and have been successfully leveraged in many biomedical applications including drug repurposing. Our objective is to construct a knowledge graph from literature to study the relations between Alzheimer’s disease (AD) and chemicals, drugs and dietary supplements in order to identify opportunities to prevent or delay neurodegenerative progression. We collected biomedical annotations and extracted their relations using SemRep via SemMedDB. We used both a BERT-based classifier and rule-based methods during data preprocessing to exclude noise while preserving most AD-related semantic triples. The 1,672,110 filtered triples were used to train with knowledge graph completion algorithms (i.e., TransE, DistMult, and ComplEx) to predict candidates that might be helpful for AD treatment or prevention. Results Among three knowledge graph completion models, TransE outperformed the other two (MR = 10.53, Hits@1 = 0.28). We leveraged the time-slicing technique to further evaluate the prediction results. We found supporting evidence for most highly ranked candidates predicted by our model which indicates that our approach can inform reliable new knowledge. Conclusion This paper shows that our graph mining model can predict reliable new relationships between AD and other entities (i.e., dietary supplements, chemicals, and drugs). The knowledge graph constructed can facilitate data-driven knowledge discoveries and the generation of novel hypotheses.

Lithium metal batteries suffer from poor (electro)chemical stability of the electrodes during prolonged cycling. Here, the authors report a dual function liquid electrolyte additive to form protective interphases on both electrodes to produce lab-scale high energy lithium metal batteries.

To improve the water reuse efficiency of circular recirculating aquaculture tanks in Recirculating Aquaculture Systems (RAS) this research investigated the effect of inlet pipe deployment distance d (the distance between the stream tube hole and the tank wall) and the inlet pipe deployment angle α (the acute angle between the inlet pipe jetting direction and the nearest tank wall) on the self-cleaning ability of the circular aquaculture tank under dual and single inlet pipe designs. First, waste collection experiments were conducted to figure out the preferable inlet pipe design based on the efficiency of waste collection. Then, flow field measurement experiments were carried out employing the Particle Image Velocimetry (PIV) technique to measure the flow field in the near bottom layer of the tank, and the essential mechanism of the inlet pipe deployment on the waste collection was explored by analyzing the flow field and the corresponding hydrodynamic characteristic indicators, such as the average flow velocity (vavg), the uniformity coefficient of water flow (DU50), and the water resistance coefficient (Ct). The results indicate that the variations of waste movement, flow filed and hydrodynamic indicators are consistent in both designs of dual inlet and single inlet, and merely differ in the absolute value. Based on the comprehensive analysis, the optimal performance of self-cleaning can be obtained at α=0°~10°, 20°~30°, 40°~50° for d=1/2 r, 1/4 r, and 1/50 r, respectively. At the same flow rate condition, the jetting velocity of the inlet pipe in the single inlet pipe design is significantly greater than that in the dual inlet pipe design, and the hydrodynamic characteristics of the waste collection are better. Therefore, in circulating water aquaculture production, it is recommended to design the inlet pipe in a similar aquaculture tank to the single inlet pipe with d=1/4 r, α=20°~30°, thus improving the self-cleaning ability of the aquaculture tank. The study results also provide a reference for adjusting the inlet pipe in circular recirculating aquaculture tank.

Previously we showed the generation of a protein trap library made with the gene-break transposon (GBT) in zebrafish (Danio rerio) that could be used to facilitate novel functional genome annotation towards understanding molecular underpinnings of human diseases (Ichino et al, 2020). Here, we report a significant application of this library for discovering essential genes for heart rhythm disorders such as sick sinus syndrome (SSS). SSS is a group of heart rhythm disorders caused by malfunction of the sinus node, the heart’s primary pacemaker. Partially owing to its aging-associated phenotypic manifestation and low expressivity, molecular mechanisms of SSS remain difficult to decipher. From 609 GBT lines screened, we generated a collection of 35 zebrafish insertional cardiac (ZIC) mutants in which each mutant traps a gene with cardiac expression. We further employed electrocardiographic measurements to screen these 35 ZIC lines and identified three GBT mutants with SSS-like phenotypes. More detailed functional studies on one of the arrhythmogenic mutants, GBT411, in both zebrafish and mouse models unveiled Dnajb6 as a novel SSS causative gene with a unique expression pattern within the subpopulation of sinus node pacemaker cells that partially overlaps with the expression of hyperpolarization activated cyclic nucleotide gated channel 4 (HCN4), supporting heterogeneity of the cardiac pacemaker cells.

Triple negative breast cancer (TNBC) follows a non-random pattern of metastasis to the bone and brain tissue. Prior work has found that brain-seeking breast tumor cells display altered proteomic profiles, leading to alterations in pathways related to cell signaling, cell cycle, metabolism, and extracellular matrix remodeling. Given the unique microenvironmental characteristics of brain and bone tissue, we hypothesized that brain- or bone-seeking TNBC cells may have altered morphologic or migratory phenotypes from each other, or from the parental TNBC cells, as a function of the biochemical or mechanical microenvironment. In this study, we utilized TNBC cells (MDA-MB-231) that were conditioned to metastasize solely to brain (MDA-BR) or bone (MDA-BO) tissue. We quantified characteristics such as cell morphology, migration, and stiffness in response to cues that partially mimic their final metastatic niche. We have shown that MDA-BO cells have a distinct protrusive morphology not found in MDA-P or MDA-BR. Further, MDA-BO cells migrate over a larger area when on a collagen I (abundant in bone tissue) substrate when compared to fibronectin (abundant in brain tissue). However, migration in highly confined environments was similar across the cell types. Modest differences were found in the stiffness of MDA-BR and MDA-BO cells plated on collagen I vs. fibronectin-coated surfaces. Lastly, MDA-BO cells were found to have larger focal adhesion area and density in comparison with the other two cell types. These results initiate a quantitative profile of mechanobiological phenotypes in TNBC, with future impacts aiming to help predict metastatic propensities to organ-specific sites in a clinical setting.

Myocardial infarction (MI) is regarded as a serious ischemic heart disease on a global level. The current study set out to explore the mechanism of the Notch signaling pathway in the regulation of fibrosis remodeling after the occurrence of MI. First, experimental mice were infected with recombination signal binding protein J (RBP-J) shRNA and empty adenovirus vector, followed by the establishment of MI mouse models and detection of cardiac function. After 4 weeks of MI, mice in the sh-RBP-J group were found to exhibit significantly improved cardiac function relative to the sh-NC group. Moreover, knockdown of RBP-J brought about decreased infarct area, promoted cardiac macrophages M2 polarization, reduced cardiac fibrosis, and further decreased transcription and protein expressions of inflammatory factors and fibrosis-related factors. Furthermore, downregulation of cylindromatosis (CYLD) using si-CYLD reversed the results that knockdown of RBP-J inhibited fibrogenesis and the release of inflammatory factors. Altogether, our findings indicated that the blockade of Notch signaling promotes M2 polarization of cardiac macrophages and improves cardiac function by inhibiting the imbalance of fibrotic remodeling after MI.

Myosin heavy chain 7 (MYH7) is a major causative gene for hypertrophic cardiomyopathy, but the affected signaling pathways and therapeutics remain elusive. In this research, we identified ventricle myosin heavy chain like (vmhcl) as a zebrafish homolog of human MYH7, and we generated vmhcl frameshift mutants. We noted vmhcl-based embryonic cardiac dysfunction (VEC) in the vmhcl homozygous mutants and vmhcl-based adult cardiomyopathy (VAC) phenotypes in the vmhcl heterozygous mutants. Using the VEC model, we assessed 7 known cardiomyopathy signaling pathways pharmacologically and 11 candidate genes genetically via CRISPR/Cas9 genome editing technology based on microhomology-mediated end joining (MMEJ). Both studies converged on therapeutic benefits of mTOR or mitogen-activated protein kinase (MAPK) inhibition of VEC. While mTOR inhibition rescued the enlarged nuclear size of cardiomyocytes, MAPK inhibition restored the prolonged cell shape in the VEC model. The therapeutic effects of mTOR and MAPK inhibition were later validated in the VAC model. Together, vmhcl/myh7 loss of function is sufficient to induce cardiomyopathy in zebrafish. The VEC and VAC models in zebrafish are amenable to both efficient genetic and chemical genetic tools, offering a rapid in vivo platform for discovering candidate signaling pathways of MYH7 cardiomyopathy.

Alpelisib, an oral selective and small-molecule phosphoinositide 3-kinase inhibitor, was lately approved in the United States to treat breast cancer. A sensitive method to quantify alpelisib levels in rat plasma on the basis of ultra-performance liquid chromatography–tandem mass spectrometry technique was established and validated, which was successfully employed to explore the effects of CYP3A4 inhibitors on alpelisib pharmacokinetics in rats. A C18 column named Acquity UPLC BEH C18 was applied to achieve the separation of alpelisib and internal standard duvelisib after protein precipitation with acetonitrile. The mobile phase in this study had two components, namely, acetonitrile and water having 0.1% formic acid, and a program with gradient elution method was used at a flow rate of 0.40 ml/min. Mass spectrometry in a positive multiple reaction monitoring mode was operated. In the scope of 1–5,000 ng/ml, this assay had excellent linearity. Our newly developed assay was verified in all aspects of bioanalytical method validation, involving lower limit of quantification, selectivity, accuracy and precision, calibration curve, extraction recovery, matrix effect, and stability. Then, this assay was used to detect the plasma levels of alpelisib from a drug-drug interaction investigation, where ketoconazole remarkably increased the plasma concentration of alpelisib and changed alpelisib pharmacokinetics more than itraconazole. This study will help better understand the pharmacokinetic properties of alpelisib, and further clinical studies should be done to confirm this result in patients.

A large-eddy simulation analysis technique is introduced in this paper to determine the interference effect of chamfered square cylinders, which is crucial to predict the impact of wind pressure and load on chamfered high-rise buildings. Based on the grid convergence analysis of the model and the validation of its accuracy, the aerodynamic interference effect, including the flow field distribution of parallel and tandem square cylinders with different spacing ratios has been compared and analyzed. The influence regulation and formation mechanism of the wind pressure interference effect have been explored. For side-by-side chamfered corners square cylinders, the average drag coefficient mainly shows an amplification effect, and the fluctuating lift coefficient mainly shows a reduction effect. When B/L = 1.5, the interference factor of the disturbed square cylinder reaches a maximum, which is located at the back flow field on the adjacent side. There is a clear critical spacing ratio for tandem double-cut square cylinders. When the spacing ratio exceeds the critical value, significant changes are observed in the aerodynamic performance. These include wind pressure distribution, non-Gaussian characteristics, and the interference effects of structures.

Abstract Background Vaccination has been one of the most successful public health interventions to date, and the U.S. FDA/CDC Vaccine Adverse Event Reporting System (VAERS) currently contains more than 500,000 reports for post-vaccination adverse events that occur after the administration of vaccines licensed in the United States. The VAERS dataset is huge, contains very large dimension nominal variables, and is complex due to multiple listing of vaccines and adverse symptoms in a single report. So far there has not been any statistical analysis conducted in attempting to identify the cross-board patterns on how all reported adverse symptoms are related to the vaccines. Methods For studies of the relationship between vaccines and reported adverse events, we consider a partial VAERS dataset which includes all reports filed over a period of 24 years between 1990-2013. We propose a neighboring method to process this dataset for dealing with the complications caused by multiple listing of vaccines and adverse symptoms in a single report. Then, the combined approaches based on our neighboring method and novel utilization of data visualization techniques are employed to analyze the large dimension dataset for characterization of the cross-board patterns of the relations between all reported vaccines and events. Results The results of our analysis indicate that those events or symptoms with overall high occurrence frequencies are positively correlated, and those most frequently occurred adverse symptoms are mostly uncorrelated or negatively correlated under different bacteria vaccines, but they are in many cases positively correlated under different virus vaccines, especially under flu vaccines. No particular patterns are shown under live vs. inactive vaccines. Conclusions This article identifies certain cross-board patterns of the relationship between the vaccines and the reported adverse events or symptoms. This helps for better understanding the VAERS data, and provides a useful starting point for the development of statistical models and procedures to further analyze the VAERS data.

Abstract Background We assessed the stability of BAFF, interferon, plasma cell and LDG neutrophil gene expression signatures over time, and whether changes in expression coincided with changes in SLE disease activity. Methods Two hundred forty-three patients with SLE were evaluated for disease activity, serological parameters and peripheral blood gene signatures in clinic visits (2 or more per patient) that occurred between 2009 and 2012. Levels of the BAFF gene transcript, plasma cell signature, Interferon (IFN) signature and the low density granulocytes (LDG)-associated neutrophil gene signature were assessed in PAX-gene-preserved peripheral blood by global microarray. The stability of repeated measures of gene expression was quantified using intra-class correlation coefficients. SLE disease activity was measured using the Physicians Global Assessment and the SELENA-SLEDAI index and its components. Using a mixed effects regression model we assessed: 1) the association between a patient’s average gene signature expression over time and disease activity, and 2) the association between a patient’s changes in gene expression over time and changes in disease activity. Results Gene expression signatures showed more within-person stability than systolic blood pressure. The IFN signature exhibited the most stability. Patients with high levels of BAFF and IFN transcripts tended to have significantly higher levels of musculoskeletal disease, skin disease, anti-dsDNA, and erythrocyte sedimentation rate, and lower levels of complement. However, changes in BAFF or IFN gene signatures were not associated with changes in disease activity. Similar associations were seen between the LDG gene signature and disease activity. However, when LDG increased, complement tended to increase. Patients with high levels of plasma cell gene signature tended to have higher levels of anti-dsDNA and lower levels of complement. However, unlike the other gene signatures, changes in plasma cell gene signature significantly coincided with changes in anti-dsDNA and complement. Conclusions The gene expression signatures were relatively stable within patients over time. BAFF and interferon gene expression were markers of patients with generally higher disease activity, but changes in these gene signatures did not coincide with changes in disease activity. Plasma Cell gene signature expression tracked with the traditional SLE serologic markers of anti-dsDNA and complement.

Prostate cancer (PCa) relies in part on AR-signaling for disease development and progression. Earlier, we developed drug candidate galeterone, which advanced through phase 2-clinical trials in treating castration-resistant PCa (CRPC). Subsequently, we designed, synthesized, and evaluated next-generation galeterone-analogs including VNPP433-3β which is potently efficacious against pre-clinical models of PCa. This study describes the mechanism of action of VNPP433-3β that promotes degradation of full-length AR (fAR) and its splice variant AR-V7 besides depleting MNK1/2 in in vitro and in vivo CRPC models that stably overexpresses fAR. VNPP433-3β directly engages AR within the cell and promotes proteasomal degradation of fAR and its splice variant AR-V7 by enhancing the interaction of AR with E3 ligases MDM2/CHIP but disrupting AR-HSP90 binding. Next, VNPP433-3β decreases phosphorylation of 4EBP1 and abates binding of eIF4E and eIF4G to 5′ cap of mRNA by depleting MNK1/2 with consequent depletion of phosphorylated eIF4E. Finally, RNA-seq demonstrates modulation of multiple pathways that synergistically contribute to PCa inhibition. Therefore, VNPP433-3β exerts its antitumor effect by imposing 1) transcriptional regulation of AR and AR-responsive oncogenes 2) translational regulation by disrupting mRNA-5′cap-dependent translation initiation, 3) reducing AR half-life through enhanced proteasomal degradation in vitro and AR-overexpressing tumor xenografts in vivo.

Introduction. Purkinje system and false tendons (FTs) are related to ventricular arrhythmia, but the association between Purkinje fibers and FTs is not clear. This study investigated the associations of anatomical and electrophysiological characteristics between Purkinje fibers and FTs. Methods and Results. We optimized the protocol of Lugol’s iodine solution staining of Purkinje fibers to study the anatomical structure and originated a novel electrophysiological mapping method, named the direct visual mapping (DVM) method, to study the electrophysiological characteristics. By using the above-mentioned innovations in 12 dogs, we found the following. (1) There was no Purkinje fiber found 0.5 cm–1.0 cm below the valve annulus or on the leaflets or chordae tendineae of the mitral valve or adjacent to the top 1/3 of the papillary muscle. (2) Purkinje fibers existed in all FTs, including smaller and tiny FTs. (3) The Purkinje fibers contained in the FTs extended from the proximal to the distal end, and their electrophysiological characteristics were similar to the fibers on the endocardium, including anterograde, retrograde, and decremental conduction and automaticity. Conclusions. Purkinje fibers are commonly found in FTs. The electrophysiological characteristics of the Purkinje fibers contained in FTs are similar to the fibers on the endocardium. FTs might have an anatomical and electrophysiological basis for ventricular arrhythmia.

Abstract Background Vaccine has been one of the most successful public health interventions to date. However, vaccines are pharmaceutical products that carry risks so that many adverse events (AEs) are reported after receiving vaccines. Traditional adverse event reporting systems suffer from several crucial challenges including poor timeliness. This motivates increasing social media-based detection systems, which demonstrate successful capability to capture timely and prevalent disease information. Despite these advantages, social media-based AE detection suffers from serious challenges such as labor-intensive labeling and class imbalance of the training data. Results To tackle both challenges from traditional reporting systems and social media, we exploit their complementary strength and develop a combinatorial classification approach by integrating Twitter data and the Vaccine Adverse Event Reporting System (VAERS) information aiming to identify potential AEs after influenza vaccine. Specifically, we combine formal reports which have accurately predefined labels with social media data to reduce the cost of manual labeling; in order to combat the class imbalance problem, a max-rule based multi-instance learning method is proposed to bias positive users. Various experiments were conducted to validate our model compared with other baselines. We observed that (1) multi-instance learning methods outperformed baselines when only Twitter data were used; (2) formal reports helped improve the performance metrics of our multi-instance learning methods consistently while affecting the performance of other baselines negatively; (3) the effect of formal reports was more obvious when the training size was smaller. Case studies show that our model labeled users and tweets accurately. Conclusions We have developed a framework to detect vaccine AEs by combining formal reports with social media data. We demonstrate the power of formal reports on the performance improvement of AE detection when the amount of social media data was small. Various experiments and case studies show the effectiveness of our model.

Abstract Long intergenic noncoding RNAs (lincRNAs) are increasingly recognized as potential key regulators of heart development and related diseases, but their identities and functions remain elusive. In this study, we sought to identify and characterize the cardiac lincRNA transcriptome in the experimentally accessible zebrafish model by integrating bioinformatics analysis with experimental validation. By conducting genome-wide RNA sequencing profiling of zebrafish embryonic hearts, adult hearts, and adult muscle, we generated a high-confidence set of 813 cardiac lincRNA transcripts, 423 of which are novel. Among these lincRNAs, 564 are expressed in the embryonic heart, and 730 are expressed in the adult heart, including 2 novel lincRNAs, TCONS_00000891 and TCONS_00028686, which exhibit cardiac-enriched expression patterns in adult heart. Using a method similar to a fetal gene program, we identified 51 lincRNAs with differential expression patterns between embryonic and adult hearts, among which TCONS_00009015 responded to doxorubicin-induced cardiac stress. In summary, our genome-wide systematic identification and characterization of cardiac lincRNAs lays the foundation for future studies in this vertebrate model to elucidate crucial roles for cardiac lincRNAs during heart development and cardiac diseases.

The adult zebrafish is an emerging vertebrate model for studying human cardiomyopathies; however, whether the simple zebrafish heart can model different subtypes of cardiomyopathies, such as dilated cardiomyopathy (DCM), remains elusive. Here, we generated and characterized an inherited DCM model in adult zebrafish and used this model to search for therapeutic strategies. We employed transcription activator-like effector nuclease (TALEN) genome editing technology to generate frame-shift mutants for the zebrafish ortholog of human BCL2-associated athanogene 3 (BAG3), an established DCM-causative gene. As in mammals, the zebrafish bag3 homozygous mutant (bag3e2/e2) exhibited aberrant proteostasis, as indicated by impaired autophagy flux and elevated ubiquitinated protein aggregation. Through comprehensive phenotyping analysis of the mutant, we identified phenotypic traits that resembled DCM phenotypes in mammals, including cardiac chamber enlargement, reduced ejection fraction characterized by increased end-systolic volume/body weight (ESV/BW), and reduced contractile myofibril activation kinetics. Nonbiased transcriptome analysis identified the hyperactivation of the mechanistic target of rapamycin (mTOR) signaling in bag3e2/e2 mutant hearts. Further genetic studies showed that mtorxu015/+, an mTOR haploinsufficiency mutant, repaired abnormal proteostasis, improved cardiac function and rescued the survival of the bag3e2/e2 mutant. This study established the bag3e2/e2 mutant as a DCM model in adult zebrafish and suggested mtor as a candidate therapeutic target gene for BAG3 cardiomyopathy.

Hypothermia is widely used in neurosurgery and cardiac surgeries. However, little is known about the underlying molecular mechanisms. We previously reported that the transcriptome responses of cardiomyocyte exposed to hypothermia, “The transcriptome responses of cardiomyocyte exposed to hypothermia” [4]. Herein, we provide the hypothermia inhibited proliferation of cardiomyocyte cells in vitro and the details of transcription factors in regulation of differentially expressed genes. Keywords: Hypothermia, Cardiomyocyte, Transcriptome

Here we provide evidence that RBBP4 modulates temozolomide (TMZ) sensitivity through coordinate regulation of two key DNA repair genes critical for recovery from TMZ-induced DNA damage: methylguanine-DNA-methyltransferase (MGMT) and RAD51. Disruption of RBBP4 enhanced TMZ sensitivity, induced synthetic lethality to PARP inhibition, and increased DNA damage signaling in response to TMZ. Moreover, RBBP4 silencing enhanced TMZ-induced H2AX phosphorylation and apoptosis in GBM cells. Intriguingly, RBBP4 knockdown suppressed the expression of MGMT, RAD51, and other genes in association with decreased promoter H3K9 acetylation (H3K9Ac) and increased H3K9 tri-methylation (H3K9me3). Consistent with these data, RBBP4 interacts with CBP/p300 to form a chromatin-modifying complex that binds within the promoter of MGMT, RAD51, and perhaps other genes. Globally, RBBP4 positively and negatively regulates genes involved in critical cellular functions including tumorigenesis. The RBBP4/CBP/p300 complex may provide an interesting target for developing therapy-sensitizing strategies for GBM and other tumors.

Recent scientific advances have accumulated a tremendous amount of biomedical knowledge providing novel insights into the relationship between molecular and cellular processes and diseases. Literature mining is one of the commonly used methods to retrieve and extract information from scientific publications for understanding these associations. However, due to large data volume and complicated associations with noises, the interpretability of such association data for semantic knowledge discovery is challenging. In this study, we describe an integrative computational framework aiming to expedite the discovery of latent disease mechanisms by dissecting 146,245 disease-gene associations from over 25 million of PubMed indexed articles. We take advantage of both Latent Dirichlet Allocation (LDA) modeling and network-based analysis for their capabilities of detecting latent associations and reducing noises for large volume data respectively. Our results demonstrate that (1) the LDA-based modeling is able to group similar diseases into disease topics; (2) the disease-specific association networks follow the scale-free network property; (3) certain subnetwork patterns were enriched in the disease-specific association networks; and (4) genes were enriched in topic-specific biological processes. Our approach offers promising opportunities for latent disease-gene knowledge discovery in biomedical research.

Cancer is a complex and heterogeneous disease. Genetic methods have uncovered thousands of complex tissue-specific mutation-induced effects and identified multiple disease gene targets. Important associations between cancer and other biological entities (eg, genes and drugs) in cancer network, however, are usually scattered in biomedical publications. Systematic analyses of these cancer-specific associations can help highlight the hidden associations between different cancer types and related genes/drugs. In this paper, we proposed a novel network-based computational framework to identify statistically over-expressed subnetwork patterns called network motifs (NMs) in an integrated cancer-specific drug–disease–gene network extracted from Semantic MEDLINE, a database containing extracted associations from MEDLINE abstracts. Eight significant NMs were identified and considered as the backbone of the cancer association network. Each NM corresponds to specific biological meanings. We demonstrated that such approaches will facilitate the formulization of novel cancer research hypotheses, which is critical for translational medicine research and personalized medicine in cancer.

OBJECTIVES: The sequencing by the PolyA selection is the most common approach for library preparation. With limited amount or degraded RNA, alternative protocols such as the NuGEN have been developed. However, it is not yet clear how the different library preparations affect the downstream analyses of the broad applications of RNA sequencing. METHODS AND MATERIALS: Eight human mammary epithelial cell (HMEC) lines with high quality RNA were sequenced by Illumina's mRNA-Seq PolyA selection and NuGEN ENCORE library preparation. The following analyses and comparisons were conducted: 1) the numbers of genes captured by each protocol; 2) the impact of protocols on differentially expressed gene detection between biological replicates; 3) expressed single nucleotide variant (SNV) detection; 4) non-coding RNAs, particularly lincRNA detection; and 5) intragenic gene expression. RESULTS: Sequences from the NuGEN protocol had lower (75%) alignment rate than the PolyA (over 90%). The NuGEN protocol detected fewer genes (12-20% less) with a significant portion of reads mapped to non-coding regions. A large number of genes were differentially detected between the two protocols. About 17-20% of the differentially expressed genes between biological replicates were commonly detected between the two protocols. Significantly higher numbers of SNVs (5-6 times) were detected in the NuGEN samples, which were largely from intragenic and intergenic regions. The NuGEN captured fewer exons (25% less) and had higher base level coverage variance. While 6.3% of reads were mapped to intragenic regions in the PolyA samples, the percentages were much higher (20-25%) for the NuGEN samples. The NuGEN protocol did not detect more known non-coding RNAs such as lincRNAs, but targeted small and "novel" lincRNAs. CONCLUSION: Different library preparations can have significant impacts on downstream analysis and interpretation of RNA-seq data. The NuGEN provides an alternative for limited or degraded RNA but it has limitations for some RNA-seq applications.

Proinflammatory cytokines secreted from microglia are known to induce a secondary immune response in astrocytes leading to an inflammatory loop. Cytokines also interfere with neurogenesis during aging and in neurodegenerative diseases. The present study examined the mechanism of induction of inflammatory mediators at the transcriptional level in human differentiated neuroprogenitor cells (NPCs). Interleukin-1β (IL-1β) and tumor necrosis factor-α (TNF-α) induced the expression of cytokines and chemokines in differentiated human NPCs as shown by an immune pathway-specific array. Network motif (NM) analysis of these genes revealed 118 three-node NMs, suggesting complex interactions between inflammatory mediators and transcription factors. Immunofluorescent staining showed increases in the levels of IL-8 and CXCL10 proteins in neurons and glial cells. Findings from Taqman low density array suggested the synergistic actions of IL-1β and TNF-α in the induction of a majority of inflammatory genes by a mechanism involving NF-kB and STAT-1. Nuclear localization of these transcription factors in differentiated NPCs was observed following exposure to IL-1α and TNF-α. Further studies on CXCL10, a chemokine known to be elevated in the Alzheimer's brain, showed that TNF-α is a stronger inducer of CXCL10 promoter when compared to IL-1β. The synergy between these cytokines was lost when ISRE or kB elements in CXCL10 promoter were mutated. Our findings suggest that the activation of inflammatory pathways in neurons and astrocytes through transcription factors including NF-kB and STAT-1 play important roles in neuroglial interactions and in sustaining the vicious cycle of inflammatory response.

Precise regulation of the cell cycle is crucial to the growth and development of all organisms. Understanding the regulatory mechanism of the cell cycle is crucial to unraveling many complicated diseases, most notably cancer. Multiple sources of biological data are available to study the dynamic interactions among many genes that are related to the cancer cell cycle. Integrating these informative and complementary data sources can help to infer a mutually consistent gene transcriptional regulatory network with strong similarity to the underlying gene regulatory relationships in cancer cells.We propose an integrative framework that infers gene regulatory modules from the cell cycle of cancer cells by incorporating multiple sources of biological data, including gene expression profiles, gene ontology, and molecular interaction. Among 846 human genes with putative roles in cell cycle regulation, we identified 46 transcription factors and 39 gene ontology groups. We reconstructed regulatory modules to infer the underlying regulatory relationships. Four regulatory network motifs were identified from the interaction network. The relationship between each transcription factor and predicted target gene groups was examined by training a recurrent neural network whose topology mimics the network motif(s) to which the transcription factor was assigned. Inferred network motifs related to eight well-known cell cycle genes were confirmed by gene set enrichment analysis, binding site enrichment analysis, and comparison with previously published experimental results.We established a robust method that can accurately infer underlying relationships between a given transcription factor and its downstream target genes by integrating different layers of biological data. Our method could also be beneficial to biologists for predicting the components of regulatory modules in which any candidate gene is involved. Such predictions can then be used to design a more streamlined experimental approach for biological validation. Understanding the dynamics of these modules will shed light on the processes that occur in cancer cells resulting from errors in cell cycle regulation.