Modeling preferential attraction to infected hosts in vector-borne diseases

Vector-borne infectious diseases cause more than 700,000 deaths a year and represent an increasing threat to public health worldwide. Strategies to mitigate the spread of vector-borne diseases can benefit from a thorough understanding of all mechanisms that contribute to viral propagation in human. A recent study showed that Aedes mosquitoes (the vectors for dengue and Zika virus, among others) are preferentially attracted to infected hosts. In order to determine the impact of this factor on viral spread, we built a dedicated agent-based model and parameterized it on dengue fever. We then performed a systematic study of how mosquitoes' preferential attraction for infected hosts affects viral load and persistence of the infection. Our results indicate that even small values of preferential attraction have a dramatic effect on the number of infected individuals and the persistence of the infection in the population. Taken together, our results suggests that interventions aimed at decreasing the preferential attraction of vectors for infected hosts can reduce viral transmission and thus can have public health implications

Coexpression Network Analysis of miRNA-142 Overexpression in Neuronal Cells

MicroRNAs are small noncoding RNA molecules, which are differentially expressed in diverse biological processes and are also involved in the regulation of multiple genes. A number of sites in the 3′ untranslated regions (UTRs) of different mRNAs allow complimentary binding for a microRNA, leading to their posttranscriptional regulation. The miRNA-142 is one of the microRNAs overexpressed in neurons that is found to regulate SIRT1 and MAOA genes. Differential analysis of gene expression data, which is focused on identifying up- or downregulated genes, ignores many relationships between genes affected by miRNA-142 overexpression in a cell. Thus, we applied a correlation network model to identify the coexpressed genes and to study the impact of miRNA-142 overexpression on this network. Combining multiple sources of knowledge is useful to infer meaningful relationships in systems biology. We applied coexpression model on the data obtained from wild type and miR-142 overexpression neuronal cells and integrated miRNA seed sequence mapping information to identify genes greatly affected by this overexpression. Larger differences in the enriched networks revealed that the nervous system development related genes such as TEAD2, PLEKHA6, and POGLUT1 were greatly impacted due to miRNA-142 overexpression

Multiplatform biomarker identification using a data-driven approach enables single-sample classification

Background: High-throughput gene expression profiles have allowed discovery of potential biomarkers enabling early diagnosis, prognosis and developing individualized treatment. However, it remains a challenge to identify a set of reliable and reproducible biomarkers across various gene expression platforms and laboratories for single sample diagnosis and prognosis. We address this need with our Data-Driven Reference (DDR) approach, which employs stably expressed housekeeping genes as references to eliminate platform-specific biases and non-biological variabilities. Results: Our method identifies biomarkers with “built-in” features, and these features can be interpreted consistently regardless of profiling technology, which enable classification of single-sample independent of platforms. Validation with RNA-seq data of blood platelets shows that DDR achieves the superior performance in classification of six different tumor types as well as molecular target statuses (such as MET or HER2-positive, and mutant KRAS, EGFR or PIK3CA) with smaller sets of biomarkers. We demonstrate on the three microarray datasets that our method is capable of identifying robust biomarkers for subgrouping medulloblastoma samples with data perturbation due to different microarray platforms. In addition to identifying the majority of subgroup-specific biomarkers in CodeSet of nanoString, some potential new biomarkers for subgrouping medulloblastoma were detected by our method. Conclusions: In this study, we present a simple, yet powerful data-driven method which contributes significantly to identification of robust cross-platform gene signature for disease classification of single-patient to facilitate precision medicine. In addition, our method provides a new strategy for transcriptome analysis

A novel approach to identify shared fragments in drugs and natural products

Fragment-based approaches have now become an important component of the drug discovery process. At the same time, pharmaceutical chemists are more often turning to the natural world and its extremely large and diverse collection of natural compounds to discover new leads that can potentially be turned into drugs. In this study we introduce and discuss a computational pipeline to automatically extract statistically overrepresented chemical fragments in therapeutic classes, and search for similar fragments in a large database of natural products. By systematically identifying enriched fragments in therapeutic groups, we are able to extract and focus on few fragments that are likely to be active or structurally important as scaffolds. We show that several therapeutic classes (including antibacterial, antineoplastic, and drugs active on the cardiovascular system, among others) have enriched fragments that are also found in many natural compounds. Further, our method is able to detect fragments shared by a drug and a natural product even when the global similarity between the two molecules is generally low. A further development of this computational pipeline is to help predict putative therapeutic activities of natural compounds, and to help identify novel leads for drug discovery

Identifying modular function via edge annotation in gene correlation networks using Gene Ontology search

Correlation networks provide a powerful tool for analyzing large sets of biological information. This method of high-throughput data modeling has important implications in uncovering novel knowledge of cellular function. Previous studies on other types of network modeling (protein-protein interaction networks, metabolomes, etc.) have demonstrated the presence of relationships between network structures and organization of cellular function. Studies with correlation network further confirm the existence of such network structure and biological function relationship. However, correlation networks are typically noisy and the identified network structures, such as clusters, must be further investigated to verify actual cellular function. This is traditionally done using Gene Ontology enrichment of the genes in that cluster. In this study a novel method to identify common cluster functions in correlation networks is proposed, which uses annotations of edges as opposed to the traditional annotation of node analysis. The results obtained using proposed method reveals functional relationships in clusters not visible by the traditional approach

Identifying enriched drug fragments as possible candidates for metabolic engineering

Background: Fragment-based approaches have now become an important component of the drug discovery process. At the same time, pharmaceutical chemists are more often turning to the natural world and its extremely large and diverse collection of natural compounds to discover new leads that can potentially be turned into drugs. In this study we introduce and discuss a computational pipeline to automatically extract statistically overrepresented chemical fragments in therapeutic classes, and search for similar fragments in a large database of natural products. By systematically identifying enriched fragments in therapeutic groups, we are able to extract and focus on few fragments that are likely to be active or structurally important. Results: We show that several therapeutic classes (including antibacterial, antineoplastic, and drugs active on the cardiovascular system, among others) have enriched fragments that are also found in many natural compounds. Further, our method is able to detect fragments shared by a drug and a natural product even when the global similarity between the two molecules is generally low. Conclusions: A further development of this computational pipeline is to help predict putative therapeutic activities of natural compounds, and to help identify novel leads for drug discovery

Comprehensive review of LCA studies in Civil Engineering

This review paper explores the application of Life Cycle Assessment (LCA) within the domain of civil engineering, aiming to provide a comprehensive overview of current research, methodologies, challenges, and future trends. LCA serves as a pivotal tool for assessing the environmental impact of infrastructure projects, yet gaps persist in its integration with socioeconomic dimensions, regional considerations, and dynamic modeling. By analyzing existing literature and scholarly discussions, this review identifies research gaps and proposes directions for enhancing the applicability and effectiveness of LCA in civil engineering. Moreover, it examines future trends such as the integration of advanced technologies, stakeholder engagement, and policy implementation, poised to shape the landscape of LCA practices in the civil engineering sector. Ultimately, this review paper contributes to the understanding of LCA's potential to drive sustainable decision-making in infrastructure development, paving the way for more informed and environmentally conscious practices

On Mining Biological Signals Using Correlation Networks

Correlation networks have been used in biological networks to analyze and model high-throughput biological data, such as gene expression from microarray or RNA-seq assays. Typically in biological network modeling, structures can be mined from these networks that represent biological functions; for example, a cluster of proteins in an interactome can represent a protein complex. In correlation networks built from high-throughput gene expression data, it has often been speculated or even assumed that clusters represent sets of genes that are coregulated. This research aims to validate this concept using network systems biology and data mining by identification of correlation network clusters via multiple clustering approaches and cross-validation of regulatory elements in these clusters via motif finding software. The results show that the majority (81- 100%) of genes in any given cluster will share at least one predicted transcription factor binding site. With this in mind, new regulatory relationships can be proposed using known transcription factors and their binding sites by integrating regulatory information and the network model itself

Bacteria Commonly Associated with Central Nervous System Catheter Infections Elicit Distinct CSF Proteome Signatures

Background: Cerebrospinal fluid (CSF) shunt infection is a common and devastating complication of the treatment of hydrocephalus. Timely and accurate diagnosis is essential as these infections can lead to long-term neurologic consequences including seizures, decreased intelligence quotient (IQ) and impaired school performance in children. Currently the diagnosis of shunt infection relies on bacterial culture; however, culture is not always accurate since these infections are frequently caused by bacteria capable of forming biofilms, such as Staphylococcus epidermidis, Cutibacterium acnes, and Pseudomonas aeruginosa resulting in few planktonic bacteria detectable in the CSF. Therefore, there is a critical need to identify a new rapid, and accurate method for diagnosis of CSF shunt infection with broad bacterial species coverage to improve the long-term outcomes of children suffering from these infections. Methods: To investigate potential biomarkers that would discriminate S. epidermidis, C. acnes and P. aeruginosa central nervous system (CNS) catheter infection we leveraged our previously published rat model of CNS catheter infection to perform serial CSF sampling to characterize the CSF proteome during these infections compared to sterile catheter placement. Results: P. aeruginosa infection demonstrated a far greater number of differentially expressed proteins when compared to S. epidermidis and C. acnes infection and sterile catheters, and these changes persisted throughout the 56-day time course. S. epidermidis demonstrated an intermediate number of differentially expressed proteins, primarily at early time points that dissipated over the course of infection. C. acnes induced the least amount of change in the CSF proteome when compared to the other pathogens. Conclusions: Despite the differences in the CSF proteome with each organism compared to sterile injury, several proteins were common across all bacterial species, especially at day 5 post-infection, which are candidate diagnostic biomarkers

Cross-reactivity influences changes in human influenza A virus and Epstein Barr virus specific CD8 memory T cell receptor alpha and beta repertoires between young and old

Older people have difficulty controlling infection with common viruses such as influenza A virus (IAV), RNA virus which causes recurrent infections due to a high rate of genetic mutation, and Epstein Barr virus (EBV), DNA virus which persists in B cells for life in the 95% of people that become acutely infected. We questioned whether changes in epitope-specific memory CD8 T cell receptor (TCR) repertoires to these two common viruses could occur with increasing age and contribute to waning immunity. We compared CD8 memory TCR alpha and beta repertoires in two HLA-A2+ EBV- and IAV-immune donors, young (Y) and older (O) donors to three immunodominant epitopes known to be cross-reactive, IAV-M158-66 (IAV-M1), EBV-BMLF1280-288 (EBV-BM), and EBV-BRLF1109-117 (EBV-BR). We, therefore, also designed these studies to examine if TCR cross-reactivity could contribute to changes in repertoire with increasing age. TCR high throughput sequencing showed a significant difference in the pattern of TRBV usage between Y and O. However, there were many more differences in AV and AJ usage, between the age groups suggesting that changes in TCRα usage may play a greater role in evolution of the TCR repertoire emphasizing the importance of studying TRAV repertoires. With increasing age there was a preferential retention of TCR for all three epitopes with features in their complementarity-determining region (CDR3) that increased their ease of generation, and their cross-reactive potential. Young and older donors differed in the patterns of AV/AJ and BV/BJ pairings and usage of dominant CDR3 motifs specific to all three epitopes. Both young and older donors had cross-reactive responses between these 3 epitopes, which were unique and differed from the cognate responses having features that suggested they could interact with either ligand. There was an increased tendency for the classic IAV-M1 specific clone BV19-IRSS-JB2.7/AV27-CAGGGSQGNLIF-AJ42 to appear among the cross-reactive clones, suggesting that the dominance of this clone may relate to its cross-reactivity with EBV. These results suggest that although young and older donors retain classic TCR features for each epitope their repertoires are gradually changing with age, maintaining TCRs that are cross-reactive between these two common human viruses, one with recurrent infections and the other a persistent virus which frequently reactivates