Elucidation of dynamic microRNA regulations in cancer progression using integrative machine learning.

MOTIVATION: Empowered by advanced genomics discovery tools, recent biomedical research has produced a massive amount of genomic data on (post-)transcriptional regulations related to transcription factors, microRNAs, long non-coding RNAs, epigenetic modifications and genetic variations. Computational modeling, as an essential research method, has generated promising testable quantitative models that represent complex interplay among different gene regulatory mechanisms based on these data in many biological systems. However, given the dynamic changes of interactome in chaotic systems such as cancers, and the dramatic growth of heterogeneous data on this topic, such promise has encountered unprecedented challenges in terms of model complexity and scalability. In this study, we introduce a new integrative machine learning approach that can infer multifaceted gene regulations in cancers with a particular focus on microRNA regulation. In addition to new strategies for data integration and graphical model fusion, a supervised deep learning model was integrated to identify conditional microRNA-mRNA interactions across different cancer stages. RESULTS: In a case study of human breast cancer, we have identified distinct gene regulatory networks associated with four progressive stages. The subsequent functional analysis focusing on microRNA-mediated dysregulation across stages has revealed significant changes in major cancer hallmarks, as well as novel pathological signaling and metabolic processes, which shed light on microRNAs' regulatory roles in breast cancer progression. We believe this integrative model can be a robust and effective discovery tool to understand key regulatory characteristics in complex biological systems. AVAILABILITY: http://sbbi-panda.unl.edu/pin/.

Bayesian network prior: network analysis of biological data using external knowledge.

MOTIVATION: Reverse engineering GI networks from experimental data is a challenging task due to the complex nature of the networks and the noise inherent in the data. One way to overcome these hurdles would be incorporating the vast amounts of external biological knowledge when building interaction networks. We propose a framework where GI networks are learned from experimental data using Bayesian networks (BNs) and the incorporation of external knowledge is also done via a BN that we call Bayesian Network Prior (BNP). BNP depicts the relation between various evidence types that contribute to the event 'gene interaction' and is used to calculate the probability of a candidate graph (G) in the structure learning process. RESULTS: Our simulation results on synthetic, simulated and real biological data show that the proposed approach can identify the underlying interaction network with high accuracy even when the prior information is distorted and outperforms existing methods. AVAILABILITY: Accompanying BNP software package is freely available for academic use at http://bioe.bilgi.edu.tr/BNP. CONTACT: hasan.otu@bilgi.edu.tr SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Metagenomic analysis of the microbial community in kefir grains.

Kefir grains as a probiotic have been subject to microbial community identification using culture-dependent and independent methods that target specific strains in the community, or that are based on limited 16S rRNA analysis. We performed whole genome shotgun pyrosequencing using two Turkish Kefir grains. Sequencing generated 3,682,455 high quality reads for a total of ∼1.6 Gbp of data assembled into 6151 contigs with a total length of ∼24 Mbp. Species identification mapped 88.16% and 93.81% of the reads rendering 4 Mpb of assembly that did not show any homology to known bacterial sequences. Identified communities in the two grains showed high concordance where Lactobacillus was the most abundant genus with a mapped abundance of 99.42% and 99.79%. This genus was dominantly represented by three species Lactobacillus kefiranofaciens, Lactobacillus buchneri and Lactobacillus helveticus with a total mapped abundance of 97.63% and 98.74%. We compared and verified our findings with 16S pyrosequencing and model based 16S data analysis. Our results suggest that microbial community profiling using whole genome shotgun data is feasible, can identify novel species data, and has the potential to generate a more accurate and detailed assessment of the underlying bacterial community, especially for low abundance species.

Small Extracellular Vesicles in Milk Cross the Blood-Brain Barrier in Murine Cerebral Cortex Endothelial Cells and Promote Dendritic Complexity in the Hippocampus and Brain Function in C57BL/6J Mice.

Human milk contains large amounts of small extracellular vesicles (sEVs) and their microRNA cargos, whereas infant formulas contain only trace amounts of sEVs and microRNAs. We assessed the transport of sEVs across the blood-brain barrier (BBB) and sEV accumulation in distinct regions of the brain in brain endothelial cells and suckling mice. We further assessed sEV-dependent gene expression profiles and effects on the dendritic complexity of hippocampal granule cells and phenotypes of EV depletion in neonate, juvenile and adult mice. The transfer of sEVs across the BBB was assessed by using fluorophore-labeled bovine sEVs in brain endothelial bEnd.3 monolayers and dual chamber systems, and in wild-type newborn pups fostered to sEV and cargo tracking (ECT) dams that express sEVs labeled with a CD63-eGFP fusion protein for subsequent analysis by serial two-photon tomography and staining with anti-eGFP antibodies. Effects of EVs on gene expression and dendritic architecture of granule cells was analyzed in hippocampi from juvenile mice fed sEV and RNA-depleted (ERD) and sEV and RNA-sufficient (ERS) diets by using RNA-sequencing analysis and Golgi-Cox staining followed by integrated neuronal tracing and morphological analysis of neuronal dendrites, respectively. Spatial learning and severity of kainic acid-induced seizures were assessed in mice fed ERD and ERS diets. bEnd.3 cells internalized sEVs by using a saturable transport mechanism and secreted miR-34a across the basal membrane. sEVs penetrated the entire brain in fostering experiments; major regions of accumulation included the hippocampus, cortex and cerebellum. Two hundred ninety-five genes were differentially expressed in hippocampi from mice fed ERD and ERS diets; high-confidence gene networks included pathways implicated in axon guidance and calcium signaling. Juvenile pups fed the ERD diet had reduced dendritic complexity of dentate granule cells in the hippocampus, scored nine-fold lower in the Barnes maze test of spatial learning and memory, and the severity of seizures was 5-fold higher following kainic acid administration in adult mice fed the ERD diet compared to mice fed the ERS diet. We conclude that sEVs cross the BBB and contribute toward optimal neuronal development, spatial learning and memory, and resistance to kainic acid-induced seizures in mice.

Elucidation of molecular links between obesity and cancer through microRNA regulation.

BACKGROUND: Obesity contributes to high cancer risk in humans and the mechanistic links between these two pathologies are not yet understood. Recent emerging evidence has associated obesity and cancer with metabolic abnormalities and inflammation where microRNA regulation has a strong implication. METHODS: In this study, we have developed an integrated framework to unravel obesity-cancer linkage from a microRNA regulation perspective. Different from traditional means of identifying static microRNA targets based on sequence and structure properties, our approach focused on the discovery of context-dependent microRNA-mRNA interactions that are potentially associated with disease progression via large-scale genomic analysis. Specifically, a meta-regression analysis and the integration of multi-omics information from obesity and cancers were presented to investigate the microRNA regulation in a dynamic and systematic manner. RESULTS: Our analysis has identified a total number of 2,143 unique microRNA-gene interactions in obesity and seven types of cancer. Common interactions in obesity and obesity-associated cancers are found to regulate genes in key metabolic processes such as fatty acid and arachidonic acid metabolism and various signaling pathways related to cell growth and inflammation. Additionally, modulated co-regulations among microRNAs targeting the same functional processes were reflected through the analysis. CONCLUSION: We demonstrated the statistical modeling of microRNA-mediated gene regulation can facilitate the association study between obesity and cancer. The entire framework provides a powerful tool to understand multifaceted gene regulation in complex human diseases that can be generalized in other biomedical applications.

Distinct and Shared Determinants of Cardiomyocyte Contractility in Multi-Lineage Competent Ethnically Diverse Human iPSCs.

The realization of personalized medicine through human induced pluripotent stem cell (iPSC) technology can be advanced by transcriptomics, epigenomics, and bioinformatics that inform on genetic pathways directing tissue development and function. When possible, population diversity should be included in new studies as resources become available. Previously we derived replicate iPSC lines of African American, Hispanic-Latino and Asian self-designated ethnically diverse (ED) origins with normal karyotype, verified teratoma formation, pluripotency biomarkers, and tri-lineage in vitro commitment. Here we perform bioinformatics of RNA-Seq and ChIP-seq pluripotency data sets for two replicate Asian and Hispanic-Latino ED-iPSC lines that reveal differences in generation of contractile cardiomyocytes but similar and robust differentiation to multiple neural, pancreatic, and smooth muscle cell types. We identify shared and distinct genes and contributing pathways in the replicate ED-iPSC lines to enhance our ability to understand how reprogramming to iPSC impacts genes and pathways contributing to cardiomyocyte contractility potential.

Detecting Microbial Dysbiosis Associated with Pediatric Crohn Disease Despite the High Variability of the Gut Microbiota.

The relationship between the host and its microbiota is challenging to understand because both microbial communities and their environments are highly variable. We have developed a set of techniques based on population dynamics and information theory to address this challenge. These methods identify additional bacterial taxa associated with pediatric Crohn disease and can detect significant changes in microbial communities with fewer samples than previous statistical approaches required. We have also substantially improved the accuracy of the diagnosis based on the microbiota from stool samples, and we found that the ecological niche of a microbe predicts its role in Crohn disease. Bacteria typically residing in the lumen of healthy individuals decrease in disease, whereas bacteria typically residing on the mucosa of healthy individuals increase in disease. Our results also show that the associations with Crohn disease are evolutionarily conserved and provide a mutual information-based method to depict dysbiosis.

Objective functions.

Multiple sequence alignment involves alignment of more than two sequences and is an NP-complete problem. Therefore, heuristic algorithms that use different criteria to find an approximation to the optimal solution are employed. At the heart of these approaches lie the scoring and objective functions that a given algorithm uses to compare competing solutions in constructing a multiple sequence alignment. These objective functions are often motivated by the biological paradigms that govern functional similarities and evolutionary relations. Most existing approaches utilize a progressive process where the final alignment is constructed sequentially by adding new sequences into an existing multiple sequence alignment matrix, which is dynamically updated. In doing this, the core scoring function to assess accuracies of pairwise alignments generally remains the same, while the objective functions used in intermediary steps differ. Nevertheless, the overall assessment of the final multiple sequence alignment is generally calculated by an extension of pairwise scorings. In this chapter, we explore different scoring and objective functions used in calculating the accuracy and optimization of a multiple sequence alignment and provide utilization of these criteria in popularly used multiple sequence alignment algorithms.

Whole genome sequence of a Turkish individual.

Although whole human genome sequencing can be done with readily available technical and financial resources, the need for detailed analyses of genomes of certain populations still exists. Here we present, for the first time, sequencing and analysis of a Turkish human genome. We have performed 35x coverage using paired-end sequencing, where over 95% of sequencing reads are mapped to the reference genome covering more than 99% of the bases. The assembly of unmapped reads rendered 11,654 contigs, 2,168 of which did not reveal any homology to known sequences, resulting in ∼1 Mbp of unmapped sequence. Single nucleotide polymorphism (SNP) discovery resulted in 3,537,794 SNP calls with 29,184 SNPs identified in coding regions, where 106 were nonsense and 259 were categorized as having a high-impact effect. The homo/hetero zygosity (1,415,123∶2,122,671 or 1∶1.5) and transition/transversion ratios (2,383,204∶1,154,590 or 2.06∶1) were within expected limits. Of the identified SNPs, 480,396 were potentially novel with 2,925 in coding regions, including 48 nonsense and 95 high-impact SNPs. Functional analysis of novel high-impact SNPs revealed various interaction networks, notably involving hereditary and neurological disorders or diseases. Assembly results indicated 713,640 indels (1∶1.09 insertion/deletion ratio), ranging from -52 bp to 34 bp in length and causing about 180 codon insertion/deletions and 246 frame shifts. Using paired-end- and read-depth-based methods, we discovered 9,109 structural variants and compared our variant findings with other populations. Our results suggest that whole genome sequencing is a valuable tool for understanding variations in the human genome across different populations. Detailed analyses of genomes of diverse origins greatly benefits research in genetics and medicine and should be conducted on a larger scale.