Inflammation is a beneficial host response to infection but can contribute to inflammatory disease if unregulated. The TH17 lineage of T helper (TH) cells can cause severe human inflammatory diseases. These cells exhibit both instability (they can cease to express their signature cytokine, IL-17A)1 and plasticity (they can start expressing cytokines typical of other lineages)1,2 upon in vitro re-stimulation. However, technical limitations have prevented the transcriptional profiling of pre- and post-conversion TH17 cells ex vivo during immune responses. Thus, it is unknown whether TH17 cell plasticity merely reflects change in expression of a few cytokines, or if TH17 cells physiologically undergo global genetic reprogramming driving their conversion from one T helper cell type to another, a process known as transdifferentiation3,4. Furthermore, although TH17 cell instability/plasticity has been associated with pathogenicity1,2,5, it is unknown whether this could present a therapeutic opportunity, whereby formerly pathogenic TH17 cells could adopt an anti-inflammatory fate. Here we used two new fate-mapping mouse models to track TH17 cells during immune responses to show that CD4+ T cells that formerly expressed IL-17A go on to acquire an anti-inflammatory phenotype. The transdifferentiation of TH17 into regulatory T cells was illustrated by a change in their signature transcriptional profile and the acquisition of potent regulatory capacity. Comparisons of the transcriptional profiles of pre- and postconversion TH17 cells also revealed a role for canonical TGF-β signalling and consequently for the aryl hydrocarbon receptor (AhR) in conversion. Thus, TH17 cells transdifferentiate into regulatory cells, and contribute to the resolution of inflammation. Our data suggest that TH17 cell instability and plasticity is a therapeutic opportunity for inflammatory diseases.

Summary Inflammation and macrophage foam cells are characteristic features of atherosclerotic lesions, but the mechanisms linking cholesterol accumulation to inflammation and LXR-dependent response pathways are poorly understood. To investigate this relationship, we utilized lipidomic and transcriptomic methods to evaluate the effect of diet and LDL receptor genotype on macrophage foam cell formation within the peritoneal cavities of mice. Foam cell formation was associated with significant changes in hundreds of lipid species and unexpected suppression, rather than activation, of inflammatory gene expression. We provide evidence that regulated accumulation of desmosterol underlies many of the homeostatic responses observed in macrophage foam cells, including activation of LXR target genes, inhibition of SREBP target genes, selective reprogramming of fatty acid metabolism and suppression of inflammatory response genes. These observations suggest that macrophage activation in atherosclerotic lesions results from extrinsic, pro-inflammatory signals generated within the artery wall that suppress homeostatic and anti-inflammatory functions of desmosterol.

Multi-omics data integration is one of the major challenges in the era of precision medicine. Considerable work has been done with the advent of high-throughput studies, which have enabled the data access for downstream analyses. To improve the clinical outcome prediction, a gamut of software tools has been developed. This review outlines the progress done in the field of multi-omics integration and comprehensive tools developed so far in this field. Further, we discuss the integration methods to predict patient survival at the end of the review.

Artificial neural networks (ANN) are computing architectures with many interconnections of simple neural-inspired computing elements, and have been applied to biomedical fields such as imaging analysis and diagnosis. We have developed a new ANN framework called Cox-nnet to predict patient prognosis from high throughput transcriptomics data. In 10 TCGA RNA-Seq data sets, Cox-nnet achieves the same or better predictive accuracy compared to other methods, including Cox-proportional hazards regression (with LASSO, ridge, and mimimax concave penalty), Random Forests Survival and CoxBoost. Cox-nnet also reveals richer biological information, at both the pathway and gene levels. The outputs from the hidden layer node provide an alternative approach for survival-sensitive dimension reduction. In summary, we have developed a new method for accurate and efficient prognosis prediction on high throughput data, with functional biological insights. The source code is freely available at https://github.com/lanagarmire/cox-nnet.

Omics data contain signals from the molecular, physical, and kinetic inter- and intracellular interactions that control biological systems. Matrix factorization (MF) techniques can reveal low-dimensional structure from high-dimensional data that reflect these interactions. These techniques can uncover new biological knowledge from diverse high-throughput omics data in applications ranging from pathway discovery to timecourse analysis. We review exemplary applications of MF for systems-level analyses. We discuss appropriate applications of these methods, their limitations, and focus on the analysis of results to facilitate optimal biological interpretation. The inference of biologically relevant features with MF enables discovery from high-throughput data beyond the limits of current biological knowledge - answering questions from high-dimensional data that we have not yet thought to ask.

It is crucial for researchers to optimize RNA-seq experimental designs for differential expression detection. Currently, the field lacks general methods to estimate power and sample size for RNA-Seq in complex experimental designs, under the assumption of the negative binomial distribution. We simulate RNA-Seq count data based on parameters estimated from six widely different public data sets (including cell line comparison, tissue comparison, and cancer data sets) and calculate the statistical power in paired and unpaired sample experiments. We comprehensively compare five differential expression analysis packages (DESeq, edgeR, DESeq2, sSeq, and EBSeq) and evaluate their performance by power, receiver operator characteristic (ROC) curves, and other metrics including areas under the curve (AUC), Matthews correlation coefficient (MCC), and F-measures. DESeq2 and edgeR tend to give the best performance in general. Increasing sample size or sequencing depth increases power; however, increasing sample size is more potent than sequencing depth to increase power, especially when the sequencing depth reaches 20 million reads. Long intergenic noncoding RNAs (lincRNA) yields lower power relative to the protein coding mRNAs, given their lower expression level in the same RNA-Seq experiment. On the other hand, paired-sample RNA-Seq significantly enhances the statistical power, confirming the importance of considering the multifactor experimental design. Finally, a local optimal power is achievable for a given budget constraint, and the dominant contributing factor is sample size rather than the sequencing depth. In conclusion, we provide a power analysis tool (http://www2.hawaii.edu/~lgarmire/ RNASeqPowerCalculator.htm) that captures the dispersion in the data and can serve as a practical reference under the budget constraint of RNA-Seq experiments.

Implementation of highly sophisticated technologies, such as next-generation sequencing (NGS), into routine clinical practice requires compatibility with common tumor biopsy types, such as formalin-fixed, paraffin-embedded (FFPE) and fine-needle aspiration specimens, and validation metrics for platforms, controls, and data analysis pipelines. In this study, a two-step PCR enrichment workflow was used to assess 540 known cancer-relevant variants in 16 oncogenes for high-depth sequencing in tumor samples on either mature (Illumina GAIIx) or emerging (Ion Torrent PGM) NGS platforms. The results revealed that the background noise of variant detection was elevated approximately twofold in FFPE compared with cell line DNA. Bioinformatic algorithms were optimized to accommodate this background. Variant calls from 38 residual clinical colorectal cancer FFPE specimens and 10 thyroid fine-needle aspiration specimens were compared across multiple cancer genes, resulting in an accuracy of 96.1% (95% CI, 96.1% to 99.3%) compared with Sanger sequencing, and 99.6% (95% CI, 97.9% to 99.9%) compared with an alternative method with an analytical sensitivity of 1% mutation detection. A total of 45 of 48 samples were concordant between NGS platforms across all matched regions, with the three discordant calls each represented at <10% of reads. Consequently, NGS of targeted oncogenes in real-life tumor specimens using distinct platforms addresses unmet needs for unbiased and highly sensitive mutation detection and can accelerate both basic and clinical cancer research.

MicroRNAs (miRs) can regulate many cellular functions, but their roles in regulating responses of vascular endothelial cells (ECs) to mechanical stimuli remain unexplored. We hypothesize that the physiological responses of ECs are regulated by not only mRNA and protein signaling networks, but also expression of the corresponding miRs. EC growth arrest induced by pulsatile shear (PS) flow is an important feature for flow regulation of ECs. miR profiling showed that 21 miRs are differentially expressed (8 up-and 13 downregulated) in response to 24-h PS as compared to static condition (ST). The mRNA expression profile indicates EC growth arrest under 24-h PS. Analysis of differentially expressed miRs yielded 68 predicted mRNA targets that overlapped with results of microarray mRNA profiling. Functional analysis of miR profile indicates that the cell cycle network is highly regulated. The upregulation of miR-23b and miR-27b was found to correlate with the PS-induced EC growth arrest. Inhibition of miR-23b using antagomir-23b oligonucleotide (AM23b) reversed the PSinduced E2F1 reduction and retinoblastoma (Rb) hypophosphorylation and attenuated the PS-induced G1/G0 arrest. Antagomir AM27b regulated E2F1 expression, but did not affect Rb and growth arrest. Our findings indicate that PS suppresses EC proliferation through the regulation of miR-23b and provide insights into the role of miRs in mechanotransduction.cell cycle | shear | bioinformatics | gene regulation | mechanotransduction H emodynamic forces, e.g., stretch and shear stress, act constantly on the vascular endothelial cells (ECs) to modulate EC signaling, gene expression, and physiological functions (1). Atherosclerotic lesions in the arterial tree are found mainly at branch points, where blood flow is disturbed with a limited forwarding direction, but are generally spared at the straight parts of the arterial tree, where the flow is laminar with a large forwarding direction (2). Exposure of ECs to 24 h of steady laminar shear flow at 12 dyn/cm 2 (approximating the hemodynamic force in straight parts of arteries) leads to antiproliferative (3) and antiinflammatory (4) responses. In contrast, ECs exposed to disturbed flow, mimicking the hemodynamic force at branch points, exhibit opposite responses (5, 6). The laminar shear-induced EC growth arrest involves the expression of CDK inhibitors (e.g., p21 cip , p27 kip ), tumor suppressor p53, and retinoblastoma (Rb) hypophosphorylation (3, 7). Whereas there is considerable knowledge on mechanotransduction at protein and mRNA levels, there is little information on the role of microRNAs (miRs) in this process.miRs are small noncoding RNAs (∼21-25 nucleotides) that regulate gene expression by binding to target mRNAs to cause their degradation or translational repression (8). It is estimated that miRs regulate ∼30% of human protein-coding genes. More than 800 miRs have been identified in the human genome and registered in the Sanger miRBase. These small RNAs provide a powerful mechanism for posttranscriptional cont...