Controlling gene expression with deep generative design of regulatory DNA.

Design of de novo synthetic regulatory DNA is a promising avenue to control gene expression in biotechnology and medicine. Using mutagenesis typically requires screening sizable random DNA libraries, which limits the designs to span merely a short section of the promoter and restricts their control of gene expression. Here, we prototype a deep learning strategy based on generative adversarial networks (GAN) by learning directly from genomic and transcriptomic data. Our ExpressionGAN can traverse the entire regulatory sequence-expression landscape in a gene-specific manner, generating regulatory DNA with prespecified target mRNA levels spanning the whole gene regulatory structure including coding and adjacent non-coding regions. Despite high sequence divergence from natural DNA, in vivo measurements show that 57% of the highly-expressed synthetic sequences surpass the expression levels of highly-expressed natural controls. This demonstrates the applicability and relevance of deep generative design to expand our knowledge and control of gene expression regulation in any desired organism, condition or tissue.

Antibacterial properties of Apis dorsata honey against some bacterial pathogens.

Now-a-days, different bioproducts are being used extensively for the welfare of mankind. However, for proper utility of any bioproduct, the exact biotechnological potential of that product should be explored. Honey is produced in almost every country on the planet. It has long been used as a medicinal agent in addition to its broader use as a popular food throughout the human history. It can be used to treat various diseases without causing any negative side effects. In the present study, the antibacterial potential of honey produced by A. dorsata was investigated at its variable concentrations (25, 50, 75 and 100 %) against four pathogenic bacterial species. The highest antimicrobial action was seen against E. coli at 100 % concentration of the honey while showing zone of inhibition of 37.5 ±â€¯3.5 mm. However, the lowest antibacterial action was observed against E. faecalis. The overall order of growth inhibition by the honey at its 100 % concentration for the implicated bacterial species appeared as: E. coli ˃ P. aeruginosa ˃ S. aureus ˃ E. faecalis. The honey couldn't show antibacterial action at its 25 % concentration. Our findings of the present study will be helpful for utility of the honey as an alternative medicine for curing different complications caused by microbial pathogens.

Common Spatial Pattern EEG decomposition for Phantom Limb Pain detection.

Phantom Limb Pain (PLP) is a chronic condition frequent among individuals with acquired amputation. PLP has been often investigated with the use of functional MRI focusing on the changes that take place in the sensorimotor cortex after amputation. In the present study, we investigated whether a different type of data, namely electroencephalographic (EEG) recordings, can be used to study the condition. We acquired resting state EEG data from people with and without PLP and then used machine learning for a binary classification task that differentiates the two. Common Spatial Pattern (CSP) decomposition was used as the feature extraction method and two validation schemes were followed for the classification task. Six classifiers (LDA, Log, QDA, LinearSVC, SVC and RF) were optimized through grid search and their performance compared. Two validation approaches, namely all-subjects validation and leave-one-out cross-validation (LOOCV), resulted in high classification accuracy. Most notably, the 93.7% accuracy achieved with SVC in LOOCV holds promise for good diagnostic capabilities using EEG biomarkers. In conclusion, our findings indicate that EEG data is a promising target for future research aiming at elucidating the neural mechanisms underlying PLP and its diagnosis.

Deep learning suggests that gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure.

Understanding the genetic regulatory code governing gene expression is an important challenge in molecular biology. However, how individual coding and non-coding regions of the gene regulatory structure interact and contribute to mRNA expression levels remains unclear. Here we apply deep learning on over 20,000 mRNA datasets to examine the genetic regulatory code controlling mRNA abundance in 7 model organisms ranging from bacteria to Human. In all organisms, we can predict mRNA abundance directly from DNA sequence, with up to 82% of the variation of transcript levels encoded in the gene regulatory structure. By searching for DNA regulatory motifs across the gene regulatory structure, we discover that motif interactions could explain the whole dynamic range of mRNA levels. Co-evolution across coding and non-coding regions suggests that it is not single motifs or regions, but the entire gene regulatory structure and specific combination of regulatory elements that define gene expression levels.