Adaptive Laboratory Evolution of Probiotics toward Oxidative Stress Using a Microfluidic-Based Platform.

Adaptive laboratory evolution (ALE) can be used to make bacteria less susceptible to oxidative stress. An alternative to large batch scale ALE cultures is to use microfluidic platforms, which are often more economical and more efficient. Microfluidic ALE platforms have shown promise, but many have suffered from subpar cell passaging mechanisms and poor spatial definition.

Effects of chitosan molecular weight and mass ratio with natural blue phycocyanin on physiochemical and structural stability of protein.

Phycocyanin (PC), an algae-extracted colorant, has extensive applications for its water-solubility and fresh blue shade. When PC is added to acidified media, dispersions are prone to aggregate and decolorize into cloudy systems. For palliating this matter, chitosan with high, medium, and low molecular weights (HMC, MMC, and LMC) were adopted in PC dispersions, and their protective effects were compared based on physiochemical stabilities.

Reversible disassembly-reassembly of C-phycocyanin in pressurization-depressurization cycles of high hydrostatic pressure.

Hydrostatic pressure can reversibly modulate protein-protein and protein-chromophore interactions of C-phycocyanin (C-PC) from Spirulina platensis. Small-angle X-ray scattering combined with UV-Vis spectrophotometry and protein modeling was used to explore the color and structural changes of C-PC under high pressure conditions at different pH levels. It was revealed that pressures up to 350 MPa were enough to fully disassemble C-PC from trimers to monomers at pH 7.

Structural Design and Optimization of a Resonant Micro-Accelerometer Based on Electrostatic Stiffness by an Improved Differential Evolution Algorithm.

This study designed an in-plane resonant micro-accelerometer based on electrostatic stiffness. The accelerometer adopts a one-piece proof mass structure; two double-folded beam resonators are symmetrically distributed inside the proof mass, and only one displacement is introduced under the action of acceleration, which reduces the influence of processing errors on the performance of the accelerometer. The two resonators form a differential structure that can diminish the impact of common-mode errors.

Interpretation of 'Omics dynamics in a single subject using local estimates of dispersion between two transcriptomes.

Calculating Differentially Expressed Genes (DEGs) from RNA-sequencing requires replicates to estimate gene-wise variability, a requirement that is at times financially or physiologically infeasible in clinics. By imposing restrictive transcriptome-wide assumptions limiting inferential opportunities of conventional methods (edgeR, NOISeq-sim, DESeq, DEGseq), comparing two conditions without replicates (TCWR) has been proposed, but not evaluated. Under TCWR conditions (e.

Precision drug repurposing via convergent eQTL-based molecules and pathway targeting independent disease-associated polymorphisms.

Repurposing existing drugs for new therapeutic indications can improve success rates and streamline development. Use of large-scale biomedical data repositories, including eQTL regulatory relationships and genome-wide disease risk associations, offers opportunities to propose novel indications for drugs targeting common or convergent molecular candidates associated to two or more diseases. This proposed novel computational approach scales across 262 complex diseases, building a multi-partite hierarchical network integrating (i) GWAS-derived SNP-to-disease associations, (ii) eQTL-derived SNP-to-eGene associations incorporating both cis- and trans-relationships from 19 tissues, (iii) protein target-to-drug, and (iv) drug-to-disease indications with (iv) Gene Ontology-based information theoretic semantic (ITS) similarity calculated between protein target functions.

Sucrose transporters of resistant grapevine are involved in stress resistance.

The whole promoter regions of SUTs in Vitis were firstly isolated. SUTs are involved in the adaptation to biotic and abiotic stresses. The vulnerability of Vitis vinifera to abiotic and biotic stresses limits its yields.

Developing a 'personalome' for precision medicine: emerging methods that compute interpretable effect sizes from single-subject transcriptomes.

The development of computational methods capable of analyzing -omics data at the individual level is critical for the success of precision medicine. Although unprecedented opportunities now exist to gather data on an individual's -omics profile ('personalome'), interpreting and extracting meaningful information from single-subject -omics remain underdeveloped, particularly for quantitative non-sequence measurements, including complete transcriptome or proteome expression and metabolite abundance. Conventional bioinformatics approaches have largely been designed for making population-level inferences about 'average' disease processes; thus, they may not adequately capture and describe individual variability.

Emergence of pathway-level composite biomarkers from converging gene set signals of heterogeneous transcriptomic responses.

Recent precision medicine initiatives have led to the expectation of improved clinical decisionmaking anchored in genomic data science. However, over the last decade, only a handful of new single-gene product biomarkers have been translated to clinical practice (FDA approved) in spite of considerable discovery efforts deployed and a plethora of transcriptomes available in the Gene Expression Omnibus. With this modest outcome of current approaches in mind, we developed a pilot simulation study to demonstrate the untapped benefits of developing disease detection methods for cases where the true signal lies at the pathway level, even if the pathway's gene expression alterations may be heterogeneous across patients.

Single subject transcriptome analysis to identify functionally signed gene set or pathway activity.

Analysis of single-subject transcriptome response data is an unmet need of precision medicine, made challenging by the high dimension, dynamic nature and difficulty in extracting meaningful signals from biological or stochastic noise. We have proposed a method for single subject analysis that uses a mixture model for transcript fold-change clustering from isogenically paired samples, followed by integration of these distributions with Gene Ontology Biological Processes (GO-BP) to reduce dimension and identify functional attributes. We then extended these methods to develop functional signing metrics for gene set process regulation by incorporating biological repressor relationships encoded in GO-BP as negatively_regulates edges.

N-of-1-pathways MixEnrich: advancing precision medicine via single-subject analysis in discovering dynamic changes of transcriptomes.

Background: Transcriptome analytic tools are commonly used across patient cohorts to develop drugs and predict clinical outcomes. However, as precision medicine pursues more accurate and individualized treatment decisions, these methods are not designed to address single-patient transcriptome analyses. We previously developed and validated the N-of-1-pathways framework using two methods, Wilcoxon and Mahalanobis Distance (MD), for personal transcriptome analysis derived from a pair of samples of a single patient.

kMEn: Analyzing noisy and bidirectional transcriptional pathway responses in single subjects.

Motivation: Understanding dynamic, patient-level transcriptomic response to therapy is an important step forward for precision medicine. However, conventional transcriptome analysis aims to discover cohort-level change, lacking the capacity to unveil patient-specific response to therapy. To address this gap, we previously developed two N-of-1-pathways methods, Wilcoxon and Mahalanobis distance, to detect unidirectionally responsive transcripts within a pathway using a pair of samples from a single subject.

Analysis of aggregated cell-cell statistical distances within pathways unveils therapeutic-resistance mechanisms in circulating tumor cells.

Motivation: As 'omics' biotechnologies accelerate the capability to contrast a myriad of molecular measurements from a single cell, they also exacerbate current analytical limitations for detecting meaningful single-cell dysregulations. Moreover, mRNA expression alone lacks functional interpretation, limiting opportunities for translation of single-cell transcriptomic insights to precision medicine. Lastly, most single-cell RNA-sequencing analytic approaches are not designed to investigate small populations of cells such as circulating tumor cells shed from solid tumors and isolated from patient blood samples.

eQTL networks unveil enriched mRNA master integrators downstream of complex disease-associated SNPs.

The causal and interplay mechanisms of Single Nucleotide Polymorphisms (SNPs) associated with complex diseases (complex disease SNPs) investigated in genome-wide association studies (GWAS) at the transcriptional level (mRNA) are poorly understood despite recent advancements such as discoveries reported in the Encyclopedia of DNA Elements (ENCODE) and Genotype-Tissue Expression (GTex). Protein interaction network analyses have successfully improved our understanding of both single gene diseases (Mendelian diseases) and complex diseases. Whether the mRNAs downstream of complex disease genes are central or peripheral in the genetic information flow relating DNA to mRNA remains unclear and may be disease-specific.

Dynamic changes of RNA-sequencing expression for precision medicine: N-of-1-pathways Mahalanobis distance within pathways of single subjects predicts breast cancer survival.

Motivation: The conventional approach to personalized medicine relies on molecular data analytics across multiple patients. The path to precision medicine lies with molecular data analytics that can discover interpretable single-subject signals (N-of-1). We developed a global framework, N-of-1-pathways, for a mechanistic-anchored approach to single-subject gene expression data analysis.

Accelerating precision biology and medicine with computational biology and bioinformatics.

A report on the 22nd Annual International Conference on Intelligent Systems for Molecular Biology, held in Boston, Massachusetts, USA, July 11-15, 2014.

A two-stage statistical procedure for feature selection and comparison in functional analysis of metagenomes.

Motivation: With the advance of new sequencing technologies producing massive short reads data, metagenomics is rapidly growing, especially in the fields of environmental biology and medical science. The metagenomic data are not only high dimensional with large number of features and limited number of samples but also complex with a large number of zeros and skewed distribution. Efficient computational and statistical tools are needed to deal with these unique characteristics of metagenomic sequencing data.

Accurate genome relative abundance estimation for closely related species in a metagenomic sample.

Background: Metagenomics has a great potential to discover previously unattainable information about microbial communities. An important prerequisite for such discoveries is to accurately estimate the composition of microbial communities. Most of prevalent homology-based approaches utilize solely the results of an alignment tool such as BLAST, limiting their estimation accuracy to high ranks of the taxonomy tree.