Improving data interpretability with new differential sample variance gene set tests.

Background: Gene set analysis methods have played a major role in generating biological interpretations from omics data such as gene expression datasets. However, most methods focus on detecting homogenous pattern changes in mean expression and methods detecting pattern changes in variance remain poorly explored. While a few studies attempted to use gene-level variance analysis, such approach remains under-utilized.

improves rice growth under salt stress by regulating the expression of key genes involved in salt stress response, abscisic acid signaling, and nutrient transport, among others.

Major food crops, such as rice and maize, display severe yield losses (30-50%) under salt stress. Furthermore, problems associated with soil salinity are anticipated to worsen due to climate change. Therefore, it is necessary to implement sustainable agricultural strategies, such as exploiting beneficial plant-microbe associations, for increased crop yields.

A machine learning method to process voice samples for identification of Parkinson's disease.

Machine learning approaches have been used for the automatic detection of Parkinson's disease with voice recordings being the most used data type due to the simple and non-invasive nature of acquiring such data. Although voice recordings captured via telephone or mobile devices allow much easier and wider access for data collection, current conflicting performance results limit their clinical applicability. This study has two novel contributions.

Ticagrelor inhibits platelet aggregation and reduces inflammatory burden more than clopidogrel in patients with stages 4 or 5 chronic kidney disease.

Background: No study has compared pharmacologic properties of ticagrelor and clopidogrel in non-dialysis patients with stage 4-5 chronic kidney disease (CKD).

Methods: We conducted a double-blind RCT to compare effects of ticagrelor and clopidogrel in 48 CKD, with the primary outcome of ADP-induced platelet aggregation (WBPA) after 2 weeks of DAPT. In a parallel arm, we compared effects of 2 weeks of ticagrelor plus aspirin on mean changes in WBPA and markers of thromboinflammation among non-CKD controls (n = 26) with that of CKD in the ticagrelor-arm.

Platelet-Dependent Inflammatory Dysregulation in Patients with Stages 4 or 5 Chronic Kidney Disease: A Mechanistic Clinical Study.

Background: Chronic kidney disease (CKD) is characterized by dysregulated inflammation that worsens with CKD severity. The role of platelets in modulating inflammation in stage 4 or 5 CKD remains unexplored. We investigated whether there are changes in platelet-derived thromboinflammatory markers in CKD with dual antiplatelet therapy (DAPT; aspirin 81 mg/d plus P2Y12 inhibitor).

Common gene expression patterns are observed in rice roots during associations with plant growth-promoting bacteria, Herbaspirillum seropedicae and Azospirillum brasilense.

Non-legume plants such as rice and maize can form beneficial associations with plant growth-promoting bacteria (PGPB) such as Herbaspirillum seropedicae and Azospirillum brasilense. Several studies have shown that these PGPB promote plant growth via multiple mechanisms. Our current understanding of the molecular aspects and signaling between plants like rice and PGPB like Herbaspirillum seropedicae is limited.

Milk Formula Diet Alters Bacterial and Host Protein Profile in Comparison to Human Milk Diet in Neonatal Piglet Model.

The metaproteome profiling of cecal contents collected from neonatal piglets fed pasteurized human milk (HM) or a dairy-based infant formula (MF) from postnatal day (PND) 2 to 21 were assessed. At PND 21, a subset of piglets from each group ( = 11/group) were euthanized, and cecal contents were collected for further metaproteome analysis. Cecal microbiota composition showed predominantly more Firmicutes phyla and family in the lumen of cecum of HM-fed piglets in comparison to the MF-fed group.

Resistant starch slows the progression of CKD in the 5/6 nephrectomy mouse model.

Background: Resistant Starch (RS) improves CKD outcomes. In this report, we study how RS modulates host-microbiome interactions in CKD by measuring changes in the abundance of proteins and bacteria in the gut. In addition, we demonstrate RS-mediated reduction in CKD-induced kidney damage.

Proteome-transcriptome alignment of molecular portraits achieved by self-contained gene set analysis: Consensus colon cancer subtypes case study.

Gene set analysis (GSA) has become the common methodology for analyzing transcriptomics data. However, self-contained GSA techniques are rarely, if ever, used for proteomics data analysis. Here we present a self-contained proteome level GSA of four consensus molecular subtypes (CMSs) previously established by transcriptome dissection of colon carcinoma specimens.

Differential network analysis and protein-protein interaction study reveals active protein modules in glucocorticoid resistance for infant acute lymphoblastic leukemia.

Background: Acute lymphoblastic leukemia (ALL) is the most common type of cancer diagnosed in children and Glucocorticoids (GCs) form an essential component of the standard chemotherapy in most treatment regimens. The category of infant ALL patients carrying a translocation involving the mixed lineage leukemia (MLL) gene (gene KMT2A) is characterized by resistance to GCs and poor clinical outcome. Although some studies examined GC-resistance in infant ALL patients, the understanding of this phenomenon remains limited and impede the efforts to improve prognosis.

RNA-seq reveals differentially expressed genes in rice (Oryza sativa) roots during interactions with plant-growth promoting bacteria, Azospirillum brasilense.

Major non-legume crops can form beneficial associations with nitrogen-fixing bacteria like Azospirillum brasilense. Our current understanding of the molecular aspects and signaling that occur between important crops like rice and these nitrogen-fixing bacteria is limited. In this study, we used an experimental system where the bacteria could colonize the plant roots and promote plant growth in wild type rice and symbiotic mutants (dmi3 and pollux) in rice.

Metaproteomics reveals potential mechanisms by which dietary resistant starch supplementation attenuates chronic kidney disease progression in rats.

Background: Resistant starch is a prebiotic metabolized by the gut bacteria. It has been shown to attenuate chronic kidney disease (CKD) progression in rats. Previous studies employed taxonomic analysis using 16S rRNA sequencing and untargeted metabolomics profiling.

RNA-Seq Analysis of Spinal Cord Tissues from hPFN1 Transgenic Mouse Model of ALS at Pre-symptomatic and End-Stages of Disease.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease that leads to the loss of motor neurons. The molecular mechanisms of motor neuron degeneration are largely unknown and there are currently no effective therapies to treat this disease. In this work, we report whole transcriptome profiling of spinal cords of mutant transgenic hPFN1 mice and their wildtype transgenic hPFN1 controls at a pre-symptomatic stage and at the end-stage of disease.

16S rRNA Gene-Based Metagenomic Analysis of Ozark Cave Bacteria.

The microbial diversity within cave ecosystems is largely unknown. Ozark caves maintain a year-round stable temperature (12-14 °C), but most parts of the caves experience complete darkness. The lack of sunlight and geological isolation from surface-energy inputs generate nutrient-poor conditions that may limit species diversity in such environments.

Platform-independent gene expression signature differentiates sessile serrated adenomas/polyps and hyperplastic polyps of the colon.

Background: Sessile serrated adenomas/polyps are distinguished from hyperplastic colonic polyps subjectively by their endoscopic appearance and histological morphology. However, hyperplastic and sessile serrated polyps can have overlapping morphological features resulting in sessile serrated polyps diagnosed as hyperplastic. While sessile serrated polyps can progress into colon cancer, hyperplastic polyps have virtually no risk for colon cancer.

Extracting the Strongest Signals from Omics Data: Differentially Expressed Pathways and Beyond.

The analysis of gene sets (in a form of functionally related genes or pathways) has become the method of choice for extracting the strongest signals from omics data. The motivation behind using gene sets instead of individual genes is two-fold. First, this approach incorporates pre-existing biological knowledge into the analysis and facilitates the interpretation of experimental results.

GSAR: Bioconductor package for Gene Set analysis in R.

Background: Gene set analysis (in a form of functionally related genes or pathways) has become the method of choice for analyzing omics data in general and gene expression data in particular. There are many statistical methods that either summarize gene-level statistics for a gene set or apply a multivariate statistic that accounts for intergene correlations. Most available methods detect complex departures from the null hypothesis but lack the ability to identify the specific alternative hypothesis that rejects the null.

Network-based expression analysis reveals key genes related to glucocorticoid resistance in infant acute lymphoblastic leukemia.

Purpose: Despite vast improvements that have been made in the treatment of children with acute lymphoblastic leukemia (ALL), the majority of infant ALL patients (~80 %, < 1 year of age) that carry a chromosomal translocation involving the mixed lineage leukemia (MLL) gene shows a poor response to chemotherapeutic drugs, especially glucocorticoids (GCs), which are essential components of all current treatment regimens. Although addressed in several studies, the mechanism(s) underlying this phenomenon have remained largely unknown. A major drawback of most previous studies is their primary focus on individual genes, thereby neglecting the putative significance of inter-gene correlations.

Protein-protein interaction analysis for functional characterization of helicases.

Helicases are enzymes involved in nucleic acid metabolism, playing major roles in replication, transcription, and repair. Defining helicases oligomerization state and transient and persistent protein interactions is essential for understanding of their function. In this article we review current methods for the protein-protein interaction analysis, and discuss examples of its application to the study of helicases: Pif1 and DDX3.

Gene set analysis approaches for RNA-seq data: performance evaluation and application guideline.

Transcriptome sequencing (RNA-seq) is gradually replacing microarrays for high-throughput studies of gene expression. The main challenge of analyzing microarray data is not in finding differentially expressed genes, but in gaining insights into the biological processes underlying phenotypic differences. To interpret experimental results from microarrays, gene set analysis (GSA) has become the method of choice, in particular because it incorporates pre-existing biological knowledge (in a form of functionally related gene sets) into the analysis.

Comparative evaluation of gene set analysis approaches for RNA-Seq data.

Background: Over the last few years transcriptome sequencing (RNA-Seq) has almost completely taken over microarrays for high-throughput studies of gene expression. Currently, the most popular use of RNA-Seq is to identify genes which are differentially expressed between two or more conditions. Despite the importance of Gene Set Analysis (GSA) in the interpretation of the results from RNA-Seq experiments, the limitations of GSA methods developed for microarrays in the context of RNA-Seq data are not well understood.

Gene Sets Net Correlations Analysis (GSNCA): a multivariate differential coexpression test for gene sets.

Motivation: To date, gene set analysis approaches primarily focus on identifying differentially expressed gene sets (pathways). Methods for identifying differentially coexpressed pathways also exist but are mostly based on aggregated pairwise correlations or other pairwise measures of coexpression. Instead, we propose Gene Sets Net Correlations Analysis (GSNCA), a multivariate differential coexpression test that accounts for the complete correlation structure between genes.