Machine Learning Analysis Using RNA Sequencing to Distinguish Neuromyelitis Optica from Multiple Sclerosis and Identify Therapeutic Candidates.

This study aims to identify RNA biomarkers distinguishing neuromyelitis optica (NMO) from relapsing-remitting multiple sclerosis (RRMS) and explore potential therapeutic applications leveraging machine learning (ML). An ensemble approach was developed using differential gene expression analysis and competitive ML methods, interrogating total RNA-sequencing data sets from peripheral whole blood of treatment-naïve patients with RRMS and NMO and healthy individuals. Pathway analysis of candidate biomarkers informed the biological context of disease, transcription factor activity, and small-molecule therapeutic potential. ML models differentiated between patients with NMO and RRMS, with the performance of certain models exceeding 90% accuracy. RNA biomarkers driving model performance were associated with ribosomal dysfunction and viral infection. Regulatory networks of kinases and transcription factors identified biological associations and identified potential therapeutic targets. Small-molecule candidates capable of reversing perturbed gene expression were uncovered. Mitoxantrone and vorinostat-two identified small molecules with previously reported use in patients with NMO and experimental autoimmune encephalomyelitis-reinforced discovered expression signatures and highlighted the potential to identify new therapeutic candidates. Putative RNA biomarkers were identified that accurately distinguish NMO from RRMS and healthy individuals. The application of multivariate approaches in analysis of RNA-sequencing data further enhances the discovery of unique RNA biomarkers, accelerating the development of new methods for disease detection, monitoring, and therapeutics. Integrating biological understanding further enhances detection of disease-specific signatures and possible therapeutic targets.

Characterizing dysbiosis of gut microbiome in PD: evidence for overabundance of opportunistic pathogens.

In Parkinson's disease (PD), gastrointestinal features are common and often precede the motor signs. Braak and colleagues proposed that PD may start in the gut, triggered by a pathogen, and spread to the brain. Numerous studies have examined the gut microbiome in PD; all found it to be altered, but found inconsistent results on associated microorganisms. Studies to date have been small (N = 20 to 306) and are difficult to compare or combine due to varied methodology. We conducted a microbiome-wide association study (MWAS) with two large datasets for internal replication (N = 333 and 507). We used uniform methodology when possible, interrogated confounders, and applied two statistical tests for concordance, followed by correlation network analysis to infer interactions. Fifteen genera were associated with PD at a microbiome-wide significance level, in both datasets, with both methods, with or without covariate adjustment. The associations were not independent, rather they represented three clusters of co-occurring microorganisms. Cluster 1 was composed of opportunistic pathogens and all were elevated in PD. Cluster 2 was short-chain fatty acid (SCFA)-producing bacteria and all were reduced in PD. Cluster 3 was carbohydrate-metabolizing probiotics and were elevated in PD. Depletion of anti-inflammatory SCFA-producing bacteria and elevated levels of probiotics are confirmatory. Overabundance of opportunistic pathogens is an original finding and their identity provides a lead to experimentally test their role in PD.

Analytical Validation of qPCR-Based Multivariate Index Assays in a Clinical Laboratory: Practical Challenges and Limitations.

BACKGROUND: Multivariate index assays (MIAs) to evaluate disease status and/or therapeutic efficacy are increasingly being used in clinical laboratories as laboratory-developed tests (LDTs). Before clinical use, diagnostic and analytical performance specifications of LDTs must be established. Several regulatory guidelines have been published that address specific components of validation procedures, but the interpretation for the analytical validation of MIAs is ambiguous and creates confusion when implementing a novel MIA in the clinical laboratory. CONTENT: CLSI guidelines and published methods were evaluated to develop a validation strategy to establish analytical sensitivity, precision, specificity, and stability for qPCR-based MIAs. Limitations and challenges identified while evaluating guidelines and literature and implementing this strategy are discussed in this review, including sample sourcing and integrity, laboratory contamination, and sample throughput. Due to the diversity of qPCR-based MIAs, we discuss additional considerations for researchers intending to transfer MIAs to a clinical laboratory. SUMMARY: A practical strategy to assess the analytical performance characteristics for validation of qPCR-based MIAs was developed and tested before diagnostic clinical use. Several important limitations, challenges, and considerations were identified during development of the analytical validation procedures that are not addressed in regulatory guidelines or published literature. The described strategy can provide insight for future developers of MIAs and clinical laboratories implementing MIAs as LDTs.

NFAT signaling in osteoblasts regulates the hematopoietic niche in the bone microenvironment.

Osteoblasts support hematopoietic cell development, including B lymphopoiesis. We have previously shown that the nuclear factor of activated T cells (NFAT) negatively regulates osteoblast differentiation and bone formation. Interestingly, in smooth muscle, NFAT has been shown to regulate the expression of vascular cellular adhesion molecule-1 (VCAM-1), a mediator of cell adhesion and signaling during leukocyte development. To examine whether NFAT signaling in osteoblasts regulates hematopoietic development in vivo, we generated a mouse model expressing dominant-negative NFAT driven by the 2.3 kb fragment of the collagen-αI promoter to disrupt NFAT activity in osteoblasts (dnNFAT(OB)). Bone histomorphometry showed that dnNFAT(OB) mice have significant increases in bone volume (44%) and mineral apposition rate (131%) and decreased trabecular thickness (18%). In the bone microenvironment, dnNFAT(OB) mice displayed a significant increase (87%) in Lineage(-)cKit(+)Sca-1(+) (LSK) cells and significant decreases in B220(+)CD19(-)IgM(-) pre-pro-B cells (41%) and B220(+)CD19(+)IgM(+) immature B cells (40%). Concurrent with these findings, LSK cell differentiation into B220(+) cells was inhibited when cocultured on differentiated primary osteoblasts harvested from dnNFAT(OB) mice. Gene expression and protein levels of VCAM-1 in osteoblasts decreased in dnNFAT(OB) mice compared to controls. These data suggest that osteoblast-specific NFAT activity mediates early B lymphopoiesis, possibly by regulating VCAM-1 expression on osteoblasts.