Monolithic affinity columns in 3D printed microfluidics for chikungunya RNA detection.

Mosquito-borne pathogens plague much of the world, yet rapid and simple diagnosis is not available for many affected patients. Using a custom stereolithography 3D printer, we created microfluidic devices with affinity monoliths that could retain, noncovalently attach a fluorescent tag, and detect oligonucleotide and viral RNA. We optimized the fluorescent binding and sample load times using an oligonucleotide sequence from chikungunya virus (CHIKV). We also tested the specificity of CHIKV capture relative to genetically similar Sindbis virus. Moreover, viral RNA from both viruses was flowed through capture columns to study the efficiency and specificity of the column for viral CHIKV. We detected ~107 loaded viral genome copies, which was similar to levels in clinical samples during acute infection. These results show considerable promise for development of this platform into a rapid mosquito-borne viral pathogen detection system.

Transcriptomic Meta-Analysis Identifies Upregulated Clotting and Fibrinolysis Pathways in Colorectal Cancer Tumors Containing Hereditary PMS2 Mismatch Repair Deficiency.

Lynch Syndrome is characterized by deficient mismatch repair (dMMR) components. We performed a meta-analysis of multiple RNA-sequencing datasets from patients with different dMMR variants (PMS2, MLH1, and MSH2) to better characterize the unique transcriptional profiles. Our results reveal enriched signaling pathways from tumor samples with germline mutations in the PMS2 gene including upregulation in pathways related to intrinsic and extrinsic prothrombin activation, fibrinolysis, and uPA/uPAR-mediated signaling. These pathways have been associated with tumor growth, invasiveness, and metastasis. This work provides support for further exploration into the role of PMS2 in tumor development, and as a potential therapeutic mechanism.

Secondary Transcriptomic Analysis of Triple-Negative Breast Cancer Reveals Reliable Universal and Subtype-Specific Mechanistic Markers.

Background/Objectives: Breast cancer is diagnosed in 2.3 million women each year and kills 685,000 (~30% of patients) worldwide. The prognosis for many breast cancer subtypes has improved due to treatments targeting the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). In contrast, patients with triple-negative breast cancer (TNBC) tumors, which lack all three commonly targeted membrane markers, more frequently relapse and have lower survival rates due to a lack of tumor-selective TNBC treatments. We aim to investigate TNBC mechanistic markers that could be targeted for treatment. Methods: We performed a secondary TNBC analysis of 196 samples across 10 publicly available bulk RNA-sequencing studies to better understand the molecular mechanism(s) of disease and predict robust mechanistic markers that could be used to improve the mechanistic understanding of and diagnostic capabilities for TNBC. Results: Our analysis identified ~12,500 significant differentially expressed genes (FDR-adjusted p-value < 0.05), including KIF14 and ELMOD3, and two significantly modulated pathways. Additionally, our novel findings include highly accurate mechanistic markers identified using machine learning methods, including CIDEC (97.1% accuracy alone), CD300LG, ASPM, and RGS1 (98.9% combined accuracy), as well as TNBC subtype-differentiating mechanistic markers, including the targets PDE3B, CFD, IFNG, and ADM, which have associated therapeutics that can potentially be repurposed to improve treatment options. We then experimentally and computationally validated a subset of these findings. Conclusions: The results of our analyses can be used to better understand the mechanism(s) of disease and contribute to the development of improved diagnostics and/or treatments for TNBC.

Comparison of B-Cell Lupus and Lymphoma Using a Novel Immune Imbalance Transcriptomics Algorithm Reveals Potential Therapeutic Targets.

BACKGROUND/OBJECTIVES: Systemic lupus erythematosus (lupus) and B-cell lymphoma (lymphoma) co-occur at higher-than-expected rates and primarily depend on B cells for their pathology. These observations implicate shared inflammation-related B cell molecular mechanisms as a potential cause of co-occurrence. METHODS: We consequently implemented a novel Immune Imbalance Transcriptomics (IIT) algorithm and applied IIT to lupus, lymphoma, and healthy B cell RNA-sequencing (RNA-seq) data to find shared and contrasting mechanisms that are potential therapeutic targets. RESULTS: We observed 7143 significantly dysregulated genes in both lupus and lymphoma. Of those genes, we found 5137 to have a significant immune imbalance, defined as a significant dysregulation by both diseases, as analyzed by IIT. Gene Ontology (GO) term and pathway enrichment of the IIT genes yielded immune-related "Neutrophil Degranulation" and "Adaptive Immune System", which validates that the IIT algorithm isolates biologically relevant genes in immunity and inflammation. We found that 344 IIT gene products are known targets for established and/or repurposed drugs. Among our results, we found 48 known and 296 novel lupus targets, along with 151 known and 193 novel lymphoma targets. Known disease drug targets in our IIT results further validate that IIT isolates genes with disease-relevant mechanisms. CONCLUSIONS: We anticipate the IIT algorithm, together with the shared and contrasting gene mechanisms uncovered here, will contribute to the development of immune-related therapeutic options for lupus and lymphoma patients.

A summary of the main themes and findings presented at the ASM Intermountain Branch meeting (2024).

The annual meeting for the Intermountain Branch was held in April 2024 on the campus of Brigham Young University. There were 127 branch members from Utah, Idaho, and Nevada who attended the meeting and were composed of undergraduate students, graduate or medical students, and faculty. This report highlights the diversity of, and the emerging trends in, the research conducted by American Society for Microbiology members in the Intermountain Branch.

A Novel Application of Spinning Disk Technology to Collect Plasma from Whole Blood Prior to Quantifying Plasma RNA.

The spinning disk technology has previously been utilized to isolate bacterial components from blood in hours instead of days. We hypothesized that this platform could be applied as an alternative approach to isolating plasma RNA from a whole blood sample. We consequently tested the efficacy of the spinning disk technology to extract plasma from whole blood upstream of RNA isolation and analysis. To do so, we collected plasma using either the spinning disk or the typical two-spin centrifuge method. We found that the spinning disk method results in significantly more hemolysis during collection than the conventional two-spin centrifuge method. However, when plasma RNA recovered from both collection methods was quantified using quantitative Real-Time Polymerase Chain Reaction (qRT-PCR), we found that the spinning disk method yielded a higher plasma RNA concentration than the two-spin centrifuge method. This suggests that the spinning disk may be an efficient alternative method to recover plasma RNA. Further work is needed to determine whether red blood cell RNA contamination is present in the plasma RNA extracted from spinning disk-processed plasma.

Joint Secondary Transcriptomic Analysis of Non-Hodgkin's B-Cell Lymphomas Predicts Reliance on Pathways Associated with the Extracellular Matrix and Robust Diagnostic Biomarkers.

Approximately 450,000 cases of Non-Hodgkin's lymphoma are annually diagnosed worldwide, resulting in ~240,000 deaths. An augmented understanding of the common mechanisms of pathology among larger numbers of B-cell Non-Hodgkin's Lymphoma (BCNHL) patients is sorely needed. We consequently performed a large joint secondary transcriptomic analysis of the available BCNHL RNA-sequencing projects from GEO, consisting of 322 relevant samples across ten distinct public studies, to find common underlying mechanisms and biomarkers across multiple BCNHL subtypes and patient subpopulations; limitations may include lack of diversity in certain ethnicities and age groups and limited clinical subtype diversity due to sample availability. We found ~10,400 significant differentially expressed genes (FDR-adjusted p-value < 0.05) and 33 significantly modulated pathways (Bonferroni-adjusted p-value < 0.05) when comparing BCNHL samples to non-diseased B-cell samples. Our findings included a significant class of proteoglycans not previously associated with lymphomas as well as significant modulation of genes that code for extracellular matrix-associated proteins. Our drug repurposing analysis predicted new candidates for repurposed drugs including ocriplasmin and collagenase. We also used a machine learning approach to identify robust BCNHL biomarkers that include YES1, FERMT2, and FAM98B, which have not previously been associated with BCNHL in the literature, but together provide ~99.9% combined specificity and sensitivity for differentiating lymphoma cells from healthy B-cells based on measurement of transcript expression levels in B-cells. This analysis supports past findings and validates existing knowledge while providing novel insights into the inner workings and mechanisms of transformed B-cell lymphomas that could give rise to improved diagnostics and/or therapeutics.

Mutation in Hemagglutinin Antigenic Sites in Influenza A pH1N1 Viruses from 2015-2019 in the United States Mountain West, Europe, and the Northern Hemisphere.

H1N1 influenza A virus is a respiratory pathogen that undergoes antigenic shift and antigenic drift to improve viral fitness. Tracking the evolutionary trends of H1N1 aids with the current detection and the future response to new viral strains as they emerge. Here, we characterize antigenic drift events observed in the hemagglutinin (HA) sequence of the pandemic H1N1 lineage from 2015-2019. We observed the substitutions S200P, K147N, and P154S, together with other mutations in structural, functional, and/or epitope regions in 2015-2019 HA protein sequences from the Mountain West region of the United States, the larger United States, Europe, and other Northern Hemisphere countries. We reconstructed multiple phylogenetic trees to track the relationships and spread of these mutations and tested for evidence of selection pressure on HA. We found that the prevalence of amino acid substitutions at positions 147, 154, 159, 200, and 233 significantly changed throughout the studied geographical regions between 2015 and 2019. We also found evidence of coevolution among a subset of these amino acid substitutions. The results from this study could be relevant for future epidemiological tracking and vaccine prediction efforts. Similar analyses in the future could identify additional sequence changes that could affect the pathogenicity and/or infectivity of this virus in its human host.