Application of a bioinformatic pipeline to RNA-seq data identifies novel virus-like sequence in human blood

Melnick, Marko; Gonzales, Patrick; LaRocca, Thomas J.; Song, Yuping; Wuu, Joanne; Benatar, Michael; Oskarsson, Björn; Petrucelli, Leonard; Dowell, Robin D.; Link, Christopher D.; Prudencio, Mercedes

doi:10.1093/g3journal/jkab141

Cited by 5 publications

(4 citation statements)

References 90 publications

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“…Notably, 18/20 of these genes were identified as differentially expressed between sALS patients and controls. The best‐performing classification model was applied to normalised gene expression counts derived from an independent ALS‐control whole blood RNA‐seq data set (n = 60) [ 39 ]. This resulted in an ALS‐control classification accuracy of 63.3% (sensitivity: 60.0%, specificity: 66.7%, AUC: 64.7%).…”

Section: Resultsmentioning

confidence: 99%

“…Leave‐One‐Out (LOO) cross‐validation was used to evaluate the effectiveness of selected features in classification and regression models. Validation of the classification model was also tested on an independent whole blood RNA‐seq data set consisting of 30 ALS cases and 30 controls [ 39 ] (Figure S4 ). All machine learning was conducted in Python v3.8.2 [ 40 ].…”

Section: Methodsmentioning

confidence: 99%

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RNA sequencing of peripheral blood in amyotrophic lateral sclerosis reveals distinct molecular subtypes: Considerations for biomarker discovery

Grima,

Liu,

Southwood

et al. 2023

Neuropathology Appl Neurobio

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AimAmyotrophic lateral sclerosis (ALS) is a heterogeneous neurodegenerative disease with limited therapeutic options. A key factor limiting the development of effective therapeutics is the lack of disease biomarkers. We sought to assess whether biomarkers for diagnosis, prognosis or cohort stratification could be identified by RNA sequencing (RNA‐seq) of ALS patient peripheral blood.MethodsWhole blood RNA‐seq data were generated for 96 Australian sporadic ALS (sALS) cases and 48 healthy controls (NCBI GEO accession GSE234297). Differences in sALS‐control gene expression, transcript usage and predicted leukocyte proportions were assessed, with pathway analysis used to predict the activity state of biological processes. Weighted Gene Co‐expression Network Analysis (WGCNA) and machine learning algorithms were applied to search for diagnostic and prognostic gene expression patterns. Unsupervised clustering analysis was employed to determine whether sALS patient subgroups could be detected.Results245 differentially expressed genes were identified in sALS patients relative to controls, with enrichment of immune, metabolic and stress‐related pathways. sALS patients also demonstrated switches in transcript usage across a small set of genes. We established a classification model that distinguished sALS from controls with an accuracy of 78% (sensitivity: 79%, specificity: 75%) using the expression of 20 genes. Clustering analysis identified four patient subgroups with gene expression signatures and immune cell proportions reflective of distinct peripheral effects.ConclusionsOur findings suggest that peripheral blood RNA‐seq can identify diagnostic biomarkers and distinguish molecular subtypes of sALS patients however, its prognostic value requires further investigation.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

RNA sequencing of peripheral blood in amyotrophic lateral sclerosis reveals distinct molecular subtypes: Considerations for biomarker discovery

Grima,

Liu,

Southwood

et al. 2023

Neuropathology Appl Neurobio

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“…However, many previous studies have found evidence of infections caused by various viruses and bacteria, which motivated us to conduct metagenomic analysis using RNA-seq data. [46][47][48][49][50] The alpha diversity values were similar between COPD and normal tissues, but the normal tissue samples had diverse values of alpha diversity, indicating a wider but restricted range of microbiome communities. This suggests that COPD tissues may have a distinct microbiome community compared to normal tissues.…”

Section: Discussionmentioning

confidence: 97%

Profiling of Microbial Landscape in Lung of Chronic Obstructive Pulmonary Disease Patients Using RNA Sequencing

Shin,

Kim,

Lee

et al. 2023

COPD

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The aim of the study was to use RNA sequencing (RNA-seq) data of lung from chronic obstructive pulmonary disease (COPD) patients to identify the bacteria that are most commonly detected. Additionally, the study sought to investigate the differences in these infections between normal lung tissues and those affected by COPD. Patients and Methods: We re-analyzed RNA-seq data of lung from 99 COPD patients and 93 non-COPD smokers to determine the extent to which the metagenomes differed between the two groups and to assess the reliability of the metagenomes. We used unmapped reads in the RNA-seq data that were not aligned to the human reference genome to identify more common infections in COPD patients. Results: We identified 18 bacteria that exhibited significant differences between the COPD and non-COPD smoker groups. Among these, Yersinia enterocolitica was found to be more than 30% more abundant in COPD. Additionally, we observed difference in detection rate based on smoking history. To ensure the accuracy of our findings and distinguish them from false positives, we doublecheck the metagenomic profile using Basic Local Alignment Search Tool (BLAST). We were able to identify and remove specific species that might have been misclassified as other species in Kraken2 but were actually Staphylococcus aureus, as identified by BLAST analysis. Conclusion:This study highlighted the method of using unmapped reads, which were not typically used in sequencing data, to identify microorganisms present in patients with lung diseases such as COPD. This method expanded our understanding of the microbial landscape in COPD and provided insights into the potential role of microorganisms in disease development and progression.

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“…I note that analysis of human transcriptome data in my lab has also led to the recovery of long non-repetitive sequences that lack significant nucleotide matches in GenBank. 48 Further characterization of instances of DNA or RNA “dark matter” will be required to determine their biological relevance.…”

Section: A Dark Brain Microbiome?mentioning

confidence: 99%

Is There a Brain Microbiome?

Link

2021

J Exp Neurosci

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Numerous studies have identified microbial sequences or epitopes in pathological and non-pathological human brain samples. It has not been resolved if these observations are artifactual, or truly represent population of the brain by microbes. Given the tempting speculation that resident microbes could play a role in the many neuropsychiatric and neurodegenerative diseases that currently lack clear etiologies, there is a strong motivation to determine the “ground truth” of microbial existence in living brains. Here I argue that the evidence for the presence of microbes in diseased brains is quite strong, but a compelling demonstration of resident microbes in the healthy human brain remains to be done. Dedicated animal models studies may be required to determine if there is indeed a “brain microbiome.”

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Application of a bioinformatic pipeline to RNA-seq data identifies novel virus-like sequence in human blood

Cited by 5 publications

References 90 publications

RNA sequencing of peripheral blood in amyotrophic lateral sclerosis reveals distinct molecular subtypes: Considerations for biomarker discovery

RNA sequencing of peripheral blood in amyotrophic lateral sclerosis reveals distinct molecular subtypes: Considerations for biomarker discovery

Profiling of Microbial Landscape in Lung of Chronic Obstructive Pulmonary Disease Patients Using RNA Sequencing

Is There a Brain Microbiome?

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