Unveiling overlooked pathways: The uric acid catabolism genes in the human genome.

In hominids, including Homo sapiens, uric acid is the end product of purine catabolism. In contrast, other placental mammals further degrade uric acid to (S)-allantoin by enzymes such as urate oxidase (uricase), HIU hydrolase (HIUase), and OHCU decarboxylase. Some organisms, such as frogs and fish, hydrolyze (S)-allantoin to allantoate and eventually to (S)-ureidoglycolate and urea, while marine invertebrates convert urea to ammonium. In H. sapiens, mutations in the uricase gene led to a reduction in the selective pressure for maintaining the integrity of the genes encoding the other enzymes of the purine catabolism pathway, resulting in an accumulation of uric acid. The hyperuricemia resulting from this accumulation is associated with gout, cardiovascular disease, diabetes, and preeclampsia. Many commonly used drugs, such as aspirin, can also increase uric acid levels. Despite the apparent absence of these enzymes in H. sapiens, there appears to be production of transcripts for uricase (UOX), HIUase (URAHP), OHCU decarboxylase (URAD), and allantoicase (ALLC). While some URAHP transcripts are classified as long non-coding RNAs (lncRNAs), URAD and ALLC produce protein-coding transcripts. Given the presence of these transcripts in various tissues, we hypothesized that they may play a role in the regulation of purine catabolism and the pathogenesis of diseases associated with hyperuricemia. Here, we specifically investigate the unique aspects of purine catabolism in H. sapiens, the effects mutations of the uricase gene, and the potential regulatory role of the corresponding transcripts. These findings open new avenues for research and therapeutic approaches for the treatment of hyperuricemia and related diseases.

The tidyomics ecosystem: Enhancing omic data analyses.

The growth of omic data presents evolving challenges in data manipulation, analysis, and integration. Addressing these challenges, Bioconductor1 provides an extensive community-driven biological data analysis platform. Meanwhile, tidy R programming2 offers a revolutionary standard for data organisation and manipulation. Here, we present the tidyomics software ecosystem, bridging Bioconductor to the tidy R paradigm. This ecosystem aims to streamline omic analysis, ease learning, and encourage cross-disciplinary collaborations. We demonstrate the effectiveness of tidyomics by analysing 7.5 million peripheral blood mononuclear cells from the Human Cell Atlas3, spanning six data frameworks and ten analysis tools.

The tidyomics ecosystem: enhancing omic data analyses.

The growth of omic data presents evolving challenges in data manipulation, analysis and integration. Addressing these challenges, Bioconductor provides an extensive community-driven biological data analysis platform. Meanwhile, tidy R programming offers a revolutionary data organization and manipulation standard. Here we present the tidyomics software ecosystem, bridging Bioconductor to the tidy R paradigm. This ecosystem aims to streamline omic analysis, ease learning and encourage cross-disciplinary collaborations. We demonstrate the effectiveness of tidyomics by analyzing 7.5 million peripheral blood mononuclear cells from the Human Cell Atlas, spanning six data frameworks and ten analysis tools.

SARS-CoV-2 Selectively Induces the Expression of Unproductive Splicing Isoforms of Interferon, Class I MHC, and Splicing Machinery Genes.

RNA processing is a highly conserved mechanism that serves as a pivotal regulator of gene expression. Alternative processing generates transcripts that can still be translated but lead to potentially nonfunctional proteins. A plethora of respiratory viruses, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), strategically manipulate the host's RNA processing machinery to circumvent antiviral responses. We integrated publicly available omics datasets to systematically analyze isoform-level expression and delineate the nascent peptide landscape of SARS-CoV-2-infected human cells. Our findings explore a suggested but uncharacterized mechanism, whereby SARS-CoV-2 infection induces the predominant expression of unproductive splicing isoforms in key IFN signaling, interferon-stimulated (ISGs), class I MHC, and splicing machinery genes, including IRF7, HLA-B, and HNRNPH1. In stark contrast, cytokine and chemokine genes, such as IL6 and TNF, predominantly express productive (protein-coding) splicing isoforms in response to SARS-CoV-2 infection. We postulate that SARS-CoV-2 employs an unreported tactic of exploiting the host splicing machinery to bolster viral replication and subvert the immune response by selectively upregulating unproductive splicing isoforms from antigen presentation and antiviral response genes. Our study sheds new light on the molecular interplay between SARS-CoV-2 and the host immune system, offering a foundation for the development of novel therapeutic strategies to combat COVID-19.

Role of long non-coding RNAs in the pathophysiology of Alzheimer's disease and other dementias.

Dementia is the term used to describe a group of cognitive disorders characterized by a decline in memory, thinking, and reasoning abilities that interfere with daily life activities. Examples of dementia include Alzheimer's Disease (AD), Frontotemporal dementia (FTD), Amyotrophic lateral sclerosis (ALS), Vascular dementia (VaD) and Progressive supranuclear palsy (PSP). AD is the most common form of dementia. The hallmark pathology of AD includes formation of ß-amyloid (Aß) oligomers and tau hyperphosphorylation in the brain, which induces neuroinflammation, oxidative stress, synaptic dysfunction, and neuronal apoptosis. Emerging studies have associated long non-coding RNAs (lncRNAs) with the pathogenesis and progression of the neurodegenerative diseases. LncRNAs are defined as RNAs longer than 200 nucleotides that lack the ability to encode functional proteins. LncRNAs play crucial roles in numerous biological functions for their ability to interact with different molecules, such as proteins and microRNAs, and subsequently regulate the expression of their target genes at transcriptional and post-transcriptional levels. In this narrative review, we report the function and mechanisms of action of lncRNAs found to be deregulated in different types of dementia, with the focus on AD. Finally, we discuss the emerging role of lncRNAs as biomarkers of dementias.

miRNAs in cerebrospinal fluid associated with Alzheimer's disease: A systematic review and pathway analysis using a data mining and machine learning approach.

Alzheimer's disease (AD) is the most common type and accounts for 60%-70% of the reported cases of dementia. MicroRNAs (miRNAs) are small non-coding RNAs that play a crucial role in gene expression regulation. Although the diagnosis of AD is primarily clinical, several miRNAs have been associated with AD and considered as potential markers for diagnosis and progression of AD. We sought to match AD-related miRNAs in cerebrospinal fluid (CSF) found in the GeoDataSets, evaluated by machine learning, with miRNAs listed in a systematic review, and a pathway analysis. Using machine learning approaches, we identified most differentially expressed miRNAs in Gene Expression Omnibus (GEO), which were validated by the systematic review, using the acronym PECO-Population (P): Patients with AD, Exposure (E): expression of miRNAs, Comparison (C): Healthy individuals, and Objective (O): miRNAs differentially expressed in CSF. Additionally, pathway enrichment analysis was performed to identify the main pathways involving at least four miRNAs selected. Four miRNAs were identified for differentiating between patients with and without AD in machine learning combined to systematic review, and followed the pathways analysis: miRNA-30a-3p, miRNA-193a-5p, miRNA-143-3p, miRNA-145-5p. The pathways epidermal growth factor, MAPK, TGF-beta and ATM-dependent DNA damage response, were regulated by these miRNAs, but only the MAPK pathway presented higher relevance after a randomic pathway analysis. These findings have the potential to assist in the development of diagnostic tests for AD using miRNAs as biomarkers, as well as provide understanding of the relationship between different pathophysiological mechanisms of AD.

Suppression of epithelial proliferation and tumorigenesis by immunoglobulin A.

Immunoglobulin A (IgA) is the most abundant antibody isotype produced across mammals and plays a specialized role in mucosal homeostasis 1 . Constantly secreted into the lumen of the intestine, IgA binds commensal microbiota to regulate their colonization and function 2,3 , with unclear implications for health. IgA deficiency is common in humans but is difficult to study due to its complex etiology and comorbidities 4-8 . Using genetically and environmentally controlled mice, here we show that IgA-deficient animals have a baseline alteration in the colon epithelium that increases susceptibility to multiple models of colorectal cancer. Transcriptome, imaging, and flow cytometry-based analyses revealed that, in the absence of IgA, colonic epithelial cells induce antibacterial factors and accelerate cell cycling in response to the microbiota. Oral treatment with IgA was sufficient to suppress aberrant epithelial proliferation independently of bacterial binding, suggesting that IgA provides a feedback signal to epithelial cells in parallel with its known roles in microbiome shaping. In a primary colonic organoid culture system, IgA directly suppresses epithelial growth. Conversely, the susceptibility of IgA-deficient mice to colorectal cancer was reversed by Notch inhibition to suppress the absorptive colonocyte developmental program, or by inhibition of the cytokine MIF, the receptor for which was upregulated in stem cells of IgA-deficient animals. These studies demonstrate a homeostatic function for IgA in tempering physiological epithelial responses to microbiota to maintain mucosal health.