Ribonucleic Acid Sequencing Research Articles

Abstract 5043: Predicting melanoma staging using targeted RNA sequencing data using machine learning

Abstract Introduction: Cancer staging can take essential time and expenses away from patients, both of which should be patients’ management. In this current study, we aim to develop a machine learning-based early TNM staging model. Methods: Normalized ribonucleic acid sequencing (RNA-seq) counts data for melanoma patients was extracted from The Cancer Genome Atlas (TCGA). Six different experiments were run to produce machine learning algorithms for nodal metastasis, distant metastasis, combine (nodal or distant) metastasis, and higher vs lower tumor stage (T4/T3 vs T2/T1/Tis). All datasets were split using 80/20 for training and test sets. Synthetic Minority Oversampling Technique (SMOTE) was used to address the imbalanced distribution of the outcome. The algorithm accuracies were determined by a percent of sensitivity, specificity, predictive values (positive and negative: PPV and NPV), and area under the receiver-operator curve (AUROC). Results: The best model for nodal metastasis was a random forest classifier (RF) with targeted gene expression (TE) showed higher sensitivity (98), specificity (100), PPV (100), NPV (94), and AUROC (1.00, 95%CI 0.91-0.99). TE for distant metastasis with RF, showed sensitivity (0), specificity (100), PPV (0), NPV (100), and AUROC (1.00, 95%CI 0.91-1.00). While TE for combined metastasis (nodal or distant) staging algorithm; Nodal or Distant Metastasis (TE) RF, showed sensitivity (98), specificity (100), PPV (100), NPV (99), and AUROC (1.00, 95%CI 0.88-0.98). The tumor staging (DEG and predicting higher stage i.e stage 3 or higher) algorithm; Tumor Staging (TE) RF, showed sensitivity (100), specificity (100), PPV (100), NPV (100), and AUROC (1.00, 95%CI 0.69-0.89) (Table 1). Conclusion: Our machine learning models can predict tumor staging including higher vs lower stage tumor, nodal metastasis, and combined metastasis with high accuracy. However, these results need to be further validated. Table 1. Machine learning models Experiment Sensitivity Specificity PPV NPV AUROC 95%CI Test Set Accuracy N Nodal Metastasis (Pan-Expression) DNN 66.22 16.98 52.69 26.47 0.495 0.25-0.47 54.55 479 Nodal Metastasis (Targeted Expression) DNN 81.13 100.0 100.0 88.10 0.982 0.62-0.83 90.21 479 Nodal Metastasis (Targeted Expression) RF 97.89 100.00 100.00 94 1.00 0.91-0.99 96.74 458 Distant Metastasis (Targeted Expression) RF 0.00 100.00 0.00 100 1.00 0.91-1.00 94.44 450* Nodal or Distant Metastasis (Targeted Expression) RF 97.56 100.00 100.00 98.56 1.00 0.88-0.98 98.91 458 Tumor Staging (Targeted Expression) RF 100.00 100.00 100.00 100.00 1.00 0.69-0.89 100.00 395 *27 cases with distant metastasis Citation Format: Fahad Shabbir Ahmed, Furqan Bin Irfan. Predicting melanoma staging using targeted RNA sequencing data using machine learning [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5043.

The Effect of Chitosan Treatment on the Microbial Diversity of Cold-Stored Macrobrachium rosenbergii

ABSTRACT The objective of the study was to evaluate the effect of chitosan on the pre-servation of Macrobrachium rosenbergii during cold storage. M. rosenbergii samples were divided into control and experimental groups and immersed in pure water and 1.5% chitosan solutions, respectively, followed by storage at 4°C Changes were measured in tissue sensory scores, pH (hydrogen potential), total volatile base nitrogen (TVB-N), total viable count (TVC), and K values. Further, 16S ribosomal ribonucleic acid (rRNA) sequencing was used to study the effects of chitosan on microbial diversity within prawns across storage times. Treating M. rosenbergii with chitosan inhibited increases in TVB-N, TVC, and K values of prawns, thereby extending prawn meat shelf life by two to three days. During storage, Lactococcus and Candidatus Hepatoplasma became the dominant spoilage bacteria of M. rosenbergii bacterial communities. The abundance of Lactococcus in the experimental M. rosenbergii samples was as high as 69.84% during the late storage stage, while the abundance of Candidatus Hepatoplasma in the control group was as high as 46.88% in the middle storage stage. Chitosan effectively controlled changes in the quality of M. rosenbergii meat during cold storage, altered their bacterial community structures, inhibited spoilage, and exhibited beneficial preservation effects.

Results of the phase IIa RADICAL trial of the FGFR inhibitor AZD4547 in endocrine resistant breast cancer

We conducted a phase IIa, multi-centre, open label, single arm study (RADICAL; NCT01791985) of AZD4547 (a potent and selective inhibitor of Fibroblast Growth Factor Receptor (FGFR)-1, 2 and 3 receptor tyrosine kinases) administered with anastrozole or letrozole in estrogen receptor positive metastatic breast cancer patients who had become resistant to aromatase inhibitors. After a safety run-in study to assess safety and tolerability, we recruited 52 patients. The primary endpoint was change in tumour size at 12 weeks, and secondary endpoints were to assess response at 6 weeks, 20 weeks and every 8 weeks thereafter and tolerability of the combined treatment. Two partial responses (PR) and 19 stable disease (SD) patients were observed at the 12-week time point. At 28 weeks, according to centrally reviewed Response Evaluation Criteria in Solid Tumours (RECIST) criteria, five PR and 8 SD patients were observed in 50 assessable cases. Overall, objective response rate (5 PR) was of 10%, meeting the pre-specified endpoint. Fourteen patients discontinued due to adverse events. Eleven patients had retinal pigment epithelial detachments which was asymptomatic and reversible in all but one patient. Exploratory ribonucleic acid sequencing (RNA-Seq) analysis was done on patients’ samples: 6 differentially-expressed-genes could distinguish those who benefited from the addition of AZD4547.

Local Environment Induces Differential Gene Expression in Regenerating Nerves

IntroductionApproximately 80% of amputations are complicated by neuromas. Methods for neuroma management include nerve translocation into bone and implantation into skeletal muscle grafts, which have also facilitated the development of regenerative neural interfaces to enable fixation of prosthetics with motor and sensory feedback. However, molecular-level differences between nerves in these environments have not been investigated. This study aimed to elucidate the physiology of regenerating nerves in different settings by assessing gene expression. Materials and methodsNew Zealand white rabbits underwent transfemoral amputation with sciatic nerve transposition into the femur or tacked to skeletal muscle. At 5 wk, ribonucleic acid (RNA) sequencing of samples of distal nerve terminating in bone or muscle and nerve of the contralateral limb (control) identified differentially expressed genes (DEGs) and biochemical pathways (α = 0.05). ResultsThree samples of nerve housed in bone, four of nerve tacked to muscle, and seven naïve controls were analyzed. Relative to controls, nerve housed in bone had little within-group variation and 13,028 DEGs, and nerve tacked to muscle had dramatic within-group variation and 12,811 DEGs. These samples upregulated the following pathways: lysosome, phagosome, antigen processing/presentation, and cell adhesion molecule. Relative to nerve housed in bone, nerve tacked to muscle had 12,526 DEGs, demonstrating upregulation of pathways of B-cell receptor signaling, focal adhesion, natural killer–cell mediated cytotoxicity, leukocyte transendothelial migration, and extracellular matrix–receptor interactions. ConclusionsNerve housed in bone has a more predictable molecular profile than does nerve tacked to muscle. Thus, the intramedullary canal may provide a more reliable setting for neuroma prevention and neural interfacing.

Profile of the Nasopharyngeal Microbiota Affecting the Clinical Course in COVID-19 Patients.

While populations at risk for severe SARS-CoV-2 infections have been clearly identified, susceptibility to the infection and its clinical course remain unpredictable. As the nasopharyngeal microbiota may promote the acquisition of several respiratory infections and have an impact on the evolution of their outcome, we studied the nasopharyngeal microbiota of COVID-19 patients in association with baseline disease-related clinical features compared to that of patients tested negative. We retrospectively analyzed 120 nasopharyngeal pseudonymized samples, obtained for diagnosis, divided into groups (infected patients with a favorable outcome, asymptomatic, and deceased patients) and patients tested negative for SARS-CoV-2, by using Illumina-16S ribosomal ribonucleic acid (rRNA) sequencing and specific polymerase chain reaction (PCR) targeting pathogens. We first found a depletion of anaerobes among COVID-19 patients, irrespective of the clinical presentation of the infection (p < 0.029). We detected 9 taxa discriminating patients tested positive for SARS-CoV-2 from those that were negative including Corynebacterium propinquum/pseudodiphtericum (p ≤ 0.05), Moraxella catarrhalis (p ≤ 0.05), Bacillus massiliamazoniensis (p ≤ 0.01), Anaerobacillus alkalidiazotrophicus (p ≤ 0.05), Staphylococcus capitis subsp. capitis (p ≤ 0.001), and Afipia birgiae (p ≤ 0.001) with 16S rRNA sequencing, and Streptococcus pneumoniae (p ≤ 0.01), Klebsiella pneumoniae (p ≤ 0.01), and Enterococcus faecalis (p ≤ 0.05) using real-time PCR. By designing a specific real-time PCR, we also demonstrated that C. propinquum is decreased in asymptomatic individuals compared to other SARS-CoV 2 positive patients. These findings indicate that the nasopharyngeal microbiota as in any respiratory infection plays a role in the clinical course of the disease. Further studies are needed to elucidate the potential role in the clinical course of the disease of M. catarrhalis, Corynebacterium accolens, and more specifically Corynebacterium propinquum/diphteriticum in order to include them as predictors of the severity of COVID-19.

Skin bacterial richness and diversity in intensive care unit patients with severe pneumonia

ObjectivesPatients with severe pneumonia admitted to the intensive care unit (ICU) have a high risk of mortality, and the microbiome is likely to affect the outcome of such patients. However, the composition of the skin microbiota of ICU patients with severe pneumonia remains unclear. In this study, on the basis of 16S ribosomal ribonucleic acid sequencing, we explored the difference in skin bacterial richness and diversity between the ICU patient group (PG) with severe pneumonia and the healthy control group (CG). MethodsThe diversity index and taxonomic distribution of skin bacteria were analyzed using the Quantitative Insights Into Microbial Ecology (QIIME) bioinformatics pipeline. Blood, endotracheal aspirate, and bronchoalveolar lavage fluid samples were collected from the same PG subjects for culture. ResultsCompared with the CG, the diversity of skin bacteria in the PG decreased significantly. Staphylococcus, Acinetobacter, Stenotrophomonas, Enterococcus, Halomonas, and Brevibacillus were differentially abundant in the PG, and most of these bacteria were also identified in the cultures of upper respiratory tract samples of the same PG. ConclusionWe provide evidence that healthcare-associated infection in ICU patients with severe pneumonia is strongly associated with skin microbiota, which necessitates the prevention and control of skin bacterial pathogens for these patients.

Short amplicon reverse transcription-polymerase chain reaction detects aberrant splicing in genes with low expression in blood missed by ribonucleic acid sequencing analysis for clinical diagnosis.

Use of blood RNA sequencing (RNA‐seq) as a splicing analysis tool for clinical interpretation of variants of uncertain significance (VUSs) found via whole‐genome and exome sequencing can be difficult for genes that have low expression in the blood due to insufficient read count coverage aligned to specific genes of interest. Here, we present a short amplicon reverse transcription‐polymerase chain reaction(RT‐PCR) for the detection of genes with low blood expression. Short amplicon RT‐PCR, is designed to span three exons where an exon harboring a variant is flanked by one upstream and one downstream exon. We tested short amplicon RT‐PCRs for genes that have median transcripts per million (TPM) values less than one according to the genotype‐tissue expression database. Median TPM values of genes analyzed in this study are SYN1 = 0.8549, COL1A1 = 0.6275, TCF4 = 0.4009, DSP = .2894, TTN = 0.2851, COL5A2 = 0.1036, TERT = 0.04452, NTRK2 = 0.0344, ABCA4 = 0.00744, PRPH = 0, and WT1 = 0. All these genes show insufficient exon‐spanning read coverage in our RNA‐seq data to allow splicing analysis. We successfully detected all genes tested except PRPH and WT1. Aberrant splicing was detected in SYN1, TCF4, NTRK2, TTN, and TERT VUSs. Therefore, our results show short amplicon RT‐PCR is a useful alternative for the analysis of splicing events in genes with low TPM in blood RNA for clinical diagnostics.

RNA-seq and Mitochondrial DNA Analysis of Adrenal Gland Metastatic Tissue in a Patient with Renal Cell Carcinoma

Simple SummaryWe performed mutation analysis of ribonucleic acid sequencing (RNA-seq) and whole mitochondrial genome data from tissues collected from metastatic lesions of a male patient in his 60s with metastatic renal cell carcinoma. We confirmed the common mutation sites of a mitochondrial gene containing the T3394Y, R11,807G, and G15,438R sites. Pathway analysis, using RNA-seq data, confirmed the common mutant pathway between renal cell carcinoma and metastatic adrenal carcinoma as cytokine–cytokine receptor (CCR) interaction. However, no common nuclear gene mutations were identified. The two similar sequences suggest that mtDNA-mutation-driven metastasis to the adrenal gland may affect CCR interactions and cause cancer cells to thrive.This study aimed to clarify whether genetic mutations participate in renal cell carcinoma (RCC) metastasis to the adrenal gland (AG). Our study analyzed whole mitochondrial gene and ribonucleic acid sequencing (RNA-seq) data from a male patient in his 60s with metastatic RCC. We confirmed common mutation sites in the mitochondrial gene and carried out Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis using RNA-seq data for RCC and adrenal carcinoma. Furthermore, we confirmed the common mutation sites of mitochondrial genes in which the T3394Y (p.H30Y) site transitioned from histidine (His.; H) to tyrosine (Tyr.; Y) in the NADH dehydrogenase subunit 1 (ND1) gene. The R11,807G (p.T350A) site transitioned from threonine (Thr.; T) to alanine (Ala.; A). Additionally, the G15,438R or A (p.G231D) site transitioned from glycine (Gly.; G) to aspartic acid (Asp.; D) in cytochrome b (CYTB). Furthermore, pathway analysis, using RNA-seq, confirmed the common mutant pathway between RCC and adrenal carcinoma as cytokine–cytokine receptor (CCR) interaction. Confirmation of the original mutation sites suggests that transfer to AG may be related to the CCR interaction. Thus, during metastasis to the AG, mitochondria DNA mutation may represent the initial origin of the metastasis, followed by the likely mutation of the nuclear genes.

SPCS: a spatial and pattern combined smoothing method for spatial transcriptomic expression.

High-dimensional, localized ribonucleic acid (RNA) sequencing is now possible owing to recent developments in spatial transcriptomics (ST). ST is based on highly multiplexed sequence analysis and uses barcodes to match the sequenced reads to their respective tissue locations. ST expression data suffer from high noise and dropout events; however, smoothing techniques have the promise to improve the data interpretability prior to performing downstream analyses. Single-cell RNA sequencing (scRNA-seq) data similarly suffer from these limitations, and smoothing methods developed for scRNA-seq can only utilize associations in transcriptome space (also known as one-factor smoothing methods). Since they do not account for spatial relationships, these one-factor smoothing methods cannot take full advantage of ST data. In this study, we present a novel two-factor smoothing technique, spatial and pattern combined smoothing (SPCS), that employs the k-nearest neighbor (kNN) technique to utilize information from transcriptome and spatial relationships. By performing SPCS on multiple ST slides from pancreatic ductal adenocarcinoma (PDAC), dorsolateral prefrontal cortex (DLPFC) and simulated high-grade serous ovarian cancer (HGSOC) datasets, smoothed ST slides have better separability, partition accuracy and biological interpretability than the ones smoothed by preexisting one-factor methods. Source code of SPCS is provided in Github (https://github.com/Usos/SPCS).

Exercise modifies the disease-relevant gut microbial shifts in post-traumatic osteoarthritis rats.

AimsPost-traumatic osteoarthritis (PTOA) is a subset of osteoarthritis (OA). The gut microbiome is shown to be involved in OA. However, the effect of exercise on gut microbiome in PTOA remains elusive.MethodsA total of 18 eight-week Sprague-Dawley rats were assigned into three groups: Sham/sedentary (Sham/Sed), PTOA/sedentary (PTOA/Sed), and PTOA/treadmill-walking (PTOA/TW). PTOA model was induced by transection of the anterior cruciate ligament (ACLT) and the destabilization of the medial meniscus (DMM). Treadmill-walking (15 m/min, 30 min/d, five days/week for eight weeks) was employed in the PTOA/TW group. The response of cartilage, subchondral bone, serology, and gut microbiome and their correlations were assessed.ResultsEight-week treadmill-walking was effective at maintaining the integrity of cartilage-subchondral bone unit and reducing the elevated systematic inflammation factors and microbiome-derived metabolites. Furthermore, 16S ribosomal ribonucleic acid (rRNA) sequencing showed disease-relevant microbial shifts in PTOA animals, characterized by the decreased abundance of phylum TM7 and the increase of phylum Fusobacteria. At the genus level, the abundance of Lactobacillus, Turicibacter, Adlercreutzia, and Cetobacterium were increased in the PTOA animals, while the increase of Adlercreutzia and Cetobacterium was weakened as a response to exercise. The correlation analysis showed that genus Lactobacillus and Adlercreutzia were correlated to the structural OA phenotypes, while phylum Fusobacteria and genus Cetobacterium may contribute to the effects of exercise on the diminishment of serological inflammatory factors.ConclusionExercise is effective at maintaining the integrity of cartilage-subchondral bone unit, and the exercise-induced modification of disease-relevant microbial shifts is potentially involved in the mechanisms of exercise-induced amelioration of PTOA.Cite this article: Bone Joint Res 2022;11(4):214–225.

Gene Expression and Mutational Landscape in a PMEC Patient With Low to Intermediate-High Grade Transition.

Primary pulmonary mucoepidermoid carcinoma (PMEC) is a very rare form of lung carcinoma. Due to the low incidence, little is known about its inherent genetic variation characteristics. The uniform treatment for PMEC has not been determined. In this case, we present a 45-year-old male with stage IA PMEC. The surgical specimens contained changes from low- to intermediate-to-high grade. We performed integrative analysis of whole-exome sequencing (WES-seq) and messenger ribonucleic acid sequencing (RNA-seq) to compare the molecular changes in the different lesions. Molecular testing exhibits the specimens harboring CRTC3-MAML2 fusion. The copy number gain of PDPK1 is only present in high-grade regional specimens. We also explored the level of immune infiltration by CIBERSORT. To our knowledge, this is the first report to describe a case of PMEC in the low- to intermediate–high-grade transition with multiomics analysis.

Bibliometric review of ATAC-Seq and its application in gene expression

With recent advances in high-throughput next-generation sequencing, it is possible to describe the regulation and expression of genes at multiple levels. An assay for transposase-accessible chromatin using sequencing (ATAC-seq), which uses Tn5 transposase to sequence protein-free binding regions of the genome, can be combined with chromatin immunoprecipitation coupled with deep sequencing (ChIP-seq) and ribonucleic acid sequencing (RNA-seq) to provide a detailed description of gene expression. Here, we reviewed the literature on ATAC-seq and described the characteristics of ATAC-seq publications. We then briefly introduced the principles of RNA-seq, ChIP-seq and ATAC-seq, focusing on the main features of the techniques. We built a phylogenetic tree from species that had been previously studied by using ATAC-seq. Studies of Mus musculus and Homo sapiens account for approximately 90% of the total ATAC-seq data, while other species are still in the process of accumulating data. We summarized the findings from human diseases and other species, illustrating the cutting-edge discoveries and the role of multi-omics data analysis in current research. Moreover, we collected and compared ATAC-seq analysis pipelines, which allowed biological researchers who lack programming skills to better analyze and explore ATAC-seq data. Through this review, it is clear that multi-omics analysis and single-cell sequencing technology will become the mainstream approach in future research.

Tyrosine phosphatase SHP2 exacerbates psoriasis-like skin inflammation in mice via ERK5-dependent NETosis.

Psoriasis is a chronic inflammatory skin disease, often accompanied by increased infiltration of immune cells, especially neutrophils. However, the detailed mechanism of the neutrophil function in psoriasis progression remains unclear. Here, we found that both Src homology‐2 domain‐containing protein tyrosine phosphatase‐2 (SHP2) and neutrophils were highly correlated to developing psoriasis by single‐cell ribonucleic acid (RNA) sequencing and experiment verification. The deficiency of SHP2 in neutrophils significantly alleviated psoriasis‐like phenotype in an imiquimod‐induced murine model. Interestingly, high levels of neutrophil extracellular traps (NETs) were produced in the inflamed lesions of psoriatic patients. In addition, imiquimod‐induced psoriasis‐like symptoms were remarkably ameliorated in peptidyl arginine deiminase 4 (PAD4) knockout mice, which cannot form NETs. Mechanistically, RNA‐seq analysis revealed that SHP2 promoted the formation of NETs in neutrophils via the ERK5 pathway. Functionally, this mechanism resulted in the infiltration of pro‐inflammatory cytokines such as TNF‐α, IL‐1β, IL‐6, IL‐17A, and CXCL‐15, which enhances the inflammatory response in skin lesions and reinforces the cross‐talk between neutrophils and keratinocytes, ultimately aggravating psoriasis. Our findings uncover a role for SHP2 in NET release and subsequent cell death known as NETosis in the progression of psoriasis and suggest that SHP2 may be a promising therapeutic target for psoriasis.

Blood mRNA Expression in Alzheimer's Disease and Dementia With Lewy Bodies

The objective of this study was to investigate the expression of genes in Alzheimer's disease (AD) and dementia with Lewy bodies (DLB), both at the mild cognitive impairment (MCI) and dementia stages, to improve our understanding of disease pathophysiology and investigate the potential for diagnostic and prognostic biomarkers based on mRNA expression. Cross-sectional observational study. University research center. People with MCI with Lewy bodies (MCI-LB, n=55), MCI-AD (n=19), DLB (n=38), AD (n=24) and a cognitively unimpaired comparison group (n=28). Ribonucleic acid sequencing of whole blood. Differentially expressed genes (DEGs) were identified and gene set enrichment analysis was carried out. Compared with the cognitively unimpaired group, there were 22 DEGs in MCI-LB/DLB and 61 DEGs in MCI-AD/AD. DEGS were also identified when comparing the two disease groups. Expression of ANP32A was associated with more rapid cognitive decline in MCI-AD/AD. Gene set enrichment analysis identified downregulation in gene sets including MYC targets and oxidative phosphorylation in MCI-LB/DLB; upregulation of immune and inflammatory responses in MCI-AD/AD; and upregulation of interferon-α and -γ responses in MCI-AD/AD compared with MCI-LB/DLB. This study identified multiple DEGs in MCI-LB/DLB and MCI-AD/AD. One of these DEGs, ANP32A, may be a prognostic marker in AD. Genes related to mitochondrial function were downregulated in MCI-LB/DLB. Previously reported upregulation of genes associated with inflammation and immune responses in MCI-AD/AD was confirmed in this cohort. Differences in interferon responses between MCI-AD/AD and MCI-LB/DLB suggest that there are key differences in peripheral immune responses between these diseases.

Analyzing RNA-Seq Gene Expression Data Using Deep Learning Approaches for Cancer Classification

Ribonucleic acid Sequencing (RNA-Seq) analysis is particularly useful for obtaining insights into differentially expressed genes. However, it is challenging because of its high-dimensional data. Such analysis is a tool with which to find underlying patterns in data, e.g., for cancer specific biomarkers. In the past, analyses were performed on RNA-Seq data pertaining to the same cancer class as positive and negative samples, i.e., without samples of other cancer types. To perform multiple cancer type classification and to find differentially expressed genes, data for multiple cancer types need to be analyzed. Several repositories offer RNA-Seq data for various cancer types. In this paper, data from the Mendeley data repository for five cancer types are analyzed. As a first step, RNA-Seq values are converted to 2D images using normalization and zero padding. In the next step, relevant features are extracted and selected using Deep Learning (DL). In the last phase, classification is performed, and eight DL algorithms are used. Results and discussion are based on four different splitting strategies and k-fold cross validation for each DL classifier. Furthermore, a comparative analysis is performed with state of the art techniques discussed in literature. The results demonstrated that classifiers performed best at 70–30 split, and that Convolutional Neural Network (CNN) achieved the best overall results. Hence, CNN is the best DL model for classification among the eight studied DL models, and is easy to implement and simple to understand.

An acidic polysaccharide from Patinopecten yessoensis skirt prevents obesity and improves gut microbiota and metabolism of mice induced by high-fat diet

The prevention of obesity is an urgent need for worldwide public health. The scallop (Patinopecten yessoensis) skirt is the by-product of adductor processing. In order to explore the value of the scallop skirt, the present study isolated an acidic polysaccharide from the skirt of P. yessoensis (SPYP) and evaluated its anti-obesity effect. SPYP was characterized as an acidic heteropolysaccharide with a molecular weight of 13.58 kDa. Through stepwise acid hydrolysis followed by HPLC-MSn analysis, two core chains in SPYP was determined as heparin-like → 4)β-GlcA(1 → 4)α-GlcNAc → and chondroitin sulfate-like → 4)β-GlcA(1 → 3)β-GalNAc →. SPYP could effectively reduce body weight, decrease fat accumulation and prevent high blood lipids induced by a high-fat diet (HFD) in mice. Further 16S ribosomal ribonucleic acid (rRNA) sequencing analysis revealed that the anti-obesity effect of SPYP was notably associated with its modulation on gut microbiota. The short-chain fatty acids were also restored by SPYP supplementation compared to the HFD group. Moreover, liquid chromatography-high resolution accurate mass spectrometry (LC-HRMS) analysis demonstrated that metabolite profiles of HFD-fed mice were altered by SPYP supplementation, especially the lipid metabolism in serum and amino acid metabolism in cecal content, which may contribute to the anti-obesity effect of SPYP. The present study offers the basis for processing and application of P. yessoensis skirt.

Knotify: An Efficient Parallel Platform for RNA Pseudoknot Prediction Using Syntactic Pattern Recognition.

Obtaining valuable clues for noncoding RNA (ribonucleic acid) subsequences remains a significant challenge, acknowledging that most of the human genome transcribes into noncoding RNA parts related to unknown biological operations. Capturing these clues relies on accurate “base pairing” prediction, also known as “RNA secondary structure prediction”. As COVID-19 is considered a severe global threat, the single-stranded SARS-CoV-2 virus reveals the importance of establishing an efficient RNA analysis toolkit. This work aimed to contribute to that by introducing a novel system committed to predicting RNA secondary structure patterns (i.e., RNA’s pseudoknots) that leverage syntactic pattern-recognition strategies. Having focused on the pseudoknot predictions, we formalized the secondary structure prediction of the RNA to be primarily a parsing and, secondly, an optimization problem. The proposed methodology addresses the problem of predicting pseudoknots of the first order (H-type). We introduce a context-free grammar (CFG) that affords enough expression power to recognize potential pseudoknot pattern. In addition, an alternative methodology of detecting possible pseudoknots is also implemented as well, using a brute-force algorithm. Any input sequence may highlight multiple potential folding patterns requiring a strict methodology to determine the single biologically realistic one. We conscripted a novel heuristic over the widely accepted notion of free-energy minimization to tackle such ambiguity in a performant way by utilizing each pattern’s context to unveil the most prominent pseudoknot pattern. The overall process features polynomial-time complexity, while its parallel implementation enhances the end performance, as proportional to the deployed hardware. The proposed methodology does succeed in predicting the core stems of any RNA pseudoknot of the test dataset by performing a 76.4% recall ratio. The methodology achieved a F1-score equal to 0.774 and MCC equal 0.543 in discovering all the stems of an RNA sequence, outperforming the particular task. Measurements were taken using a dataset of 262 RNA sequences establishing a performance speed of 1.31, 3.45, and 7.76 compared to three well-known platforms. The implementation source code is publicly available under knotify github repo.

Association of the Kynurenine Pathway of Tryptophan Metabolism With Human Immunodeficiency Virus-Related Gut Microbiota Alterations and Visceral Adipose Tissue Accumulation.

The aim of the study was to investigate the association between human immunodeficiency virus (HIV)-related gut microbiota changes, alterations in the kynurenine (Kyn) pathway of tryptophan (Trp) metabolism, and visceral adipose tissue in the context of HIV infection. Three hundred eighty-three people with HIV (PWH) were included from the Copenhagen comorbidity in HIV infection (COCOMO) study. Gut microbiota composition was analyzed by 16S ribosomal ribonucleic acid sequencing. Plasma metabolites were analyzed by liquid chromatography-tandem mass spectrometry. Visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) areas were measured by single-slice computed tomography (CT) scan (4th lumbar vertebra). The HIV-related gut microbiota alterations were associated with lower Trp (β -.01; 95% confidence interval [CI], -0.03 to -0.00) and higher Kyn-to-Trp ratio (β 0.03; 95% CI, 0.01-0.05), which in turn was associated with higher VAT-to-SAT ratio (β 0.50; 95% CI, 0.10-0.90) and larger VAT area (β 30.85; 95% CI, 4.43-57.28). In mediation analysis, the Kyn-to-Trp ratio mediated 10% (P = .023) of the association between the VAT-to-SAT ratio and HIV-related gut microbiota. Our data suggest HIV-related gut microbiota compositional changes and gut microbial translocation as potential drivers of high Kyn-to-Trp ratio in PWH. In turn, increased activity in the Kyn pathway of Trp metabolism was associated with larger visceral adipose tissue area. Taken together, our findings suggest a possible role for this pathway in the gut-adipose tissue axis in the context of HIV infection.

Prediction of Bacterial sRNAs Using Sequence-Derived Features and Machine Learning.

Small ribonucleic acid (sRNA) sequences are 50–500 nucleotide long, noncoding RNA (ncRNA) sequences that play an important role in regulating transcription and translation within a bacterial cell. As such, identifying sRNA sequences within an organism’s genome is essential to understand the impact of the RNA molecules on cellular processes. Recently, numerous machine learning models have been applied to predict sRNAs within bacterial genomes. In this study, we considered the sRNA prediction as an imbalanced binary classification problem to distinguish minor positive sRNAs from major negative ones within imbalanced data and then performed a comparative study with six learning algorithms and seven assessment metrics. First, we collected numerical feature groups extracted from known sRNAs previously identified in Salmonella typhimurium LT2 (SLT2) and Escherichia coli K12 (E. coli K12) genomes. Second, as a preliminary study, we characterized the sRNA-size distribution with the conformity test for Benford’s law. Third, we applied six traditional classification algorithms to sRNA features and assessed classification performance with seven metrics, varying positive-to-negative instance ratios, and utilizing stratified 10-fold cross-validation. We revisited important individual features and feature groups and found that classification with combined features perform better than with either an individual feature or a single feature group in terms of Area Under Precision-Recall curve (AUPR). We reconfirmed that AUPR properly measures classification performance on imbalanced data with varying imbalance ratios, which is consistent with previous studies on classification metrics for imbalanced data. Overall, eXtreme Gradient Boosting (XGBoost), even without exploiting optimal hyperparameter values, performed better than the other five algorithms with specific optimal parameter settings. As a future work, we plan to extend XGBoost further to a large amount of published sRNAs in bacterial genomes and compare its classification performance with recent machine learning models’ performance.

EL-RMLocNet: An explainable LSTM network for RNA-associated multi-compartment localization prediction

Subcellular localization of Ribonucleic Acid (RNA) molecules provide significant insights into the functionality of RNAs and helps to explore their association with various diseases. Predominantly developed single-compartment localization predictors (SCLPs) lack to demystify RNA association with diverse biochemical and pathological processes mainly happen through RNA co-localization in multiple compartments. Limited multi-compartment localization predictors (MCLPs) manage to produce decent performance only for target RNA class of particular sub-type. Further, existing computational approaches have limited practical significance and potential to optimize therapeutics due to the poor degree of model explainability. The paper in hand presents an explainable Long Short-Term Memory (LSTM) network “EL-RMLocNet”, predictive performance and interpretability of which are optimized using a novel GeneticSeq2Vec statistical representation learning scheme and attention mechanism for accurate multi-compartment localization prediction of different RNAs solely using raw RNA sequences. GeneticSeq2Vec generates optimized statistical vectors of raw RNA sequences by capturing short and long range relations of nucleotide k-mers. Using sequence vectors generated by GeneticSeq2Vec scheme, Long Short Term Memory layers extract most informative features, weighting of which on the basis of discriminative potential for accurate multi-compartment localization prediction is performed using attention layer. Through reverse engineering, weights of statistical feature space are mapped to nucleotide k-mers patterns to make multi-compartment localization prediction decision making transparent and explainable for different RNA classes and species. Empirical evaluation indicates that EL-RMLocNet outperforms state-of-the-art predictor for subcellular localization prediction of 4 different RNA classes by an average accuracy figure of 8% for Homo Sapiens species and 6% for Mus Musculus species. EL-RMLocNet is freely available as a web server at (https://sds_genetic_analysis.opendfki.de/subcellular_loc/).