RNA sequencing: advances, challenges and opportunities

Thousands of oscillating LncRNAs in the mouse testis

The long noncoding RNAs (lncRNAs) are involved in numerous fundamental biological processes, including circadian regulation. Although recent studies have revealed insights into the functions of lncRNAs, how the lncRNAs regulate circadian rhythms still requires a deeper investigation. In this study, we generate two datasets of RNA-seq profiles of the mouse (Mus musculus) testis under light-dark (LD) cycle. The first dataset included 18,613 unannotated transcripts measured at 12 time points, each with duplicate samples, under LD conditions; while the second dataset included 21,414 unannotated transcripts measured at six time points, each with three replicates, under desynchronized and control conditions. We identified 5964 testicular lncRNAs in each dataset by BLASTing these transcripts against the known mouse lncRNAs from the NONCODE database. MetaCycle analyses were performed to identify 519, 475, and 494 rhythmically expressed mouse testicular lncRNAs in the 12-time-point dataset, the six-time-point control dataset, and the six-time-point desynchronized dataset, respectively. A comparison of the expression profiles of the lncRNAs under desynchronized and control conditions revealed that 427 rhythmically expressed lncRNAs from the control condition became arrhythmic under the desynchronized condition, suggesting a possible loss of rhythmicity. In contrast, 446 arrhythmic lncRNAs from the control condition became rhythmic under the desynchronized condition, suggesting a possible gain of rhythmicity. Interestingly, 48 lncRNAs were rhythmically expressed under both desynchronized and control conditions. These oscillating lncRNAs were divided into morning lncRNAs, evening lncRNAs, and night lncRNAs based on their time-course expression patterns. We interrogated the promoter regions of these rhythmically expressed mouse testicular lncRNAs to predict their possible regulation by the E-box, D-box, or RORE promoter motifs. GO and KEGG analyses were performed to identify the possible biological functions of these rhythmically expressed mouse testicular lncRNAs. Further, we conducted conservation analyses of the rhythmically expressed mouse testicular lncRNAs with lncRNAs from humans, rats, and zebrafish, and uncovered three mouse testicular lncRNAs conserved across these four species. Finally, we computationally predicted the conserved lncRNA-encoded peptides and their 3D structures from each of the four species. Taken together, our study revealed thousands of rhythmically expressed lncRNAs in the mouse testis, setting the stage for further computational and experimental validations.

Integrated single-cell and bulk RNA sequencing identifies POSTN as a potential biomarker and therapeutic target for rheumatoid arthritis

BACKGROUND

This study aimed to integrate single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data to identify potential biomarkers and therapeutic targets for rheumatoid arthritis (RA).

METHOD

Firstly, we obtained the synovial scRNA-seq data from the Immport database and bulk RNA-seq data from the Gene Expression Omnibus (GEO) database. Then, we used weighted gene correlation network analysis (WGCNA) to screen for module genes most relevant to RA and intersected them with the differentially expressed genes (DEGs) obtained from scRNA-seq and bulk RNA-seq to obtain intersecting genes. Next, we constructed a protein-protein interaction (PPI) network of hub genes using the STRING database and Cytoscape software and validated its expression using external validation cohorts. Finally, we performed immune cell infiltration analysis using CIBERSORT and explored the expression and drug binding activity of key gene using clinical samples and molecular docking, respectively.

RESULT

We identified six cellular subgroups through dimensionality reduction and clustering, and fibroblasts may be the most important cell cluster in RA based on pseudotime and cell-cell communication analyses. Subsequently, we intersected module genes with DEGs obtained from scRNA-seq and bulk RNA-seq and constructed a PPI network of hub genes (BGN, COL11A1, COL1A1, GUCY1A1, POSTN). In external validation cohorts, POSTN was highly expressed and demonstrated the highest diagnostic performance (AUC = 0.716). In subsequent analyses, we defined POSTN as a key gene and found that its expression level was positively correlated with M2 macrophages in immune cell infiltration analysis. Additionally, POSTN was upregulated in clinical samples and exhibited favorable binding activity with nine anti-rheumatoid arthritis drugs (affinity ≤ -6.0 kcal/mol).

CONCLUSION

Through bioinformatics analysis, clinical sample validation, and molecular docking, we found that POSTN was highly expressed in RA and stably bound to common anti-rheumatoid arthritis drugs, which will provide new insights into potential biomarkers and therapeutic targets for RA.

Screening of reference genes for gene expression study in different tissues from the transcriptome data of the vector leafhopper Psammotettix striatus

Selecting appropriate reference genes is crucial for ensuring the accuracy and reliability of gene expression study using reverse transcription-quantitative PCR (RT-qPCR). To screen the optimal reference genes for analyzing gene expression in different tissues of the vector leafhopper Psammotettix striatus which causes extensive damage to a wide range of crops by vectoring multiple plant pathogenic microorganisms, the transcriptome data from Malpighian tubules (MTs) of P. striatus were mined. Twenty alternative candidate reference genes were initially selected for screening, among which seven genes with diverse Gene Ontology (GO) annotations were choosed as candidate reference genes, i.e., ribosomal protein L7A (RPL7A), ribosomal protein S28 (RPS28), ribosomal protein L22 (RPL22), ribosomal protein LP2 (RPLP2), H3 histone family 3A (H3F3A), elongation factor 1γ (EF-1γ), and elongation factor 1α (EF-1α). Gene expression levels in different tissues of P. striatus adults were examined using RT-qPCR, and their expression stability was analyzed using multiple reference gene screening software. This study revealed EF-1α as the most abundantly expressed gene, while RPL22 exhibited the lowest expression levels. EF-1α showed the most stable expression, whereas RPS28 showed the least stability. Various software tools confirmed EF-1α as the most stable single reference gene, and EF-1α and RPLP2 an optimal combination. This study provides a foundation for future investigation of the transmission of pathogenic microorganisms mediated by the vector leafhoppers, the function of the MTs, the biosynthesis of brochosomes, the coevolutionary processes and nutritional interactions of symbionts and host insects, and the gene expression study of other sap-sucking insects.

Preservation of cfRNA in cytological supernatants for cfDNA & cfRNA double detection in non-small cell lung cancer patients

BACKGROUD

Supernatants from various cytological samples, including body cavity effusion, sputum, bronchoalveolar lavage fluid (BALF), and needle aspiration, have been validated for detecting genetic alterations using cell-free DNA (cfDNA) in patients with non-small cell lung cancer (NSCLC). However, the sensitivity of fusion variations detection remains challenging. The protection of cell-free RNA (cfRNA) is critical for resolving the issue.

METHODS

A protective solution (PS) was applied for preserving cfRNA in cytological supernatant (CS), and the quality of protected cfRNA was assessed by cycle threshold (CT) values from reverse transcription quantitative polymerase chain reaction (RT-qPCR). Furthermore, we collected an additional set of malignant cytological and matched tumor samples from 84 NSCLC patients, cfDNA & cfRNA extraction and double detection for driver gene mutations was validated using the multi-gene mutations detection by RT-qPCR.

RESULTS

Under the optimal protection system, 91.0% (101/111) of cfRNA were protected effectively. Among the 84 NSCLC patient samples, seven cytological samples failed the tests. In comparison with tumor samples, the overall sensitivity and specificity of detecting driver genes of supernatant cfDNA and cfRNA were 93.8% (74/77) and 100% (77/77), respectively. Notably, when focusing exclusively on patients with fusion gene changes, both sensitivity and specificity reached 100% (11/11) for EML4-ALK, ROS1, RET fusions, and MET ex14 skipping.

CONCLUSION

These findings suggest that cfDNA & cfRNA extraction and double detection strategy recommended in this study improve the accuracy of driver genes mutations test, especially for RNA-based assay.

Unraveling flavivirus pathogenesis: from bulk to single-cell RNA-sequencing strategies

The global spread of flaviviruses has triggered major outbreaks worldwide, significantly impacting public health, society, and economies. This has intensified research efforts to understand how flaviviruses interact with their hosts and manipulate the immune system, underscoring the need for advanced research tools. RNA-sequencing (RNA-seq) technologies have revolutionized our understanding of flavivirus infections by offering transcriptome analysis to dissect the intricate dynamics of virus-host interactions. Bulk RNA-seq provides a macroscopic overview of gene expression changes in virus-infected cells, offering insights into infection mechanisms and host responses at the molecular level. Single-cell RNA sequencing (scRNAseq) provides unprecedented resolution by analyzing individual infected cells, revealing remarkable cellular heterogeneity within the host response. A particularly innovative advancement, virus-inclusive single-cell RNA sequencing (viscRNA-seq), addresses the challenges posed by non-polyadenylated flavivirus genomes, unveiling intricate details of virus-host interactions. In this review, we discuss the contributions of bulk RNA-seq, scRNA-seq, and viscRNA-seq to the field, exploring their implications in cell line experiments and studies on patients infected with various flavivirus species. Comprehensive transcriptome analyses from RNA-seq technologies are pivotal in accelerating the development of effective diagnostics and therapeutics, paving the way for innovative treatments and enhancing our preparedness for future outbreaks.

NERD-seq: a novel approach of Nanopore direct RNA sequencing that expands representation of non-coding RNAs

Non-coding RNAs (ncRNAs) are frequently documented RNA modification substrates. Nanopore Technologies enables the direct sequencing of RNAs and the detection of modified nucleobases. Ordinarily, direct RNA sequencing uses polyadenylation selection, studying primarily mRNA gene expression. Here, we present NERD-seq, which enables detection of multiple non-coding RNAs, excluded by the standard approach, alongside natively polyadenylated transcripts. Using neural tissues as a proof of principle, we show that NERD-seq expands representation of frequently modified non-coding RNAs, such as snoRNAs, snRNAs, scRNAs, srpRNAs, tRNAs, and rRFs. NERD-seq represents an RNA-seq approach to simultaneously study mRNA and ncRNA epitranscriptomes in brain tissues and beyond.

scSwinFormer: A Transformer-Based Cell-Type Annotation Method for scRNA-Seq Data Using Smooth Gene Embedding and Global Features

Single-cell omics techniques have made it possible to analyze individual cells in biological samples, providing us with a more detailed understanding of cellular heterogeneity and biological systems. Accurate identification of cell types is critical for single-cell RNA sequencing (scRNA-seq) analysis. However, scRNA-seq data are usually high dimensional and sparse, posing a great challenge to analyze scRNA-seq data. Existing cell-type annotation methods are either constrained in modeling scRNA-seq data or lack consideration of long-term dependencies of characterized genes. In this work, we developed a Transformer-based deep learning method, scSwinFormer, for the cell-type annotation of large-scale scRNA-seq data. Sequence modeling of scRNA-seq data is performed using the smooth gene embedding module, and then, the potential dependencies of genes are captured by the self-attention module. Subsequently, the global information inherent in scRNA-seq data is synthesized using the Cell Token, thereby facilitating accurate cell-type annotation. We evaluated the performance of our model against current state-of-the-art scRNA-seq cell-type annotation methods on multiple real data sets. ScSwinFormer outperforms the current state-of-the-art scRNA-seq cell-type annotation methods in both external and benchmark data set experiments.

Analysis on morphological characteristics and identification of candidate genes during the flowering development of alfalfa

Flower development is a crucial and complex process in the reproductive stage of plants, which involves the interaction of multiple endogenous signals and environmental factors. However, regulatory mechanism of flower development was unknown in alfalfa (Medicago sativa). In this study, the three stages of flower development of 'M. sativa cv. Gannong No. 5' (G5) and its early flowering and multi flowering mutant (MG5) were comparatively analyzed by transcriptomics. The results showed that compared with late bud stage (S1), 14287 and 8351 differentially expressed genes (DEGs) were identified at early flower stage (S2) in G5 and MG5, and 19941 and 19469 DEGs were identified at late flower stage (S3). Compared with S2, 9574 and 10870 DEGs were identified at S3 in G5 and MG5, respectively. Venn analysis revealed that 547 DEGs were identified among the three comparison groups. KEGG pathway enrichment analysis showed that these genes were involved in the development of alfalfa flowers through redox pathways and plant hormone signaling pathways. Key candidate genes including SnRK2, BSK, GID1, DELLA and CRE1, for regulating the development from buds to mature flowers in alfalfa were screened. In addition, differential expression of transcription factors such as MYB, AP2, bHLH, C2C2, MADS-box, NAC, bZIP, B3 and AUX/IAA also played an important role in this process. The results laid a theoretical foundation for studying the molecular mechanisms of the development process from buds to mature flowers in alfalfa.

PLEKv2: predicting lncRNAs and mRNAs based on intrinsic sequence features and the coding-net model

BACKGROUND

Long non-coding RNAs (lncRNAs) are RNA transcripts of more than 200 nucleotides that do not encode canonical proteins. Their biological structure is similar to messenger RNAs (mRNAs). To distinguish between lncRNA and mRNA transcripts quickly and accurately, we upgraded the PLEK alignment-free tool to its next version, PLEKv2, and constructed models tailored for both animals and plants.

RESULTS

PLEKv2 can achieve 98.7% prediction accuracy for human datasets. Compared with classical tools and deep learning-based models, this is 8.1%, 3.7%, 16.6%, 1.4%, 4.9%, and 48.9% higher than CPC2, CNCI, Wen et al.'s CNN, LncADeep, PLEK, and NcResNet, respectively. The accuracy of PLEKv2 was > 90% for cross-species prediction. PLEKv2 is more effective and robust than CPC2, CNCI, LncADeep, PLEK, and NcResNet for primate datasets (including chimpanzees, macaques, and gorillas). Moreover, PLEKv2 is not only suitable for non-human primates that are closely related to humans, but can also predict the coding ability of RNA sequences in plants such as Arabidopsis.

CONCLUSIONS

The experimental results illustrate that the model constructed by PLEKv2 can distinguish lncRNAs and mRNAs better than PLEK. The PLEKv2 software is freely available at https://sourceforge.net/projects/plek2/ .

Phenomics in Livestock Research: Bottlenecks and Promises of Digital Phenotyping and Other Quantification Techniques on a Global Scale

Bottlenecks in moving genomics to real-life applications also include phenomics. This is true not only for genomics medicine and public health genomics but also in ecology and livestock phenomics. This expert narrative review explores the intricate relationship between genetic makeup and observable phenotypic traits across various biological levels in the context of livestock research. We unpack and emphasize the significance of precise phenotypic data in selective breeding outcomes and examine the multifaceted applications of phenomics, ranging from improvement to assessing welfare, reproductive traits, and environmental adaptation in livestock. As phenotypic traits exhibit strong correlations, their measurement alongside specific biological outcomes provides insights into performance, overall health, and clinical endpoints like morbidity and disease. In addition, automated assessment of livestock holds potential for monitoring the dynamic phenotypic traits across various species, facilitating a deeper comprehension of how they adapt to their environment and attendant stressors. A key challenge in genetic improvement in livestock is predicting individuals with optimal fitness without direct measurement. Temporal predictions from unmanned aerial systems can surpass genomic predictions, offering in-depth data on livestock. In the near future, digital phenotyping and digital biomarkers may further unravel the genetic intricacies of stress tolerance, adaptation and welfare aspects of animals enabling the selection of climate-resilient and productive livestock. This expert review thus delves into challenges associated with phenotyping and discusses technological advancements shaping the future of biological research concerning livestock.

RNA-sequencing exploration on SIR2 and SOD genes in Polyalthia longifolia leaf methanolic extracts (PLME) mediated anti-aging effects in Saccharomyces cerevisiae BY611 yeast cells

Polyalthia longifolia is well-known for its abundance of polyphenol content and traditional medicinal uses. Previous research has demonstrated that the methanolic extract of P. longifolia leaves (PLME, 1 mg/mL) possesses anti-aging properties in Saccharomyces cerevisiae BY611 yeast cells. Building on these findings, this study delves deeper into the potential antiaging mechanism of PLME, by analyzing the transcriptional responses of BY611 cells treated with PLME using RNA-sequencing (RNA-seq) technology. The RNA-seq analysis results identified 1691 significantly (padj < 0.05) differentially expressed genes, with 947 upregulated and 744 downregulated genes. Notably, the expression of three important aging-related genes, SIR2, SOD1, and SOD2, showed a significant difference following PLME treatment. The subsequent integration of these targeted genes with GO and KEGG pathway analysis revealed the multifaceted nature of PLME's anti-aging effects in BY611 yeast cells. Enriched GO and KEGG analysis showed that PLME treatment promotes the upregulation of SIR2, SOD1, and SOD2 genes, leading to a boosted cellular antioxidant defense system, reduced oxidative stress, regulated cell metabolism, and maintain genome stability. These collectively increased longevities in PLME-treated BY611 yeast cells and indicate the potential anti-aging action of PLME through the modulation of SIR2 and SOD genes. The present study provided novel insights into the roles of SIR2, SOD1, and SOD2 genes in the anti-aging effects of PLME treatment, offering promising interventions for promoting healthy aging.

GAM-MDR: probing miRNA-drug resistance using a graph autoencoder based on random path masking

MicroRNAs (miRNAs) are found ubiquitously in biological cells and play a pivotal role in regulating the expression of numerous target genes. Therapies centered around miRNAs are emerging as a promising strategy for disease treatment, aiming to intervene in disease progression by modulating abnormal miRNA expressions. The accurate prediction of miRNA-drug resistance (MDR) is crucial for the success of miRNA therapies. Computational models based on deep learning have demonstrated exceptional performance in predicting potential MDRs. However, their effectiveness can be compromised by errors in the data acquisition process, leading to inaccurate node representations. To address this challenge, we introduce the GAM-MDR model, which combines the graph autoencoder (GAE) with random path masking techniques to precisely predict potential MDRs. The reliability and effectiveness of the GAM-MDR model are mainly reflected in two aspects. Firstly, it efficiently extracts the representations of miRNA and drug nodes in the miRNA-drug network. Secondly, our designed random path masking strategy efficiently reconstructs critical paths in the network, thereby reducing the adverse impact of noisy data. To our knowledge, this is the first time that a random path masking strategy has been integrated into a GAE to infer MDRs. Our method was subjected to multiple validations on public datasets and yielded promising results. We are optimistic that our model could offer valuable insights for miRNA therapeutic strategies and deepen the understanding of the regulatory mechanisms of miRNAs. Our data and code are publicly available at GitHub:https://github.com/ZZCrazy00/GAM-MDR.

Machine Learning-Assisted Direct RNA Sequencing with Epigenetic RNA Modification Detection via Quantum Tunneling

RNA sequence information holds immense potential as a drug target for diagnosing various RNA-mediated diseases and viral/bacterial infections. Massively parallel complementary DNA (c-DNA) sequencing helps but results in a loss of valuable information about RNA modifications, which are often associated with cancer evolution. Herein, we report machine learning (ML)-assisted high throughput RNA sequencing with inherent RNA modification detection using a single-molecule, long-read, and label-free quantum tunneling approach. The ML tools achieve classification accuracy as high as 100% in decoding RNA and 98% for decoding both RNA and RNA modifications simultaneously. The relationships between input features and target values have been well examined through Shapley additive explanations. Furthermore, transmission and sensitivity readouts enable the recognition of RNA and its modifications with good selectivity and sensitivity. Our approach represents a starting point for ML-assisted direct RNA sequencing that can potentially decode RNA and its epigenetic modifications at the molecular level.

Single-cell RNA sequencing in ovarian cancer: revealing new perspectives in the tumor microenvironment

Single-cell sequencing technology has emerged as a pivotal tool for unraveling the complexities of the ovarian tumor microenvironment (TME), which is characterized by its cellular heterogeneity and intricate cell-to-cell interactions. Ovarian cancer (OC), known for its high lethality among gynecologic malignancies, presents significant challenges in treatment and diagnosis, partly due to the complexity of its TME. The application of single-cell sequencing in ovarian cancer research has enabled the detailed characterization of gene expression profiles at the single-cell level, shedding light on the diverse cell populations within the TME, including cancer cells, stromal cells, and immune cells. This high-resolution mapping has been instrumental in understanding the roles of these cells in tumor progression, invasion, metastasis, and drug resistance. By providing insight into the signaling pathways and cell-to-cell communication mechanisms, single-cell sequencing facilitates the identification of novel therapeutic targets and the development of personalized medicine approaches. This review summarizes the advancement and application of single-cell sequencing in studying the stromal components and the broader TME in OC, highlighting its implications for improving diagnosis, treatment strategies, and understanding of the disease's underlying biology.

Combining multiomics to analyze the molecular mechanism of hair follicle cycle change in cashmere goats from Inner Mongolia

Sheep body size can directly reflect the growth rates and fattening rates of sheep and is also an important index for measuring the growth performance of meat sheep.Inner Mongolia Cashmere Goat is a local excellent breed of cashmere and meat dual-purpose, which is a typical heterogeneous indumentum. The hair follicles cycle through periods of vigorous growth (anagen), a regression caused by apoptosis (catagen), and relative rest (telogen). At present, it is not clear which genes affect the cycle transformation of hair follicles and unclear how proteins impact the creation and expansion of hair follicles.we using multi-omics joint analysis methodologies to investigated the possible pathways of transformation and apoptosis in goat hair follicles. The results showed that 917,1,187, and 716 proteins were specifically expressed in anagen, catagen andtelogen. The result of gene ontology (GO) annotation showed that differentially expressed proteins (DEPs) are in different growth cycle periods, and enriched GO items are mostly related to the transformation of cells and proteins. The Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment result indicated that the apoptosis process has a great impact on hair follicle's growth cycle. The results of the protein interaction network of differential proteins showed that the ribosomal protein family (RPL4, RPL8, RPS16, RPS18, RPS2, RPS27A, RPS3) was the core protein in the network. The results of combined transcriptome and proteomics analysis showed that there were 16,34, and 26 overlapped DEGs and DEPs in the comparison of anagen VS catagen, catagen VS telogen and anagen VS telogen, of which API5 plays an important role in regulating protein and gene expression levels. We focused on API5 and Ribosomal protein and found that API5 affected the apoptosis process of hair follicles, and ribosomal protein was highly expressed in the resting stage of hair follicles. They are both useful as molecular marker candidate genes to study hair follicle growth and apoptosis,and they both have an essential function in the cycle transition process of hair follicles. The results of this study may provide a theoretical basis for further research on the growth and development of hair follicles in Inner Mongolian Cashmere goats.

Engineering circular RNA for molecular and metabolic reprogramming

The role of messenger RNA (mRNA) in biological systems is extremely versatile. However, it's extremely short half-life poses a fundamental restriction on its application. Moreover, the translation efficiency of mRNA is also limited. On the contrary, circular RNAs, also known as circRNAs, are a common and stable form of RNA found in eukaryotic cells. These molecules are synthesized via back-splicing. Both synthetic circRNAs and certain endogenous circRNAs have the potential to encode proteins, hence suggesting the potential of circRNA as a gene expression machinery. Herein, we aim to summarize all engineering aspects that allow exogenous circular RNA (circRNA) to prolong the time that proteins are expressed from full-length RNA signals. This review presents a systematic engineering approach that have been devised to efficiently assemble circRNAs and evaluate several aspects that have an impact on protein production derived from. We have also reviewed how optimization of the key components of circRNAs, including the topology of vector, 5' and 3' untranslated sections, entrance site of the internal ribosome, and engineered aptamers could be efficiently impacting the translation machinery for molecular and metabolic reprogramming. Collectively, molecular and metabolic reprogramming present a novel way of regulating distinctive cellular features, for instance growth traits to neoplastic cells, and offer new possibilities for therapeutic inventions.

RNA-Seq Reveals Pathways Responsible for Meat Quality Characteristic Differences between Two Yunnan Indigenous Chicken Breeds and Commercial Broilers

Poultry is a source of meat that is in great demand in the world. The quality of meat is an imperative point for shoppers. To explore the genes controlling meat quality characteristics, the growth and meat quality traits and muscle transcriptome of two indigenous Yunnan chicken breeds, Wuding chickens (WDs) and Daweishan mini chickens (MCs), were compared with Cobb broilers (CBs). The growth and meat quality characteristics of these two indigenous breeds were found to differ from CB. In particular, the crude fat (CF), inosine monophosphate content, amino acid (AA), and total fatty acid (TFA) content of WDs were significantly higher than those of CBs and MCs. In addition, it was found that MC pectoralis had 420 differentially expressed genes (DEGs) relative to CBs, and WDs had 217 DEGs relative to CBs. Among them, 105 DEGs were shared. The results of 10 selected genes were also confirmed by qPCR. The differentially expressed genes were six enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) biological pathways including lysosomes, phagosomes, PPAR signaling pathways, cell adhesion molecules, cytokine-cytokine receptor interaction, and phagosome sphingolipid metabolism. Interestingly, four genes (LPL, GK, SCD, and FABP7) in the PPAR signal pathway related to fatty acid (FA) metabolism were elevated in WD muscles, which may account for higher CF, inosine monophosphate content, and AA and FA contents, key factors affecting meat quality. This work laid the foundation for improving the meat quality of Yunnan indigenous chickens, especially WD. In future molecular breeding, the genes in this study can be used as molecular screening markers and applied to the molecular breeding of chicken quality characteristics.

Combined transcriptomic and metabolomic analysis revealed that pH changes affected the expression of carbohydrate and ribosome biogenesis-related genes in Aspergillus niger SICU-33

The process of carbohydrate metabolism and genetic information transfer is an important part of the study on the effects of the external environment on microbial growth and development. As one of the most significant environmental parameters, pH has an important effect on mycelial growth. In this study, the effects of environmental pH on the growth and nutrient composition of Aspergillus niger (A. niger) filaments were determined. The pH values of the medium were 5, 7, and 9, respectively, and the molecular mechanism was further investigated by transcriptomics and metabolomics methods. The results showed that pH 5 and 9 significantly inhibited filament growth and polysaccharide accumulation of A. niger. Further, the mycelium biomass of A. niger and the crude polysaccharide content was higher when the medium's pH was 7. The DEGs related to ribosome biogenesis were the most abundant, and the downregulated expression of genes encoding XRN1, RRM, and RIO1 affected protein translation, modification, and carbohydrate metabolism in fungi. The dynamic changes of pargyline and choline were in response to the oxidative metabolism of A. niger SICU-33. The ribophorin_I enzymes and DL-lactate may be important substances related to pH changes during carbohydrate metabolism of A.niger SICU-33. The results of this study provide useful transcriptomic and metabolomic information for further analyzing the bioinformatic characteristics of A. niger and improving the application in ecological agricultural fermentation.

Next generation sequencing technologies to address aberrant mRNA translation in cancer

In this review, we explore the transformative impact of next generation sequencing technologies in the realm of translatomics (the study of how translational machinery acts on a genome-wide scale). Despite the expectation of a direct correlation between mRNA and protein content, the complex regulatory mechanisms that affect this relationship remark the limitations of standard RNA-seq approaches. Then, the review characterizes crucial techniques such as polysome profiling, ribo-seq, trap-seq, proximity-specific ribosome profiling, rnc-seq, tcp-seq, qti-seq and scRibo-seq. All these methods are summarized within the context of cancer research, shedding light on their applications in deciphering aberrant translation in cancer cells. In addition, we encompass databases and bioinformatic tools essential for researchers that want to address translatome analysis in the context of cancer biology.

Microbial metabolite deoxycholic acid-mediated ferroptosis exacerbates high-fat diet-induced colonic inflammation

High-fat diet (HFD) has long been recognized as risk factors for the development and progression of ulcerative colitis (UC), but the exact mechanism remained elusive. Here, HFD increased intestinal deoxycholic acid (DCA) levels, and DCA further exacerbated colonic inflammation. Transcriptome analysis revealed that DCA triggered ferroptosis pathway in colitis mice. Mechanistically, DCA upregulated hypoxia-inducible factor-2α (HIF-2α) and divalent metal transporter-1 (DMT1) expression, causing the ferrous ions accumulation and ferroptosis in intestinal epithelial cells, which was reversed by ferroptosis inhibitor ferrostatin-1. DCA failed to promote colitis and ferroptosis in intestine-specific HIF-2α-null mice. Notably, byak-angelicin inhibited DCA-induced pro-inflammatory and pro-ferroptotic effects through blocking the up-regulation of HIF-2α by DCA. Moreover, fat intake was positively correlated with disease activity in UC patients consuming HFD, with ferroptosis being more pronounced. Collectively, our findings demonstrated that HFD exacerbated colonic inflammation by promoting DCA-mediated ferroptosis, providing new insights into diet-related bile acid dysregulation in UC.

Next-Generation Sequencing-Based Spatial Transcriptomics: A Perspective from Barcoding Chemistry

Gene expression profiling of tissue cells with spatial context is in high demand to reveal cell types, locations, and intercellular or molecular interactions for physiological and pathological studies. With rapid advances in barcoding chemistry and sequencing chemistry, spatially resolved transcriptome (SRT) techniques have emerged to quantify spatial gene expression in tissue samples by correlating transcripts with their spatial locations using diverse strategies. These techniques provide both physical tissue structure and molecular characteristics and are poised to revolutionize many fields, such as developmental biology, neuroscience, oncology, and histopathology. In this context, this Perspective focuses on next-generation sequencing-based SRT methods, particularly highlighting spatial barcoding chemistry. It delves into optically manipulated spatial indexing methods and DNA array-barcoded spatial indexing methods by exploring current advances, challenges, and future development directions in this nascent field.

Transcriptomics confirms IRF1 as a key regulator of pyroptosis in diabetic retinopathy

BACKGROUND

Diabetic retinopathy (DR) is a retinal microvascular complication caused by hyperglycemia, which can lead to visual impairment or blindness. Pyroptosis is a type of inflammation-related programmed cell death, activated by caspase-1, resulting in the maturation of IL-1β and IL-18 and the rupture of the cell membrane. RNA sequencing (RNA-seq) is a high-throughput sequencing technique that reveals the presence and quantity of RNA in the genome at a specific time point, i.e., the transcriptome. RNA-seq can analyze gene expression levels, splicing variants, mutations, fusions, editing and other post-transcriptional modifications, as well as gene expression differences between different samples or conditions. It has been widely used in biological and medical research, clinical diagnosis and new drug development. This study aimed to establish an in vitro model of diabetic retinopathy by culturing human retinal endothelial cells (HREC) with high glucose (30 mmol/L), and to detect their transcriptome expression by RNA-seq, screen for key genes related to pyroptosis, and validate the sequencing results by subsequent experiments.

METHODS

We used RNA-seq to detect the transcriptome expression differences between HREC cells cultured with high glucose and control group, and identified differentially expressed genes by GO/KEGG analysis. We constructed a PPI network and determined the key genes by Cytoscape software and CytoHubba plugin. We validated the expression of related factors by Western Blot, qPCR and ELISA.

RESULTS

We performed GO and KEGG analysis on the RNA-seq data and found differentially expressed genes. We used Cytoscape and CytoHubba plugin to screen out IRF1 as the key gene, and then detected the expression of IRF1 in HREC under high glucose and control group by Western Blot and qPCR. We found that the expression of Caspase-1, GSDMD and IL-1β proteins in HREC under high glucose increased, while the expression of these proteins decreased after the inhibition of IRF1 by siRNA. ELISA showed that the secretion of IL-1β in HREC under high glucose increased, while the inhibition of IRF1 reduced the secretion of IL-1β. These results indicate that IRF1 plays an important role in DR, and provides a new target and strategy for the prevention and treatment of this disease.

ProBac-seq, a bacterial single-cell RNA sequencing methodology using droplet microfluidics and large oligonucleotide probe sets

Methods that measure the transcriptomic state of thousands of individual cells have transformed our understanding of cellular heterogeneity in eukaryotic cells since their introduction in the past decade. While simple and accessible protocols and commercial products are now available for the processing of mammalian cells, these existing technologies are incompatible with use in bacterial samples for several fundamental reasons including the absence of polyadenylation on bacterial messenger RNA, the instability of bacterial transcripts and the incompatibility of bacterial cell morphology with existing methodologies. Recently, we developed ProBac sequencing (ProBac-seq), a method that overcomes these technical difficulties and provides high-quality single-cell gene expression data from thousands of bacterial cells by using messenger RNA-specific probes. Here we provide details for designing large oligonucleotide probe sets for an organism of choice, amplifying probe sets to produce sufficient quantities for repeated experiments, adding unique molecular indexes and poly-A tails to produce finalized probes, in situ probe hybridization and single-cell encapsulation and library preparation. This protocol, from the probe amplification to the library preparation, requires ~7 d to complete. ProBac-seq offers several advantages over other methods by capturing only the desired target sequences and avoiding nondesired transcripts, such as highly abundant ribosomal RNA, thus enriching for signal that better informs on cellular state. The use of multiple probes per gene can detect meaningful single-cell signals from cells expressing transcripts to a lesser degree or those grown in minimal media and other environmentally relevant conditions in which cells are less active. ProBac-seq is also compatible with other organisms that can be profiled by in situ hybridization techniques.

Benchmarking of methods for DNA methylome deconvolution

Defining the number and abundance of different cell types in tissues is important for understanding disease mechanisms as well as for diagnostic and prognostic purposes. Typically, this is achieved by immunohistological analyses, cell sorting, or single-cell RNA-sequencing. Alternatively, cell-specific DNA methylome information can be leveraged to deconvolve cell fractions from a bulk DNA mixture. However, comprehensive benchmarking of deconvolution methods and modalities was not yet performed. Here we evaluate 16 deconvolution algorithms, developed either specifically for DNA methylome data or more generically. We assess the performance of these algorithms, and the effect of normalization methods, while modeling variables that impact deconvolution performance, including cell abundance, cell type similarity, reference panel size, method for methylome profiling (array or sequencing), and technical variation. We observe differences in algorithm performance depending on each these variables, emphasizing the need for tailoring deconvolution analyses. The complexity of the reference, marker selection method, number of marker loci and, for sequencing-based assays, sequencing depth have a marked influence on performance. By developing handles to select the optimal analysis configuration, we provide a valuable source of information for studies aiming to deconvolve array- or sequencing-based methylation data.

Epitranscriptome insights into Riccia fluitans L. (Marchantiophyta) aquatic transition using nanopore direct RNA sequencing

BACKGROUND

Riccia fluitans, an amphibious liverwort, exhibits a fascinating adaptation mechanism to transition between terrestrial and aquatic environments. Utilizing nanopore direct RNA sequencing, we try to capture the complex epitranscriptomic changes undergone in response to land-water transition.

RESULTS

A significant finding is the identification of 45 differentially expressed genes (DEGs), with a split of 33 downregulated in terrestrial forms and 12 upregulated in aquatic forms, indicating a robust transcriptional response to environmental changes. Analysis of N6-methyladenosine (m6A) modifications revealed 173 m6A sites in aquatic and only 27 sites in the terrestrial forms, indicating a significant increase in methylation in the former, which could facilitate rapid adaptation to changing environments. The aquatic form showed a global elongation bias in poly(A) tails, which is associated with increased mRNA stability and efficient translation, enhancing the plant's resilience to water stress. Significant differences in polyadenylation signals were observed between the two forms, with nine transcripts showing notable changes in tail length, suggesting an adaptive mechanism to modulate mRNA stability and translational efficiency in response to environmental conditions. This differential methylation and polyadenylation underline a sophisticated layer of post-transcriptional regulation, enabling Riccia fluitans to fine-tune gene expression in response to its living conditions.

CONCLUSIONS

These insights into transcriptome dynamics offer a deeper understanding of plant adaptation strategies at the molecular level, contributing to the broader knowledge of plant biology and evolution. These findings underscore the sophisticated post-transcriptional regulatory strategies Riccia fluitans employs to navigate the challenges of aquatic versus terrestrial living, highlighting the plant's dynamic adaptation to environmental stresses and its utility as a model for studying adaptation mechanisms in amphibious plants.

Detection and identification of single ribonucleotide monophosphates using a dual in-plane nanopore sensor made in a thermoplastic via replication

We report the generation of ∼8 nm dual in-plane pores fabricated in a thermoplastic via nanoimprint lithography (NIL). These pores were connected in series with nanochannels, one of which served as a flight tube to allow the identification of single molecules based on their molecular-dependent apparent mobilities (i.e., dual in-plane nanopore sensor). Two different thermoplastics were investigated including poly(methyl methacrylate), PMMA, and cyclic olefin polymer, COP, as the substrate for the sensor both of which were sealed using a low glass transition cover plate (cyclic olefin co-polymer, COC) that could be thermally fusion bonded to the PMMA or COP substrate at a temperature minimizing nanostructure deformation. Unique to these dual in-plane nanopore sensors was two pores flanking each side of the nanometer flight tube (50 × 50 nm, width × depth) that was 10 μm in length. The utility of this dual in-plane nanopore sensor was evaluated to not only detect, but also identify single ribonucleotide monophosphates (rNMPs) by using the travel time (time-of-flight, ToF), the resistive pulse event amplitude, and the dwell time. In spite of the relatively large size of these in-plane pores (∼8 nm effective diameter), we could detect via resistive pulse sensing (RPS) single rNMP molecules at a mass load of 3.9 fg, which was ascribed to the unique structural features of the nanofluidic network and the use of a thermoplastic with low relative dielectric constants, which resulted in a low RMS noise level in the open pore current. Our data indicated that the identification accuracy of individual rNMPs was high, which was ascribed to an improved chromatographic contribution to the nano-electrophoresis apparent mobility. With the ToF data only, the identification accuracy was 98.3%. However, when incorporating the resistive pulse sensing event amplitude and dwell time in conjunction with the ToF and analyzed via principal component analysis (PCA), the identification accuracy reached 100%. These findings pave the way for the realization of a novel chip-based single-molecule RNA sequencing technology.

The Microglial Transcriptome of Age-Associated Deep Subcortical White Matter Lesions Suggests a Neuroprotective Response to Blood-Brain Barrier Dysfunction

Age-associated deep-subcortical white matter lesions (DSCLs) are an independent risk factor for dementia, displaying high levels of CD68+ microglia. This study aimed to characterize the transcriptomic profile of microglia in DSCLs and surrounding radiologically normal-appearing white matter (NAWM) compared to non-lesional control white matter. CD68+ microglia were isolated from white matter groups (n = 4 cases per group) from the Cognitive Function and Ageing Study neuropathology cohort using immuno-laser capture microdissection. Microarray gene expression profiling, but not RNA-sequencing, was found to be compatible with immuno-LCM-ed post-mortem material in the CFAS cohort and identified significantly differentially expressed genes (DEGs). Functional grouping and pathway analysis were assessed using the Database for Annotation Visualization and Integrated Discovery (DAVID) software, and immunohistochemistry was performed to validate gene expression changes at the protein level. Transcriptomic profiling of microglia in DSCLs compared to non-lesional control white matter identified 181 significant DEGs (93 upregulated and 88 downregulated). Functional clustering analysis in DAVID revealed dysregulation of haptoglobin-haemoglobin binding (Enrichment score 2.5, p = 0.017), confirmed using CD163 immunostaining, suggesting a neuroprotective microglial response to blood-brain barrier dysfunction in DSCLs. In NAWM versus control white matter, microglia exhibited 347 DEGs (209 upregulated, 138 downregulated), with significant dysregulation of protein de-ubiquitination (Enrichment score 5.14, p < 0.001), implying an inability to maintain protein homeostasis in NAWM that may contribute to lesion spread. These findings enhance understanding of microglial transcriptomic changes in ageing white matter pathology, highlighting a neuroprotective adaptation in DSCLs microglia and a potentially lesion-promoting phenotype in NAWM microglia.

LINK-A: unveiling its functional role and clinical significance in human tumors

LINK-A, also recognized as LINC01139, has emerged as a key oncological lncRNA in cancer. LINK-A is upregulated in solid and liquid tumor samples, including breast cancer, ovarian cancer, glioma, non-small-cell lung cancer, and mantle cell lymphoma. Notably, LINK-A is involved in regulating critical cancer-related pathways, such as AKT and HIF1α signaling, and is implicated in a range of oncogenic activities, including cell proliferation, apoptosis, epithelial-mesenchymal transition (EMT), cell invasion and migration, and glycolysis reprogramming. LINK-A's differential expression and its correlation with clinical features enable it to be a promising biomarker for cancer diagnosis, prognosis, and the stratification of tumor progression. Additionally, LINK-A's contribution to the development of resistance to cancer therapies, including AKT inhibitors and immunotherapy, underscores its potential as a therapeutic target. This review provides a comprehensive overview of the available data on LINK-A, focusing on its molecular regulatory pathways and clinical significance. By exploring the multifaceted nature of LINK-A in cancer, the review aims to offer a valuable resource for future research directions, potentially guiding the development of novel therapeutic strategies targeting this lncRNA in cancer treatment.

Bulk RNA-sequencing, single-cell RNA-sequencing analysis, and experimental validation reveal iron metabolism-related genes CISD2 and CYP17A1 are potential diagnostic markers for recurrent pregnancy loss

BACKGROUND

Recurrent pregnancy loss (RPL) is associated with variable causes. Its etiology remains unexplained in about half of the cases, with no effective treatment available. Individuals with RPL have an irregular iron metabolism. In the present study, we identified key genes impacting iron metabolism that could be used for diagnosing and treating RPL.

METHODS

We obtained gene expression profiles from the Gene Expression Omnibus (GEO) database. The Molecular Signatures Database was used to identify 14 gene sets related to iron metabolism, comprising 520 iron metabolism genes. Differential analysis and a weighted gene co-expression network analysis (WGCNA) of gene expression revealed two iron metabolism-related hub genes. Reverse transcriptase-polymerase chain reaction (RT-PCR) and immunohistochemistry were used on clinical samples to confirm our results. The receiver operating characteristic (ROC) analysis and immune infiltration analysis were conducted. In addition, we analyzed the distribution of genes and performed CellChat analysis by single-cell RNA sequencing.

RESULTS

The expression of two hub genes, namely, CDGSH iron sulfur domain 2 (CISD2)and Cytochrome P450 family 17 subfamily A member 1 (CYP17A1), were reduced in RPL, as verified by both qPCR and immunohistochemistry. The Gene Ontology (GO) analysis revealed the genes predominantly engaged in autophagy and iron metabolism. The area under the curve (AUC) demonstrated better diagnostic performance for RPL using CISD2 and CYP17A1. The single-cell transcriptomic analysis of RPL demonstrated that CISD2 is expressed in the majority of cell subpopulations, whereas CYP17A1 is not. The cell cycle analysis revealed highly active natural killer (NK) cells that displayed the highest communications with other cells, including the strongest interaction with macrophages through the migratory inhibitory factor (MIF) pathway.

CONCLUSIONS

Our study suggested that CISD2 and CYP17A1 genes are involved in abnormal iron metabolism, thereby contributing to RPL. These genes could be used as potential diagnostic and therapeutic markers for RPL.

Three-dimensional assessments are necessary to determine the true, spatially-resolved composition of tissues

Methods for spatially resolved cellular profiling using thinly cut sections have enabled in-depth quantitative tissue mapping to study inter-sample and intra-sample differences in normal human anatomy and disease onset and progression. These methods often profile extremely limited regions, which may impact the evaluation of heterogeneity due to tissue sub-sampling. Here, we applied CODA, a deep learning-based tissue mapping platform, to reconstruct the three-dimensional (3D) microanatomy of grossly normal and cancer-containing human pancreas biospecimens obtained from individuals who underwent pancreatic resection. To compare inter- and intra-sample heterogeneity, we assessed bulk and spatially resolved tissue composition in a cohort of two-dimensional (2D) whole slide images (WSIs) and a cohort of thick slabs of pancreas tissue that were digitally reconstructed in 3D from serial sections. To demonstrate the marked under sampling of 2D assessments, we simulated the number of WSIs and tissue microarrays (TMAs) necessary to represent the compositional heterogeneity of 3D data within 10% error to reveal that tens of WSIs and hundreds of TMA cores are sometimes needed. We show that spatial correlation of different pancreatic structures decay significantly within a span of microns, demonstrating that 2D histological sections may not be representative of their neighboring tissues. In sum, we demonstrate that 3D assessments are necessary to accurately assess tissue composition in normal and abnormal specimens and in order to accurately determine neoplastic content. These results emphasize the importance of intra-sample heterogeneity in tissue mapping efforts.

Improving drug response prediction via integrating gene relationships with deep learning

Predicting the drug response of cancer cell lines is crucial for advancing personalized cancer treatment, yet remains challenging due to tumor heterogeneity and individual diversity. In this study, we present a deep learning-based framework named Deep neural network Integrating Prior Knowledge (DIPK) (DIPK), which adopts self-supervised techniques to integrate multiple valuable information, including gene interaction relationships, gene expression profiles and molecular topologies, to enhance prediction accuracy and robustness. We demonstrated the superior performance of DIPK compared to existing methods on both known and novel cells and drugs, underscoring the importance of gene interaction relationships in drug response prediction. In addition, DIPK extends its applicability to single-cell RNA sequencing data, showcasing its capability for single-cell-level response prediction and cell identification. Further, we assess the applicability of DIPK on clinical data. DIPK accurately predicted a higher response to paclitaxel in the pathological complete response (pCR) group compared to the residual disease group, affirming the better response of the pCR group to the chemotherapy compound. We believe that the integration of DIPK into clinical decision-making processes has the potential to enhance individualized treatment strategies for cancer patients.

Unlocking melanoma Suppression: Insights from Plasma-Induced potent miRNAs through PI3K-AKT-ZEB1 axis

INTRODUCTION

Melanoma is a rare but highly malignant form of skin cancer. Although recent targeted and immune-based therapies have improved survival rates by 10-15%, effective melanoma treatment remains challenging. Therefore, novel, combinatorial therapy options such as non-thermal atmospheric pressure plasma (NTP) are being investigated to inhibit and prevent chemoresistance. Although several studies have reported the apoptotic and inhibitory effects of reactive oxygen species produced by NTP in the context of melanoma, the intricate molecular network that determines the role of microRNAs (miRNAs) in regulating NTP-mediated cell death remains unexplored.

OBJECTIVES

This study aimed to explore the molecular mechanisms and miRNA networks regulated by NTP-induced oxidative stress in melanoma cells.

METHODS

Melanoma cells were exposed to NTP and then subjected to high-throughput miRNA sequencing to identify NTP-regulated miRNAs. Various biological processes and underlying molecular mechanisms were assessed using Alamar Blue, propidium iodide (PI) uptake, cell migration, and clonogenic assays followed by qRT-PCR and flow cytometry.

RESULTS

NTP exposure for 3 min was sufficient to modulate the expression of several miRNAs, inhibiting cell growth. Persistent NTP exposure for 5 min increased differential miRNA regulation, PI uptake, and the expression of genes involved in cell cycle arrest and death. qPCR confirmed that miR-200b-3p and miR-215-5p upregulation contributed to decreased cell viability and migration. Mechanistically, inhibiting miR-200b-3p and miR-215-5p in SK-2 cells enhancedZEB1, PI3K, and AKT expression, increasing cell proliferation and viability.

CONCLUSION

This study demonstrated that NTP exposure for 5 min results in the differential regulation of miRNAs related to the PI3K-AKT-ZEB1 axis and cell cycle dysregulation to facilitate melanoma suppression.

GERWR: Identifying the Key Pathogenicity- Associated sRNAs of Magnaporthe Oryzae Infection in Rice Based on Graph Embedding and Random Walk With Restart

Rice blast, caused by Magnaporthe oryzae(M.oryzae), is a destructive rice disease that reduces rice yield by 10% to 30% annually. It also affects other cereal crops such as barley, wheat, rye, millet, sorghum, and maize. Small RNAs (sRNAs) play an essential regulatory role in fungus-plant interaction during the fungal invasion, but studies on pathogenic sRNAs during the fungal invasion of plants based on multi-omics data integration are rare. This paper proposes a novel approach called Graph Embedding combined with Random Walk with Restart (GERWR) to identify pathogenic sRNAs based on multi-omics data integration during M.oryzae invasion. By constructing a multi-omics network (MRMO), we identified 29 pathogenic sRNAs of rice blast fungus. Further analysis revealed that these sRNAs regulate rice genes in a many-to-many relationship, playing a significant regulatory role in the pathogenesis of rice blast disease. This paper explores the pathogenic factors of rice blast disease from the perspective of multi-omics data analysis, revealing the inherent connection between pathogenic factors of different omics. It has essential scientific significance for studying the pathogenic mechanism of rice blast fungus, the rice blast fungus-rice model system, and the pathogen-host interaction in related fields.

Non-severe thermal burn injuries induce long-lasting downregulation of gene expression in cortical excitatory neurons and microglia

Burn injuries are devastating traumas, often leading to life-long consequences that extend beyond the observable burn scar. In the context of the nervous system, burn injury patients commonly develop chronic neurological disorders and have been suggested to have impaired motor cortex function, but the long-lasting impact on neurons and glia in the brain is unknown. Using a mouse model of non-severe burn injury, excitatory and inhibitory neurons in the primary motor cortex were labelled with fluorescent proteins using adeno-associated viruses (AAVs). A total of 5 weeks following the burn injury, virus labelled excitatory and inhibitory neurons were isolated using fluorescence-activated cell sorting (FACS). In addition, microglia and astrocytes from the remaining cortical tissue caudal to the motor cortex were immunolabelled and isolated with FACS. Whole transcriptome RNA-sequencing was used to identify any long-lasting changes to gene expression in the different cell types. RNA-seq analysis showed changes to the expression of a small number of genes with known functions in excitatory neurons and microglia, but not in inhibitory neurons or astrocytes. Specifically, genes related to GABA-A receptors in excitatory neurons and several cellular functions in microglia were found to be downregulated in burn injured mice. These findings suggest that non-severe burn injuries lead to long lasting transcriptomic changes in the brain, but only in specific cell types. Our findings provide a broad overview of the long-lasting impact of burn injuries on the central nervous system which may help identify potential therapeutic targets to prevent neurological dysfunction in burn patients.

Endophytic fungi as a potential source of anti-cancer drug

Endophytes are considered one of the major sources of bioactive compounds used in different aspects of health care including cancer treatment. When colonized, they either synthesize these bioactive compounds as a part of their secondary metabolite production or augment the host plant machinery in synthesising such bioactive compounds. Hence, the study of endophytes has drawn the attention of the scientific community in the last few decades. Among the endophytes, endophytic fungi constitute a major portion of endophytic microbiota. This review deals with a plethora of anti-cancer compounds derived from endophytic fungi, highlighting alkaloids, lignans, terpenes, polyketides, polyphenols, quinones, xanthenes, tetralones, peptides, and spirobisnaphthalenes. Further, this review emphasizes modern methodologies, particularly omics-based techniques, asymmetric dihydroxylation, and biotic elicitors, showcasing the dynamic and evolving landscape of research in this field and describing the potential of endophytic fungi as a source of anticancer drugs in the future.

Comparison of RNA-Seq and microarray in the prediction of protein expression and survival prediction

Gene expression profiling using RNA-sequencing (RNA-seq) and microarray technologies is widely used in cancer research to identify biomarkers for clinical endpoint prediction. We compared the performance of these two methods in predicting protein expression and clinical endpoints using The Cancer Genome Atlas (TCGA) datasets of lung cancer, colorectal cancer, renal cancer, breast cancer, endometrial cancer, and ovarian cancer. We calculated the correlation coefficients between gene expression measured by RNA-seq or microarray and protein expression measured by reverse phase protein array (RPPA). In addition, after selecting the top 103 survival-related genes, we compared the random forest survival prediction model performance across test platforms and cancer types. Both RNA-seq and microarray data were retrieved from TCGA dataset. Most genes showed similar correlation coefficients between RNA-seq and microarray, but 16 genes exhibited significant differences between the two methods. The BAX gene was recurrently found in colorectal cancer, renal cancer, and ovarian cancer, and the PIK3CA gene belonged to renal cancer and breast cancer. Furthermore, the survival prediction model using microarray was better than the RNA-seq model in colorectal cancer, renal cancer, and lung cancer, but the RNA-seq model was better in ovarian and endometrial cancer. Our results showed good correlation between mRNA levels and protein measured by RPPA. While RNA-seq and microarray performance were similar, some genes showed differences, and further clinical significance should be evaluated. Additionally, our survival prediction model results were controversial.

Classifying breast cancer using multi-view graph neural network based on multi-omics data

Introduction: As the evaluation indices, cancer grading and subtyping have diverse clinical, pathological, and molecular characteristics with prognostic and therapeutic implications. Although researchers have begun to study cancer differentiation and subtype prediction, most of relevant methods are based on traditional machine learning and rely on single omics data. It is necessary to explore a deep learning algorithm that integrates multi-omics data to achieve classification prediction of cancer differentiation and subtypes. Methods: This paper proposes a multi-omics data fusion algorithm based on a multi-view graph neural network (MVGNN) for predicting cancer differentiation and subtype classification. The model framework consists of a graph convolutional network (GCN) module for learning features from different omics data and an attention module for integrating multi-omics data. Three different types of omics data are used. For each type of omics data, feature selection is performed using methods such as the chi-square test and minimum redundancy maximum relevance (mRMR). Weighted patient similarity networks are constructed based on the selected omics features, and GCN is trained using omics features and corresponding similarity networks. Finally, an attention module integrates different types of omics features and performs the final cancer classification prediction. Results: To validate the cancer classification predictive performance of the MVGNN model, we conducted experimental comparisons with traditional machine learning models and currently popular methods based on integrating multi-omics data using 5-fold cross-validation. Additionally, we performed comparative experiments on cancer differentiation and its subtypes based on single omics data, two omics data, and three omics data. Discussion: This paper proposed the MVGNN model and it performed well in cancer classification prediction based on multiple omics data.

Peripheral inflammation-induced changes in songbird brain gene expression: 3' mRNA transcriptomic approach

Species-specific neural inflammation can be induced by profound immune signalling from periphery to brain. Recent advances in transcriptomics offer cost-effective approaches to study this regulation. In a population of captive zebra finch (Taeniopygia guttata), we compare the differential gene expression patterns in lipopolysaccharide (LPS)-triggered peripheral inflammation revealed by RNA-seq and QuantSeq. The RNA-seq approach identified more differentially expressed genes but failed to detect any inflammatory markers. In contrast, QuantSeq results identified specific expression changes in the genes regulating inflammation. Next, we adopted QuantSeq to relate peripheral and brain transcriptomes. We identified subtle changes in the brain gene expression during the peripheral inflammation (e.g. up-regulation in AVD-like and ACOD1 expression) and detected co-structure between the peripheral and brain inflammation. Our results suggest benefits of the 3'end transcriptomics for association studies between peripheral and neural inflammation in genetically heterogeneous models and identify potential targets for the future brain research in birds.

A Circular RNA Derived from the Pumilio 1 Gene Could Regulate PTEN in Human Cumulus Cells

CircRNAs are a class of non-coding RNAs able to regulate gene expression at multiple levels. Their involvement in physiological processes, as well as their altered regulation in different human diseases, both tumoral and non-tumoral, is well documented. However, little is known about their involvement in female reproduction. This study aims to identify circRNAs potentially involved in reproductive women's health. Candidate circRNAs expressed in ovary and sponging miRNAs, already known to be expressed in the ovary, were selected by a computational approach. Using real time PCR, we verified their expression and identified circPUM1 as the most interesting candidate circRNA for further analyses. We assessed the expression of circPUM1 and its linear counterpart in all the follicle compartments and, using a computational and experimental approach, identified circPUM1 direct and indirect targets, miRNAs and mRNAs, respectively, in cumulus cells. We found that both circPUM1 and its mRNA host gene are co-expressed in all the follicle compartments and proposed circPUM1 as a potential regulator of PTEN, finding a strong positive correlation between circPUM1 and PTEN mRNA. These results suggest a possible regulation of PTEN by circPUM1 in cumulus cells and point out the important role of circRNA inside the pathways related to follicle growth and oocyte maturation.

Uncovering transcriptomic biomarkers for enhanced diagnosis of methamphetamine use disorder: a comprehensive review

Introduction

Methamphetamine use disorder (MUD) is a chronic relapsing disorder characterized by compulsive Methamphetamine (MA) use despite its detrimental effects on physical, psychological, and social well-being. The development of MUD is a complex process that involves the interplay of genetic, epigenetic, and environmental factors. The treatment of MUD remains a significant challenge, with no FDA-approved pharmacotherapies currently available. Current diagnostic criteria for MUD rely primarily on self-reporting and behavioral assessments, which have inherent limitations owing to their subjective nature. This lack of objective biomarkers and unidimensional approaches may not fully capture the unique features and consequences of MA addiction.

Methods

We performed a literature search for this review using the Boolean search in the PubMed database.

Results

This review explores existing technologies for identifying transcriptomic biomarkers for MUD diagnosis. We examined non-invasive tissues and scrutinized transcriptomic biomarkers relevant to MUD. Additionally, we investigated transcriptomic biomarkers identified for diagnosing, predicting, and monitoring MUD in non-invasive tissues.

Discussion

Developing and validating non-invasive MUD biomarkers could address these limitations, foster more precise and reliable diagnostic approaches, and ultimately enhance the quality of care for individuals with MA addiction.

Inferring Interaction Networks from Transcriptomic Data: Methods and Applications

Transcriptomic data is a treasure trove in modern molecular biology, as it offers a comprehensive viewpoint into the intricate nuances of gene expression dynamics underlying biological systems. This genetic information must be utilized to infer biomolecular interaction networks that can provide insights into the complex regulatory mechanisms underpinning the dynamic cellular processes. Gene regulatory networks and protein-protein interaction networks are two major classes of such networks. This chapter thoroughly investigates the wide range of methodologies used for distilling insightful revelations from transcriptomic data that include association-based methods (based on correlation among expression vectors), probabilistic models (using Bayesian and Gaussian models), and interologous methods. We reviewed different approaches for evaluating the significance of interactions based on the network topology and biological functions of the interacting molecules and discuss various strategies for the identification of functional modules. The chapter concludes with highlighting network-based techniques of prioritizing key genes, outlining the centrality-based, diffusion- based, and subgraph-based methods. The chapter provides a meticulous framework for investigating transcriptomic data to uncover assembly of complex molecular networks for their adaptable analyses across a broad spectrum of biological domains.

Transcriptomic and Metabolomic Analysis of Liver Cirrhosis

BACKGROUND

Liver cirrhosis is one of the leading causes of decreased life expectancy worldwide. However, the molecular mechanisms underlying liver cirrhosis remain unclear. In this study, we performed a comprehensive analysis using transcriptome and metabolome sequencing to explore the genes, pathways, and interactions associated with liver cirrhosis.

METHODS

We performed transcriptome and metabolome sequencing of blood samples from patients with cirrhosis and healthy controls (1:1 matched for sex and age). We validated the differentially expressed microRNA (miRNA) and mRNAs using real-time quantitative polymerase chain reaction.

RESULTS

For transcriptome analysis, we screened for differentially expressed miRNAs and mRNAs, analyzed mRNAs to identify possible core genes and pathways, and performed coanalysis of miRNA and mRNA sequencing results. In terms of the metabolome, we screened five pathways that were substantially enriched in the differential metabolites. Next, we identified the metabolites with the most pronounced differences among these five metabolic pathways. We performed receiver operating characteristic (ROC) curve analysis of these five metabolites to determine their diagnostic efficacy for cirrhosis. Finally, we explored possible links between the transcriptome and metabolome.

CONCLUSION

Based on sequencing and bioinformatics, we identified miRNAs and genes that were differentially expressed in the blood of patients with liver cirrhosis. By exploring pathways and disease-specific networks, we identified unique biological mechanisms. In terms of metabolomes, we identified novel biomarkers and explored their diagnostic efficacy. We identified possible common pathways in the transcriptome and metabolome that could serve as candidates for further studies.

Analyzing RNA posttranscriptional modifications to decipher the epitranscriptomic code

The discovery of RNA silencing has revealed that non-protein-coding sequences (ncRNAs) can cover essential roles in regulatory networks and their malfunction may result in severe consequences on human health. These findings have prompted a general reassessment of the significance of RNA as a key player in cellular processes. This reassessment, however, will not be complete without a greater understanding of the distribution and function of the over 170 variants of the canonical ribonucleotides, which contribute to the breathtaking structural diversity of natural RNA. This review surveys the analytical approaches employed for the identification, characterization, and detection of RNA posttranscriptional modifications (rPTMs). The merits of analyzing individual units after exhaustive hydrolysis of the initial biopolymer are outlined together with those of identifying their position in the sequence of parent strands. Approaches based on next generation sequencing and mass spectrometry technologies are covered in depth to provide a comprehensive view of their respective merits. Deciphering the epitranscriptomic code will require not only mapping the location of rPTMs in the various classes of RNAs, but also assessing the variations of expression levels under different experimental conditions. The fact that no individual platform is currently capable of meeting all such demands implies that it will be essential to capitalize on complementary approaches to obtain the desired information. For this reason, the review strived to cover the broadest possible range of techniques to provide readers with the fundamental elements necessary to make informed choices and design the most effective possible strategy to accomplish the task at hand.

From cancer big data to treatment: Artificial intelligence in cancer research

In recent years, developing the idea of "cancer big data" has emerged as a result of the significant expansion of various fields such as clinical research, genomics, proteomics and public health records. Advances in omics technologies are making a significant contribution to cancer big data in biomedicine and disease diagnosis. The increasingly availability of extensive cancer big data has set the stage for the development of multimodal artificial intelligence (AI) frameworks. These frameworks aim to analyze high-dimensional multi-omics data, extracting meaningful information that is challenging to obtain manually. Although interpretability and data quality remain critical challenges, these methods hold great promise for advancing our understanding of cancer biology and improving patient care and clinical outcomes. Here, we provide an overview of cancer big data and explore the applications of both traditional machine learning and deep learning approaches in cancer genomic and proteomic studies. We briefly discuss the challenges and potential of AI techniques in the integrated analysis of omics data, as well as the future direction of personalized treatment options in cancer.

Revolutionizing Malaria Vector Control: The Importance of Accurate Species Identification through Enhanced Molecular Capacity

Many factors, such as the resistance to pesticides and a lack of knowledge of the morphology and molecular structure of malaria vectors, have made it more challenging to eradicate malaria in numerous malaria-endemic areas of the globe. The primary goal of this review is to discuss malaria vector control methods and the significance of identifying species in vector control initiatives. This was accomplished by reviewing methods of molecular identification of malaria vectors and genetic marker classification in relation to their use for species identification. Due to its specificity and consistency, molecular identification is preferred over morphological identification of malaria vectors. Enhanced molecular capacity for species identification will improve mosquito characterization, leading to accurate control strategies/treatment targeting specific mosquito species, and thus will contribute to malaria eradication. It is crucial for disease epidemiology and surveillance to accurately identify the Plasmodium spp. that are causing malaria in patients. The capacity for disease surveillance will be significantly increased by the development of more accurate, precise, automated, and high-throughput diagnostic techniques. In conclusion, although morphological identification is quick and achievable at a reduced cost, molecular identification is preferred for specificity and sensitivity. To achieve the targeted malaria elimination goal, proper identification of vectors using accurate techniques for effective control measures should be prioritized.

Integrating genomics and genome editing for orphan crop improvement: a bridge between orphan crops and modern agriculture system

Domestication of orphan crops could be explored by editing their genomes. Genome editing has a lot of promise for enhancing agricultural output, and there is a lot of interest in furthering breeding in orphan crops, which are sometimes plagued with unwanted traits that resemble wild cousins. Consequently, applying model crop knowledge to orphan crops allows for the rapid generation of targeted allelic diversity and innovative breeding germplasm. We explain how plant breeders could employ genome editing as a novel platform to accelerate the domestication of semi-domesticated or wild plants, resulting in a more diversified base for future food and fodder supplies. This review emphasizes both the practicality of the strategy and the need to invest in research that advances our understanding of plant genomes, genes, and cellular systems. Planting more of these abandoned orphan crops could help alleviate food scarcities in the challenge of future climate crises.

Ambiguous genes due to aligners and their impact on RNA-seq data analysis

The main scope of the study is ambiguous genes, i.e. genes whose expression is difficult to estimate from the data produced by next-generation sequencing technologies. We focused on the RNA sequencing (RNA-Seq) type of experiment performed on the Illumina platform. It is crucial to identify such genes and understand the cause of their difficulty, as these genes may be involved in some diseases. By giving misleading results, they could contribute to a misunderstanding of the cause of certain diseases, which could lead to inappropriate treatment. We thought that the ambiguous genes would be difficult to map because of their complex structure. So we looked at RNA-seq analysis using different mappers to find genes that would have different measurements from the aligners. We were able to identify such genes using a generalized linear model with two factors: mappers and groups introduced by the experiment. A large proportion of ambiguous genes are pseudogenes. High sequence similarity of pseudogenes to functional genes may indicate problems in alignment procedures. In addition, predictive analysis verified the performance of difficult genes in classification. The effectiveness of classifying samples into specific groups was compared, including the expression of difficult and not difficult genes as covariates. In almost all cases considered, ambiguous genes have less predictive power.

Hydrated lime promoted the polysaccharide content and affected the transcriptomes of Lentinula edodes during brown film formation

Brown film formation, a unique developmental stage in the life cycle of Lentinula edodes, is essential for the subsequent development of fruiting bodies in L. edodes cultivation. The pH of mushroom growth substrates are usually adjusted with hydrated lime, yet the effects of hydrated lime on cultivating L. edodes and the molecular mechanisms associated with the effects have not been studied systemically. We cultivated L. edodes on substrates supplemented with 0% (CK), 1% (T1), 3% (T2), and 5% (T3) hydrated lime (Ca (OH)2), and applied transcriptomics and qRT-PCR to study gene expression on the brown film formation stage. Hydrated lime increased polysaccharide contents in L. edodes, especially in T2, where the 5.3% polysaccharide content was approximately 1.5 times higher than in the CK. The addition of hydrated lime in the substrate promoted laccase, lignin peroxidase and manganese peroxidase activities, implying that hydrated lime improved the ability of L. edodes to decompose lignin and provide nutrition for its growth and development. Among the annotated 9,913 genes, compared to the control, 47 genes were up-regulated and 52 genes down-regulated in T1; 73 genes were up-regulated and 44 were down-regulated in T2; and 125 genes were up-regulated and 65 genes were down-regulated in T3. Differentially expressed genes (DEGs) were enriched in the amino acid metabolism, lipid metabolism and carbohydrate metabolism related pathways. The carbohydrate-active enzyme genes up-regulated in the hydrated lime treatments were mostly glycosyl hydrolase genes. The results will facilitate future optimization of L. edodes cultivation techniques and possibly shortening the production cycle.

Virulence factors of the genus Fusarium with targets in plants

Fusarium spp. comprise various species of filamentous fungi that cause severe diseases in plant crops of both agricultural and forestry interest. These plant pathogens produce a wide range of molecules with diverse chemical structures and biological activities. Genetic functional analyses of some of these compounds have shown their role as virulence factors (VF). However, their mode of action and contributions to the infection process for many of these molecules are still unknown. This review aims to analyze the state of the art in Fusarium VF, emphasizing their biological targets on the plant hosts. It also addresses the current experimental approaches to improve our understanding of their role in virulence and suggests relevant research questions that remain to be answered with a greater focus on species of agroeconomic importance. In this review, a total of 37 confirmed VF are described, including 22 proteinaceous and 15 non-proteinaceous molecules, mainly from Fusarium oxysporum and Fusarium graminearum and, to a lesser extent, in Fusarium verticillioides and Fusarium solani.

Transcriptome profiles of Streptomyces clavuligerus strains producing different titers of clavulanic acid

Streptomyces clavuligerus NRRL 3585 is a native producer of clavulanic acid (CA), a clinically used β-lactamase inhibitor, and is widely used as an industrial strain for the production of antibiotics. Selective random mutagenesis has successfully generated the improved CA-producing S. clavuligerus mutant strains as well as the strain with the loss of CA biosynthesis. To understand the molecular mechanisms associated with the improved CA-production potential, genome-scale RNA-sequencing-based transcriptional data were obtained for the wild-type S. clavuligerus strain and its three mutant strains. Total RNA samples for each strain were collected across four different growth stages, and all 32 sequencing data points exhibited an average Phred score of 36. The high-quality genome-scale transcriptional profile of S. clavuligerus strains with varied CA biosynthetic potential provides valuable insights and new opportunities for discovering efficient metabolic engineering strategies for the development of improved industrial strains.