The transcriptional landscape of the yeast genome defined by RNA sequencing

Transcriptomic analysis reveals potential biomarkers for early-onset pre-eclampsia using integrative bioinformatics and LASSO based approach

Pre-eclampsia (PE) is a severe vascular disorder during pregnancy, significantly affecting maternal and fetal health worldwide. However, the exact molecular mechanism of its pathophysiology remains unclear, highlighting the need for reliable early diagnostic methods. Our primary aim of this study was to identify key genes (KGs) that may affect the outcome of patients with PE via integrated bioinformatics analysis. We analysed a gene expression dataset from the national center for biotechnology information (NCBI) sequence read archive (SRA) database and performed standard preprocessing steps, including quality assessment, trimming, genome alignment, and feature counts. Following this, normalization and differentially expressed genes (DEGs) were performed using Deseq2, which identified 781 DEGs were identified comprising 457 upregulated and 324 downregulated genes. Identified DEGs were significantly enriched in the cytokine interaction pathway and cellular calcium ion homeostasis. PPI network analysis revealed eight KGs (CXCL8, GAPDH, MMP9, SPP1, PTGS2, LEP, FGF7, and FGF10). These KGs were further found to be regulated by ten transcription factors (TFs), among which NF-kB1 and RELA consistently interact with all the KGs, and four microRNAs (miRNAs) such as hsa-mir-335-5p, has-mir-16a-5p, has-let-7b-5p, and has-mir-204-5p. The least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation (CV) confirmed all eight KGs may act as potential biomarkers based on their coefficients. Among these, GAPDH, SPP1, FGF7, and FGF10 emerged as novel biomarkers. Additionally, receiver operating characteristic (ROC) curve analysis for these novel biomarkers showed an area under the curve (AUC) of 0.869, demonstrating strong discriminatory power between the healthy and EOPE groups. The drug-gene interaction was performed by DrugMap database revealed an important interaction of GAPDH and FGF7 with FDA-approved drugs, indicating their therapeutic significance in PE. This analysis also facilitates drug repurposing for PE treatment.

Beyond the ORF: Paralog-specific regulation of RPS7/eS7 mRNAs via 3'-UTRs and promoter sequences

In a classical view, each paralogous ribosomal protein (RP) is equally synthesized and integrated into the ribosome. Therefore, RP-paralog mRNAs are generally believed to be similarly regulated on their transcription and/or stability. In this paper, we report that two Rps7p/eS7 paralogs of Saccharomyces cerevisiae are differently regulated; deletion of RPS7A upregulates RPS7B paralogous mRNA expression but not vice versa. Their 3'-UTR sequences critically regulated the stabilities of both RPS7A and RPS7B mRNAs. Alterations in these sequences led to a diminished expression of RPS7A and RPS7B mRNAs in a transcript-dependent manner, suggesting that RPS7-paralog mRNAs have different properties for their expression and/or stability. The C-terminal tagging of the ORF and mutation analyses in the 3'-UTR indicate that both RPS7-paralog mRNAs critically rely on their 3'-UTRs for mRNA expressions and/or stabilities. We also found that activities of both RPS7A and RPS7B promoters are regulated by abundance of Rps7Ap and that Fhl1p, a key transcriptional regulator of RP genes, is essential for transcription of RPS7B but not RPS7A while simultaneous deletion of a consensus sequence for Fhl1p in the RPS7A promoter region and the FHL1 gene completely abolishes the promoter activity. These results indicate that yeast has a distinct buffering system for Rps7p production between the two RPS7-paralogs, which is sensitive to variation on their 3'-UTRs and is partially mediated in a transcription-dependent manner.

Genome-Wide Association Study and RNA-Seq Elucidate the Genetic Mechanisms Behind Aphid (Rhopalosiphum maidis F.) Resistance in Maize

Maize is a crucial food crop and industrial raw material, significantly contributing to national food security. Aphids are one of the most prevalent and destructive pests in maize production, necessitating the exploration of pest-resistant germplasm and the development of resistant varieties as the most fundamental and effective strategy for mitigating aphid-induced damage. This study established an aphid resistance evaluation system and identified 17 elite resistant inbred lines through multi-year screening. A genome-wide association study (GWAS) revealed 22 significant single-nucleotide polymorphisms (SNPs) associated with aphid resistance, including genes involved in benzoxazinoid (Bx) biosynthesis (such as Bx2), insect resistance-related transcription factors (such as WRKY23), plant lectins, and other resistance pathways. RNA-seq analysis of the samples before and after aphid infestation detected 1037 differentially expressed genes (DEGs) in response to aphid infestation, with KEGG enrichment highlighting benzoxazinoid biosynthesis and starch/sucrose metabolism as primary response pathways. Integrating GWAS and RNA-seq results revealed the presence of several benzoxazinoid synthesis-related genes on the short arm of chromosome 4 (Chr4S). FMqRrm1, a Kompetitive Allele-Specific PCR (KASP) marker, was derived from the Chr4S region. We subsequently utilized this marker for marker-assisted selection (MAS) to introgress the Chr4S region from the aphid-resistant inbred line into two aphid-susceptible inbred lines. The results demonstrated that the Chr4S favorable allele significantly reduced aphid occurrence by 1.5 to 2.1 grades. This study provides a critical theoretical foundation and practical guidance for understanding the molecular mechanism of aphid resistance in maize and molecular breeding for aphid resistance.

Machine Learning-Based Analysis of Differentially Expressed Genes in the Muscle Transcriptome Between Beef Cattle and Dairy Cattle

Muscle is a crucial component of cattle, playing a vital role in determining the final quality of beef. This study aimed to identify candidate genes associated with muscle growth and lipid metabolism in beef and dairy cattle by utilizing the public database of the National Center for Biotechnology Information (NCBI) to download bovine muscle transcriptome data. Through differential expression analysis, weighted gene co-expression network analysis (WGCNA), and SHapley Additive exPlanation (SHAP) explains machine learning models, we integrated and screened for relevant genes. The results showed a total of 2588 differentially expressed genes (DEGs), with 933 upregulated and 1655 downregulated in beef cattle compared to dairy cattle. In the WGCNA, the purple, black, green, red, brown, and blue modules were identified as significant modules. Based on the results of five different machine learning models, the Adaptive Boosting (AdaBoost) model demonstrated superior classification performance (accuracy = 0.84) compared to the other four models and was therefore selected as the optimal model. SHAP analysis was then employed to interpret the results, yielding the top 500 SHAP genes. In combination with DEGs and WGCNA, a total of 117 genes were identified. Subsequent functional enrichment analysis of these 117 genes revealed significant enrichment in pathways such as lipoprotein metabolic process, muscle contraction, and cytoskeleton in muscle cells, followed by interaction network analysis of genes and pathways. Ultimately, the APOA1, ACTB, S1PR1, PKLR, and SLC27A6 genes were identified as potential key regulators of lipid metabolism and muscle growth in beef and dairy cattle. In summary, this study provides a feasible method for handling large-scale transcriptome data and lays a foundation for future research on meat quality and improving the economic benefits of Holstein cattle.

Comparative Transcriptomics and Intestinal Microbiome Analysis Provide Insights into the Semi-Terrestrial Adaptation of Helice tientsinensis

Helice tientsinensis, a Grapsidae family member, can adapt to terrestrial and semi-terrestrial environments. This study used transcriptomic and microbiome analyses to explore its adaptation mechanisms. Transcriptome analysis showed gene changes related to cytoskeleton-motor, water-osmotic pressure, and energy metabolism. For example, DST was upregulated in the aquatic environment compared to the semi-terrestrial one, and SPAST was downregulated in some groups. ATP2A and SLC6A3 were upregulated with osmotic regulation, and IDH3 was upregulated when comparing the aquatic and semi-terrestrial habitats; at the same time, many energy-related genes were downregulated between the terrestrial and semi-terrestrial habitats. Regarding the gut microbiota, no significant differences in alpha diversity were found between habitats, but there were differences at the genus level. Pseudomonas and Malaciobacter were more abundant in the aquatic habitat, and Dietzia in the semi-terrestrial one. These results provide insights into H. tientsinensis' terrestrial adaptation, benefiting crustacean evolution study and aquaculture.

Transcriptomic Insights into the Degradation Mechanisms of Fomitopsis pinicola and Its Host Preference for Coniferous over Broadleaf Deadwood

The degradation of deadwood is a vital ecological process for geochemical cycling and biodiversity conservation, with two main routes of fungal degradation: brown and white rot. Brown rot fungi cause severe destruction of wood cellulose and lead to brown and modified lignin residue. Fomitopsis pinicola is a typical brown rot fungus with a distinct host preference for coniferous trees. The mechanisms through which this fungus degrades coniferous and broadleaf wood remain poorly understood. Therefore, in this study, a 60-day cultivation experiment involving F. pinicola growing on deadwood strips of Pinus koraiensis and Betula platyphylla separately was performed. A comparative transcriptome analysis was carried out to explore the mechanisms underlying the differences in degradation, in terms of both physicochemical properties and transcriptomic data. The findings revealed that the host preference of F. pinicola resulted in the more efficient degradation of coniferous wood than broadleaf wood, accompanied by higher gene expression levels. GO enrichment analysis indicated that this preference was primarily associated with the hydrolytic enzyme family and processes related to the Fenton reaction, which is characteristic of brown rot fungi. Furthermore, the KEGG pathways showed that the DEGs were enriched in mainly included histidine metabolism, fatty acid degradation, and so on, indicating underlying carbohydrate and lipid metabolism processes. These results support P. pinicola's strong ability to degrade the deadwood lignin of P. koraiensis, reflecting its adaptive evolution in host selection and choice of different ecological niches.

Transcriptome analysis and temporal expression patterns of wing development-related genes in Lymantria dispar (Lepidoptera: Erebidae)

Spongy moth, Lymantria dispar Linnaeus (Lepidoptera: Erebidae), stands as a pervasive international threat, marked by its designation as one of the "world's 100 worst invasive species" by IUCN, owing to its voracious leaf-eating habits encompassing over 500 plant species. Its strong flight ability facilitates its spread and invasion. The present study aims to uncover differential gene expression, utilizing the Illumina Novaseq6000 sequencing platform for comprehensive transcriptome sequencing and bioinformatic analysis of total RNA extracted from larvae and pupae. Results revealed pivotal processes of protein functional structure conformation, transport, and signal transduction in functional gene annotation during the 2 developmental stages of spongy moth. 18 functional genes, namely, Distal-less (Dll), Wingless (Wg), Decapentaplegic (Dpp), Hedgehog (Hh), Cubitus interruptus (Ci), Patched (Ptc), Apterous (Ap), Serrate (Ser), Fringe (Fng), Achaete (Ac), Engrailed (En), Vestigial (Vg), Scute (Sc), Invected (Inv), Scalloped (Sd), Ultrabithorax (Ubx), Serum Response Factor (SRF), and Spalt-major, associated with wing development were identified, and their expression levels were meticulously assessed through real-time quantitative PCR (RT-qPCR) in 1st-6th instar larvae and male and female pupae wing discs. The results showed that 18 genes exhibited expression. Furthermore, the relative expression values of wing development-related genes were significantly higher in the pupae stage than in the larval stage. The relative expression values of male and female pupae were also significantly different. The RT-qPCR results were in general agreement with the results of transcriptome analysis. This study establishes a foundational understanding of the developmental mechanisms governing the formation of spongy moth wings.

Transcriptome Sequencing Analysis of the Effects of Metformin on the Regeneration of Planarian Dugesia japonica

BACKGROUND

Metformin is a widely used oral hypoglycemic agent for treating type 2 diabetes. Planarians, with their remarkable regenerative abilities, are frequently employed as model organisms in stem cell and regeneration studies. This study aimed to investigate the effects of metformin on planarian regeneration, focusing on the regeneration of eyespots after amputation.

METHODS

Regenerating planarians with amputated eyespots were exposed to various concentrations of metformin. The regeneration time of the eyespots was measured to assess the effects of metformin. Subsequently, a 1 mmol/L metformin treatment for 24 h was applied to the planarians, followed by transcriptome analysis to identify differentially expressed genes (DEGs). The gene expression was validated through qPCR. The full-length gene of casein kinase 1α (DjCK1α) was cloned using RACE technology. DjCK1α interference was performed to examine its role in regeneration.

RESULTS

Low concentrations of metformin significantly reduced the regeneration time of planarians. Transcriptome analysis identified 113 DEGs, including 61 upregulated and 52 downregulated genes. GO and KEGG enrichment analyses were conducted. Notably, DjCK1α, a key gene involved in regeneration, was selected for further validation. qPCR confirmed that DjCK1α was significantly upregulated. The interference of DjCK1α prolonged the regeneration time of the eyespots of planarians cultured in water, while treatment with metformin did not promote the eyespot regeneration of the DjCK1α-interfered planarians.

CONCLUSIONS

The results suggest that metformin accelerates planarian eyespot regeneration, potentially through the regulation of DjCK1α. This study provides the first transcriptome-based analysis of drug effects on regeneration in planarians, highlighting the role of metformin in the regeneration process.

Comparative analysis of genotype imputation strategies for SNPs calling from RNA-seq

BACKGROUND

RNA sequencing (RNA-seq) is a powerful tool for transcriptome profiling, enabling integrative studies of expression quantitative trait loci (eQTL). As it identifies fewer genetic variants than DNA sequencing (DNA-seq), reference panel-based genotype imputation is often required to enhance its utility.

RESULTS

This study evaluated the accuracy of genotype imputation using SNPs called from RNA-seq data (RNA-SNPs). SNP features from 6,567 RNA-seq samples across 28 pig tissues were used to mask whole genome sequencing (WGS) data, with the Pig Genomic Reference Panel (PGRP) serving as the reference panel. Three imputation software tools (i.e., Beagle, Minimac4, and Impute5) were employed to perform the imputation. The result showed that RNA-SNPs achieved higher imputation accuracy (CR: 0.895 ~ 0.933; r²: 0.745 ~ 0.817) than SNPs from GeneSeek Genomic Profiler Porcine SNP50 BeadChip (Chip-SNPs) (CR: 0.873 ~ 0.909; r²: 0.629 ~ 0.698), and lower accuracy in "intergenic" regions. After imputation, quality control (QC) by minor allele frequency (MAF) and imputation quality (DR²) could improve r² but reduce SNP retention. Among software, Minimac4 takes the least runtime in single-thread setting, while Beagle performed best in multi-thread setting and phasing. Impute5 takes up minimal memory usage but requires the maximum runtime. All tools demonstrated comparable global accuracy (CR: 0.906 ~ 0.917; r²: 0.780 ~ 0.787).

CONCLUSIONS

This study offers practical guidance for conducting RNA-SNP imputation strategies in genome and transcriptome research.

Transcription termination promotes splicing efficiency and fidelity in a compact genome

Splicing of terminal introns is coupled to 3'-end processing by cleavage and polyadenylation (CPA) of mRNAs in mammalian genes. Whether this functional coupling is universally conserved across eukaryotes is unknown. Here we show using long read RNA sequencing in S . cerevisiae that splicing inactivation does not result in widespread CPA impairment and that inactivation of CPA does not lead to global splicing defects. However, 5'-extensions due to termination defects from upstream genes lead to splicing inhibition in a length-dependent manner. Additionally, for some extended RNAs resulting from failed termination, we observed decreased splicing fidelity resulting in novel intergenic and long-range intragenic splicing events. These results argue against a broad coupling of splicing to CPA in S . cerevisiae but show that efficient CPA-mediated transcription termination is critical for splicing fidelity and efficiency in a compact genome.

Particulate hexavalent chromium inhibits global transcription of genes in DNA repair pathways, particularly targeting homologous recombination repair, base excision repair, mismatch repair and microhomology-mediated end-joining

Hexavalent chromium [Cr(VI)] is a human lung carcinogen with widespread exposure. How Cr(VI) causes cancer is poorly understood, but chromosome instability plays a central role. Inhibition of DNA repair pathways leads to chromosome instability; however, despite the importance of these pathways in the mechanism of Cr(VI)-induced lung carcinogenesis, there are no data considering in-depth analysis on the transcriptional changes of genes involved in them. This study characterized the global transcriptional changes of mRNA expression after Cr(VI) exposure focusing on DNA repair pathways. The repair pathways considered included homologous recombination repair, non-homologous end joining, microhomology-directed end-joining, single strand annealing, mismatch repair, base excision repair, nucleotide excision repair and crosslink repair. Normal human lung fibroblast cells were exposed to increasing zinc chromate concentrations for 24, 72 or 120 h then RNA was extracted and sequenced. Our results indicate Cr(VI) causes differential expression of genes in lung cancer pathways and downregulates expression of some genes in all 8 DNA repair pathways. Homologous recombination repair, mismatch repair, base excision repair and microhomology-directed end-joining were the most affected pathways. This study provides a critical in-depth analysis of the effects of Cr(VI) on DNA repair pathways and contributes new insights into the mechanism of Cr(VI)-carcinogenesis.

Graph neural networks for single-cell omics data: a review of approaches and applications

Rapid advancement of sequencing technologies now allows for the utilization of precise signals at single-cell resolution in various omics studies. However, the massive volume, ultra-high dimensionality, and high sparsity nature of single-cell data have introduced substantial difficulties to traditional computational methods. The intricate non-Euclidean networks of intracellular and intercellular signaling molecules within single-cell datasets, coupled with the complex, multimodal structures arising from multi-omics joint analysis, pose significant challenges to conventional deep learning operations reliant on Euclidean geometries. Graph neural networks (GNNs) have extended deep learning to non-Euclidean data, allowing cells and their features in single-cell datasets to be modeled as nodes within a graph structure. GNNs have been successfully applied across a broad range of tasks in single-cell data analysis. In this survey, we systematically review 107 successful applications of GNNs and their six variants in various single-cell omics tasks. We begin by outlining the fundamental principles of GNNs and their six variants, followed by a systematic review of GNN-based models applied in single-cell epigenomics, transcriptomics, spatial transcriptomics, proteomics, and multi-omics. In each section dedicated to a specific omics type, we have summarized the publicly available single-cell datasets commonly utilized in the articles reviewed in that section, totaling 77 datasets. Finally, we summarize the potential shortcomings of current research and explore directions for future studies. We anticipate that this review will serve as a guiding resource for researchers to deepen the application of GNNs in single-cell omics.

Optimizing Human Cell-Free System for Efficient Protein Production

We present a highly efficient human HEK293-based cell-free protein synthesis (CFPS) system capable of producing up to 300 μg/ml reporter protein. One of the limitations of the CFPS systems with respect to protein yield has been the decline of the protein-synthesizing activity of the system upon prolonged incubation. Though factors contributing to this decline in activity have been investigated in yeast, little is known about the factors in mammalian systems. We find that a rapid depletion of the components of the energy-regeneration system is a major factor behind the decreasing protein-synthesis activity in the HEK293-derived system. In addition, we demonstrate that a functional CFPS system can be prepared from other mammalian cell lines as evidenced by our use of a human neuroblastoma SH-SY5Y-derived CFPS system. We also find that exogenous creatine kinase (CK) is dispensable for the functionality of the energy-regeneration system in HEK293 due to the presence of a sufficiently high endogenous CK activity in an HEK293 cell-free extract.

From non-coding to coding: The importance of long non-coding RNA translation in de novo gene birth

Recent emerging evidence demonstrates that some long non-coding RNAs (lncRNAs) can indeed be translated into functional polypeptides. These discoveries are pivotal for understanding de novo gene birth, the process by which new genes evolve from previously non-genic regions. In this review, we first introduce key methods, such as Ribo-seq and translation initiation site detection by translation complex analysis, for identifying coding sequences within lncRNAs and highlight examples of functional polypeptides derived from lncRNAs across species. These polypeptides play essential roles in maintaining cellular homeostasis and contribute to pathological processes, including cancer. However, because not all lncRNA-derived polypeptides appear to be functional, these lncRNAs may act as gene reservoirs. We also discuss how lncRNAs change their intracellular localization, how lncRNA-derived polypeptides evade immune surveillance, and how they gradually evolve into typical coding RNAs, providing evidence for the evolutionary model of de novo gene birth.

Technical considerations and review of urinary microRNAs as biomarkers for chronic kidney disease in dogs and cats

MicroRNAs (miRNAs or miRs) are small, non-coding RNAs that play a crucial role in gene regulation, making them potential biomarkers for various diseases. In the field of veterinary medicine, there is a growing interest in exploring the diagnostic and therapeutic potential of miRNAs in kidney diseases affecting dogs and cats. This review focuses on the use of urinary miRNAs as biomarkers for chronic kidney disease (CKD) in these companion animals. We introduce miRNAs, their biogenesis, and their presence in biofluids, particularly within exosomes, and discuss studies investigating miRNAs in kidney tissue and urine. We acknowledge the challenges associated with miRNA studies, including preanalytical factors such as biological variation, sample collection/processing, storage conditions, and experimental design. We highlight the importance of technical considerations, such as sample pooling, sequencing depth, multiplexing, and the various steps of the miRNA experimental workflow. Furthermore, we discuss RNA isolation methods, small RNA sequencing data analysis, and the use of quantitative reverse transcription PCR (qRT-PCR) and droplet digital PCR for verification. We emphasize the importance of internal controls, spike-ins, and normalization methods to minimize technical variation and ensure reliable results in qRT-PCR analysis. This review concludes that while urinary miRNAs hold promise as non-invasive biomarkers for CKD in dogs and cats, addressing the challenges and standardization of protocols is vital for the successful translation of this research into clinical practice.

KAS-ATAC reveals the genome-wide single-stranded accessible chromatin landscape of the human genome

Gene regulation in most eukaryotes involves two fundamental processes: alterations in genome packaging by nucleosomes, with active cis-regulatory elements (CREs) generally characterized by open-chromatin configuration, and transcriptional activation. Mapping these physical properties and biochemical activities, through profiling chromatin accessibility and active transcription, is a key tool for understanding the logic and mechanisms of transcription and its regulation. However, the relationship between these two states has not been accessible to simultaneous measurement. To this end, we developed KAS-ATAC, a combination of the kethoxal-assisted ssDNA sequencing (KAS-seq) and assay for transposase-accessible chromatin using sequencing (ATAC-seq) methods for mapping single-stranded DNA (and thus active transcription) and chromatin accessibility, respectively, enabling the genome-wide identification of DNA fragments that are simultaneously accessible and contain ssDNA. We use KAS-ATAC to evaluate levels of active transcription over different CRE classes, to estimate absolute levels of transcribed accessible DNA over CREs, to map nucleosomal configurations associated with RNA polymerase activities, and to assess transcription factor association with transcribed DNA through transcription factor binding site (TFBS) footprinting. We observe lower levels of transcription over distal enhancers compared with promoters and distinct nucleosomal configurations around transcription initiation sites associated with active transcription. We find that most TFs associate equally with transcribed and nontranscribed DNA, but a few factors specifically do not exhibit footprints over ssDNA-containing fragments. We anticipate KAS-ATAC to continue to derive useful insights into chromatin organization and transcriptional regulation in other contexts in the future.

Translation efficiency covariation across cell types is a conserved organizing principle of mammalian transcriptomes

Characterization of shared patterns of RNA expression between genes across conditions has led to the discovery of regulatory networks and novel biological functions. However, it is unclear if such coordination extends to translation, a critical step in gene expression. Here, we uniformly analyzed 3,819 ribosome profiling datasets from 117 human and 94 mouse tissues and cell lines. We introduce the concept of Translation Efficiency Covariation (TEC), identifying coordinated translation patterns across cell types. We nominate potential mechanisms driving shared patterns of translation regulation. TEC is conserved across human and mouse cells and helps uncover gene functions. Moreover, our observations indicate that proteins that physically interact are highly enriched for positive covariation at both translational and transcriptional levels. Our findings establish translational covariation as a conserved organizing principle of mammalian transcriptomes.

From Images to Genes: Radiogenomics Based on Artificial Intelligence to Achieve Non-Invasive Precision Medicine in Cancer Patients

With the increasing demand for precision medicine in cancer patients, radiogenomics emerges as a promising frontier. Radiogenomics is originally defined as a methodology for associating gene expression information from high-throughput technologies with imaging phenotypes. However, with advancements in medical imaging, high-throughput omics technologies, and artificial intelligence, both the concept and application of radiogenomics have significantly broadened. In this review, the history of radiogenomics is enumerated, related omics technologies, the five basic workflows and their applications across tumors, the role of AI in radiogenomics, the opportunities and challenges from tumor heterogeneity, and the applications of radiogenomics in tumor immune microenvironment. The application of radiogenomics in positron emission tomography and the role of radiogenomics in multi-omics studies is also discussed. Finally, the challenges faced by clinical transformation, along with future trends in this field is discussed.

Differential gene expression analysis pipelines and bioinformatic tools for the identification of specific biomarkers: A review

In recent years, the role of bioinformatics and computational biology together with omics techniques and transcriptomics has gained tremendous importance in biomedicine and healthcare, particularly for the identification of biomarkers for precision medicine and drug discovery. Differential gene expression (DGE) analysis is one of the most used techniques for RNA-sequencing (RNA-seq) data analysis. This tool, which is typically used in various RNA-seq data processing applications, allows the identification of differentially expressed genes across two or more sample sets. Functional enrichment analyses can then be performed to annotate and contextualize the resulting gene lists. These studies provide valuable information about disease-causing biological processes and can help in identifying molecular targets for novel therapies. This review focuses on differential gene expression (DGE) analysis pipelines and bioinformatic techniques commonly used to identify specific biomarkers and discuss the advantages and disadvantages of these techniques.

Separable roles of the DNA damage response kinase Mec1ATR and its activator Rad24RAD17 during meiotic recombination

During meiosis, programmed DNA double-strand breaks (DSBs) are formed by the topoisomerase-like enzyme, Spo11, activating the DNA damage response (DDR) kinase Mec1ATR via the checkpoint clamp loader, Rad24RAD17. At single loci, loss of Mec1 and Rad24 activity alters DSB formation and recombination outcome, but their genome-wide roles have not been examined in detail. Here, we utilise two strategies-deletion of the mismatch repair protein, Msh2, and control of meiotic prophase length via regulation of the Ndt80 transcription factor-to help characterise the roles Mec1 and Rad24 play in meiotic recombination by enabling genome-wide mapping of meiotic progeny. In line with previous studies, we observe severely impacted spore viability and a reduction in the frequency of recombination upon deletion of RAD24-driven by a shortened prophase. By contrast, loss of Mec1 function increases recombination frequency, consistent with its role in DSB trans-interference, and has less effect on spore viability. Despite these differences, complex multi-chromatid events initiated by closely spaced DSBs-rare in wild-type cells-occur more frequently in the absence of either Rad24 or Mec1, suggesting a loss of spatial regulation at the level of DSB formation in both. Mec1 and Rad24 also have important roles in the spatial regulation of crossovers (COs). Upon loss of either Mec1 or Rad24, CO distributions become more random-suggesting reductions in the global manifestation of interference. Such effects are similar to, but less extreme than, the phenotype of 'ZMM' mutants such as zip3Δ, and may be driven by reductions in the proportion of interfering COs. Collectively, in addition to shared roles in CO regulation, our results highlight separable roles for Rad24 as a pro-CO factor, and for Mec1 as a regulator of recombination frequency, the loss of which helps to suppress any broader defects in CO regulation caused by abrogation of the DDR.

Multi-omic human pancreatic islet endoplasmic reticulum and cytokine stress response mapping provides type 2 diabetes genetic insights

Endoplasmic reticulum (ER) and inflammatory stress responses contribute to islet dysfunction in type 2 diabetes (T2D). Comprehensive genomic understanding of these human islet stress responses and whether T2D-associated genetic variants modulate them is lacking. Here, comparative transcriptome and epigenome analyses of human islets exposed ex vivo to these stressors revealed 30% of expressed genes and 14% of islet cis-regulatory elements (CREs) as stress responsive, modulated largely in an ER- or cytokine-specific fashion. T2D variants overlapped 86 stress-responsive CREs, including 21 induced by ER stress. We linked the rs6917676-T T2D risk allele to increased islet ER-stress-responsive CRE accessibility and allele-specific β cell nuclear factor binding. MAP3K5, the ER-stress-responsive putative rs6917676 T2D effector gene, promoted stress-induced β cell apoptosis. Supporting its pro-diabetogenic role, MAP3K5 expression correlated inversely with human islet β cell abundance and was elevated in T2D β cells. This study provides genome-wide insights into human islet stress responses and context-specific T2D variant effects.

Based proteomics analyses reveal response mechanisms of Apis mellifera (Hymenoptera: Apidae) against the heat stress

Heat stress can significantly affect the survival, metabolism, and reproduction of honeybees. It is important to understand the proteomic changes of honeybees under heat stress to understand the molecular mechanism behind heat resistance. However, the proteomic changes of honeybees under heat stress are poorly understood. We analyzed the proteomic changes of Apis mellifera Ligustica (Hymenoptera: Apidae) under heat stress using mass spectrometry-based proteomics with TMT (Tandem mass tags) stable isotope labeling. A total of 3,799 proteins were identified, 85 of which differentially abundance between experimental groups. The most significant categories affected by heat stress were associated with transcription and translation processes, metabolism, and stress-resistant pathways. We found that heat stress altered the protein profiles in A. mellifera, with momentous resist proteins being upregulated in heat groups. These results show a proof of molecular details that A. mellifera can respond to heat stress by increasing resist proteins. Our findings add research basis for studying the molecular mechanisms of honeybees' resistance to heat stress. The differentially expressed proteins identified in this study can be used as biomarkers of heat stress in bees, and provide a foundation for future research on honeybees under heat stress. Our in-depth proteomic analysis provides new insights into how bees cope with heat stress.

Investigating the Activities of CAF20 and ECM32 in the Regulation of PGM2 mRNA Translation

Translation is a fundamental process in biology, and understanding its mechanisms is crucial to comprehending cellular functions and diseases. The regulation of this process is closely linked to the structure of mRNA, as these regions prove vital to modulating translation efficiency and control. Thus, identifying and investigating these fundamental factors that influence the processing and unwinding of structured mRNAs would be of interest due to the widespread impact in various fields of biology. To this end, we employed a computational approach and identified genes that may be involved in the translation of structured mRNAs. The approach is based on the enrichment of interactions and co-expression of genes with those that are known to influence translation and helicase activity. The in silico prediction found CAF20 and ECM32 to be highly ranked candidates that may play a role in unwinding mRNA. The activities of neither CAF20 nor ECM32 have previously been linked to the translation of PGM2 mRNA or other structured mRNAs. Our follow-up investigations with these two genes provided evidence of their participation in the translation of PGM2 mRNA and several other synthetic structured mRNAs.

RNA Sequencing Analysis of Patients with Chronic Hepatitis B Treated Using PEGylated Interferon

Purpose

Worldwide, chronic hepatitis B virus (CHB) infection is a public health concern, ultimately leading to liver cirrhosis and hepatocellular carcinoma. Currently, patients with CHB can be treated using polyethylene glycol (PEG)ylated interferon (PEG-IFN) antiviral therapy, which has both immune modulatory and antiviral properties. This study aimed to reveal the mechanism underlying the effect of PEG-IFN therapy, to rationally optimize this therapeutic option.

Patients and Methods

Ten patients with CHB who were positive for the hepatitis B virus e antigen (HBeAg) and were receiving PEG-IFN treatment were enrolled. Clinical and virological parameters were monitored during 48 weeks of treatment. In addition, peripheral blood mononuclear cells (PBMCs) were collected from the 10 patients at 0, 24, and 36 weeks. RNA sequencing technology was used to analyze the RNA expression profile in the PBMC samples.

Results

Following PEG-IFN treatment, we identified 217 differentially expressed genes (DEGs), most of which were upregulated. Gene ontology enrichment analysis of the DEGs revealed that they were enriched in 29 clusters, mainly associated with "antiviral defense", "innate immunity", "immunity", "defense response to virus", "response to virus", "type I interferon signaling pathway", "negative regulation of viral genome replication", "innate immune response", and "RNA-binding".

Conclusion

After PEG-IFN treatment, a certain mRNA expression profile was observed in patients with CHB, providing further mechanistic insights into the antiviral effect of this therapy.

Density estimation for ordinal biological sequences and its applications

Biological sequences do not come at random. Instead, they appear with particular frequencies that reflect properties of the associated system or phenomenon. Knowing how biological sequences are distributed in sequence space is thus a natural first step toward understanding the underlying mechanisms. Here we propose a method for inferring the probability distribution from which a sample of biological sequences were drawn for the case where the sequences are composed of elements that admit a natural ordering. Our method is based on Bayesian field theory, a physics-based machine learning approach, and can be regarded as a nonparametric extension of the traditional maximum entropy estimate. As an example, we use it to analyze the aneuploidy data pertaining to gliomas from The Cancer Genome Atlas project. In addition, we demonstrate two follow-up analyses that can be performed with the resulting probability distribution. One of them is to investigate the associations among the sequence sites. This provides a way to infer the governing biological grammar. The other is to study the global geometry of the probability landscape, which allows us to look at the problem from an evolutionary point of view. It can be seen that this methodology enables us to learn from a sample of sequences about how a biological system or phenomenon in the real world works.

Network modulation at stable states

Advances in microarray and sequencing technologies have made possible the interrogation of biological processes at increasing levels of complexity. The underlying biomolecular networks contain large numbers of nodes, yet interactions within the networks are not known precisely. In the absence of accurate models, one may inquire if it is possible to find relationships between the states of such networks under external changes, and in particular, if such relationships can be model-independent. In this paper we introduce a class of such relationships. The results are based on the observation that changes to the equilibrium state of a network due to an alteration in an external input are "small" compared to the change in the input, a phenomenon we refer to as network modulation. It relies on the stability of the state. One consequence of network modulation is that response surfaces containing expression profiles of different mutants of an organism are low-dimensional linear subspaces. As an example, the expression profile of a double-knockout mutant generally lies close to the plane defined by the expression profiles of the wild-type and those of the two single-knockout mutants. This assertion is validated using experimental data from the sleep-deprivation network of Drosophila and the oxygen-deprivation network of Escherichia coli. The linearity of response surfaces is crucial in the design of a feedback control algorithm to move the underlying network from an initial state to a prespecified target state.

OmicShare tools: A zero-code interactive online platform for biological data analysis and visualization

The OmicShare tools platform is a user-friendly online resource for data analysis and visualization, encompassing 161 bioinformatic tools. Users can easily track the progress of projects in real-time through an overview interface. The platform has a powerful interactive graphics engine that allows for the custom-tailored modification of charts generated from analyses. The visually appealing charts produced by OmicShare improve data interpretability and meet the requirements for publication. It has been acknowledged in over 4000 publications and is available in https://www.omicshare.com/tools/.

Body fluid multiomics in 3PM-guided ischemic stroke management: health risk assessment, targeted protection against health-to-disease transition, and cost-effective personalized approach are envisaged

Because of its rapid progression and frequently poor prognosis, stroke is the third major cause of death in Europe and the first one in China. Many independent studies demonstrated sufficient space for prevention interventions in the primary care of ischemic stroke defined as the most cost-effective protection of vulnerable subpopulations against health-to-disease transition. Although several studies identified molecular patterns specific for IS in body fluids, none of these approaches has yet been incorporated into IS treatment guidelines. The advantages and disadvantages of individual body fluids are thoroughly analyzed throughout the paper. For example, multiomics based on a minimally invasive approach utilizing blood and its components is recommended for real-time monitoring, due to the particularly high level of dynamics of the blood as a body system. On the other hand, tear fluid as a more stable system is recommended for a non-invasive and patient-friendly holistic approach appropriate for health risk assessment and innovative screening programs in cost-effective IS management. This article details aspects essential to promote the practical implementation of highlighted achievements in 3PM-guided IS management.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-024-00376-2.

Pleiotropic effects of PAB1 deletion: Extensive changes in the yeast proteome, transcriptome, and translatome

Cytoplasmic poly(A)-binding protein (PABPC; Pab1 in yeast) is thought to be involved in multiple steps of post-transcriptional control, including translation initiation, translation termination, and mRNA decay. To understand both the direct and indirect roles of PABPC in more detail, we have employed mass spectrometry to assess the abundance of the components of the yeast proteome, as well as RNA-Seq and Ribo-Seq to analyze changes in the abundance and translation of the yeast transcriptome, in cells lacking the PAB1 gene. We find that pab1Δ cells manifest drastic changes in the proteome and transcriptome, as well as defects in translation initiation and termination. Defects in translation initiation and the stabilization of specific classes of mRNAs in pab1Δ cells appear to be partly indirect consequences of reduced levels of specific initiation factors, decapping activators, and components of the deadenylation complex in addition to the general loss of Pab1's direct role in these processes. Cells devoid of Pab1 also manifested a nonsense codon readthrough phenotype indicative of a defect in translation termination. Collectively, our results indicate that, unlike the loss of simpler regulatory proteins, elimination of cellular Pab1 is profoundly pleiotropic and disruptive to numerous aspects of post-transcriptional regulation.

Orchestration of antiviral responses within the infected central nervous system

Many newly emerging and re-emerging viruses have neuroinvasive potential, underscoring viral encephalitis as a global research priority. Upon entry of the virus into the CNS, severe neurological life-threatening conditions may manifest that are associated with high morbidity and mortality. The currently available therapeutic arsenal against viral encephalitis is rather limited, emphasizing the need to better understand the conditions of local antiviral immunity within the infected CNS. In this review, we discuss new insights into the pathophysiology of viral encephalitis, with a focus on myeloid cells and CD8+ T cells, which critically contribute to protection against viral CNS infection. By illuminating the prerequisites of myeloid and T cell activation, discussing new discoveries regarding their transcriptional signatures, and dissecting the mechanisms of their recruitment to sites of viral replication within the CNS, we aim to further delineate the complexity of antiviral responses within the infected CNS. Moreover, we summarize the current knowledge in the field of virus infection and neurodegeneration and discuss the potential links of some neurotropic viruses with certain pathological hallmarks observed in neurodegeneration.

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.

CleanUpRNAseq: An R/Bioconductor Package for Detecting and Correcting DNA Contamination in RNA-Seq Data

RNA sequencing (RNA-seq) has become a standard method for profiling gene expression, yet genomic DNA (gDNA) contamination carried over to the sequencing library poses a significant challenge to data integrity. Detecting and correcting this contamination is vital for accurate downstream analyses. Particularly, when RNA samples are scarce and invaluable, it becomes essential not only to identify but also to correct gDNA contamination to maximize the data's utility. However, existing tools capable of correcting gDNA contamination are limited and lack thorough evaluation. To fill the gap, we developed CleanUpRNAseq, which offers a comprehensive set of functionalities for identifying and correcting gDNA-contaminated RNA-seq data. Our package offers three correction methods for unstranded RNA-seq data and a dedicated approach for stranded data. Through rigorous validation on published RNA-seq datasets with known levels of gDNA contamination and real-world RNA-seq data, we demonstrate CleanUpRNAseq's efficacy in detecting and correcting detrimental levels of gDNA contamination across diverse library protocols. CleanUpRNAseq thus serves as a valuable tool for post-alignment quality assessment of RNA-seq data and should be integrated into routine workflows for RNA-seq data analysis. Its incorporation into OneStopRNAseq should significantly bolster the accuracy of gene expression quantification and differential expression analysis of RNA-seq data.

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.

The Transcriptional Landscape of Coding and Noncoding RNAs in Recurrent and Nonrecurrent Colon Cancer

A number of patients with colon cancer with local or local advanced disease suffer from recurrence and there is an urgent need for better prognostic biomarkers in this setting. Here, the transcriptomic landscape of mRNAs, long noncoding RNAs, snRNAs, small nucleolar RNAs (snoRNAs), small Cajal body-specific RNAs, pseudogenes, and circular RNAs, as well as RNAs denoted as miscellaneous RNAs, was profiled by total RNA sequencing. In addition to well-known coding and noncoding RNAs, differential expression analysis also uncovered transcripts that have not been implicated previously in colon cancer, such as RNA5SP149, RNU4-2, and SNORD3A. Moreover, there was a profound global up-regulation of snRNA pseudogenes, snoRNAs, and rRNA pseudogenes in more advanced tumors. A global down-regulation of circular RNAs in tumors relative to normal tissues was observed, although only a few were expressed differentially between tumor stages. Many previously undescribed transcripts, including RNU6-620P, RNU2-20P, VTRNA1-3, and RNA5SP60, indicated strong prognostic biomarker potential in receiver operating characteristics analyses. In summary, this study unveiled numerous differentially expressed RNAs across various classes between recurrent and nonrecurrent colon cancer. Notably, there was a significant global up-regulation of snRNA pseudogenes, snoRNAs, and rRNA pseudogenes in advanced tumors. Many of these newly discovered candidates demonstrate a strong prognostic potential for stage II colon cancer.

Transcriptome analysis: a powerful tool to understand individual microbial behaviors and interactions in ecosystems

Transcriptome analysis is a powerful tool for studying microbial ecology, especially individual microbial functions in an ecosystem and their interactions. With the development of high-throughput sequencing technology, great progress has been made in analytical methods for microbial communities in natural environments. 16S rRNA gene amplicon sequencing (ie microbial community structure analysis) and shotgun metagenome analysis have been widely used to determine the composition and potential metabolic capability of microorganisms in target environments without requiring culture. However, even if the types of microorganisms present and their genes are known, it is difficult to determine what they are doing in an ecosystem. Gene expression analysis (transcriptome analysis; RNA-seq) is a powerful tool to address these issues. The history and basic information of gene expression analysis, as well as examples of studies using this method to analyze microbial ecosystems, are presented.

Massively integrated coexpression analysis reveals transcriptional regulation, evolution and cellular implications of the yeast noncanonical translatome

BACKGROUND

Recent studies uncovered pervasive transcription and translation of thousands of noncanonical open reading frames (nORFs) outside of annotated genes. The contribution of nORFs to cellular phenotypes is difficult to infer using conventional approaches because nORFs tend to be short, of recent de novo origins, and lowly expressed. Here we develop a dedicated coexpression analysis framework that accounts for low expression to investigate the transcriptional regulation, evolution, and potential cellular roles of nORFs in Saccharomyces cerevisiae.

RESULTS

Our results reveal that nORFs tend to be preferentially coexpressed with genes involved in cellular transport or homeostasis but rarely with genes involved in RNA processing. Mechanistically, we discover that young de novo nORFs located downstream of conserved genes tend to leverage their neighbors' promoters through transcription readthrough, resulting in high coexpression and high expression levels. Transcriptional piggybacking also influences the coexpression profiles of young de novo nORFs located upstream of genes, but to a lesser extent and without detectable impact on expression levels. Transcriptional piggybacking influences, but does not determine, the transcription profiles of de novo nORFs emerging nearby genes. About 40% of nORFs are not strongly coexpressed with any gene but are transcriptionally regulated nonetheless and tend to form entirely new transcription modules. We offer a web browser interface ( https://carvunislab.csb.pitt.edu/shiny/coexpression/ ) to efficiently query, visualize, and download our coexpression inferences.

CONCLUSIONS

Our results suggest that nORF transcription is highly regulated. Our coexpression dataset serves as an unprecedented resource for unraveling how nORFs integrate into cellular networks, contribute to cellular phenotypes, and evolve.

The loci of environmental adaptation in a model eukaryote

While the underlying genetic changes have been uncovered in some cases of adaptive evolution, the lack of a systematic study prevents a general understanding of the genomic basis of adaptation. For example, it is unclear whether protein-coding or noncoding mutations are more important to adaptive evolution and whether adaptations to different environments are brought by genetic changes distributed in diverse genes and biological processes or concentrated in a core set. We here perform laboratory evolution of 3360 Saccharomyces cerevisiae populations in 252 environments of varying levels of stress. We find the yeast adaptations to be primarily fueled by large-effect coding mutations overrepresented in a relatively small gene set, despite prevalent antagonistic pleiotropy across environments. Populations generally adapt faster in more stressful environments, partly because of greater benefits of the same mutations in more stressful environments. These and other findings from this model eukaryote help unravel the genomic principles of environmental adaptation.

Cactus: A user-friendly and reproducible ATAC-Seq and mRNA-Seq analysis pipeline for data preprocessing, differential analysis, and enrichment analysis

The ever decreasing cost of Next-Generation Sequencing coupled with the emergence of efficient and reproducible analysis pipelines has rendered genomic methods more accessible. However, downstream analyses are basic or missing in most workflows, creating a significant barrier for non-bioinformaticians. To help close this gap, we developed Cactus, an end-to-end pipeline for analyzing ATAC-Seq and mRNA-Seq data, either separately or jointly. Its Nextflow-, container-, and virtual environment-based architecture ensures efficient and reproducible analyses. Cactus preprocesses raw reads, conducts differential analyses between conditions, and performs enrichment analyses in various databases, including DNA-binding motifs, ChIP-Seq binding sites, chromatin states, and ontologies. We demonstrate the utility of Cactus in a multi-modal and multi-species case study as well as by showcasing its unique capabilities as compared to other ATAC-Seq pipelines. In conclusion, Cactus can assist researchers in gaining comprehensive insights from chromatin accessibility and gene expression data in a quick, user-friendly, and reproducible manner.

Assessing RNA-Seq Workflow Methodologies Using Shannon Entropy

RNA-seq faces persistent challenges due to the ongoing, expanding array of data processing workflows, none of which have yet achieved standardization to date. It is imperative to determine which method most effectively preserves biological facts. Here, we used Shannon entropy as a tool for depicting the biological status of a system. Thus, we assessed the measurement of Shannon entropy by several RNA-seq workflow approaches, such as DESeq2 and edgeR, but also by combining nine normalization methods with log2 fold change on paired samples of TCGA RNA-seq representing datasets of 515 patients and spanning 12 different cancer types with 5-year overall survival rates ranging from 20% to 98%. Our analysis revealed that TPM, RLE, and TMM normalization, coupled with a threshold of log2 fold change ≥1, for identifying differentially expressed genes, yielded the best results. We propose that Shannon entropy can serve as an objective metric for refining the optimization of RNA-seq workflows and mRNA sequencing technologies.

An epigenetic toolbox for conservation biologists

Ongoing climatic shifts and increasing anthropogenic pressures demand an efficient delineation of conservation units and accurate predictions of populations' resilience and adaptive potential. Molecular tools involving DNA sequencing are nowadays routinely used for these purposes. Yet, most of the existing tools focusing on sequence-level information have shortcomings in detecting signals of short-term ecological relevance. Epigenetic modifications carry valuable information to better link individuals, populations, and species to their environment. Here, we discuss a series of epigenetic monitoring tools that can be directly applied to various conservation contexts, complementing already existing molecular monitoring frameworks. Focusing on DNA sequence-based methods (e.g. DNA methylation, for which the applications are readily available), we demonstrate how (a) the identification of epi-biomarkers associated with age or infection can facilitate the determination of an individual's health status in wild populations; (b) whole epigenome analyses can identify signatures of selection linked to environmental conditions and facilitate estimating the adaptive potential of populations; and (c) epi-eDNA (epigenetic environmental DNA), an epigenetic-based conservation tool, presents a non-invasive sampling method to monitor biological information beyond the mere presence of individuals. Overall, our framework refines conservation strategies, ensuring a comprehensive understanding of species' adaptive potential and persistence on ecologically relevant timescales.

A Spatial Transcriptomics Browser for Discovering Gene Expression Landscapes across Microscopic Tissue Sections

A crucial feature of life is its spatial organization and compartmentalization on the molecular, cellular, and tissue levels. Spatial transcriptomics (ST) technology has opened a new chapter of the sequencing revolution, emerging rapidly with transformative effects across biology. This technique produces extensive and complex sequencing data, raising the need for computational methods for their comprehensive analysis and interpretation. We developed the ST browser web tool for the interactive discovery of ST images, focusing on different functional aspects such as single gene expression, the expression of functional gene sets, as well as the inspection of the spatial patterns of cell-cell interactions. As a unique feature, our tool applies self-organizing map (SOM) machine learning to the ST data. Our SOM data portrayal method generates individual gene expression landscapes for each spot in the ST image, enabling its downstream analysis with high resolution. The performance of the spatial browser is demonstrated by disentangling the intra-tumoral heterogeneity of melanoma and the microarchitecture of the mouse brain. The integration of machine-learning-based SOM portrayal into an interactive ST analysis environment opens novel perspectives for the comprehensive knowledge mining of the organization and interactions of cellular ecosystems.

Disregarding multimappers leads to biases in the functional assessment of NGS data

BACKGROUND

Standard ChIP-seq and RNA-seq processing pipelines typically disregard sequencing reads whose origin is ambiguous ("multimappers"). This usual practice has potentially important consequences for the functional interpretation of the data: genomic elements belonging to clusters composed of highly similar members are left unexplored.

RESULTS

In particular, disregarding multimappers leads to the underrepresentation in epigenetic studies of recently active transposable elements, such as AluYa5, L1HS and SVAs. Furthermore, this common strategy also has implications for transcriptomic analysis: members of repetitive gene families, such the ones including major histocompatibility complex (MHC) class I and II genes, are under-quantified.

CONCLUSION

Revealing inherent biases that permeate routine tasks such as functional enrichment analysis, our results underscore the urgency of broadly adopting multimapper-aware bioinformatic pipelines -currently restricted to specific contexts or communities- to ensure the reliability of genomic and transcriptomic studies.

TIR predictor and optimizer: Web-tools for accurate prediction of translation initiation rate and precision gene design in Saccharomyces cerevisiae

Translation initiation is the primary determinant of the rate of protein production. The variation in the rate with which this step occurs can cause up to three orders of magnitude differences in cellular protein levels. Several mRNA features, including mRNA stability in proximity to the start codon, coding sequence length, and presence of specific motifs in the mRNA molecule, have been shown to influence the translation initiation rate. These molecular factors acting at different strengths allow precise control of in vivo translation initiation rate and thus the rate of protein synthesis. However, despite the paramount importance of translation initiation rate in protein synthesis, accurate prediction of the absolute values of initiation rate remains a challenge. In fact, as of now, there is no available model for predicting the initiation rate in Saccharomyces cerevisiae. To address this, we train a machine learning model for predicting the in vivo initiation rate in S. cerevisiae transcripts. The model is trained using a diverse set of mRNA transcripts, enabling the comparison of initiation rates across different transcripts. Our model exhibited excellent accuracy in predicting the translation initiation rate and demonstrated its effectiveness with both endogenous and exogenous transcripts. Then, by combining the machine learning model with the Monte-Carlo search algorithm, we have also devised a method to optimize the nucleotide sequence of any gene to achieve a specific target initiation rate. The machine learning model we've developed for predicting translation initiation rates, along with the gene optimization method, are deployed as a web server. Both web servers are accessible for free at the following link: ajeetsharmalab.com/TIRPredictor. Thus, this research advances our fundamental understanding of translation initiation processes, with direct applications in biotechnology.

Truncated protein isoforms generate diversity of protein localization and function in yeast

Genome-wide measurement of ribosome occupancy on mRNAs has enabled empirical identification of translated regions, but high-confidence detection of coding regions that overlap annotated coding regions has remained challenging. Here, we report a sensitive and robust algorithm that revealed the translation of 388 N-terminally truncated proteins in budding yeast-more than 30-fold more than previously known. We extensively experimentally validated them and defined two classes. The first class lacks large portions of the annotated protein and tends to be produced from a truncated transcript. We show that two such cases, Yap5truncation and Pus1truncation, have condition-specific regulation and distinct functions from their respective annotated isoforms. The second class of truncated protein isoforms lacks only a small region of the annotated protein and is less likely to be produced from an alternative transcript isoform. Many display different subcellular localizations than their annotated counterpart, representing a common strategy for dual localization of otherwise functionally identical proteins. A record of this paper's transparent peer review process is included in the supplemental information.

Penicillium chrysogenum: Beyond the penicillin

Almost one century after the Sir Alexander Fleming's fortuitous discovery of penicillin and the identification of the fungal producer as Penicillium notatum, later Penicillium chrysogenum (currently reidentified as Penicillium rubens), the molecular mechanisms behind the massive production of penicillin titers by industrial strains could be considered almost fully characterized. However, this filamentous fungus is not only circumscribed to penicillin, and instead, it seems to be full of surprises, thereby producing important metabolites and providing expanded biotechnological applications. This review, in addition to summarizing the classical role of P. chrysogenum as penicillin producer, highlights its ability to generate an array of additional bioactive secondary metabolites and enzymes, together with the use of this microorganism in relevant biotechnological processes, such as bioremediation, biocontrol, production of bioactive nanoparticles and compounds with pharmaceutical interest, revalorization of agricultural and food-derived wastes or the enhancement of food industrial processes and the agricultural production.

Comparative Transcriptomics of Fat Bodies between Symbiotic and Quasi-Aposymbiotic Adult Females of Blattella germanica with Emphasis on the Metabolic Integration with Its Endosymbiont Blattabacterium and Its Immune System

We explored the metabolic integration of Blattella germanica and its obligate endosymbiont Blattabacterium cuenoti by the transcriptomic analysis of the fat body of quasi-aposymbiotic cockroaches, where the endosymbionts were almost entirely removed with rifampicin. Fat bodies from quasi-aposymbiotic insects displayed large differences in gene expression compared to controls. In quasi-aposymbionts, the metabolism of phenylalanine and tyrosine involved in cuticle sclerotization and pigmentation increased drastically to compensate for the deficiency in the biosynthesis of these amino acids by the endosymbionts. On the other hand, the uricolytic pathway and the biosynthesis of uric acid were severely decreased, probably because the reduced population of endosymbionts was unable to metabolize urea to ammonia. Metabolite transporters that could be involved in the endosymbiosis process were identified. Immune system and antimicrobial peptide (AMP) gene expression was also reduced in quasi-aposymbionts, genes encoding peptidoglycan-recognition proteins, which may provide clues for the maintenance of the symbiotic relationship, as well as three AMP genes whose involvement in the symbiotic relationship will require additional analysis. Finally, a search for AMP-like factors that could be involved in controlling the endosymbiont identified two orphan genes encoding proteins smaller than 200 amino acids underexpressed in quasi-aposymbionts, suggesting a role in the host-endosymbiont relationship.

Non-Coding RNAs: Regulators of Stress, Ageing, and Developmental Decisions in Yeast?

Cells must change their properties in order to adapt to a constantly changing environment. Most of the cellular sensing and regulatory mechanisms described so far are based on proteins that serve as sensors, signal transducers, and effectors of signalling pathways, resulting in altered cell physiology. In recent years, however, remarkable examples of the critical role of non-coding RNAs in some of these regulatory pathways have been described in various organisms. In this review, we focus on all classes of non-coding RNAs that play regulatory roles during stress response, starvation, and ageing in different yeast species as well as in structured yeast populations. Such regulation can occur, for example, by modulating the amount and functional state of tRNAs, rRNAs, or snRNAs that are directly involved in the processes of translation and splicing. In addition, long non-coding RNAs and microRNA-like molecules are bona fide regulators of the expression of their target genes. Non-coding RNAs thus represent an additional level of cellular regulation that is gradually being uncovered.

New Empirical Bayes Models to Jointly Analyze Multiple RNA-Sequencing Data in a Hypophosphatasia Disease Study

Hypophosphatasia is a rare inherited metabolic disorder caused by the deficiency of tissue-nonspecific alkaline phosphatase. More severe and early onset cases present symptoms of muscle weakness, diminished motor coordination, and epileptic seizures. These neurological manifestations are poorly characterized. Thus, it is urgent to discover novel differentially expressed genes for investigating the genetic mechanisms underlying the neurological manifestations of hypophosphatasia. RNA-sequencing data offer a high-resolution and highly accurate transcript profile. In this study, we apply an empirical Bayes model to RNA-sequencing data acquired from the spinal cord and neocortex tissues of a mouse model, individually, to more accurately estimate the genetic effects without bias. More importantly, we further develop two integration methods, weighted gene approach and weighted Z method, to incorporate two RNA-sequencing data into a model for enhancing the effects of genetic markers in the diagnostics of hypophosphatasia disease. The simulation and real data analysis have demonstrated the effectiveness of our proposed integration methods, which can maximize genetic signals identified from the spinal cord and neocortex tissues, minimize the prediction error, and largely improve the prediction accuracy in risk prediction.

Cross-evaluation of E. coli's operon structures via a whole-cell model suggests alternative cellular benefits for low- versus high-expressing operons

Many bacteria use operons to coregulate genes, but it remains unclear how operons benefit bacteria. We integrated E. coli's 788 polycistronic operons and 1,231 transcription units into an existing whole-cell model and found inconsistencies between the proposed operon structures and the RNA-seq read counts that the model was parameterized from. We resolved these inconsistencies through iterative, model-guided corrections to both datasets, including the correction of RNA-seq counts of short genes that were misreported as zero by existing alignment algorithms. The resulting model suggested two main modes by which operons benefit bacteria. For 86% of low-expression operons, adding operons increased the co-expression probabilities of their constituent proteins, whereas for 92% of high-expression operons, adding operons resulted in more stable expression ratios between the proteins. These simulations underscored the need for further experimental work on how operons reduce noise and synchronize both the expression timing and the quantity of constituent genes. A record of this paper's transparent peer review process is included in the supplemental information.

Extended stop codon context predicts nonsense codon readthrough efficiency in human cells

Protein synthesis terminates when a stop codon enters the ribosome's A-site. Although termination is efficient, stop codon readthrough can occur when a near-cognate tRNA outcompetes release factors during decoding. Seeking to understand readthrough regulation we used a machine learning approach to analyze readthrough efficiency data from published HEK293T ribosome profiling experiments and compared it to comparable yeast experiments. We obtained evidence for the conservation of identities of the stop codon, its context, and 3'-UTR length (when termination is compromised), but not the P-site codon, suggesting a P-site tRNA role in readthrough regulation. Models trained on data from cells treated with the readthrough-promoting drug, G418, accurately predicted readthrough of premature termination codons arising from CFTR nonsense alleles that cause cystic fibrosis. This predictive ability has the potential to aid development of nonsense suppression therapies by predicting a patient's likelihood of improvement in response to drugs given their nonsense mutation sequence context.