Best practices on the differential expression analysis of multi-species RNA-seq

Dysregulation of mRNA expression by hsa-miR-186 overexpression in arsenic-induced skin carcinogenesis

Dysregulated miRNA expression contributes to development of arsenic-induced cutaneous squamous cell carcinoma (cSCC). hsa-miR-186 (miR-186) is overexpressed in arsenical cSCC tissues as well as in preclinical cell line model of arsenical cSCC. Simultaneous miR-186 overexpression and chronic inorganic trivalent arsenite (iAs; 100 nM) exposure transformed human HaCaT cell line preferentially over miR-186 overexpression or iAs exposure alone. Both iAs and miR-186 regulate the expression of wide range of mRNA targets. However, how their interaction impacts the transcriptome-wide mRNA expression landscape ushering in cancer is unknown. We performed longitudinal RNA-seq analysis in passage-matched HaCaT cell clones (±miR-186 overexpression) with simultaneous chronic iAs exposure (0/100 nM) at 12 and 29 weeks. We determined the impact of each factor and their interaction towards differential gene expression and pathway dysregulation employing two different statistical approaches (t-statistic and 2-factor ANOVA). We show that a core set of pathways are dysregulated deterministically irrespective of the statistical approach chosen, possibly representing necessary changes for transformation. The data suggest that each clonal line could take a unique route to dysregulate this core set of pathways necessary for transformation, highlighting the possible role of stochasticity in cancer development. Evidence is presented to sift the strengths and weaknesses of each statistical methodology in providing biological understanding of events that play crucial roles in carcinogenesis in large datasets with multiple contributing variables.

Sequencing-based analysis of microbiomes

Microbiomes occupy a range of niches and, in addition to having diverse compositions, they have varied functional roles that have an impact on agriculture, environmental sciences, and human health and disease. The study of microbiomes has been facilitated by recent technological and analytical advances, such as cheaper and higher-throughput DNA and RNA sequencing, improved long-read sequencing and innovative computational analysis methods. These advances are providing a deeper understanding of microbiomes at the genomic, transcriptional and translational level, generating insights into their function and composition at resolutions beyond the species level.

Genetic Approaches for Identifying and Characterizing Effectors in Bacterial Pathogens

Microbial pathogens have coevolved with their hosts, often for millions of years, and in the process have developed a variety of virulence mechanisms to ensure their survival, typically at the host's expense. At the center of this host-pathogen warfare are proteins called effectors that are delivered by bacteria into their host where they alter the intracellular environment to promote bacterial proliferation. Many effectors are believed to have been acquired by the bacteria from their host during evolution, explaining why researchers are keen to understand their function, as this information may provide insight into both microbial virulence strategies and biological processes that happen within our own cells. Help for accomplishing this goal has come from the recent development of increasingly powerful genetic approaches, which are the focus of this review.

Interspecific cross-talk: The catalyst driving microbial biosynthesis of secondary metabolites

Microorganisms co-exist and co-evolve in nature, forming intricate ecological communities. The interspecies cross-talk within these communities creates and sustains their great biosynthetic potential, making them an important source of natural medicines and high-value-added chemicals. However, conventional investigations into microbial metabolites are typically carried out in pure cultures, resulting in the absence of specific activating factors and consequently causing a substantial number of biosynthetic gene clusters to remain silent. This, in turn, hampers the in-depth exploration of microbial biosynthetic potential and frequently presents researchers with the challenge of rediscovering compounds. In response to this challenge, the coculture strategy has emerged to explore microbial biosynthetic capabilities and has shed light on the study of cross-talk mechanisms. These elucidated mechanisms will contribute to a better understanding of complex biosynthetic regulations and offer valuable insights to guide the mining of secondary metabolites. This review summarizes the research advances in microbial cross-talk mechanisms, with a particular focus on the mechanisms that activate the biosynthesis of secondary metabolites. Additionally, the instructive value of these mechanisms for developing strategies to activate biosynthetic pathways is discussed. Moreover, challenges and recommendations for conducting in-depth studies on the cross-talk mechanisms are presented.

Deciphering transcript architectural complexity in bacteria and archaea

RNA transcripts are potential therapeutic targets, yet bacterial transcripts have uncharacterized biodiversity. We developed an algorithm for transcript prediction called tp.py using it to predict transcripts (mRNA and other RNAs) in Escherichia coli K12 and E2348/69 strains (Bacteria:gamma-Proteobacteria), Listeria monocytogenes strains Scott A and RO15 (Bacteria:Firmicute), Pseudomonas aeruginosa strains SG17M and NN2 strains (Bacteria:gamma-Proteobacteria), and Haloferax volcanii (Archaea:Halobacteria). From >5 million E. coli K12 and >3 million E. coli E2348/69 newly generated Oxford Nanopore Technologies direct RNA sequencing reads, 2,487 K12 mRNAs and 1,844 E2348/69 mRNAs were predicted, with the K12 mRNAs containing more than half of the predicted E. coli K12 proteins. While the number of predicted transcripts varied by strain based on the amount of sequence data used, across all strains examined, the predicted average size of the mRNAs was 1.6-1.7 kbp, while the median size of the 5'- and 3'-untranslated regions (UTRs) were 30-90 bp. Given the lack of bacterial and archaeal transcript annotation, most predictions were of novel transcripts, but we also predicted many previously characterized mRNAs and ncRNAs, including post-transcriptionally generated transcripts and small RNAs associated with pathogenesis in the E. coli E2348/69 LEE pathogenicity islands. We predicted small transcripts in the 100-200 bp range as well as >10 kbp transcripts for all strains, with the longest transcript for two of the seven strains being the nuo operon transcript, and for another two strains it was a phage/prophage transcript. This quick, easy, and reproducible method will facilitate the presentation of transcripts, and UTR predictions alongside coding sequences and protein predictions in bacterial genome annotation as important resources for the research community.IMPORTANCEOur understanding of bacterial and archaeal genes and genomes is largely focused on proteins since there have only been limited efforts to describe bacterial/archaeal RNA diversity. This contrasts with studies on the human genome, where transcripts were sequenced prior to the release of the human genome over two decades ago. We developed software for the quick, easy, and reproducible prediction of bacterial and archaeal transcripts from Oxford Nanopore Technologies direct RNA sequencing data. These predictions are urgently needed for more accurate studies examining bacterial/archaeal gene regulation, including regulation of virulence factors, and for the development of novel RNA-based therapeutics and diagnostics to combat bacterial pathogens, like those with extreme antimicrobial resistance.

Complex transcriptional regulations of a hyperparasitic quadripartite system in giant viruses infecting protists

Hyperparasitism is a common pattern in nature that is not limited to cellular organisms. Giant viruses infecting protists can be hyperparasitized by smaller ones named virophages. In addition, both may carry episomal DNA molecules known as transpovirons in their particles. They all share transcriptional regulatory elements that dictate the expression of their genes within viral factories built by giant viruses in the host cytoplasm. This suggests the existence of interactions between their respective transcriptional networks. Here we investigated Acanthamoeba castellanii cells infected by a giant virus (megavirus chilensis), and coinfected with a virophage (zamilon vitis) and/or a transpoviron (megavirus vitis transpoviron). Infectious cycles were monitored through time-course RNA sequencing to decipher the transcriptional program of each partner and its impact on the gene expression of the others. We found highly diverse transcriptional responses. While the giant virus drastically reshaped the host cell transcriptome, the transpoviron had no effect on the gene expression of any of the players. In contrast, the virophage strongly modified the giant virus gene expression, albeit transiently, without altering the protein composition of mature viral particles. The virophage also induced the overexpression of transpoviron genes, likely through the indirect upregulation of giant virus-encoded transcription factors. Together, these analyses document the intricated transcriptionally regulated networks taking place in the infected cell.

MME and PTPRC: key renal biomarkers in lupus nephritis

Background

Lupus nephritis (LN) is an autoimmune-related kidney disease with a poor prognosis, however the potential pathogenic mechanism remains unclear and there is a lack of precise biomarkers. Therefore, a thorough screening and identification of renal markers in LN are immensely beneficial to the research on its pathogenic mechanisms and treatment strategies.

Methods

We utilized bioinformatics to analyze the differentially expressed genes (DEGs) at the transcriptome level of three clusters: total renal, glomeruli, and renal tubulointerstitium in the GEO database to discover potential renal biomarkers of LN. We utilized NephroSeq datasets and measured mRNA and protein levels in the kidneys of MRL/lpr mice to confirm the expression of key DEGs.

Results

Seven significantly differential genes (EGR1, MME, PTPRC, RORC, MX1, ZBTB16, FKBP5) were revealed from the transcriptome database of GSE200306, which were mostly enriched in the pathway of the hematopoietic cell lineage and T cell differentiation respectively by KEGG and GO analysis. The seven hot differential genes were verified to have consistent change trends using three datasets from NephroSeq database. The receiver operating characteristic (ROC) curve indicated that five DEGs (PTPRC, MX1, EGR1, MME and RORC) exhibited a higher diagnostic ROC value in both the glomerulus and tubulointerstitium group. Validation of core genes using MRL/lpr mice showed that MME and PTPRC exhibit significantly differential mRNA and protein expression patterns in mouse kidneys like the datasets.

Conclusions

This study identified seven key renal biomarkers through bioinformatics analysis using the GEO and NephroSeq databases. It was identified that MME and PTPRC may have a high predictive value as renal biomarkers in the pathogenesis of LN, as confirmed by animal validation.

Key role of CYP17A1 in Leydig cell function and testicular development in Qianbei Ma goats

Reproductive traits are vital economic parameters in goat production, and boosting the reproductive capacity of breeding rams is crucial for enhancing the profitability of goat farming. Currently, research on the reproductive performance of Qianbei Ma goats mainly centers on investigating mechanisms associated with prolificacy and estrous ovulation in ewes, with limited emphasis on ram reproductive aspects. This study used scanning electron microscopy and enzyme-linked immunosorbent assay (ELISA) to profile the morphology of testis and the dynamic changes of Luteinizing Hormone (LH), Follicle-Stimulating Hormone (FSH), and Testosterone (T) in serum at different developmental stages of Qianbei Ma goats. Meanwhile, transcriptome sequencing technology was used to investigate the mRNA expression patterns in testicular tissues at different developmental stages: newborn (0 M), puberty (6 M), sexual maturity (12 M), and physical maturity (18 M). The results showed that the diameter, circumference, and area of the testicular seminiferous tubules gradually increased with age. The levels of T and LH in serum significantly increased from 0 to 6 months after birth (p < 0.05), followed by a stabilization of T levels and a significant decrease in LH levels (p < 0.05). Meanwhile, FSH shows a decreasing trend between 0 and 18 months after birth. A total of 26,437 differentially expressed genes were identified in 6 comparison groups, which involve various biological processes such as immunity, growth, metabolism, development, and reproduction, and are significantly enriched in signaling pathways related to testicular development and spermatogenesis. WGCNA analysis identified 6 regions significantly associated with testicular development and spermatogenesis, and selected 320 genes for constructing a PPI network. Ten candidate genes related to testicular development and spermatogenesis were identified, including TP53, PLK4, RPS9, PFN4, ACTB, CYP17A1, GPX4, CLDN1, AMH and DHH. Of these, the CYP17A1 gene promotes interstitial cell proliferation, and promotes T synthesis. This study provides a theoretical basis and data support for promoting efficient breeding of goats and early breeding of excellent male goats.

Updated annotation and meta-analysis of Brugia malayi transcriptomics data reveals consistent transcriptional profiles across time and space with some study-specific differences in adult female worm transcriptional profiles

Genomics, transcriptomics, and proteomics have significantly advanced our understanding of obligately host-associated microbes, where interrogation of the biology is often limited by the complexity of the biological system and limited tools. This includes the causative agents of many neglected tropical diseases, including filarial nematodes. Therefore, numerous transcriptomics studies have been undertaken on filarial nematodes. Most of these transcriptomics studies focus on Brugia malayi, which causes lymphatic filariasis and is a laboratory model for human filarial disease. Here, we undertook a meta-analysis of the publicly available B. malayi transcriptomics data enabling the direct cross comparison of samples from almost a dozen studies. This reanalysis highlights the consistency of transcriptomics results across many different studies and experimental designs from across the globe for over a decade of research, across many different generations of a sequencing technology, library preparation protocols, and differential expression tools. Males and microfilariae across samples had similar expression profiles. However, female samples were clustered into two differential expression patterns that were significantly different from one another. Largely, we confirm previous results for all studies reanalyzed including tissue-specific gene expression and anti-Wolbachia doxycycline treatment of microfilaria. However, we did not detect previously reported differential expression upon in vitro or in vivo treatment with ivermectin, albendazole, and DEC, instead identifying a consistent lack of transcriptomic change upon exposure to these anthelminthic drugs. Updated annotation has been provided that denotes poorly supported genes including those overlapping rRNAs.

Environmental community transcriptomics: strategies and struggles

Transcriptomics is the study of RNA transcripts, the portion of the genome that is transcribed, in a specific cell, tissue, or organism. Transcriptomics provides insight into gene expression patterns, regulation, and the underlying mechanisms of cellular processes. Community transcriptomics takes this a step further by studying the RNA transcripts from environmental assemblies of organisms, with the intention of better understanding the interactions between members of the community. Community transcriptomics requires successful extraction of RNA from a diverse set of organisms and subsequent analysis via mapping those reads to a reference genome or de novo assembly of the reads. Both, extraction protocols and the analysis steps can pose hurdles for community transcriptomics. This review covers advances in transcriptomic techniques and assesses the viability of applying them to community transcriptomics.

Diverse genetic alteration dysregulates neuropeptide and intracellular signalling in the suprachiasmatic nuclei

In mammals, intrinsic 24 h or circadian rhythms are primarily generated by the suprachiasmatic nuclei (SCN). Rhythmic daily changes in the transcriptome and proteome of SCN cells are controlled by interlocking transcription-translation feedback loops (TTFLs) of core clock genes and their proteins. SCN cells function as autonomous circadian oscillators, which synchronize through intercellular neuropeptide signalling. Physiological and behavioural rhythms can be severely disrupted by genetic modification of a diverse range of genes and proteins in the SCN. With the advent of next generation sequencing, there is unprecedented information on the molecular profile of the SCN and how it is affected by genetically targeted alteration. However, whether the expression of some genes is more readily affected by genetic alteration of the SCN is unclear. Here, using publicly available datasets from recent RNA-seq assessments of the SCN from genetically altered and control mice, we evaluated whether there are commonalities in transcriptome dysregulation. This was completed for four different phases across the 24 h cycle and was augmented by Gene Ontology Molecular Function (GO:MF) and promoter analysis. Common differentially expressed genes (DEGs) and/or enriched GO:MF terms included signalling molecules, their receptors, and core clock components. Finally, examination of the JASPAR database indicated that E-box and CRE elements in the promoter regions of several commonly dysregulated genes. From this analysis, we identify differential expression of genes coding for molecules involved in SCN intra- and intercellular signalling as a potential cause of abnormal circadian rhythms.

How does the age of control individuals hinder the identification of target genes for Huntington's disease?

Several studies have compared the transcriptome across various brain regions in Huntington's disease (HD) gene-positive and neurologically normal individuals to identify potential differentially expressed genes (DEGs) that could be pharmaceutical or prognostic targets for HD. Despite adhering to technical recommendations for optimal RNA-Seq analysis, none of the genes identified as upregulated in these studies have yet demonstrated success as prognostic or therapeutic targets for HD. Earlier studies included samples from neurologically normal individuals older than the HD gene-positive group. Considering the gradual transcriptional changes induced by aging in the brain, we posited that utilizing samples from older controls could result in the misidentification of DEGs. To validate our hypothesis, we reanalyzed 146 samples from this study, accessible on the SRA database, and employed Propensity Score Matching (PSM) to create a "virtual" control group with a statistically comparable age distribution to the HD gene-positive group. Our study underscores the adverse impact of using neurologically normal individuals over 75 as controls in gene differential expression analysis, resulting in false positives and negatives. We conclusively demonstrate that using such old controls leads to the misidentification of DEGs, detrimentally affecting the discovery of potential pharmaceutical and prognostic markers. This underscores the pivotal role of considering the age of control samples in RNA-Seq analysis and emphasizes its inclusion in evaluating best practices for such investigations. Although our primary focus is HD, our findings suggest that judiciously selecting age-appropriate control samples can significantly improve best practices in differential expression analysis.

In vitro and vivo anti-tumor activity and mechanisms of the new cryptotanshinone derivative 11 against hepatocellular carcinoma

Global burden of hepatocellular carcinoma (HCC) is increasing. Chemotherapy and immunotherapy are the prevailing options for therapy. Developing new therapeutic strategies for HCC patients is still highly desirable. Recent studies demonstrate that cryptotanshinone is capable of inhibiting tumor growth in HCC and induces antitumor immunity in vitro. In our previous research, we discovered a new cryptotanshinone derivative 11 as an effective immunoregulatory enzyme indoleamine 2, 3-dioxygenase 1 (IDO1) inhibitor. This study aims to evaluate its in vitro and in vivo antitumor activity against hepatocellular carcinoma. 11 displayed robust anti-proliferative activity against HCC cell lines and promoted apoptosis of HCC cell line through the mitochondrial-mediated apoptotic pathway. In H22 tumor-bearing mice models, 11 exhibited significant in vivo anti-tumor activity with different administration routes. And no obvious toxicity was observed. RNA-seq analysis demonstrated the differential expressed genes and alteration of key pathways associated with immune responses after administration of 11. Up-regulation of anti-tumor cytokines and down-regulation of cytokines that promote tumor growth were indicated and further validated. Our study demonstrates that 11 exhibits promising anti-tumor activity both in vitro and in vivo against hepatocellular carcinoma cancer. It is a lead compound for HCC immunotherapy and is worthy for further development.

Induction of proteasomal activity in mammalian cells by lifespan-extending tRNA synthetase inhibitors

We have recently shown that multiple tRNA synthetase inhibitors can greatly increase lifespan in multiple models by acting through the conserved transcription factor ATF4. Here, we show that these compounds, and several others of the same class, can greatly upregulate mammalian ATF4 in cells in vitro, in a dose dependent manner. Further, RNASeq analysis of these cells pointed toward changes in protein turnover. In subsequent experiments here we show that multiple tRNA synthetase inhibitors can greatly upregulate activity of the ubiquitin proteasome system (UPS) in cells in an ATF4-dependent manner. The UPS plays an important role in the turnover of many damaged or dysfunctional proteins in an organism. Increasing UPS activity has been shown to enhance the survival of Huntington's disease cell models, but there are few known pharmacological enhancers of the UPS. Additionally, we see separate ATF4 dependent upregulation of macroautophagy upon treatment with tRNA synthetase inhibitors. Protein degradation is an essential cellular process linked to many important human diseases of aging such as Alzheimer's disease and Huntington's disease. These drugs' ability to enhance proteostasis more broadly could have wide-ranging implications in the treatment of important age-related neurodegenerative diseases.

A single workflow for multi-species blood transcriptomics

BACKGROUND

Blood transcriptomic analysis is widely used to provide a detailed picture of a physiological state with potential outcomes for applications in diagnostics and monitoring of the immune response to vaccines. However, multi-species transcriptomic analysis is still a challenge from a technological point of view and a standardized workflow is urgently needed to allow interspecies comparisons.

RESULTS

Here, we propose a single and complete total RNA-Seq workflow to generate reliable transcriptomic data from blood samples from humans and from animals typically used in preclinical models. Blood samples from a maximum of six individuals and four different species (rabbit, non-human primate, mouse and human) were extracted and sequenced in triplicates. The workflow was evaluated using different wet-lab and dry-lab criteria, including RNA quality and quantity, the library molarity, the number of raw sequencing reads, the Phred-score quality, the GC content, the performance of ribosomal-RNA and globin depletion, the presence of residual DNA, the strandness, the percentage of coding genes, the number of genes expressed, and the presence of saturation plateau in rarefaction curves. We identified key criteria and their associated thresholds to be achieved for validating the transcriptomic workflow. In this study, we also generated an automated analysis of the transcriptomic data that streamlines the validation of the dataset generated.

CONCLUSIONS

Our study has developed an end-to-end workflow that should improve the standardization and the inter-species comparison in blood transcriptomics studies. In the context of vaccines and drug development, RNA sequencing data from preclinical models can be directly compared with clinical data and used to identify potential biomarkers of value to monitor safety and efficacy.

Chronic arsenic exposure induces malignant transformation of human HaCaT cells through both deterministic and stochastic changes in transcriptome expression

Biological processes are inherently stochastic, i.e., are partially driven by hard to predict random probabilistic processes. Carcinogenesis is driven both by stochastic and deterministic (predictable non-random) changes. However, very few studies systematically examine the contribution of stochastic events leading to cancer development. In differential gene expression studies, the established data analysis paradigms incentivize expression changes that are uniformly different across the experimental versus control groups, introducing preferential inclusion of deterministic changes at the expense of stochastic processes that might also play a crucial role in the process of carcinogenesis. In this study, we applied simple computational techniques to quantify: (i) The impact of chronic arsenic (iAs) exposure as well as passaging time on stochastic gene expression and (ii) Which genes were expressed deterministically and which were expressed stochastically at each of the three stages of cancer development. Using biological coefficient of variation as an empirical measure of stochasticity we demonstrate that chronic iAs exposure consistently suppressed passaging related stochastic gene expression at multiple time points tested, selecting for a homogenous cell population that undergo transformation. Employing multiple balanced removal of outlier data, we show that chronic iAs exposure induced deterministic and stochastic changes in the expression of unique set of genes, that populate largely unique biological pathways. Together, our data unequivocally demonstrate that both deterministic and stochastic changes in transcriptome-wide expression are critical in driving biological processes, pathways and networks towards clonal selection, carcinogenesis, and tumor heterogeneity.

Unravelling the role of long non-coding RNAs in modulating the Hedgehog pathway in cancer

Cancer is a multifactorial pathological condition characterized by uncontrolled cellular proliferation, genomic instability, and evasion of regulatory mechanisms. It arises from the accumulation of genetic mutations confer selective growth advantages, leading to malignant transformation and tumor formation. The intricate interplay between LncRNAs and the Hedgehog pathway has emerged as a captivating frontier in cancer research. The Hedgehog pathway, known for its fundamental roles in embryonic development and tissue homeostasis, is frequently dysregulated in various cancers, contributing to aberrant cellular proliferation, survival, and differentiation. The Hh pathway is crucial in organizing growth and maturation processes in multicellular organisms. It plays a pivotal role in the initiation of tumors as well as in conferring resistance to conventional therapeutic approaches. The crosstalk among the Hh pathway and lncRNAs affects the expression of Hh signaling components through various transcriptional and post-transcriptional processes. Numerous pathogenic processes, including both non-malignant and malignant illnesses, have been identified to be induced by this interaction. The dysregulation of lncRNAs has been associated with the activation or inhibition of the Hh pathway, making it a potential therapeutic target against tumorigenesis. Insights into the functional significance of LncRNAs in Hedgehog pathway modulation provide promising avenues for diagnostic and therapeutic interventions. The dysregulation of LncRNAs in various cancer types underscores their potential as biomarkers for early detection and prognostication. Additionally, targeting LncRNAs associated with the Hedgehog pathway presents an innovative strategy for developing precision therapeutics to restore pathway homeostasis and impede cancer progression. This review aims to elucidate the complex regulatory network orchestrated by LncRNAs, unravelling their pivotal roles in modulating the Hedgehog pathway and influencing cancer progression.

A combination of burn wound injury and Pseudomonas infection elicits unique gene expression that enhances bacterial pathogenicity

IMPORTANCE

The interaction between an underlying disease process and a specific pathogen may lead to the unique expression of genes that affect bacterial pathogenesis. These genes may not be observed during infection in the absence of, or with a different underlying process or infection during the underlying process with a different pathogen. To test this hypothesis, we used Nanostring technology to compare gene transcription in a murine-burned wound infected with P. aeruginosa. The Nanostring probeset allowed the simultaneous direct comparison of immune response gene expression in both multiple host tissues and P. aeruginosa in conditions of burn alone, infection alone, and burn with infection. While RNA-Seq is used to discover novel transcripts, NanoString could be a technique to monitor specific changes in transcriptomes between samples and bypass the additional adjustments for multispecies sample processing or the need for the additional steps of alignment and assembly required for RNASeq. Using Nanostring, we identified arginine and IL-10 as important contributors to the lethal outcome of burned mice infected with P. aeruginosa. While other examples of altered gene transcription are in the literature, our study suggests that a more systematic comparison of gene expression in various underlying diseases during infection with specific bacterial pathogens may lead to the identification of unique host-pathogen interactions and result in more precise therapeutic interventions.

CoSIA: an R Bioconductor package for CrOss Species Investigation and Analysis

SUMMARY

High-throughput sequencing technologies have enabled cross-species comparative transcriptomic studies; however, there are numerous challenges for these studies due to biological and technical factors. We developed CoSIA (Cross-Species Investigation and Analysis), a Bioconductor R package and Shiny app that provides an alternative framework for cross-species transcriptomic comparison of non-diseased wild-type RNA sequencing gene expression data from Bgee across tissues and species (human, mouse, rat, zebrafish, fly, and nematode) through visualization of variability, diversity, and specificity metrics.

AVAILABILITY AND IMPLEMENTATION

https://github.com/lasseignelab/CoSIA.

TsImpute: an accurate two-step imputation method for single-cell RNA-seq data

MOTIVATION

Single-cell RNA sequencing (scRNA-seq) technology has enabled discovering gene expression patterns at single cell resolution. However, due to technical limitations, there are usually excessive zeros, called "dropouts," in scRNA-seq data, which may mislead the downstream analysis. Therefore, it is crucial to impute these dropouts to recover the biological information.

RESULTS

We propose a two-step imputation method called tsImpute to impute scRNA-seq data. At the first step, tsImpute adopts zero-inflated negative binomial distribution to discriminate dropouts from true zeros and performs initial imputation by calculating the expected expression level. At the second step, it conducts clustering with this modified expression matrix, based on which the final distance weighted imputation is performed. Numerical results based on both simulated and real data show that tsImpute achieves favorable performance in terms of gene expression recovery, cell clustering, and differential expression analysis.

AVAILABILITY AND IMPLEMENTATION

The R package of tsImpute is available at https://github.com/ZhengWeihuaYNU/tsImpute.

Syringaresinol alleviates IgG immune complex induced acute lung injury via activating PPARγ and suppressing pyroptosis

Acute lung injury (ALI) is a life-threatening condition characterized by severe lung inflammation and tissue damage. In this study, we investigate the potential therapeutic efficacy of (+)-Syringaresinol (SYG), a natural compound known for its antioxidant and anti-inflammatory properties, in alleviating ALI induced by IgG immune complexes (IgG-IC). Using MH-S cells as a model, we explore SYG's ability to target peroxisome proliferator-activated receptor gamma (PPARγ) and its anti-inflammatory properties. Our comprehensive investigation aims to elucidate the specific molecular mechanisms underlying SYG's effects against pyroptosis, as revealed through transcriptomic analysis. Validation in C57BL/6 mice provides in vivo support. Our findings indicate that SYG effectively mitigates IgG-IC-induced lung damage, as evidenced by a significant reduction in lung inflammation and tissue injury. SYG treatment notably decreases pro-inflammatory cytokine levels (TNF-α, IL-6, IL-1β) in both lung tissue and cells. Molecular docking analysis reveals SYG's robust binding to PPARγ, leading to the inhibition of IgG-IC-induced inflammatory signaling pathways. Additionally, transcriptomic analysis unveils SYG's potential in suppressing macrophage pyroptosis, potentially through the downregulation of key inflammatory mediators (NLRP3, GSDMD, Caspase-1). In summary, our study presents compelling evidence supporting SYG as an effective therapeutic agent for ALI. SYG's activation of PPARγ contributes to the suppression of NF-κB and C/EBPs expression, thereby mitigating inflammation. Moreover, SYG demonstrates the ability to inhibit macrophage pyroptosis by targeting the NLRP3/GSDMD/caspase-1 axis.

Wolbachia endosymbionts manipulate the self-renewal and differentiation of germline stem cells to reinforce fertility of their fruit fly host

The alphaproteobacterium Wolbachia pipientis infects arthropod and nematode species worldwide, making it a key target for host biological control. Wolbachia-driven host reproductive manipulations, such as cytoplasmic incompatibility (CI), are credited for catapulting these intracellular bacteria to high frequencies in host populations. Positive, perhaps mutualistic, reproductive manipulations also increase infection frequencies, but are not well understood. Here, we identify molecular and cellular mechanisms by which Wolbachia influences the molecularly distinct processes of germline stem cell (GSC) self-renewal and differentiation. We demonstrate that wMel infection rescues the fertility of flies lacking the translational regulator mei-P26 and is sufficient to sustain infertile homozygous mei-P26-knockdown stocks indefinitely. Cytology revealed that wMel mitigates the impact of mei-P26 loss through restoring proper pMad, Bam, Sxl, and Orb expression. In Oregon R files with wild-type fertility, wMel infection elevates lifetime egg hatch rates. Exploring these phenotypes through dual-RNAseq quantification of eukaryotic and bacterial transcripts revealed that wMel infection rescues and offsets many gene expression changes induced by mei-P26 loss at the mRNA level. Overall, we show that wMel infection beneficially reinforces host fertility at mRNA, protein, and phenotypic levels, and these mechanisms may promote the emergence of mutualism and the breakdown of host reproductive manipulations.

Elucidating the Corneal Endothelial Cell Proliferation Capacity through an Interspecies Transcriptome Comparison

The regenerative capacity of corneal endothelial cells (CECs) differs between species; in bigger mammals, CECs are arrested in a non-proliferative state. Damage to these cells can compromise their function causing corneal opacity. Corneal transplantation is the current treatment for the recovery of clear eyesight, but the donor tissue demand is higher than the availability and there is a need to develop novel treatments. Interestingly, rabbit CECs retain a high proliferative profile and can repopulate the endothelium. There is a lack of fundamental knowledge to explain these differences. Gaining information on their transcriptomic variances could allow the identification of CEC proliferation drivers. In this study, human, sheep, and rabbit CECs are analyzed at the transcriptomic level. To understand the differences across each species, a pipeline for the analysis of pathways with different activities is generated. The results reveal that 52 pathways have different activity when comparing species with non-proliferative CECs (human and sheep) to species with proliferative CECs (rabbit). The results show that Notch and TGF-β pathways have increased activity in species with non-proliferative CECs, which might be associated with their low proliferation. Overall, this study illustrates transcriptomic pathway-level differences that can provide leads to develop novel therapies to regenerate the corneal endothelium.

The endohyphal microbiome: current progress and challenges for scaling down integrative multi-omic microbiome research

As microbiome research has progressed, it has become clear that most, if not all, eukaryotic organisms are hosts to microbiomes composed of prokaryotes, other eukaryotes, and viruses. Fungi have only recently been considered holobionts with their own microbiomes, as filamentous fungi have been found to harbor bacteria (including cyanobacteria), mycoviruses, other fungi, and whole algal cells within their hyphae. Constituents of this complex endohyphal microbiome have been interrogated using multi-omic approaches. However, a lack of tools, techniques, and standardization for integrative multi-omics for small-scale microbiomes (e.g., intracellular microbiomes) has limited progress towards investigating and understanding the total diversity of the endohyphal microbiome and its functional impacts on fungal hosts. Understanding microbiome impacts on fungal hosts will advance explorations of how "microbiomes within microbiomes" affect broader microbial community dynamics and ecological functions. Progress to date as well as ongoing challenges of performing integrative multi-omics on the endohyphal microbiome is discussed herein. Addressing the challenges associated with the sample extraction, sample preparation, multi-omic data generation, and multi-omic data analysis and integration will help advance current knowledge of the endohyphal microbiome and provide a road map for shrinking microbiome investigations to smaller scales. Video Abstract.

[A transcriptomic study of osteoporosis induced by ketogenic diet in mice]

OBJECTIVE

To investigate the molecular mechanism of osteoporosis caused by ketogenic diet (KD) using transcriptomic analysis.

METHODS

Sixteen 8-week-old female C57BL/6J mice were divided into KD group and sham group for feeding with KD and normal diet for 3 months, respectively. Body weight, blood glucose and blood ketone levels of the mice were measured every two weeks. Microstructure of the cancellous bone in the distal femur was observed with Micro-CT. Total RNA was extracted from bone marrow cells for transcriptomic analysis and bioinformatics analysis. RT-qPCR was used to verify the expression levels of the genes with significant differential expression between the groups.

RESULTS

KD obviously weakened the microstructure of the cancellous bone in mice. Compared with those in the sham group, the mice in KD group showed 165 differentially expressed genes (94 up-regulated and 71 down-regulated ones), including Acot1, Mpig6b, Gp9, Ppbp, Slc2a9, etc. KEGG pathway enrichment analysis showed obvious enrichment of the Apelin signaling pathway, PI3K- Akt signaling pathway and ECM-receptor interaction signal transduction pathway with greater number of differential genes. RTqPCR results showed that the 5 differential genes screened by transcriptomics were significantly upregulated in KD group, among which Acot1, Mpig6b and Ppbp were upregulated by over two folds (2.49 ± 0.665, 2.58 ± 0.470, and 2.59 ± 0.611, respectively), suggesting their involvement in KD-induced osteoporosis.

CONCLUSION

The differentially expressed genes and enriched pathways identified in the mouse models provide new clues for studying the molecular mechanism and prevention of KD-induced osteoporosis.

CoSIA: an R Bioconductor package for CrOss Species Investigation and Analysis

High throughput sequencing technologies have enabled cross-species comparative transcriptomic studies; however, there are numerous challenges for these studies due to biological and technical factors. We developed CoSIA (Cross-Species Investigation and Analysis), an Bioconductor R package and Shiny app that provides an alternative framework for cross-species transcriptomic comparison of non-diseased wild-type RNA sequencing gene expression data from Bgee across tissues and species (human, mouse, rat, zebrafish, fly, and nematode) through visualization of variability, diversity, and specificity metrics.

The Longitudinal Analysis of Convergent Antibody VDJ Regions in SARS-CoV-2-Positive Patients Using RNA-Seq

Severe acute respiratory syndrome-related coronavirus-2 (SARS-CoV-2) is an ongoing pandemic that continues to evolve and reinfect individuals. To understand the convergent antibody responses that evolved over the course of the pandemic, we evaluated the immunoglobulin repertoire of individuals infected by different SARS-CoV-2 variants for similarity between patients. We utilized four public RNA-seq data sets collected between March 2020 and March 2022 from the Gene Expression Omnibus (GEO) in our longitudinal analysis. This covered individuals infected with Alpha and Omicron variants. In total, from 269 SARS-CoV-2-positive patients and 26 negative patients, 629,133 immunoglobulin heavy-chain variable region V(D)J sequences were reconstructed from sequencing data. We grouped samples based on the SARS-CoV-2 variant type and/or the time they were collected from patients. Our comparison of patients within each SARS-CoV-2-positive group found 1011 common V(D)Js (same V gene, J gene and CDR3 amino acid sequence) shared by more than one patient and no common V(D)Js in the noninfected group. Taking convergence into account, we clustered based on similar CDR3 sequence and identified 129 convergent clusters from the SARS-CoV-2-positive groups. Within the top 15 clusters, 4 contain known anti-SARS-CoV-2 immunoglobulin sequences with 1 cluster confirmed to cross-neutralize variants from Alpha to Omicron. In our analysis of longitudinal groups that include Alpha and Omicron variants, we find that 2.7% of the common CDR3s found within groups were also present in more than one group. Our analysis reveals common and convergent antibodies, which include anti-SARS-CoV-2 antibodies, in patient groups over various stages of the pandemic.

Transcriptome-Powered Pluripotent Stem Cell Differentiation for Regenerative Medicine

Pluripotent stem cells are endless sources for in vitro engineering human tissues for regenerative medicine. Extensive studies have demonstrated that transcription factors are the key to stem cell lineage commitment and differentiation efficacy. As the transcription factor profile varies depending on the cell type, global transcriptome analysis through RNA sequencing (RNAseq) has been a powerful tool for measuring and characterizing the success of stem cell differentiation. RNAseq has been utilized to comprehend how gene expression changes as cells differentiate and provide a guide to inducing cellular differentiation based on promoting the expression of specific genes. It has also been utilized to determine the specific cell type. This review highlights RNAseq techniques, tools for RNAseq data interpretation, RNAseq data analytic methods and their utilities, and transcriptomics-enabled human stem cell differentiation. In addition, the review outlines the potential benefits of the transcriptomics-aided discovery of intrinsic factors influencing stem cell lineage commitment, transcriptomics applied to disease physiology studies using patients' induced pluripotent stem cell (iPSC)-derived cells for regenerative medicine, and the future outlook on the technology and its implementation.

Comparative Genomic and Transcriptomic Profiling Revealed the Molecular Basis of Starch Promoting the Growth and Proliferation of Balantioides coli

Carbohydrates are the main source of nutrition for B. coli, supplying energy for cell growth and development. The research aimed at investigating the mechanism of starch on the growth and replication of B. coli. Single-cell separation was used to isolate single trophozoites of B. coli under a stereomicroscope, transcriptomic profiling was conducted based on the SMART-seq2 single-cell RNA-seq method. Comparative genomic analysis was performed on B. coli and eight other ciliates to obtain specific and expanded gene families of B. coli. GO and KEGG enrichment analysis were used to analyze the key genes of B. coli under the action of starch in the present study. The results of single-cell RNA-seq depicts starch affected the growth and replication of B. coli in two ways: (1) the cell cycle was positively promoted by the activation of the cAMP/PKA signaling pathway via glycolysis; (2) the cell autophagy was suppressed through the PI3K/AKT/mTOR pathway. Genes involved in endocytosis, carbohydrate utilization, and the cAMP/PKA signaling pathway were highly enriched in both specific and expanded gene families of B. coli. Starch can be ingested and hydrolyzed into glucose, in turn affecting various biological processes of B. coli. The molecular mechanism of the effect of starch on the growth and proliferation of B. coli by promoting cell cycle and inhibiting the autophagy of trophozoites has been elucidated in our study.

Complexities of mammalian transcriptome revealed by targeted RNA enrichment techniques

Studies using highly sensitive targeted RNA enrichment methods have shown that a large portion of the human transcriptome remains to be discovered and that most of the genome is transcribed in a complex, interleaved fashion characterized by a complex web of transcripts emanating from protein coding and noncoding loci. These results resonate with those from single-cell transcriptome profiling endeavors that reveal the existence of multiple novel, cell type-specific transcripts and clearly demonstrate that our understanding of the complexities of the human transcriptome is far from being complete. Here, we review the current status of the targeted RNA enrichment techniques, their application to the discovery of novel cell type-specific transcripts, and their impact on our understanding of the human genome and transcriptome.

DELongSeq for efficient detection of differential isoform expression from long-read RNA-seq data

Conventional gene expression quantification approaches, such as microarrays or quantitative PCR, have similar variations of estimates for all genes. However, next-generation short-read or long-read sequencing use read counts to estimate expression levels with much wider dynamic ranges. In addition to the accuracy of estimated isoform expression, efficiency, which measures the degree of estimation uncertainty, is also an important factor for downstream analysis. Instead of read count, we present DELongSeq, which employs information matrix of EM algorithm to quantify uncertainty of isoform expression estimates to improve estimation efficiency. DELongSeq uses random-effect regression model for the analysis of DE isoform, in that within-study variation represents variable precision in isoform expression estimation and between-study variation represents variation in isoform expression levels across samples. More importantly, DELongSeq allows 1 case versus 1 control comparison of differential expression, which has specific application scenarios in precision medicine (such as before versus after treatment, or tumor versus stromal tissues). Through extensive simulations and analysis of several RNA-Seq datasets, we show that the uncertainty quantification approach is computationally reliable, and can improve the power of differential expression (DE) analysis of isoforms or genes. In summary, DELongSeq allows for efficient detection of differential isoform/gene expression from long-read RNA-Seq data.

Protective effects of MNQ against Lipopolysaccharide-induced inflammatory damage in bovine ovarian follicular granulosa cells in Vitro

Inflammation of the reproductive tract in dairy cows lead to functional disorders of follicular granulosa cells (GCs) in mammalian ovaries resulting in infertility and serious losses to the livestock industry. Lipopolysaccharide (LPS) can induce an inflammatory response in follicular granulosa cells in vitro. The aim of this study was to investigate the cellular regulatory mechanism of MNQ (2-methoxy-1,4-naphthoquinone) on eliminating the inflammatory response and restoring normal functions for bovine ovarian follicular GCs cultured in vitro exposed to LPS. The cytotoxicity of MNQ and LPS on GCs were detected by MTT method to determine the safe concentration. The relative expression of inflammatory factors and steroid synthesis-related genes were detected by qRT-PCR. The concentration of steroid hormones in the culture broth were detected by ELISA. Differential gene expressions were analyzed by RNA-seq. There were no toxic effects on GCs at MNQ and LPS concentrations of less than 3 µM and 10 µg/mL, respectively and treated in 12 h. The relative expressions of IL-6, IL-1β and TNF-α were significantly higher in the LPS group compared with the CK group when GCs cultured in vitro were treated with the above concentrations and times (P < 0.05), but significantly lower in the MNQ+LPS group compared with the LPS group (P < 0.05). The levels of E₂ and P₄ in the culture solution were significantly reduced in the LPS group compared to the CK group (P < 0.05), and restored in the MNQ+LPS group. The relative expressions of CYP19A1, CYP11A1, 3β-HSD, and STAR were significantly decreased in the LPS group compared with the CK group (P < 0.05), while the MNQ+LPS group also recovered to some extent. There were 407 differential genes shared by LPS vs CK and MNQ+LPS vs LPS by RNA-seq analysis, which were mainly enriched in steroid biosynthesis and TNF signaling pathway. We screened 10 genes for analysis and found consistent results for RNA-seq and qRT-PCR. In this study, we confirmed the protective effect of MNQ, an extract from Impatiens balsamina L, on LPS-induced inflammatory responses in bovine follicular granulosa cells in vitro as well as functional damage, and acted through steroid biosynthesis and TNF signaling pathways.

Approaches for sRNA Analysis of Human RNA-Seq Data: Comparison, Benchmarking

Expression analysis of small noncoding RNA (sRNA), including microRNA, piwi-interacting RNA, small rRNA-derived RNA, and tRNA-derived small RNA, is a novel and quickly developing field. Despite a range of proposed approaches, selecting and adapting a particular pipeline for transcriptomic analysis of sRNA remains a challenge. This paper focuses on the identification of the optimal pipeline configurations for each step of human sRNA analysis, including reads trimming, filtering, mapping, transcript abundance quantification and differential expression analysis. Based on our study, we suggest the following parameters for the analysis of human sRNA in relation to categorical analyses with two groups of biosamples: (1) trimming with the lower length bound = 15 and the upper length bound = Read length - 40% Adapter length; (2) mapping on a reference genome with bowtie aligner with one mismatch allowed (-v 1 parameter); (3) filtering by mean threshold > 5; (4) analyzing differential expression with DESeq2 with adjusted p-value < 0.05 or limma with p-value < 0.05 if there is very little signal and few transcripts.

Oligodendrocyte lineage cells: Advances in development, disease, and heterogeneity

Oligodendrocyte progenitor cells (OPCs) originate in the ventricular zone (VZ) of the brain and spinal cord, and their primary function is to differentiate into oligodendrocytes (OLs). Studies have shown that OPCs and OLs are pathologically and physiologically heterogeneous. Previous transcriptome analyses used Bulk RNA-seq, which compares average gene expression in cells and does not allow for heterogeneity. In recent years, the development of single-cell sequencing (scRNA-seq) and single-cell nuclear sequencing (snRNA-seq) has allowed us to study an individual cell. In this review, sc/snRNA-seq was used to study the different subpopulations of OL lineage cells, their developmental trajectories, and their applications in related diseases. These techniques can distinguish different subpopulations of cells, and identify differentially expressed genes in particular cell types under certain conditions, such as treatment or disease. It is of great significance to the study of the occurrence, prevention, and treatment of various diseases.

Structure of co-expression networks of Bifidobacterium species in response to human milk oligosaccharides

Biological systems respond to environmental perturbations and a large diversity of compounds through gene interactions, and these genetic factors comprise complex networks. Experimental information from transcriptomic studies has allowed the identification of gene networks that contribute to our understanding of microbial adaptations. In this study, we analyzed the gene co-expression networks of three Bifidobacterium species in response to different types of human milk oligosaccharides (HMO) using weighted gene co-expression analysis (WGCNA). RNA-seq data obtained from Geo Datasets were obtained for Bifidobacterium longum subsp. Infantis, Bifidobacterium bifidum and Bifidobacterium longum subsp. Longum. Between 10 and 20 co-expressing modules were obtained for each dataset. HMO-associated genes appeared in the modules with more genes for B. infantis and B. bifidum, in contrast with B. longum. Hub genes were identified in each module, and in general they participated in conserved essential processes. Certain modules were differentially enriched with LacI-like transcription factors, and others with certain metabolic pathways such as the biosynthesis of secondary metabolites. The three Bifidobacterium transcriptomes showed distinct regulation patterns for HMO utilization. HMO-associated genes in B. infantis co-expressed in two modules according to their participation in galactose or N-Acetylglucosamine utilization. Instead, B. bifidum showed a less structured co-expression of genes participating in HMO utilization. Finally, this category of genes in B. longum clustered in a small module, indicating a lack of co-expression with main cell processes and suggesting a recent acquisition. This study highlights distinct co-expression architectures in these bifidobacterial genomes during HMO consumption, and contributes to understanding gene regulation and co-expression in these species of the gut microbiome.

A Phylogenetic Framework to Simulate Synthetic Interspecies RNA-Seq Data

Interspecies RNA-Seq datasets are increasingly common, and have the potential to answer new questions about the evolution of gene expression. Single-species differential expression analysis is now a well-studied problem that benefits from sound statistical methods. Extensive reviews on biological or synthetic datasets have provided the community with a clear picture on the relative performances of the available methods in various settings. However, synthetic dataset simulation tools are still missing in the interspecies gene expression context. In this work, we develop and implement a new simulation framework. This tool builds on both the RNA-Seq and the phylogenetic comparative methods literatures to generate realistic count datasets, while taking into account the phylogenetic relationships between the samples. We illustrate the usefulness of this new framework through a targeted simulation study, that reproduces the features of a recently published dataset, containing gene expression data in adult eye tissue across blind and sighted freshwater crayfish species. Using our simulated datasets, we perform a fair comparison of several approaches used for differential expression analysis. This benchmark reveals some of the strengths and weaknesses of both the classical and phylogenetic approaches for interspecies differential expression analysis, and allows for a reanalysis of the crayfish dataset. The tool has been integrated in the R package compcodeR, freely available on Bioconductor.

Comparative transcriptome and metabolome analyses of four Panax species explore the dynamics of metabolite biosynthesis

Background

The genus Panax in the Araliaceae family has been used as traditional medicinal plants worldwide and is known to biosynthesize ginsenosides and phytosterols. However, genetic variation between Panax species has influenced their biosynthetic pathways is not fully understood.

Methods

Simultaneous analysis of transcriptomes and metabolomes obtained from adventitious roots of two tetraploid species (Panax ginseng and P. quinquefolius) and two diploid species (P. notoginseng and P. vietnamensis) revealed the diversity of their metabolites and related gene expression profiles.

Results

The transcriptome analysis showed that 2,3-OXIDOSQUALENE CYCLASEs (OSCs) involved in phytosterol biosynthesis are upregulated in the diploid species, while the expression of OSCs contributing to ginsenoside biosynthesis is higher in the tetraploid species. In agreement with these results, the contents of dammarenediol-type ginsenosides were higher in the tetraploid species relative to the diploid species.

Conclusion

These results suggest that a whole-genome duplication event has influenced the triterpene biosynthesis pathway in tetraploid Panax species during their evolution or ecological adaptation. This study provides a basis for further efforts to explore the genetic variation of the Panax genus.

Bioinformatic and experimental methods to identify and validate bacterial RNA-human RNA interactions

Ample evidence supports the importance of the microbiota on human health and disease. Recent studies suggest that extracellular vesicles are an important means of bacterial-host communication, in part via the transport of small RNAs (sRNAs). Bacterial sRNAs have been shown to co-precipitate with human and mouse RNA-induced silencing complex, hinting that some may regulate gene expression as eukaryotic microRNAs do. Bioinformatic tools, including those that can incorporate an sRNA's secondary structure, can be used to predict interactions between bacterial sRNAs and human messenger RNAs (mRNAs). Validation of these potential interactions using reproducible experimental methods is essential to move the field forward. This review will cover the evidence of interspecies communication via sRNAs, bioinformatic tools currently available to identify potential bacterial sRNA-host (specifically, human) mRNA interactions, and experimental methods to identify and validate those interactions.

Identification of Novel m6A-Related Long Non-Coding RNA Signatures for Cholangiocarcinoma Using Integrated Bioinformatics Analyses

Traditional methods used to treat cholangiocarcinoma are less effective, and the identification of new CHOL signature genes can help in the early clinical diagnosis and intervention of cholangiocarcinoma. In this work, we used integrated bioinformatics analysis to find new m6a-associated lncRNA signatures in cholangiocarcinoma. Pearson correlation test was used to identify m6A-lncRNAs by co-expression analysis of m6A-mrna and lncRNAs. we then selected m6A-lncRNAs co-expressed with METTL3 and METTL14 genes and screened for DEm6A-lncRNAs by comparing expression differences. we then used R package of Spearman coefficient correlation analysis to investigate the relevance of m6A-lncrna expression in CHOL. To determine the relative levels of immune cell infiltration, we performed ssGSEA analysis on all samples using the R package, and then we used graphs to illustrate the differences in immune cell infiltration between the CHOL and NC groups. The results of this study will help to identify new CHOL-causing biosignatures, which are important for the early clinical detection and management of CHOL.

Advances in experimental and computational methodologies for the study of microbial-surface interactions at different omics levels

The study of the biological response of microbial cells interacting with natural and synthetic interfaces has acquired a new dimension with the development and constant progress of advanced omics technologies. New methods allow the isolation and analysis of nucleic acids, proteins and metabolites from complex samples, of interest in diverse research areas, such as materials sciences, biomedical sciences, forensic sciences, biotechnology and archeology, among others. The study of the bacterial recognition and response to surface contact or the diagnosis and evolution of ancient pathogens contained in archeological tissues require, in many cases, the availability of specialized methods and tools. The current review describes advances in in vitro and in silico approaches to tackle existing challenges (e.g., low-quality sample, low amount, presence of inhibitors, chelators, etc.) in the isolation of high-quality samples and in the analysis of microbial cells at genomic, transcriptomic, proteomic and metabolomic levels, when present in complex interfaces. From the experimental point of view, tailored manual and automatized methodologies, commercial and in-house developed protocols, are described. The computational level focuses on the discussion of novel tools and approaches designed to solve associated issues, such as sample contamination, low quality reads, low coverage, etc. Finally, approaches to obtain a systems level understanding of these complex interactions by integrating multi omics datasets are presented.

Biofilm Microenvironment-Mediated MoS2 Nanoplatform with Its Photothermal/Photodynamic Synergistic Antibacterial Molecular Mechanism and Wound Healing Study

Drug-resistant bacterial infections pose a serious threat to human public health. Biofilm formation is one of the main factors contributing to the development of bacterial resistance, characterized by a hypoxic and microacidic microenvironment. Traditional antibiotic treatments have been ineffective against multidrug-resistant (MDR) bacteria. Novel monotherapies have had little success. On the basis of the photothermal effect, molybdenum disulfide (MoS₂) nanoparticles were used to link quaternized polyethylenimine (QPEI), dihydroporphyrin e6 (Ce6), and Panax notoginseng saponins (PNS) in a zeolitic imidazolate framework-8 (ZIF-8). A multifunctional nanoplatform (MQCP@ZIF-8) was constructed with dual response to pH and near-infrared light (NIR), which resulted in synergistic photothermal and photodynamic antibacterial effects. The nanoplatform exhibited a photothermal conversion efficiency of 56%. It inhibited MDR Escherichia coli (E. coli) and MDR Staphylococcus aureus (S. aureus) by more than 95% and effectively promoted wound healing in mice infected with MDR S. aureus. The nanoplatform induced the death of MDR bacteria by promoting biofilm ablation, disrupting bacterial cell membranes and intracellular DNA, and interfering with intracellular material and energy metabolism. In this study, a multifunctional nanoplatform with good antibacterial effect was developed. The molecular mechanisms of MDR bacteria were also elucidated for possible clinical application.

dRNASb: a systems biology approach to decipher dynamics of host-pathogen interactions using temporal dual RNA-seq data

Infection triggers a dynamic cascade of reciprocal events between host and pathogen wherein the host activates complex mechanisms to recognise and kill pathogens while the pathogen often adjusts its virulence and fitness to avoid eradication by the host. The interaction between the pathogen and the host results in large-scale changes in gene expression in both organisms. Dual RNA-seq, the simultaneous detection of host and pathogen transcripts, has become a leading approach to unravelling complex molecular interactions between the host and the pathogen and is particularly informative for intracellular organisms. The amount of in vitro and in vivo dual RNA-seq data is rapidly growing, which demands computational pipelines to effectively analyse such data. In particular, holistic, systems-level, and temporal analyses of dual RNA-seq data are essential to enable further insights into the host-pathogen transcriptional dynamics and potential interactions. Here, we developed an integrative network-driven bioinformatics pipeline, dRNASb, a systems biology-based computational pipeline to analyse temporal transcriptional clusters, incorporate molecular interaction networks (e.g. protein-protein interactions), identify topologically and functionally key transcripts in host and pathogen, and associate host and pathogen temporal transcriptome to decipher potential between-species interactions. The pipeline is applicable to various dual RNA-seq data from different species and experimental conditions. As a case study, we applied dRNASb to analyse temporal dual RNA-seq data of Salmonella-infected human cells, which enabled us to uncover genes contributing to the infection process and their potential functions and to identify putative associations between host and pathogen genes during infection. Overall, dRNASb has the potential to identify key genes involved in bacterial growth or host defence mechanisms for future uses as therapeutic targets.

Reducing costs for DNA and RNA sequencing by sample pooling using a metagenomic approach

DNA and RNA sequencing are widely used techniques to investigate genomic modifications and gene expression. The costs for sequencing dropped dramatically in the last decade. However, due to material and labor intense steps, the sample preparation costs could not keep up with that pace. About 80% of the total costs occur prior to sequencing during DNA/RNA extraction, enrichment steps and subsequent library preparation. In this study, we investigate the potential of pooling different organisms samples prior to DNA/RNA extraction to significantly reduce costs in preparative steps. Similar to the common procedure of ligated DNA tags to pool (c)DNA samples, sequence diversity of different organisms intrinsically provide unique sequences that allow separation of reads after sequencing. With this approach, sample pooling can occur before DNA/RNA isolation and library preparation. We show that pooled sequencing of three related bacterial organisms is possible without loss of data quality at a cost reduction of approx. 50% in DNA- and RNA-seq approaches. Furthermore, we show that this approach is highly efficient down to the level of a shared genus and is, therefore, widely applicable in sequencing facilities and companies with diverse sample pools.

Multi-Omics Techniques for Analysis Antifungal Mechanisms of Lipopeptides Produced by Bacillus velezensis GS-1 against Magnaporthe oryzae In Vitro

Magnaporthe oryzae is a fungal pathogen that causes rice blast, a highly destructive disease. In the present study, the bacteria strain GS-1 was isolated from the rhizosphere soil of ginseng and identified as Bacillus velezensis through 16S rRNA gene sequencing, whole genome assembly, and average nucleotide identity analysis. B. velezensis strain GS-1 exhibited significant antagonistic activity to several plant fungal pathogens. Through whole genome sequencing, 92 Carbohydrate-Active Enzymes and 13 gene clusters that encoded for secondary metabolites were identified. In addition, strain GS-1 was able to produce the lipopeptide compounds, surfactin, fengycin, and plantazolicin. The inhibitory effects of lipopeptide compounds on M. oryzae were confirmed, and the antagonistic mechanism was explored using transcriptomics and metabolomics analysis. Differential expressed genes (DEGs) and differential accumulated metabolites (DAMs) revealed that the inhibition of M. oryzae by lipopeptide produced by GS-1 downregulated the expression of genes involved in amino acid metabolism, sugar metabolism, oxidative phosphorylation, and autophagy. These results may explain why GS-1 has antagonistic activity to fungal pathogens and revealed the mechanisms underlying the inhibitory effects of lipopeptides produced by GS-1 on fungal growth, which may provide a theoretical basis for the potential application of B. velezensis GS-1 in future plant protection.

Urgent need for consistent standards in functional enrichment analysis

Gene set enrichment tests (a.k.a. functional enrichment analysis) are among the most frequently used methods in computational biology. Despite this popularity, there are concerns that these methods are being applied incorrectly and the results of some peer-reviewed publications are unreliable. These problems include the use of inappropriate background gene lists, lack of false discovery rate correction and lack of methodological detail. To ascertain the frequency of these issues in the literature, we performed a screen of 186 open-access research articles describing functional enrichment results. We find that 95% of analyses using over-representation tests did not implement an appropriate background gene list or did not describe this in the methods. Failure to perform p-value correction for multiple tests was identified in 43% of analyses. Many studies lacked detail in the methods section about the tools and gene sets used. An extension of this survey showed that these problems are not associated with journal or article level bibliometrics. Using seven independent RNA-seq datasets, we show misuse of enrichment tools alters results substantially. In conclusion, most published functional enrichment studies suffered from one or more major flaws, highlighting the need for stronger standards for enrichment analysis.

Functional enrichment analysis is a commonly used technique to identify trends in large scale biological datasets. In biomedicine, functional enrichment analysis of gene expression data is frequently applied to identify disease and drug mechanisms. While enrichment tests were once primarily conducted with complicated computer scripts, web-based tools are becoming more widely used. Users can paste a list of genes into a website and receive enrichment results in a matter of seconds. Despite the popularity of these tools, there are concerns that statistical problems and incomplete reporting are compromising research quality. In this article, we conducted a systematic examination of published enrichment analyses and assessed whether (i) any statistical flaws were present and (ii) sufficient methodological detail is provided such that the study could be replicated. We found that lack of methodological detail and errors in statistical analysis were widespread, which undermines the reliability and reproducibility of these research articles. A set of best practices is urgently needed to raise the quality of published work.

Using Omics to Study Leprosy, Tuberculosis, and Other Mycobacterial Diseases

Mycobacteria are members of the Actinomycetales order, and they are classified into one family, Mycobacteriaceae. More than 20 mycobacterial species cause disease in humans. The Mycobacterium group, called the Mycobacterium tuberculosis complex (MTBC), has nine closely related species that cause tuberculosis in animals and humans. TB can be detected worldwide and one-fourth of the world's population is contaminated with tuberculosis. According to the WHO, about two million dies from it, and more than nine million people are newly infected with TB each year. Mycobacterium tuberculosis (M. tuberculosis) is the most potential causative agent of tuberculosis and prompts enormous mortality and morbidity worldwide due to the incompletely understood pathogenesis of human tuberculosis. Moreover, modern diagnostic approaches for human tuberculosis are inefficient and have many lacks, while MTBC species can modulate host immune response and escape host immune attacks to sustain in the human body. "Multi-omics" strategies such as genomics, transcriptomics, proteomics, metabolomics, and deep sequencing technologies could be a comprehensive strategy to investigate the pathogenesis of mycobacterial species in humans and offer significant discovery to find out biomarkers at the early stage of disease in the host. Thus, in this review, we attempt to understand an overview of the mission of "omics" approaches in mycobacterial pathogenesis, including tuberculosis, leprosy, and other mycobacterial diseases.

Therapeutic inhibition of GAS6-AS1/YBX1/MYC axis suppresses cell propagation and disease progression of acute myeloid leukemia

BACKGROUND

Acute myeloid leukemia (AML) is the most common type of leukemia in adults. Its therapy has not significantly improved during the past four decades despite intense research efforts. New molecularly targeted therapies are in great need. The proto-oncogene c-Myc (MYC) is an attractive target due to its transactivation role in multiple signaling cascades. Deregulation of the MYC is considered one of a series of oncogenic events required for tumorigenesis. However, limited knowledge is available on which mechanism underlie MYC dysregulation and how long non-coding RNAs (lncRNAs) are involved in MYC dysregulation in AML.

METHODS

AML microarray chips and public datasets were screened to identify novel lncRNA GAS6-AS1 was dysregulated in AML. Gain or loss of functional leukemia cell models were produced, and in vitro and in vivo experiments were applied to demonstrate its leukemogenic phenotypes. Interactive network analyses were performed to define intrinsic mechanism.

RESULTS

We identified GAS6-AS1 was overexpressed in AML, and its aberrant function lead to more aggressive leukemia phenotypes and poorer survival outcomes. We revealed that GAS6-AS1 directly binds Y-box binding protein 1 (YBX1) to facilitate its interaction with MYC, leading to MYC transactivation and upregulation of IL1R1, RAB27B and other MYC target genes associated with leukemia progression. Further, lentiviral-based GAS6-AS1 silencing inhibited leukemia progression in vivo.

CONCLUSIONS

Our findings revealed a previously unappreciated role of GAS6-AS1 as an oncogenic lncRNA in AML progression and prognostic prediction. Importantly, we demonstrated that therapeutic targeting of the GAS6-AS1/YBX1/MYC axis inhibits AML cellular propagation and disease progression. Our insight in lncRNA associated MYC-driven leukemogenesis may contribute to develop new anti-leukemia treatment strategies.