The transcriptional landscape of the yeast genome defined by RNA sequencing

Simultaneous Single-Cell Profiling of the Transcriptome and Accessible Chromatin Using SHARE-seq

The ability to analyze the transcriptomic and epigenomic states of individual single cells has in recent years transformed our ability to measure and understand biological processes. Recent advancements have focused on increasing sensitivity and throughput to provide richer and deeper biological insights at the cellular level. The next frontier is the development of multiomic methods capable of analyzing multiple features from the same cell, such as the simultaneous measurement of the transcriptome and the chromatin accessibility of candidate regulatory elements. In this chapter, we discuss and describe SHARE-seq (Simultaneous high-throughput ATAC, and RNA expression with sequencing) for carrying out simultaneous chromatin accessibility and transcriptome measurements in single cells, together with the experimental and analytical considerations for achieving optimal results.

Deletion of histone demethylase Lsd1 (Kdm1a) during retinal development leads to defects in retinal function and structure

Purpose

The purpose of this study was to investigate the role of Lysine specific demethylase 1 (Lsd1) in murine retinal development. LSD1 is a histone demethylase that can demethylate mono- and di-methyl groups on H3K4 and H3K9. Using Chx10-Cre and Rho-iCre75 driver lines, we generated novel transgenic mouse lines to delete Lsd1 in most retinal progenitor cells or specifically in rod photoreceptors. We hypothesize that Lsd1 deletion will cause global morphological and functional defects due to its importance in neuronal development.

Methods

We tested the retinal function of young adult mice by electroretinogram (ERG) and assessed retinal morphology by in vivo imaging by fundus photography and SD-OCT. Afterward, eyes were enucleated, fixed, and sectioned for subsequent hematoxylin and eosin (H&E) or immunofluorescence staining. Other eyes were plastic fixed and sectioned for electron microscopy.

Results

In adult Chx10-Cre Lsd1fl/fl mice, we observed a marked reduction in a-, b-, and c-wave amplitudes in scotopic conditions compared to age-matched control mice. Photopic and flicker ERG waveforms were even more sharply reduced. Modest reductions in total retinal thickness and outer nuclear layer (ONL) thickness were observed in SD-OCT and H&E images. Lastly, electron microscopy revealed significantly shorter inner and outer segments and immunofluorescence showed modest reductions in specific cell type populations. We did not observe any obvious functional or morphological defects in the adult Rho-iCre75 Lsd1fl/fl animals.

Conclusion

Lsd1 is necessary for neuronal development in the retina. Adult Chx10-Cre Lsd1fl/fl mice show impaired retinal function and morphology. These effects were fully manifested in young adults (P30), suggesting that Lsd1 affects early retinal development in mice.

Transcriptome analysis creates a new era of precision medicine for managing recurrent hepatocellular carcinoma

The high incidence of hepatocellular carcinoma (HCC) recurrence negatively impacts outcomes of patients treated with curative intent despite advances in surgical techniques and other locoregional liver-targeting therapies. Over the past few decades, the emergence of transcriptome analysis tools, including real-time quantitative reverse transcription PCR, microarrays, and RNA sequencing, has not only largely contributed to our knowledge about the pathogenesis of recurrent HCC but also led to the development of outcome prediction models based on differentially expressed gene signatures. In recent years, the single-cell RNA sequencing technique has revolutionized our ability to study the complicated crosstalk between cancer cells and the immune environment, which may benefit further investigations on the role of different immune cells in HCC recurrence and the identification of potential therapeutic targets. In the present article, we summarized the major findings yielded with these transcriptome methods within the framework of a causal model consisting of three domains: primary cancer cells; carcinogenic stimuli; and tumor microenvironment. We provided a comprehensive review of the insights that transcriptome analyses have provided into diagnostics, surveillance, and treatment of HCC recurrence.

Gene expression prediction based on neighbour connection neural network utilizing gene interaction graphs

Having observed that gene expressions have a correlation, the Library of Integrated Network-based Cell-Signature program selects 1000 landmark genes to predict the remaining gene expression value. Further works have improved the prediction result by using deep learning models. However, these models ignore the latent structure of genes, limiting the accuracy of the experimental results. We therefore propose a novel neural network named Neighbour Connection Neural Network(NCNN) to utilize the gene interaction graph information. Comparing to the popular GCN model, our model incorperates the graph information in a better manner. We validate our model under two different settings and show that our model promotes prediction accuracy comparing to the other models.

Comparative Research: Regulatory Mechanisms of Ribosomal Gene Transcription in Saccharomyces cerevisiae and Schizosaccharomyces pombe

Restricting ribosome biosynthesis and assembly in response to nutrient starvation is a universal phenomenon that enables cells to survive with limited intracellular resources. When cells experience starvation, nutrient signaling pathways, such as the target of rapamycin (TOR) and protein kinase A (PKA), become quiescent, leading to several transcription factors and histone modification enzymes cooperatively and rapidly repressing ribosomal genes. Fission yeast has factors for heterochromatin formation similar to mammalian cells, such as H3K9 methyltransferase and HP1 protein, which are absent in budding yeast. However, limited studies on heterochromatinization in ribosomal genes have been conducted on fission yeast. Herein, we shed light on and compare the regulatory mechanisms of ribosomal gene transcription in two species with the latest insights.

A Computational Workflow for Analysis of 3' Tag-Seq Data

RNA-sequencing (RNA-seq) is a gold-standard method to profile genome-wide changes in gene expression. RNA-seq uses high-throughput sequencing technology to quantify the amount of RNA in a biological sample. With the increasing popularity of RNA-seq, many variations on the protocol have been proposed to extract unique and relevant information from biological samples. 3' Tag-Seq (also called TagSeq, 3' Tag-RNA-Seq, and Quant-Seq 3' mRNA-Seq) is one RNA-seq variation where the 3' end of the transcript is selected and amplified to yield one copy of cDNA from each transcript in the biological sample. We present a simple, easy-to-use, and publicly available computational workflow to analyze 3' Tag-Seq data. The workflow begins by trimming sequence adapters from raw FASTQ files. The trimmed sequence reads are checked for quality using FastQC and aligned to the reference genome, and then read counts are obtained using STAR. Differential gene expression analysis is performed using DESeq2, based on differential analysis of gene count data. The outputs of this workflow are MA plots, tables of differentially expressed genes, and UpSet plots. This protocol is intended for users specifically interested in analyzing 3' Tag-Seq data, and thus normalizations based on transcript length are not performed within the workflow. Future updates to this workflow could include custom analyses based on the gene counts table as well as data visualization enhancements. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol: Running the 3' Tag-Seq workflow Support Protocol: Generating genome indices.

Insight into the Neuroprotective Effect of Genistein-3'-Sodium Sulfonate Against Neonatal Hypoxic-Ischaemic Brain Injury in Rats by Bioinformatics

Therapeutic hypothermia (TH) is the only intervention approved for the treatment of neonatal hypoxic-ischaemic encephalopathy (HIE), but its treatment window is narrow (within 6 h after birth), and its efficacy is not ideal. Thus, alternative treatments are urgently needed. Our previous studies showed that genistein-3'-sodium sulfonate (GSS), a derivative of genistein (Gen), has a strong neuroprotective effect in rats with ischaemic stroke, but its role in HIE is unclear. A hypoxia-ischaemia (HI) brain injury model was established in neonatal male Sprague‒Dawley (SD) rats. Twenty-four hours after reperfusion, rats treated with GSS were assessed for cerebral infarction, neurological function, and neuronal damage. RNA-Seq and bioinformatics analysis were used to explore differentially expressed genes (DEGs) and regulated signalling pathways, which were subsequently validated by Western blotting and immunofluorescence. In this study, we found that GSS not only significantly reduced the size of brain infarcts and alleviated nerve damage in rats with HIE but also inhibited neuronal loss and degeneration in neonatal rats with HIE. A total of 2170 DEGs, of which 1102 were upregulated and 1068 were downregulated, were identified in the GSS group compared with the HI group. In an analysis based on Kyoto Encyclopedia of Genes and Genomes (KEGG) categories, the downregulated DEGs were significantly enriched in the pathways "Phagosome", "NF-κB signalling", and "Complement and coagulation cascades", amongst others. Meanwhile, the upregulated DEGs were significantly enriched in the pathways "Neurodegeneration", "Glutamatergic synapse", and "Calcium signalling pathway", amongst others. These results indicate that GSS intervenes in the process of HIE-induced brain injury by participating in multiple pathways, which suggests potential candidate drugs for the treatment of HIE.

Attempts for deriving extended pluripotent stem cells from common marmoset embryonic stem cells

Extended pluripotent stem cells (EPSCs) derived from mice and humans showed an enhanced potential for chimeric formation. By exploiting transcriptomic approaches, we assessed the differences in gene expression profile between extended EPSCs derived from mice and humans, and those newly derived from the common marmoset (marmoset; Callithrix jacchus). Although the marmoset EPSC-like cells displayed a unique colony morphology distinct from murine and human EPSCs, they displayed a pluripotent state akin to embryonic stem cells (ESCs), as confirmed by gene expression and immunocytochemical analyses of pluripotency markers and three-germ-layer differentiation assay. Importantly, the marmoset EPSC-like cells showed interspecies chimeric contribution to mouse embryos, such as E6.5 blastocysts in vitro and E6.5 epiblasts in vivo in mouse development. Also, we discovered that the perturbation of gene expression of the marmoset EPSC-like cells from the original ESCs resembled that of human EPSCs. Taken together, our multiple analyses evaluated the efficacy of the method for the derivation of marmoset EPSCs.

A dynamical stochastic model of yeast translation across the cell cycle

Translation is a central step in gene expression, however its quantitative and time-resolved regulation is poorly understood. We developed a discrete, stochastic model for protein translation in S. cerevisiae in a whole-transcriptome, single-cell context. A "base case" scenario representing an average cell highlights translation initiation rates as the main co-translational regulatory parameters. Codon usage bias emerges as a secondary regulatory mechanism through ribosome stalling. Demand for anticodons with low abundancy is shown to cause above-average ribosome dwelling times. Codon usage bias correlates strongly both with protein synthesis rates and elongation rates. Applying the model to a time-resolved transcriptome estimated by combining data from FISH and RNA-Seq experiments, it could be shown that increased total transcript abundance during the cell cycle decreases translation efficiency at single transcript level. Translation efficiency grouped by gene function shows highest values for ribosomal and glycolytic genes. Ribosomal proteins peak in S phase while glycolytic proteins rank highest in later cell cycle phases.

De Novo Transcriptome Profiling of Naegleria fowleri Trophozoites and Cysts via RNA Sequencing

Naegleria fowleri is a pathogenic free-living amoeba, commonly found around the world in warm, fresh water and soil. N. fowleri trophozoites can infect humans by entering the brain through the nose and causing usually fatal primary amebic meningoencephalitis (PAM). Trophozoites can encyst to survive under unfavorable conditions such as cold temperature, starvation, and desiccation. Recent technological advances in genomics and bioinformatics have provided unique opportunities for the identification and pre-validation of pathogen-related and environmental resistance through improved understanding of the biology of pathogenic N. fowleri trophozoites and cysts at a molecular level. However, genomic and transcriptomic data on differential expression genes (DEGs) between trophozoites and cysts of N. fowleri are very limited. Here, we report transcriptome Illumina RNA sequencing (RNA-seq) for N. fowleri trophozoites and cysts and de novo transcriptome assembly. RNA-seq libraries were generated from RNA extracted from N. fowleri sampled from cysts, and a reference transcriptome was generated through the assembly of trophozoite data. In the database, the assembly procedure resulted in 42,220 contigs with a mean length of 11,254 nucleotides and a C+G content of 37.21%. RNA sequencing showed that 146 genes in cysts of N. fowleri indicated 2-fold upregulation in comparison with trophozoites of N. fowleri, and 163 genes were downregulated; these genes were found to participate in the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. The KEGG pathway included metabolic (131 sequences) and genetic information processing (66 sequences), cellular processing (43 sequences), environmental information processing (22 sequences), and organismal system (20 sequences) pathways. On the other hand, an analysis of 11,254 sequences via the Gene Ontology database showed that their annotations contained 1069 biological processes including the cellular process (228 sequences) and metabolic process (214 sequences); 923 cellular components including cells (240 sequences) and cell parts (225 sequences); and 415 molecular functions including catalytic activities (195 sequences) and binding processes (186 sequences). Differential expression levels increased in cysts of N. fowleri compared to trophozoites of N. fowleri, which were mainly categorized as serine/threonine protease, kinase, and lipid metabolism-related proteins. These results may provide new insights into pathogen-related genes or environment-resistant genes in the pathogenesis of N. fowleri.

Using Single-Cell RNA Sequencing and MicroRNA Targeting Data to Improve Colorectal Cancer Survival Prediction

Colorectal cancer has proven to be difficult to treat as it is the second leading cause of cancer death for both men and women worldwide. Recent work has shown the importance of microRNA (miRNA) in the progression and metastasis of colorectal cancer. Here, we develop a metric based on miRNA-gene target interactions, previously validated to be associated with colorectal cancer. We use this metric with a regularized Cox model to produce a small set of top-performing genes related to colon cancer. We show that using the miRNA metric and a Cox model led to a meaningful improvement in colon cancer survival prediction and correct patient risk stratification. We show that our approach outperforms existing methods and that the top genes identified by our process are implicated in NOTCH3 signaling and general metabolism pathways, which are essential to colon cancer progression.

Monitoring Mechano-Regulation of Gene Expression by RNA Sequencing

The advent of high-throughput sequencing techniques has revolutionized biological research. One such method is RNA sequencing, which has become a relatively affordable and routine method for quantifying and comparing gene expression changes over desired experimental conditions. Along with the popularity of the method, a myriad of user-friendly, open-source computational tools have also emerged for differential gene expression analyses. Correspondingly, decades of mechanobiology research have established that mechanical cues, both alone and/or in combination with biochemical signals, can be powerful regulators of transcriptional programs and consequently cell state/fate transitions. Thus, it has become possible to investigate both universal and specific temporally resolved transcriptional responses upon mechanical stimulation genome-wide. This chapter will describe methods to analyze transcriptional changes in response to extrinsic mechanical stretch.

RNA-seq preprocessing and sample size considerations for gene network inference

Background

Gene network inference (GNI) methods have the potential to reveal functional relationships between different genes and their products. Most GNI algorithms have been developed for microarray gene expression datasets and their application to RNA-seq data is relatively recent. As the characteristics of RNA-seq data are different from microarray data, it is an unanswered question what preprocessing methods for RNA-seq data should be applied prior to GNI to attain optimal performance, or what the required sample size for RNA-seq data is to obtain reliable GNI estimates.

Results

We ran 9144 analysis of 7 different RNA-seq datasets to evaluate 300 different preprocessing combinations that include data transformations, normalizations and association estimators. We found that there was no single best performing preprocessing combination but that there were several good ones. The performance varied widely over various datasets, which emphasized the importance of choosing an appropriate preprocessing configuration before GNI. Two preprocessing combinations appeared promising in general: First, Log-2 TPM (transcript per million) with Variance-stabilizing transformation (VST) and Pearson Correlation Coefficient (PCC) association estimator. Second, raw RNA-seq count data with PCC. Along with these two, we also identified 18 other good preprocessing combinations. Any of these algorithms might perform best in different datasets. Therefore, the GNI performances of these approaches should be measured on any new dataset to select the best performing one for it. In terms of the required biological sample size of RNA-seq data, we found that between 30 to 85 samples were required to generate reliable GNI estimates.

Conclusions

This study provides practical recommendations on default choices for data preprocessing prior to GNI analysis of RNA-seq data to obtain optimal performance results.

RNA Preparation and RNA-Seq Bioinformatics for Comparative Transcriptomics

The principal transcriptome analysis is the determination of differentially expressed genes across experimental conditions. For this, the next-generation sequencing of RNA (RNA-seq) has several advantages over other techniques, such as the capability of detecting all the transcripts in one assay over RT-qPCR, such as its higher accuracy and broader dynamic range over microarrays or the ability to detect novel transcripts, including non-coding RNA molecules, at nucleotide-level resolution over both techniques. Despite these advantages, many microbiology laboratories have not yet applied RNA-seq analyses to their investigations. The high cost of the equipment for next-generation sequencing is no longer an issue since this intermediate part of the analysis can be provided by commercial or central services. Here, we detail a protocol for the first part of the analysis, the RNA extraction and an introductory protocol to the bioinformatics analysis of the sequencing data to generate the differential expression results.

RNA Sequencing Analysis of Saliva exRNA

Next-generation sequencing (NGS) methodologies are rapidly developing. However, RNA Sequencing of saliva is challenging due to low abundance and integrity of extracellular RNA, as well as large amounts of bacterial RNAs that may be encountered in saliva. In addition, the literature about human salivary extracellular RNA is very scarce. Therefore, in our chapter, we present the most appropriate protocols for saliva collection, pre- and post-processing, including bioinformatic analysis of salivary RNA Sequencing data. However, the choice of the proper method for RNA extraction, cDNA library preparation, and computational pipeline can make a significant impact on the final quality of data and their interpretation.

Inference of 3D genome architecture by modeling overdispersion of Hi-C data

MOTIVATION

We address the challenge of inferring a consensus 3D model of genome architecture from Hi-C data. Existing approaches most often rely on a two-step algorithm: first, convert the contact counts into distances, then optimize an objective function akin to multidimensional scaling (MDS) to infer a 3D model. Other approaches use a maximum likelihood approach, modeling the contact counts between two loci as a Poisson random variable whose intensity is a decreasing function of the distance between them. However, a Poisson model of contact counts implies that the variance of the data is equal to the mean, a relationship that is often too restrictive to properly model count data.

RESULTS

We first confirm the presence of overdispersion in several real Hi-C datasets, and we show that the overdispersion arises even in simulated datasets. We then propose a new model, called Pastis-NB, where we replace the Poisson model of contact counts by a negative binomial one, which is parametrized by a mean and a separate dispersion parameter. The dispersion parameter allows the variance to be adjusted independently from the mean, thus better modeling overdispersed data. We compare the results of Pastis-NB to those of several previously published algorithms, both MDS-based and statistical methods. We show that the negative binomial inference yields more accurate structures on simulated data, and more robust structures than other models across real Hi-C replicates and across different resolutions.

AVAILABILITY AND IMPLEMENTATION

A Python implementation of Pastis-NB is available at https://github.com/hiclib/pastis under the BSD license.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

RNA-Seq Data Processing in Plant-Pathogen Interaction System: A Case Study

In RNA-seq data processing, short reads are usually aligned from one species against its own genome sequence; however, in plant-pathogen interaction systems, reads from both host and pathogen samples are blended together. In contrast with single-genome analyses, both pathogen and host reference genomes are involved in the alignment process. In such circumstances, the order in which the alignment is carried out, whether the host or pathogen is aligned first, or if both genomes are aligned simultaneously, influences the read counts of certain genes. This is a problem, especially at advanced infection stages. It is crucial to have an appropriate strategy for aligning the reads to their respective genomes, yet the existing strategies of either sequential or parallel alignment become problematic when mapping mixed reads to their corresponding reference genomes. The challenge lies in the determination of which reads belong to which species, especially when homology exists between the host and pathogen genomes. This chapter proposes a combo-genome alignment strategy, which was compared with existing alignment scenarios. Simulation results demonstrated that the degree of discrepancy in the results is correlated with phylogenetic distance of the two species in the mixture which was attributable to the extent of homology between the two genomes involved. This correlation was also found in the analysis using two real RNA-seq datasets of Fusarium-challenged wheat plants. Comparisons of the three RNA-seq processing strategies on three simulation datasets and two real Fusarium-infected wheat datasets showed that an alignment to a combo-genome, consisting of both host and pathogen genomes, improves mapping quality as compared to sequential alignment procedures.

Analyzing Prokaryotic Transcriptomics in the Light of Genome Data with the MicroScope Platform

Large-scale genome sequencing and the increasingly massive use of high-throughput approaches produce a vast amount of new information that completely transforms our understanding of thousands of microbial species occurring in our environment. However, despite the development of powerful bioinformatics approaches, full interpretation of the content of these genomes remains a difficult task. To address this challenge, the MicroScope platform has been developed. It is an integrated Web platform for management, annotation, comparative analysis, and visualization of microbial genomes ( https://mage.genoscope.cns.fr/microscope ). Launched in 2005, the platform has been under continuous development and provides analyzes for complete and ongoing genome projects together with metabolic network reconstruction and transcriptomic experiments allowing users to improve the understanding of gene functions. MicroScope platform is widely used by microbiologists from academia and industry all around the world for collaborative studies and expert annotation. It enables collaborative work in a rich comparative genomic context and improves community-based curation efforts. Here, we describe the protocol to follow for the integration and analysis of transcriptomics data in the Microscope platform. The chapter reviews each key step from the experimental design to the analysis and interpretation of the experiment data and results. The integration of transcriptomics data gives a dynamic view of the genome by allowing the users to improve the understanding of gene functions by interpreting them in the light of regulatory cell processes. Moreover, they can also contribute to the refinement of genome annotation through the discovery of new genes and help to fill metabolic gaps.

Upcoming progress of transcriptomics studies on plants: An overview

Transcriptome sequencing or RNA-Sequencing is a high-resolution, sensitive and high-throughput next-generation sequencing (NGS) approach used to study non-model plants and other organisms. In other words, it is an assembly of RNA transcripts from individual or whole samples of functional and developmental stages. RNA-Seq is a significant technique for identifying gene predictions and mining functional analysis that improves gene ontology understanding mechanisms of biological processes, molecular functions, and cellular components, but there is limited information available on this topic. Transcriptomics research on different types of plants can assist researchers to understand functional genes in better ways and regulatory processes to improve breeding selection and cultivation practices. In recent years, several advancements in RNA-Seq technology have been made for the characterization of the transcriptomes of distinct cell types in biological tissues in an efficient manner. RNA-Seq technologies are briefly introduced and examined in terms of their scientific applications. In a nutshell, it introduces all transcriptome sequencing and analysis techniques, as well as their applications in plant biology research. This review will focus on numerous existing and forthcoming strategies for improving transcriptome sequencing technologies for functional gene mining in various plants using RNA- Seq technology, based on the principles, development, and applications.

Distance-dependent inhibition of translation initiation by downstream out-of-frame AUGs is consistent with a Brownian ratchet process of ribosome scanning

BACKGROUND

Eukaryotic ribosomes are widely presumed to scan mRNA for the AUG codon to initiate translation in a strictly 5'-3' movement (i.e., strictly unidirectional scanning model), so that ribosomes initiate translation exclusively at the 5' proximal AUG codon (i.e., the first-AUG rule).

RESULTS

We generate 13,437 yeast variants, each with an ATG triplet placed downstream (dATGs) of the annotated ATG (aATG) codon of a green fluorescent protein. We find that out-of-frame dATGs can inhibit translation at the aATG, but with diminishing strength over increasing distance between aATG and dATG, undetectable beyond ~17 nt. This phenomenon is best explained by a Brownian ratchet mechanism of ribosome scanning, in which the ribosome uses small-amplitude 5'-3' and 3'-5' oscillations with a net 5'-3' movement to scan the AUG codon, thereby leading to competition for translation initiation between aAUG and a proximal dAUG. This scanning model further predicts that the inhibitory effect induced by an out-of-frame upstream AUG triplet (uAUG) will diminish as uAUG approaches aAUG, which is indeed observed among the 15,586 uATG variants generated in this study. Computational simulations suggest that each triplet is scanned back and forth approximately ten times until the ribosome eventually migrates to downstream regions. Moreover, this scanning process could constrain the evolution of sequences downstream of the aATG to minimize proximal out-of-frame dATG triplets in yeast and humans.

CONCLUSIONS

Collectively, our findings uncover the basic process by which eukaryotic ribosomes scan for initiation codons, and how this process could shape eukaryotic genome evolution.

Unleashing the power within short-read RNA-seq for plant research: Beyond differential expression analysis and toward regulomics

RNA-seq has become a state-of-the-art technique for transcriptomic studies. Advances in both RNA-seq techniques and the corresponding analysis tools and pipelines have unprecedently shaped our understanding in almost every aspects of plant sciences. Notably, the integration of huge amount of RNA-seq with other omic data sets in the model plants and major crop species have facilitated plant regulomics, while the RNA-seq analysis has still been primarily used for differential expression analysis in many less-studied plant species. To unleash the analytical power of RNA-seq in plant species, especially less-studied species and biomass crops, we summarize recent achievements of RNA-seq analysis in the major plant species and representative tools in the four types of application: (1) transcriptome assembly, (2) construction of expression atlas, (3) network analysis, and (4) structural alteration. We emphasize the importance of expression atlas, coexpression networks and predictions of gene regulatory relationships in moving plant transcriptomes toward regulomics, an omic view of genome-wide transcription regulation. We highlight what can be achieved in plant research with RNA-seq by introducing a list of representative RNA-seq analysis tools and resources that are developed for certain minor species or suitable for the analysis without species limitation. In summary, we provide an updated digest on RNA-seq tools, resources and the diverse applications for plant research, and our perspective on the power and challenges of short-read RNA-seq analysis from a regulomic point view. A full utilization of these fruitful RNA-seq resources will promote plant omic research to a higher level, especially in those less studied species.

Improving Coconut Using Modern Breeding Technologies: Challenges and Opportunities

Coconut (Cocos nucifera L.) is a perennial palm with a wide range of distribution across tropical islands and coastlines. Multitude use of coconut by nature is important in the socio-economic fabric framework among rural smallholders in producing countries. It is a major source of income for 30 million farmers, while 60 million households rely on the coconut industry directly as farm workers and indirectly through the distribution, marketing, and processing of coconut and coconut-based products. Stagnant production, inadequate planting materials, the effects of climate change, as well as pests and diseases are among the key issues that need to be urgently addressed in the global coconut industry. Biotechnology has revolutionized conventional breeding approaches in creating genetic variation for trait improvement in a shorter period of time. In this review, we highlighted the challenges of current breeding strategies and the potential of biotechnological approaches, such as genomic-assisted breeding, next-generation sequencing (NGS)-based genotyping and genome editing tools in improving the coconut. Also, combining these technologies with high-throughput phenotyping approaches and speed breeding could speed up the rate of genetic gain in coconut breeding to solve problems that have been plaguing the industry for decades.

Comparison of Metagenomics and Metatranscriptomics Tools: A Guide to Making the Right Choice

The study of microorganisms is a field of great interest due to their environmental (e.g., soil contamination) and biomedical (e.g., parasitic diseases, autism) importance. The advent of revolutionary next-generation sequencing techniques, and their application to the hypervariable regions of the 16S, 18S or 23S ribosomal subunits, have allowed the research of a large variety of organisms more in-depth, including bacteria, archaea, eukaryotes and fungi. Additionally, together with the development of analysis software, the creation of specific databases (e.g., SILVA or RDP) has boosted the enormous growth of these studies. As the cost of sequencing per sample has continuously decreased, new protocols have also emerged, such as shotgun sequencing, which allows the profiling of all taxonomic domains in a sample. The sequencing of hypervariable regions and shotgun sequencing are technologies that enable the taxonomic classification of microorganisms from the DNA present in microbial communities. However, they are not capable of measuring what is actively expressed. Conversely, we advocate that metatranscriptomics is a "new" technology that makes the identification of the mRNAs of a microbial community possible, quantifying gene expression levels and active biological pathways. Furthermore, it can be also used to characterise symbiotic interactions between the host and its microbiome. In this manuscript, we examine the three technologies above, and discuss the implementation of different software and databases, which greatly impact the obtaining of reliable results. Finally, we have developed two easy-to-use pipelines leveraging Nextflow technology. These aim to provide everything required for an average user to perform a metagenomic analysis of marker genes with QIMME2 and a metatranscriptomic study using Kraken2/Bracken.

Autophagy in the normal and diseased cornea

The cornea and covering tear film are together the 'objective lens' of the eye through which 80% of light is refracted. Despite exposure to a physically harsh and at times infectious or toxic environment, transparency essential for sight is in most cases maintained. Such resiliency makes the avascular cornea a superb model for the exploration of autophagy in the regulation of homeostasis with relevancy to all organs. Nonetheless, missense mutations and inflammation respectively clog or apparently overwhelm autophagic flux to create dystrophies much like in neurodegenerative diseases or further exacerbate inflammation. Here there is opportunity to generate novel topical therapies towards the restoration of homeostasis with potential broad application.

An improved imaging system that corrects MS2-induced RNA destabilization

The MS2 and MS2-coat protein (MS2-MCP) imaging system is widely used to study messenger RNA (mRNA) spatial distribution in living cells. Here, we report that the MS2-MCP system destabilizes some tagged mRNAs by activating the nonsense-mediated mRNA decay pathway. We introduce an improved version, which counteracts this effect by increasing the efficiency of translation termination of the tagged mRNAs. Improved versions were developed for both yeast and mammalian systems.

YMLA: A comparative platform to carry out functional enrichment analysis for multiple gene lists in yeast

Comparative analysis among multiple gene lists on their functional features is now a routine task due to the advancement of high-throughput experiments. Several enrichment analysis tools were developed in the past. However, these tools mainly focus on one gene list and contain only gene ontology or interaction features. What makes it worse, comparative investigation and customized feature set reanalysis are still unavailable. Therefore, we constructed the YMLA (Yeast Multiple List Analyzer) platform in this research. YMLA includes 39 yeast features and facilitates comparative analysis among multiple gene lists via tabular views, heatmaps, and network plots. Moreover, the customized feature set reanalysis function was implemented in YMLA to help form mechanism hypotheses based on a selected enriched feature subset. We demonstrated the biological applicability of YMLA via example lists consisting of genes with top/bottom translation efficiency values. The analysis results provided by YMLA reveal novel facts consistent with previous experiments. YMLA is available at https://cosbi7.ee.ncku.edu.tw/YMLA/.

Non-coding antisense transcripts: fine regulation of gene expression in cancer

Natural antisense transcripts (NATs) are coding or non-coding RNA sequences transcribed on the opposite direction from the same genomic locus. NATs are widely distributed throughout the human genome and seem to play crucial roles in physiological and pathological processes, through newly described and targeted mechanisms. NATs represent the intricate complexity of the genome organization and constitute another layer of potential targets in disease. Here, we focus on the interesting and unique role of non-coding NATs in cancer, paying particular attention to those acting as miRNA sponges.

Omics technologies in allergy and asthma research: An EAACI position paper

Allergic diseases and asthma are heterogenous chronic inflammatory conditions with several distinct complex endotypes. Both environmental and genetic factors can influence the development and progression of allergy. Complex pathogenetic pathways observed in allergic disorders present a challenge in patient management and successful targeted treatment strategies. The increasing availability of high-throughput omics technologies, such as genomics, epigenomics, transcriptomics, proteomics, and metabolomics allows studying biochemical systems and pathophysiological processes underlying allergic responses. Additionally, omics techniques present clinical applicability by functional identification and validation of biomarkers. Therefore, finding molecules or patterns characteristic for distinct immune-inflammatory endotypes, can subsequently influence its development, progression, and treatment. There is a great potential to further increase the effectiveness of single omics approaches by integrating them with other omics, and nonomics data. Systems biology aims to simultaneously and longitudinally understand multiple layers of a complex and multifactorial disease, such as allergy, or asthma by integrating several, separated data sets and generating a complete molecular profile of the condition. With the use of sophisticated biostatistics and machine learning techniques, these approaches provide in-depth insight into individual biological systems and will allow efficient and customized healthcare approaches, called precision medicine. In this EAACI Position Paper, the Task Force "Omics technologies in allergic research" broadly reviewed current advances and applicability of omics techniques in allergic diseases and asthma research, with a focus on methodology and data analysis, aiming to provide researchers (basic and clinical) with a desk reference in the field. The potential of omics strategies in understanding disease pathophysiology and key tools to reach unmet needs in allergy precision medicine, such as successful patients' stratification, accurate disease prognosis, and prediction of treatment efficacy and successful prevention measures are highlighted.

Phosphoproteomics of extracellular vesicles integrated with multiomics analysis reveals novel kinase networks for lung cancer

Phosphorylation regulates the functions of proteins and aberrant phosphorylation often leads to a variety of diseases, including cancers. Extracellular vesicles (EVs) are important messengers in the microenvironment and their proteome contributes to cancer genesis and metastasis, while the kinases that driving EVs proteins' phosphorylation are less known. Clinical tissue samples from 13 patients with non-small-cell lung cancer (NSCLC) were utilized to isolate cancer EVs and adjacent normal EVs. Through quantitative phosphoproteomics analysis, 2473 phosphorylation sites on 1567 proteins were successfully identified and quantified. Accordingly, 152 kinases were identified, and 25 of them were differentially expressed. Based on Tied Diffusion through Interacting Events (TieDIE) algorithm, we integrated genomic and transcriptomic data sets of NSCLC from TCGA with our phosphoproteome data set to construct signaling networks. Through database integration and multiomics enrichment analysis, a compact network of 234 nodes with 1599 edges was constructed, which consisted of 34 transcription factors, 33 kinases, 63 aberrant genes, and 172 linking proteins. Rarely studied phosphorylation sites were specifically enriched. Key phosphoproteins of network nodes were validated in patients' EVs, including MAPK6^S189 , IKBKE^S172 , SRC^Y530 , CDK7^S164 , and CDK1^T14 . These networks depict intrinsic signal-regulation derived from EVs' phosphoproteins, providing a comprehensive and pathway-based strategy for in-depth lung cancer research.

On taming the effect of transcript level intra-condition count variation during differential expression analysis: A story of dogs, foxes and wolves

The evolution of RNA-seq technologies has yielded datasets of scientific value that are often generated as condition associated biological replicates within expression studies. With expanding data archives opportunity arises to augment replicate numbers when conditions of interest overlap. Despite correction procedures for estimating transcript abundance, a source of ambiguity is transcript level intra-condition count variation; as indicated by disjointed results between analysis tools. We present TVscript, a tool that removes reference-based transcripts associated with intra-condition count variation above specified thresholds and we explore the effects of such variation on differential expression analysis. Initially iterative differential expression analysis involving simulated counts, where levels of intra-condition variation and sets of over represented transcripts are explicitly specified, was performed. Then counts derived from inter- and intra-study data representing brain samples of dogs, wolves and foxes (wolves vs. dogs and aggressive vs. tame foxes) were used. For simulations, the sensitivity in detecting differentially expressed transcripts increased after removing hyper-variable transcripts, although at levels of intra-condition variation above 5% detection became unreliable. For real data, prior to applying TVscript, ≈20% of the transcripts identified as being differentially expressed were associated with high levels of intra-condition variation, an over representation relative to the reference set. As transcripts harbouring such variation were removed pre-analysis, a discordance from 26 to 40% in the lists of differentially expressed transcripts is observed when compared to those obtained using the non-filtered reference. The removal of transcripts possessing intra-condition variation values within (and above) the 97^th and 95^th percentiles, for wolves vs. dogs and aggressive vs. tame foxes, maximized the sensitivity in detecting differentially expressed transcripts as a result of alterations within gene-wise dispersion estimates. Through analysis of our real data the support for seven genes with potential for being involved with selection for tameness is provided. TVscript is available at: https://sourceforge.net/projects/tvscript/.

Transcriptomic Harmonization as the Way for Suppressing Cross-Platform Bias and Batch Effect

(1) Background: Emergence of methods interrogating gene expression at high throughput gave birth to quantitative transcriptomics, but also posed a question of inter-comparison of expression profiles obtained using different equipment and protocols and/or in different series of experiments. Addressing this issue is challenging, because all of the above variables can dramatically influence gene expression signals and, therefore, cause a plethora of peculiar features in the transcriptomic profiles. Millions of transcriptomic profiles were obtained and deposited in public databases of which the usefulness is however strongly limited due to the inter-comparison issues; (2) Methods: Dozens of methods and software packages that can be generally classified as either flexible or predefined format harmonizers have been proposed, but none has become to the date the gold standard for unification of this type of Big Data; (3) Results: However, recent developments evidence that platform/protocol/batch bias can be efficiently reduced not only for the comparisons of limited transcriptomic datasets. Instead, instruments were proposed for transforming gene expression profiles into the universal, uniformly shaped format that can support multiple inter-comparisons for reasonable calculation costs. This forms a basement for universal indexing of all or most of all types of RNA sequencing and microarray hybridization profiles; (4) Conclusions: In this paper, we attempted to overview the landscape of modern approaches and methods in transcriptomic harmonization and focused on the practical aspects of their application.

Origins, evolution, and physiological implications of de novo genes in yeast

De novo gene birth is the process by which new genes emerge in sequences that were previously noncoding. Over the past decade, researchers have taken advantage of the power of yeast as a model and a tool to study the evolutionary mechanisms and physiological implications of de novo gene birth. We summarize the mechanisms that have been proposed to explicate how noncoding sequences can become protein-coding genes, highlighting the discovery of pervasive translation of the yeast transcriptome and its presumed impact on evolutionary innovation. We summarize current best practices for the identification and characterization of de novo genes. Crucially, we explain that the field is still in its nascency, with the physiological roles of most young yeast de novo genes identified thus far still utterly unknown. We hope this review inspires researchers to investigate the true contribution of de novo gene birth to cellular physiology and phenotypic diversity across yeast strains and species.

Controlling gene expression with deep generative design of regulatory DNA

Design of de novo synthetic regulatory DNA is a promising avenue to control gene expression in biotechnology and medicine. Using mutagenesis typically requires screening sizable random DNA libraries, which limits the designs to span merely a short section of the promoter and restricts their control of gene expression. Here, we prototype a deep learning strategy based on generative adversarial networks (GAN) by learning directly from genomic and transcriptomic data. Our ExpressionGAN can traverse the entire regulatory sequence-expression landscape in a gene-specific manner, generating regulatory DNA with prespecified target mRNA levels spanning the whole gene regulatory structure including coding and adjacent non-coding regions. Despite high sequence divergence from natural DNA, in vivo measurements show that 57% of the highly-expressed synthetic sequences surpass the expression levels of highly-expressed natural controls. This demonstrates the applicability and relevance of deep generative design to expand our knowledge and control of gene expression regulation in any desired organism, condition or tissue.

Design of de novo synthetic regulatory DNA is a promising avenue to control gene expression in biotechnology and medicine. Here the authors present EspressionGAN, a generative adversarial network that uses genomic and transcriptomic data to generate regulatory sequences.

Screening and transcriptomic analysis of the ethanol-tolerant mutant Saccharomyces cerevisiae YN81 for high-gravity brewing

Ethanol stress is one of the major limiting factors for high-gravity brewing. Breeding of yeast strain with high ethanol tolerance, and revealing the ethanol tolerance mechanism of Saccharomyces cerevisiae is of great significance to the production of high-gravity beer. In this study, the mutant YN81 was obtained by ultraviolet-diethyl sulfate (UV-DES) cooperative mutagenesis from parental strain CS31 used in high-gravity craft beer brewing. The ethanol tolerance experiment results showed that cell growth and viability of YN81 were significantly greater than that of CS31 under ethanol stress. The ethanol tolerance mechanisms of YN81 were studied through observation of cell morphology, intracellular trehalose content, and transcriptomic analysis. Results from scanning electron microscope (SEM) showed alcohol toxicity caused significant changes in the cell morphology of CS31, while the cell morphology of YN81 changed slightly, indicating the cell morphology of CS31 got worse (the formation of hole and cell wrinkle). In addition, compared with ethanol-free stress, the trehalose content of YN81 and CS31 increased dramatically under ethanol stress, but there was no significant difference between YN81 and CS31, whether with or without ethanol stress. GO functional annotation analysis showed that under alcohol stress, the number of membrane-associated genes in YN81 was higher than that without alcohol stress, as well as CS31, while membrane-associated genes in YN81 were expressed more than CS31 under alcohol stress. KEGG functional enrichment analysis showed unsaturated fatty acid synthesis pathways and amino acid metabolic pathways were involved in ethanol tolerance of YN81. The mutant YN81 and its ethanol tolerance mechanism provide an optimal strain and theoretical basis for high-gravity craft beer brewing.

Preparation, characterization, and osteogenic activity mechanism of casein phosphopeptide-calcium chelate

Casein phosphopeptides (CPPs) are good at calcium-binding and intestinal calcium absorption, but there are few studies on the osteogenic activity of CPPs. In this study, the preparation of casein phosphopeptide calcium chelate (CPP-Ca) was optimized on the basis of previous studies, and its peptide-calcium chelating activity was characterized. Subsequently, the effects of CPP-Ca on the proliferation, differentiation, and mineralization of MC3T3-E1 cells were studied, and the differentiation mechanism of CPP-Ca on MC3T3-E1 cells was further elucidated by RNA sequencing (RNA-seq). The results showed that the calcium chelation rate of CPPs was 23.37%, and the calcium content of CPP-Ca reached 2.64 × 10⁵ mg/kg. The test results of Ultraviolet–Visible absorption spectroscopy (UV) and Fourier transform infrared spectroscopy (FTIR) indicated that carboxyl oxygen and amino nitrogen atoms of CPPs might be chelated with calcium during the chelation. Compared with the control group, the proliferation of MC3T3-E1 cells treated with 250 μg/mL of CPP-Ca increased by 21.65%, 26.43%, and 28.43% at 24, 48, and 72 h, respectively, and the alkaline phosphatase (ALP) activity and mineralized calcium nodules of MC3T3-E1 cells were notably increased by 55% and 72%. RNA-seq results showed that 321 differentially expressed genes (DEGs) were found in MC3T3-E1 cells treated with CPP-Ca, including 121 upregulated and 200 downregulated genes. Gene ontology (GO) revealed that the DEGs mainly played important roles in the regulation of cellular components. The enrichment of the Kyoto Encyclopedia of Genes and Genomes Database (KEGG) pathway indicated that the AMPK, PI3K-Akt, MAPK, and Wnt signaling pathways were involved in the differentiation of MC3T3-E1 cells. The results of a quantitative real-time PCR (qRT-PCR) showed that compared with the blank control group, the mRNA expressions of Apolipoprotein D (APOD), Osteoglycin (OGN), and Insulin-like growth factor (IGF1) were significantly increased by 2.6, 2.0 and 3.0 times, respectively, while the mRNA levels of NOTUM, WIF1, and LRP4 notably decreased to 2.3, 2.1, and 4.2 times, respectively, which were consistent both in GO functional and KEGG enrichment pathway analysis. This study provided a theoretical basis for CPP-Ca as a nutritional additive in the treatment and prevention of osteoporosis.

Molecular mechanism of high-production tannase of Aspergillus carbonarius NCUF M8 after ARTP mutagenesis: revealed by RNA-seq and molecular docking

BACKGROUND

Tannase is an enzyme produced by microbial fermentation and is widely used in the food industry; however, the molecular mechanism of tannase production by Aspergillus has not yet been studied. This study was conducted to reveal the differences in Aspergillus carbonarius tannase enzymatic characterization, secondary structures and molecular mechanisms after treatment of the strain with atmospheric and room temperature plasma (ARTP).

RESULTS

The results showed that the specific activity of tannase was improved by ARTP treatment, and it showed higher thermostability and tolerance to metal ions and additives. The enzymatic characterization and molecular docking results indicated that tannase had a higher affinity and catalytic rate with tannic acid as a substrate after ARTP treatment. In addition, the docking results indicated that Aspergillus tannases may catalyze tannic acid by forming two hydrogen-bonding networks with neighboring residues. RNA-seq analysis indicated that changes in steroid biosynthesis, glutathione metabolism, glycerolipid metabolism, oxidative phosphorylation pathway and mitogen-activated protein kinase signaling pathways might be crucial reasons for the high production of tannase.

CONCLUSION

ARTP enhanced the yield and properties of A. carbonarius tannase by changing the enzyme structure and cell metabolism. This study provides a theoretical basis for elucidating the molecular mechanism underlying high production of Aspergillus tannases. © 2022 Society of Chemical Industry.

In silico Identification of Immune Cell-Types and Pathways Involved in Chronic Spontaneous Urticaria

Background

The immunopathogenesis of chronic spontaneous urticaria (CSU) is poorly understood, but recent research suggests that patients can be divided into autoallergic and autoimmune subtypes. Given that not all patients can be controlled with current treatment regimens, including anti-IgE monoclonal antibodies, a better understanding of the immune pathways involved in CSU may enable the repurposing of monoclonal antibodies used for other dermatologic diseases (e.g., Th2 and Th17 inhibitors). Therefore, we investigated the implicated immune cells and pathways by reanalyzing publicly available transcriptomic data.

Methods

Microarray data of CSU and healthy control (HC) skin and blood were obtained from the Gene Expression Omnibus (GSE72542, GSE57178). Differentially expressed genes were defined as a false discovery rate <0.05 and a |log₂ fold change| ≥1. Pathway analyses were conducted using ToppGene and KEGG. Cell-type enrichment was determined by CIBERSORT and xCell and was correlated with clinical characteristics.

Results

Th2 (IL-4/13 signaling) and Th17-related (IL-17/23 signaling) pathways were upregulated in lesional compared to non-lesional and HC samples. In non-lesional versus lesional samples, CIBERSORT analysis revealed increased regulatory T-cells (Treg) and resting mast cells. xCell analysis established that Th1 and Th2 scores were not significantly different between lesional and HC samples. However, Th2 scores in both lesional and non-lesional samples correlated positively with disease severity. Few differentially expressed genes and pathways were identified between CSU and HC blood samples.

Conclusion

Our results support the involvement of Th2 and Th17-related genes and pathways in CSU. Th2 scores associate with disease severity, which indicates the clinical relevance of these findings. Increased resting mast cell and Treg scores in non-lesional samples may suggest local suppression of wheal formation. Moreover, disease activity seemed to be restricted to the skin as there were limited findings from blood. Larger studies using next-generation sequencing will be helpful to confirm these results.

scDLC: a deep learning framework to classify large sample single-cell RNA-seq data

Background

Using single-cell RNA sequencing (scRNA-seq) data to diagnose disease is an effective technique in medical research. Several statistical methods have been developed for the classification of RNA sequencing (RNA-seq) data, including, for example, Poisson linear discriminant analysis (PLDA), negative binomial linear discriminant analysis (NBLDA), and zero-inflated Poisson logistic discriminant analysis (ZIPLDA). Nevertheless, few existing methods perform well for large sample scRNA-seq data, in particular when the distribution assumption is also violated.

Results

We propose a deep learning classifier (scDLC) for large sample scRNA-seq data, based on the long short-term memory recurrent neural networks (LSTMs). Our new scDLC does not require a prior knowledge on the data distribution, but instead, it takes into account the dependency of the most outstanding feature genes in the LSTMs model. LSTMs is a special recurrent neural network, which can learn long-term dependencies of a sequence.

Conclusions

Simulation studies show that our new scDLC performs consistently better than the existing methods in a wide range of settings with large sample sizes. Four real scRNA-seq datasets are also analyzed, and they coincide with the simulation results that our new scDLC always performs the best. The code named “scDLC” is publicly available at https://github.com/scDLC-code/code.

Supplementary Information

The online version contains supplementary material available at (10.1186/s12864-022-08715-1).

RNA Instant Quality Check: Alignment-Free RNA-Degradation Detection

With the constant increase of large-scale genomic data projects, automated and high-throughput quality assessment becomes a crucial component of any analysis. Whereas small projects often have a more homogeneous design and a manageable structure allowing for a manual per-sample analysis of quality, large-scale studies tend to be much more heterogeneous and complex. Many quality metrics have been developed to assess the quality of an individual sample on the raw read level. Degradation effects are typically assessed based on the RNA integrity (RIN) score, or on postalignment data. In this study, we show that single commonly used quality criteria such as the RIN score alone are not sufficient to ensure RNA sample quality. We developed a new approach and provide an efficient tool that estimates RNA sample degradation by computing the 5'/3' bias based on all genes in an alignment-free manner. That enables degradation assessment right after data generation and not during the analysis procedure allowing for early intervention in the sample handling process. Our analysis shows that this strategy is fast, robust to annotation and differences in library size, and provides complementary quality information to RIN scores enabling the accurate identification of degraded samples.

A survey of transcriptome complexity using full-length isoform sequencing in the tea plant Camellia sinensis

Tea is one of the most popular beverages and its leaves are rich in catechins, contributing to the diverse flavor as well as beneficial for human health. However, the study of the post-transcriptional regulatory mechanism affecting the synthesis of catechins remains insufficient. Here, we sequenced the transcriptome using PacBio sequencing technology and obtained 63,111 full-length high-quality isoforms, including 1302 potential novel genes and 583 highly reliable fusion transcripts. We also identified 1204 lncRNAs with high quality, containing 188 known and 1016 novel lncRNAs. In addition, 311 mis-annotated genes were corrected based on the high-quality Isoseq reads. A large number of alternative splicing (AS) events (3784) and alternative polyadenylation (APA) genes (18,714) were analyzed, accounting for 8.84% and 43.7% of the total annotated genes, respectively. We also found that 2884 genes containing AS and APA features exhibited higher expression levels than other genes. These genes are mainly involved in amino acid biosynthesis, carbon fixation in photosynthetic organisms, phenylalanine, tyrosine, tryptophan biosynthesis, and pyruvate metabolism, suggesting that they play an essential role in the catechins content of tea polyphenols. Our results further improved the level of genome annotation and indicated that post-transcriptional regulation plays a crucial part in synthesizing catechins.

Application of Genetic, Genomic and Biological Pathways in Improvement of Swine Feed Efficiency

Despite the significant improvement of feed efficiency (FE) in pigs over the past decades, feed costs remain a major challenge for producers profitability. Improving FE is a top priority for the global swine industry. A deeper understanding of the biology underlying FE is crucial for making progress in genetic improvement of FE traits. This review comprehensively discusses the topics related to the FE in pigs including: measurements, genetics, genomics, biological pathways and the advanced technologies and methods involved in FE improvement. We first provide an update of heritability for different FE indicators and then characterize the correlations of FE traits with other economically important traits. Moreover, we present the quantitative trait loci (QTL) and possible candidate genes associated with FE in pigs and outline the most important biological pathways related to the FE traits in pigs. Finally, we present possible ways to improve FE in swine including the implementation of genomic selection, new technologies for measuring the FE traits, and the potential use of genome editing and omics technologies.

Low Temperature Stress Tolerance: An Insight Into the Omics Approaches for Legume Crops

The change in climatic conditions is the major cause for decline in crop production worldwide. Decreasing crop productivity will further lead to increase in global hunger rate. Climate change results in environmental stress which has negative impact on plant-like deficiencies in growth, crop yield, permanent damage, or death if the plant remains in the stress conditions for prolonged period. Cold stress is one of the main abiotic stresses which have already affected the global crop production. Cold stress adversely affects the plants leading to necrosis, chlorosis, and growth retardation. Various physiological, biochemical, and molecular responses under cold stress have revealed that the cold resistance is more complex than perceived which involves multiple pathways. Like other crops, legumes are also affected by cold stress and therefore, an effective technique to mitigate cold-mediated damage is critical for long-term legume production. Earlier, crop improvement for any stress was challenging for scientific community as conventional breeding approaches like inter-specific or inter-generic hybridization had limited success in crop improvement. The availability of genome sequence, transcriptome, and proteome data provides in-depth sight into different complex mechanisms under cold stress. Identification of QTLs, genes, and proteins responsible for cold stress tolerance will help in improving or developing stress-tolerant legume crop. Cold stress can alter gene expression which further leads to increases in stress protecting metabolites to cope up the plant against the temperature fluctuations. Moreover, genetic engineering can help in development of new cold stress-tolerant varieties of legume crop. This paper provides a general insight into the "omics" approaches for cold stress in legume crops.

Data mining and mathematical models in cancer prognosis and prediction

Cancer is a fetal and complex disease. Individual differences of the same cancer type or the same patient at different stages of cancer development may require distinct treatments. Pathological differences are reflected in tissues, cells and gene levels etc. The interactions between the cancer cells and nearby microenvironments can also influence the cancer progression and metastasis. It is a huge challenge to understand all of these mechanistically and quantitatively. Researchers applied pattern recognition algorithms such as machine learning or data mining to predict cancer types or classifications. With the rapidly growing and available computing powers, researchers begin to integrate huge data sets, multi-dimensional data types and information. The cells are controlled by the gene expressions determined by the promoter sequences and transcription regulators. For example, the changes in the gene expression through these underlying mechanisms can modify cell progressing in the cell-cycle. Such molecular activities can be governed by the gene regulations through the underlying gene regulatory networks, which are essential for cancer study when the information and gene regulations are clear and available. In this review, we briefly introduce several machine learning methods of cancer prediction and classification which include Artificial Neural Networks (ANNs), Decision Trees (DTs), Support Vector Machine (SVM) and naive Bayes. Then we describe a few typical models for building up gene regulatory networks such as Correlation, Regression and Bayes methods based on available data. These methods can help on cancer diagnosis such as susceptibility, recurrence, survival etc. At last, we summarize and compare the modeling methods to analyze the development and progression of cancer through gene regulatory networks. These models can provide possible physical strategies to analyze cancer progression in a systematic and quantitative way.

Shambhala-2: A Protocol for Uniformly Shaped Harmonization of Gene Expression Profiles of Various Formats

Uniformly shaped harmonization of gene expression profiles is central for the simultaneous comparison of multiple gene expression datasets. It is expected to operate with the gene expression data obtained using various experimental methods and equipment, and to return harmonized profiles in a uniform shape. Such uniformly shaped expression profiles from different initial datasets can be further compared directly. However, current harmonization techniques have strong limitations that prevent their broad use for bioinformatic applications. They can either operate with only up to two datasets/platforms or return data in a dynamic format that will be different for every comparison under analysis. This also does not allow for adding new data to the previously harmonized dataset(s), which complicates the analysis and increases calculation costs. We propose here a new method termed Shambhala-2 that can transform multi-platform expression data into a universal format that is identical for all harmonizations made using this technique. Shambhala-2 is based on sample-by-sample cubic conversion of the initial expression dataset into a preselected shape of the reference definitive dataset. Using 8390 samples of 12 healthy human tissue types and 4086 samples of colorectal, kidney, and lung cancer tissues, we verified Shambhala-2's capacity in restoring tissue-specific expression patterns for seven microarray and three RNA sequencing platforms. Shambhala-2 performed well for all tested combinations of RNAseq and microarray profiles, and retained gene-expression ranks, as evidenced by high correlations between different single- or aggregated gene expression metrics in pre- and post-Shambhalized samples, including preserving cancer-specific gene expression and pathway activation features. © 2022 Wiley Periodicals LLC. Basic Protocol: Shambhala-2 harmonizer Alternate Protocol 1: Linear Shambhala/Shambhala-1 Alternate Protocol 2: Alternative (flexible-format and uniformly shaped) normalization methods Support Protocol 1: Watermelon multisection (WM) Support Protocol 2: Calculation of cancer-to-normal log-fold-change (LFC) and pathway activation level (PAL).

Transcription elongation is finely tuned by dozens of regulatory factors

Understanding the complex network that regulates transcription elongation requires the quantitative analysis of RNA polymerase II (Pol II) activity in a wide variety of regulatory environments. We performed native elongating transcript sequencing (NET-seq) in 41 strains of Saccharomyces cerevisiae lacking known elongation regulators, including RNA processing factors, transcription elongation factors, chromatin modifiers, and remodelers. We found that the opposing effects of these factors balance transcription elongation and antisense transcription. Different sets of factors tightly regulate Pol II progression across gene bodies so that Pol II density peaks at key points of RNA processing. These regulators control where Pol II pauses with each obscuring large numbers of potential pause sites that are primarily determined by DNA sequence and shape. Antisense transcription varies highly across the regulatory landscapes analyzed, but antisense transcription in itself does not affect sense transcription at the same locus. Our findings collectively show that a diverse array of factors regulate transcription elongation by precisely balancing Pol II activity.

Antimicrobial Resistance Profile by Metagenomic and Metatranscriptomic Approach in Clinical Practice: Opportunity and Challenge

The burden of bacterial resistance to antibiotics affects several key sectors in the world, including healthcare, the government, and the economic sector. Resistant bacterial infection is associated with prolonged hospital stays, direct costs, and costs due to loss of productivity, which will cause policy makers to adjust their policies. Current widely performed procedures for the identification of antibiotic-resistant bacteria rely on culture-based methodology. However, some resistance determinants, such as free-floating DNA of resistance genes, are outside the bacterial genome, which could be potentially transferred under antibiotic exposure. Metagenomic and metatranscriptomic approaches to profiling antibiotic resistance offer several advantages to overcome the limitations of the culture-based approach. These methodologies enhance the probability of detecting resistance determinant genes inside and outside the bacterial genome and novel resistance genes yet pose inherent challenges in availability, validity, expert usability, and cost. Despite these challenges, such molecular-based and bioinformatics technologies offer an exquisite advantage in improving clinicians’ diagnoses and the management of resistant infectious diseases in humans. This review provides a comprehensive overview of next-generation sequencing technologies, metagenomics, and metatranscriptomics in assessing antimicrobial resistance profiles.

The tomato yellow leaf curl virus C4 protein alters the expression of plant developmental genes correlating to leaf upward cupping phenotype in tomato

Tomato yellow leaf curl virus (TYLCV), a monopartite begomovirus in the family Geminiviridae, is efficiently transmitted by the whitefly, Bemisia tabaci, and causes serious economic losses to tomato crops around the world. TYLCV-infected tomato plants develop distinctive symptoms of yellowing and leaf upward cupping. In recent years, excellent progress has been made in the characterization of TYLCV C4 protein function as a pathogenicity determinant in experimental plants, including Nicotiana benthamiana and Arabidopsis thaliana. However, the molecular mechanism leading to disease symptom development in the natural host plant, tomato, has yet to be characterized. The aim of the current study was to generate transgenic tomato plants expressing the TYLCV C4 gene and evaluate differential gene expression through comparative transcriptome analysis between the transgenic C4 plants and the transgenic green fluorescent protein (Gfp) gene control plants. Transgenic tomato plants expressing TYLCV C4 developed phenotypes, including leaf upward cupping and yellowing, that are similar to the disease symptoms expressed on tomato plants infected with TYLCV. In a total of 241 differentially expressed genes identified in the transcriptome analysis, a series of plant development-related genes, including transcription factors, glutaredoxins, protein kinases, R-genes and microRNA target genes, were significantly altered. These results provide further evidence to support the important function of the C4 protein in begomovirus pathogenicity. These transgenic tomato plants could serve as basic genetic materials for further characterization of plant receptors that are interacting with the TYLCV C4.