Rumen-protected glucose stimulates the secretion of reproductive hormones and the mTOR/AKT signaling pathway in the ovaries of early postpartum

This study was conducted to determine the response of the reproductive hormones and the mTOR/AKT/PI3K pathway in the ovaries of postpartum dairy cows with dietary rumen-protected glucose (RPG). Twelve Holstein cows were randomly assigned to two groups (n = 6/group): the control group (CT) and the RPG group. Blood samples were collected on d 1, 7, and 14 after calving for the gonadal hormone assay. The expression of the gonadal hormones receptors and PI3K/mTOR/AKT pathways were detected using RT-PCR and Western blot. The RPG addition increased the plasma LH, E2, and P4 concentrations on d 14 after calving and upregulated the mRNA and protein expressions of the ERα, ERβ, 17β-HSD, FSHR, LHR, and CYP17A1 but downregulated StAR expression. Immunohistochemical analysis identified higher expressions of the FSHR and LHR in the ovaries of RPG-fed cows compared to CT cows. Furthermore, the protein expressions of p-AKT/AKT and p-mTOR/mTOR were significantly increased in the ovaries of RPG-fed cows compared to the CT group, but the addition of RPG did not alter the protein expression of p-PI3K/PI3K. In conclusion, the current results indicated that dietary RPG supplementation regulated gonadotropin secretion and stimulated expression of hormone receptors and the mTOR/AKT pathway in the ovaries of early postpartum dairy cows. RPG may be beneficial for the recovery of ovarian activity in post-calving dairy cows.

Clinical and biological significance of RNA N6-methyladenosine regulators in Alzheimer disease

RNA N6-methyladenosine (m6A) regulators are essential for a variety of biological functions, such as early development, viral infections, and cancer. However, their roles in Alzheimer disease (AD) are still not very clear. Here, 16 significant m6A regulators were identified using difference analysis between AD patients and non-demented controls based on the GSE132903 dataset from the Gene Expression Omnibus database. Using these 16 m6A regulators, a nomogram model was established to predict the prevalence of AD. We found that patients could obtain a good clinical benefit based on this model. In addition, we revealed 2 distinct m6A patterns and 2 distinct m6A gene patterns in AD and demonstrated their prognostic and risk assessment significance. This present work comprehensively evaluated the functions of m6A regulators in the diagnosis and subtype classification of AD. These results suggested they have potential prognostic and risk assessment significance in AD.

Diagnostic accuracy of novel mRNA blood biomarkers of infection to predict outcomes in emergency department patients with undifferentiated abdominal pain

Abdominal pain represents greater than 20% of US Emergency Department (ED) visits due to a wide range of illnesses. There are currently no reliable blood biomarkers to predict serious outcomes in patients with abdominal pain. Our previous studies have identified three mRNA transcripts related to innate immune activation: alkaline phosphatase (ALPL), interleukin-8 receptor-β (IL8RB), and defensin-1 (DEFA1) as promising candidates to detect an intra-abdominal infection. The objective of this study was to evaluate the accuracy of these mRNA biomarkers to predict likely infection, hospitalization and surgery in Emergency Department patients with undifferentiated abdominal pain. We prospectively enrolled Emergency Department patients with undifferentiated abdominal pain who received an abdominal CT scan as part of their evaluation. Clinical outcomes were abstracted from the CT scan and medical records. mRNA biomarker levels were calculated independent of the clinical outcomes and their accuracy was assessed to predict infectious diagnoses, surgery and hospital admission. 89 patients were enrolled; 21 underwent surgery; 47 underwent hospital admission; and, no deaths were observed within 30 days. In identifying which cases were likely infectious, mRNA biomarkers' AUC values were: ALPL, 0.83; DEFA1 0.51; IL8RB, 0.74; and ALPL + IL8RB, 0.79. In predicting which Emergency Department patients would receive surgery, the AUC values were: ALPL, 0.75; DEFA1, 0.58; IL8RB, 0.75; and ALPL + IL8RB, 0.76. In predicting hospital admission, the AUC values were: ALPL, 0.78; DEFA1, 0.52; IL8RB, 0.74; and, ALPL + IL8RB, 0.77. For predicting surgery, ALPL + IL8RB's positive likelihood ratio (LR) was 3.97; negative LR (NLR) was 0.70. For predicting hospital admission, the same marker's positive LR was 2.80 with an NLR of 0.45. Where the primary cause for admission was a potentially infectious disorder, 33 of 34 cases (97%) had positive RNA scores. In a pragmatic, prospective diagnostic accuracy trial in Emergency Department patients with undifferentiated abdominal pain, mRNA biomarkers showed good accuracy to identify patients with potential infection, as well as those needing surgery or hospital admission.

Transcriptome profiling for precision cancer medicine using shallow nanopore cDNA sequencing

Transcriptome profiling is a mainstay of translational cancer research and is increasingly finding its way into precision oncology. While bulk RNA sequencing (RNA-seq) is widely available, high investment costs and long data return time are limiting factors for clinical applications. We investigated a portable nanopore long-read sequencing device (MinION, Oxford Nanopore Technologies) for transcriptome profiling of tumors. In particular, we investigated the impact of lower coverage than that of larger sequencing devices by comparing shallow nanopore RNA-seq data with short-read RNA-seq data generated using reversible dye terminator technology (Illumina) for ten samples representing four cancer types. Coupled with ShaNTi (Shallow Nanopore sequencing for Transcriptomics), a newly developed data processing pipeline, a turnaround time of five days was achieved. The correlation of normalized gene-level counts between nanopore and Illumina RNA-seq was high for MinION but not for very low-throughput Flongle flow cells (r = 0.89 and r = 0.24, respectively). A cost-saving approach based on multiplexing of four samples per MinION flow cell maintained a high correlation with Illumina data (r = 0.56-0.86). In addition, we compared the utility of nanopore and Illumina RNA-seq data for analysis tools commonly applied in translational oncology: (1) Shallow nanopore and Illumina RNA-seq were equally useful for inferring signaling pathway activities with PROGENy. (2) Highly expressed genes encoding kinases targeted by clinically approved small-molecule inhibitors were reliably identified by shallow nanopore RNA-seq. (3) In tumor microenvironment composition analysis, quanTIseq performed better than CIBERSORT, likely due to higher average expression of the gene set used for deconvolution. (4) Shallow nanopore RNA-seq was successfully applied to detect fusion genes using the JAFFAL pipeline. These findings suggest that shallow nanopore RNA-seq enables rapid and biologically meaningful transcriptome profiling of tumors, and warrants further exploration in precision cancer medicine studies.

Synthetic Assembly DNA Cloning to Build Plasmids for Multiplexed Transgenic Selection, Counterselection or Any Other Genetic Strategies Using Drosophila melanogaster

We recently described a drug-based selectable and counterselectable genetic platform for the animal model system Drosophila melanogaster, consisting of four resistance and two sensitivity markers that allow direct selection for, or counterselection against, a desired genotype. This platform eliminates the need to identify modified progeny by traditional laborious screening using the dominant eye and body color markers, white+ and yellow+ , respectively. The four resistance markers permit selection of animals using G418 sulfate, puromycin HCl, blasticidin S, or hygromycin B, while the two sensitivity markers allow counterselection of animals against ganciclovir or acyclovir and 5-fluorocytosine. The six markers can be used alone or in combination to perform co-selection, combination selection, and counterselection, as well as co-counterselection. To make this novel selection and counterselection genetics platform easily accessible to and rapidly implementable by the scientific community, we used a synthetic assembly DNA cloning platform, GoldenBraid 2.0 (GB2.0). GB2.0 relies on two Type IIs restriction enzymes that are alternatingly used during successive cloning steps to make increasingly complex genetic constructs. Here we describe, as an example, how to perform synthetic assembly DNA cloning using GB2.0 to build such complex plasmids via the assembly of both components of the binary LexA/LexA-Op overexpression system, a G418 sulfate-selectable LexA transactivator plasmid, and a blasticidin S-selectable LexA-Op responder plasmid. We demonstrate the functionality of these plasmids by including the expression pattern obtained after co-injection, followed by co-selection using G418 sulfate and blasticidin S, resulting in co-transgenesis of both plasmids. Protocols are provided on how to obtain, adapt, and clone DNA parts for synthetic assembly cloning after de novo DNA synthesis or PCR amplification of desired DNA parts and how to assemble those DNA parts into multipartite transcription units, followed by how to further assemble multiple transcription units into genetic constructs of increasing complexity to perform multiplexed transgenic selection and counterselection, or any other genetic strategies using Drosophila melanogaster. The protocols we present can be easily adapted to incorporate any of the six selectable and counterselectable markers, or any other, markers, to generate plasmids of unmatched complexity for various genetic applications. A protocol on how to generate transgenic animals using these synthetically assembled plasmids is described in an accompanying Current Protocols article (Venken, Matinyan, Gonzalez, & Dierick, 2023). © 2023 Wiley Periodicals LLC. Basic Protocol 1: Obtaining and cloning a de novo-synthesized DNA part for synthetic assembly DNA cloning Basic Protocol 2: Obtaining and cloning a DNA part amplified by PCR from existing DNA resources for synthetic assembly DNA cloning Alternate Protocol: Obtaining, adapting, and cloning a DNA part amplified by PCR from existing DNA resources for synthetic assembly DNA cloning Basic Protocol 3: Synthetic assembly DNA cloning of individual DNA parts into a multipartite transcription unit Basic Protocol 4: Synthetic assembly DNA cloning of multiple transcription units into genetic constructs of increasing complexity.

Identification of key genes related to immune infiltration in cirrhosis via bioinformatics analysis

Cirrhosis is the most common subclass of liver disease worldwide and correlated to immune infiltration. However, the immune-related molecular mechanism underlying cirrhosis remains obscure. Two gene expression profiles GSE89377 and GSE139602 were investigated to identify differentially expressed genes (DEGs) related to cirrhosis. Enrichment analysis for DEGs was conducted. Next, the immune infiltration of DEGs was evaluated using CIBERSORT algorithm. The hub DEGs with tight connectivity were identified using the String and Cytoscape databases, and the expression difference of these hub genes between normal liver and cirrhosis samples was determined. Moreover, in order to evaluate the discriminatory ability of hub genes and obtained the area under the receiver operating characteristic curve values in the GSE89377 and GSE139602 datasets. Finally, the association between hub DEGs and immune cell infiltration was explored by Spearman method. Among the 299 DEGs attained, 136 were up-regulated and 163 were down-regulated. Then the enrichment function analysis of DEGs and CIBERSORT algorithm showed significant enrichment in immune and inflammatory responses. And four hub DEGs (ACTB, TAGLN, VIM, SOX9) were identified, which also showed a diagnostic value in the GSE89377 and GSE 139,602 datasets. Finally, the immune infiltration analysis indicated that, these hub DEGs were highly related to immune cells. This study revealed key DEGs involved in inflammatory immune responses of cirrhosis, which could be used as biomarkers for diagnosis or therapeutic targets of cirrhosis.

Serial Recombineering Cloning to Build Selectable and Tagged Genomic P[acman] BAC Clones for Selection Transgenesis and Functional Gene Analysis using Drosophila melanogaster

Transgenes with genomic DNA fragments that encompass genes of interest are the gold standard for complementing null alleles in rescue experiments in the fruit fly Drosophila melanogaster. Of particular interest are genomic DNA clones available as bacterial artificial chromosomes (BACs) or fosmids from publicly available genomic DNA libraries. Genes contained within BAC and fosmid clones can be easily modified by recombineering cloning to insert peptide or protein tags to localize, visualize, or manipulate gene products, and to create point mutations or deletions for structure-function analysis of the inserted genes. However, since transgenesis efficiency is inversely correlated with transgene size, obtaining transgenic animals for increasingly larger BAC and fosmid clones requires increasingly laborious screening efforts using the transgenesis marker commonly used for these transgenes, the dominant eye color marker white+ . We recently described a drug-based selectable genetic platform for Drosophila melanogaster, which included four resistance markers that allow direct selection of transgenic animals, eliminating the need to identify transgenic progeny by laborious phenotypic screening. By integrating these resistance markers into BAC transgenes, we were able to isolate animals containing large transgenes by direct selection, avoiding laborious screening. Here we present procedures on how to upgrade BAC clones by serial recombineering cloning to build both selectable and tagged BAC transgenes, for selection transgenesis and functional gene analysis, respectively. We illustrate these procedures using a BAC clone encompassing the gene encoding the synaptic vesicle protein, cysteine string protein. We demonstrate that the modified BAC clone, serially recombineered with a selectable marker for selection transgenesis and an N-terminal green fluorescent protein tag for gene expression analysis, is functional by showing the expression pattern obtained after successful selection transgenesis. The protocols cover: (1) cloning and preparation of the recombineering templates needed for serial recombineering cloning to incorporate selectable markers and protein tags; (2) preparing electrocompetent cells needed to perform serial recombineering cloning; and (3) the serial recombineering workflow to generate both selectable and tagged genomic BAC reporter transgenes for selection transgenesis and functional gene analysis in Drosophila melanogaster. The protocols we describe can be easily adapted to incorporate any of four selectable markers, protein tags, or any other modification for structure-function analysis of the genes present within any of the BAC or fosmid clones. A protocol for generating transgenic animals using serially recombineered BAC clones is presented in an accompanying Current Protocols article (Venken, Matinyan, Gonzalez, & Dierick, 2023a). © 2023 Wiley Periodicals LLC. Basic Protocol 1: Cloning and preparation of recombineering templates used for serial recombineering cloning. Basic Protocol 2: Making electrocompetent cells of the bacterial strains used to perform serial recombineering cloning or induction of plasmid copy number. Basic Protocol 3: Serial recombineering cloning to generate both selectable and tagged genomic P[acman] BAC reporter transgenes for selection transgenesis and gene expression analysis in Drosophila melanogaster.

Biologically informed deep learning to query gene programs in single-cell atlases

The increasing availability of large-scale single-cell atlases has enabled the detailed description of cell states. In parallel, advances in deep learning allow rapid analysis of newly generated query datasets by mapping them into reference atlases. However, existing data transformations learned to map query data are not easily explainable using biologically known concepts such as genes or pathways. Here we propose expiMap, a biologically informed deep-learning architecture that enables single-cell reference mapping. ExpiMap learns to map cells into biologically understandable components representing known 'gene programs'. The activity of each cell for a gene program is learned while simultaneously refining them and learning de novo programs. We show that expiMap compares favourably to existing methods while bringing an additional layer of interpretability to integrative single-cell analysis. Furthermore, we demonstrate its applicability to analyse single-cell perturbation responses in different tissues and species and resolve responses of patients who have coronavirus disease 2019 to different treatments across cell types.

Lymphedema as first clinical presentation of Cantu Syndrome: reversed phenotyping after identification of gain-of-function variant in ABCC9

Cantu Syndrome (CS), [OMIM #239850] is characterized by hypertrichosis, osteochondrodysplasia, and cardiomegaly. CS is caused by gain-of-function (GOF) variants in the KCNJ8 or ABCC9 genes that encode pore-forming Kir6.1 and regulatory SUR2 subunits of ATP-sensitive potassium (KATP) channels. Many subjects with CS also present with the complication of lymphedema. A previously uncharacterized, heterozygous ABCC9 variant, p.(Leu1055_Glu1058delinsPro), termed indel1055, was identified in an individual diagnosed with idiopathic lymphedema. The variant was introduced into the equivalent position of rat SUR2A, and inside-out patches were used to characterize the KATP channels formed by Kir6.2 and WT or mutant SUR2A subunits coexpressed in Cosm6 cells. The indel1055 variant causes gain-of-function of the channel, with an increase of the IC50 for ATP inhibition compared to WT. Retrospective consideration of this individual reveals clear features of Cantu Syndrome. An additional heterozygous ABCC9 variant, p.(Ile419Thr), was identified in a second individual diagnosed with lymphedema. In this case, there were no additional features consistent with CS, and the properties of p.(Ile416Thr) (the corresponding mutation in rat SUR2A)--containing channels were not different from WT. This proof-of-principle study shows that idiopathic lymphedema may actually be a first presentation of otherwise unrecognized Cantu Syndrome, but molecular phenotyping of identified variants is necessary to confirm relevance.

An RNA-seq time series of the medaka pituitary gland during sexual maturation

Directing both organismal homeostasis and physiological adaptation, the pituitary is a key endocrine gland in all vertebrates. One of its major tasks is to coordinate sexual maturation through the production and release of hormones stimulating gonad development. In order to study its developmental dynamics in the model fish medaka (Oryzias latipes), we sampled both the pituitary and the ovaries of 68 female fish. Of these, 55 spanned the entire course of sexual maturation from prepubertal juveniles to spawning adults. An additional 13 showed either considerably faster or slower growth and development than the majority of fish. We used histological examination of the ovaries to determine a histological maturation stage, and analyzed the pituitary glands using RNA-seq optimized for low input. Taken together, these data reveal the timing of hormone production priorities, and form a comprehensive resource for the study of their regulation.

Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review

Cancer is a term that denotes a group of diseases caused by the abnormal growth of cells that can spread in different parts of the body. According to the World Health Organization (WHO), cancer is the second major cause of death after cardiovascular diseases. Gene expression can play a fundamental role in the early detection of cancer, as it is indicative of the biochemical processes in tissue and cells, as well as the genetic characteristics of an organism. Deoxyribonucleic acid (DNA) microarrays and ribonucleic acid (RNA)-sequencing methods for gene expression data allow quantifying the expression levels of genes and produce valuable data for computational analysis. This study reviews recent progress in gene expression analysis for cancer classification using machine learning methods. Both conventional and deep learning-based approaches are reviewed, with an emphasis on the application of deep learning models due to their comparative advantages for identifying gene patterns that are distinctive for various types of cancers. Relevant works that employ the most commonly used deep neural network architectures are covered, including multi-layer perceptrons, as well as convolutional, recurrent, graph, and transformer networks. This survey also presents an overview of the data collection methods for gene expression analysis and lists important datasets that are commonly used for supervised machine learning for this task. Furthermore, we review pertinent techniques for feature engineering and data preprocessing that are typically used to handle the high dimensionality of gene expression data, caused by a large number of genes present in data samples. The paper concludes with a discussion of future research directions for machine learning-based gene expression analysis for cancer classification.

Genome-wide characterization of the PP2C gene family in peanut (Arachis hypogaea L.) and the identification of candidate genes involved in salinity-stress response

Plant protein phosphatase 2C (PP2C) play important roles in response to salt stress by influencing metabolic processes, hormone levels, growth factors, etc. Members of the PP2C family have been identified in many plant species. However, they are rarely reported in peanut. In this study, 178 PP2C genes were identified in peanut, which were unevenly distributed across the 20 chromosomes, with segmental duplication in 78 gene pairs. AhPP2Cs could be divided into 10 clades (A-J) by phylogenetic analysis. AhPP2Cs had experienced segmental duplications and strong purifying selection pressure. 22 miRNAs from 14 different families were identified, targeting 57 AhPP2C genes. Gene structures and motifs analysis exhibited PP2Cs in subclades AI and AII had high structural and functional similarities. Phosphorylation sites of AhPP2C45/59/134/150/35/121 were predicted in motifs 2 and 4, which located within the catalytic site at the C-terminus. We discovered multiple MYB binding factors and ABA response elements in the promoter regions of the six genes (AhPP2C45/59/134/150/35/121) by cis-elements analysis. GO and KEGG enrichment analysis confirmed AhPP2C-A genes in protein binding, signal transduction, protein modification process response to abiotic stimulus through environmental information processing. Based on RNA-Seq data of 22 peanut tissues, clade A AhPP2Cs showed a varying degree of tissue specificity, of which, AhPP2C35 and AhPP2C121 specifically expressed in seeds, while AhPP2C45/59/134/150 expressed in leaves and roots. qRT-PCR indicated that AhPP2C45 and AhPP2C134 displayed significantly up-regulated expression in response to salt stress. These results indicated that AhPP2C45 and AhPP2C134 could be candidate PP2Cs conferring salt tolerance. These results provide further insights into the peanut PP2C gene family and indicate PP2Cs potentially involved in the response to salt stress, which can now be further investigated in peanut breeding efforts to obtain cultivars with improved salt tolerance.

Highly efficient and robust π-FISH rainbow for multiplexed in situ detection of diverse biomolecules

In the unprecedented single-cell sequencing and spatial multiomics era of biology, fluorescence in situ hybridization (FISH) technologies with higher sensitivity and robustness, especially for detecting short RNAs and other biomolecules, are greatly desired. Here, we develop the robust multiplex π-FISH rainbow method to detect diverse biomolecules (DNA, RNA, proteins, and neurotransmitters) individually or simultaneously with high efficiency. This versatile method is successfully applied to detect gene expression in different species, from microorganisms to plants and animals. Furthermore, we delineate the landscape of diverse neuron subclusters by decoding the spatial distribution of 21 marker genes via only two rounds of hybridization. Significantly, we combine π-FISH rainbow with hybridization chain reaction to develop π-FISH+ technology for short nucleic acid fragments, such as microRNA and prostate cancer anti-androgen therapy-resistant marker ARV7 splicing variant in circulating tumour cells from patients. Our study provides a robust biomolecule in situ detection technology for spatial multiomics investigation and clinical diagnosis.

Data mining combines bioinformatics discover immunoinfiltration-related gene SERPINE1 as a biomarker for diagnosis and prognosis of stomach adenocarcinoma

Stomach adenocarcinoma (STAD) is a type of cancer which often at itsadvanced stage apon diagnosis and mortality in clinical practice. Several factors influencethe prognosis of STAD, including the expression and regulation of immune cells in the tumor microenvironment. We here investigated the biomarkers related to the diagnosis and prognosis of gastric cancer, hoping to provide insights for the diagnosis and treatment of gastric cancer in the future. STAD and normal patient RNA sequencing data sets were accessed from the cancer genome atlas (TCGA database). Differential genes were determined and obtained by using the R package DESeq2. The stromal, immune, and ESTIMATE scores are calculated by the ESTIMATE algorithm, followed by the modular genes screening using the R package WGCNA. Subsequently, the intersection between the modular gene and the differential gene was taken and the STRING database was used for PPI network module analysis. The R packages clusterProfiler, enrichplot, and ggplot2 were used for GO and KEGG enrichment analysis. Cox regression analysis was used to screen survival-related genes, and finally, the R package Venn Diagram was used to take the intersection and obtain 7 hub genes. The time-dependent ROC curve and Kaplan-Meier survival curve were used to find the SERPINE1 gene, which plays a critical role in prognosis. Finally, the expression pattern, clinical characteristics, and regulatory mechanism of SERPINE1 were analyzed in STAD. We revealed that the expression of SERPINE1 was significantly increased in the samples from STAD compared with normal samples. Cox regression, time-dependent ROC, and Kaplan-Meier survival analyses demonstrated that SERPINE1 was significantly related to the adverse prognosis of STAD patients. The expression of SERPINE1 increased with the progression of T, N, and M classification of the tumor. In addition, the results of immune infiltration analysis indicated that the immune cells' expression were higher in high SERPINE1 expression group than that in low SERPINE1 expression group, including CD4⁺ T cells, B cells, CD8⁺ T cells, macrophages, neutrophils and other immune cells. SERPINE1 was closely related to immune cells in the STAD immune microenvironment and had a synergistic effect with the immune checkpoints PD1 and PD-L1. In conclusion, we proved that SERPINE1 is a promising prognostic and diagnostic biomarker for STAD and a potential target for immunotherapy.

Statistical Power Analysis for Designing Bulk, Single-Cell, and Spatial Transcriptomics Experiments: Review, Tutorial, and Perspectives

Gene expression profiling technologies have been used in various applications such as cancer biology. The development of gene expression profiling has expanded the scope of target discovery in transcriptomic studies, and each technology produces data with distinct characteristics. In order to guarantee biologically meaningful findings using transcriptomic experiments, it is important to consider various experimental factors in a systematic way through statistical power analysis. In this paper, we review and discuss the power analysis for three types of gene expression profiling technologies from a practical standpoint, including bulk RNA-seq, single-cell RNA-seq, and high-throughput spatial transcriptomics. Specifically, we describe the existing power analysis tools for each research objective for each of the bulk RNA-seq and scRNA-seq experiments, along with recommendations. On the other hand, since there are no power analysis tools for high-throughput spatial transcriptomics at this point, we instead investigate the factors that can influence power analysis.

Global analysis of the association between pig muscle fatty acid composition and gene expression using RNA-Seq

Fatty acids (FAs) play an essential role as mediators of cell signaling and signal transduction, affecting metabolic homeostasis and determining meat quality in pigs. However, FAs are transformed by the action of several genes, such as those encoding desaturases and elongases of FAs in lipogenic tissues. The aim of the current work was to identify candidate genes, biological processes, and pathways involved in the modulation of intramuscular FA profile from longissimus dorsi muscle. FA profile by gas chromatography of methyl esters and gene expression by RNA-Seq were determined in 129 Iberian × Duroc backcrossed pigs. An association analysis between the muscle transcriptome and its FA profile was performed, followed by a concordance and functional analysis. Overall, a list of well-known (e.g., PLIN1, LEP, ELOVL6, SC5D, NCOA2, ACSL1, MDH1, LPL, LGALS12, TFRC, GOT1, and FBP1) and novel (e.g., TRARG1, TANK, ENSSSCG00000011196, and ENSSSCG00000038429) candidate genes was identified, either in association with specific or several FA traits. Likewise, several of these genes belong to biological processes and pathways linked to energy, lipid, and carbohydrate metabolism, which seem determinants in the modulation of FA compositions. This study can contribute to elucidate the complex relationship between gene expression and FA profile in pig muscle.

Identification and characterization of Dof genes in Cerasus humilis

Introduction

Dof genes encode plant-specific transcription factors, which regulate various biological processes such as growth, development, and secondary metabolite accumulation.

Methods

We conducted whole-genome analysis of Chinese dwarf cherry (Cerasus humilis) to identify ChDof genes and characterize the structure, motif composition, cis-acting elements, chromosomal distribution, and collinearity of these genes as well as the physical and chemical properties, amino acid sequences, and phylogenetic evolution of the encoded proteins.

Results

The results revealed the presence of 25 ChDof genes in C. humilis genome. All 25 ChDof genes could be divided into eight groups, and the members of the same group had similar motif arrangement and intron-exon structure. Promoter analysis showed that cis-acting elements responsive to abscisic acid, low temperature stress, and light were dominant. Transcriptome data revealed that most ChDof genes exhibited tissue-specific expression. Then, we performed by qRT-PCR to analyze the expression patterns of all 25 ChDof genes in fruit during storage. The results indicated that these genes exhibited different expression patterns, suggesting that they played an important role in fruit storage.

Discussion

The results of this study provide a basis for further investigation of the biological function of Dof genes in C. humilis fruit.

Multiplexed transcriptome discovery of RNA-binding protein binding sites by antibody-barcode eCLIP

Ultraviolet crosslinking and immunoprecipitation (CLIP) methodologies enable the identification of RNA binding sites of RNA-binding proteins (RBPs). Despite improvements in the library preparation of RNA fragments, the enhanced CLIP (eCLIP) protocol requires 4 days of hands-on time and lacks the ability to process several RBPs in parallel. We present a new method termed antibody-barcode eCLIP that utilizes DNA-barcoded antibodies and proximity ligation of the DNA oligonucleotides to RBP-protected RNA fragments to interrogate several RBPs simultaneously. We observe performance comparable with that of eCLIP with the advantage of dramatically increased scaling while maintaining the same material requirement of a single eCLIP experiment.

Nonnegative spatial factorization applied to spatial genomics

Nonnegative matrix factorization (NMF) is widely used to analyze high-dimensional count data because, in contrast to real-valued alternatives such as factor analysis, it produces an interpretable parts-based representation. However, in applications such as spatial transcriptomics, NMF fails to incorporate known structure between observations. Here, we present nonnegative spatial factorization (NSF), a spatially-aware probabilistic dimension reduction model based on transformed Gaussian processes that naturally encourages sparsity and scales to tens of thousands of observations. NSF recovers ground truth factors more accurately than real-valued alternatives such as MEFISTO in simulations, and has lower out-of-sample prediction error than probabilistic NMF on three spatial transcriptomics datasets from mouse brain and liver. Since not all patterns of gene expression have spatial correlations, we also propose a hybrid extension of NSF that combines spatial and nonspatial components, enabling quantification of spatial importance for both observations and features. A TensorFlow implementation of NSF is available from https://github.com/willtownes/nsf-paper .

Genomic characterization and expression analysis of TCP transcription factors in Setaria italica and Setaria viridis

The plant-specific TCP transcription factor plays important roles in plant development and environment adaptation. Setaria italica and Setaria viridis, the C4 model plants, can grow on drought or arid soils. However, there is no systematic information about the genomic dissection and the expression of Setaria TCP genes. A total of 22 TCP genes were both identified from S. italica and S. viridis genomes. They all contained bHLH domain and were grouped into three main clades (PCF, CIN, and CYC/TB1). The TCP genes in the same clades shared similar gene structures. Cis-element in the TCP promoter regions were analyzed and associated with hormones and stress responsiveness. Ten TCP genes were predicted to be targets of miRNA319. Moreover, gene ontology analysis indicated three SiTCP and three SvTCP genes were involved in the regulation of shoot development, and SiTCP16/SvTCP16 were clustered together with tillering controlling gene TB1. The TCP genes were differentially expressed in the organs, but SiTCP/SvTCP orthologs shared similar expression patterns. Ten SiTCP members were downregulated under drought or salinity stresses, indicating they may play regulatory roles in abiotic stresses. The study provides detailed information regarding Setaria TCP genes, providing the theoretical basis for agricultural applications.

The sirtuin family in health and disease

Sirtuins (SIRTs) are nicotine adenine dinucleotide(+)-dependent histone deacetylases regulating critical signaling pathways in prokaryotes and eukaryotes, and are involved in numerous biological processes. Currently, seven mammalian homologs of yeast Sir2 named SIRT1 to SIRT7 have been identified. Increasing evidence has suggested the vital roles of seven members of the SIRT family in health and disease conditions. Notably, this protein family plays a variety of important roles in cellular biology such as inflammation, metabolism, oxidative stress, and apoptosis, etc., thus, it is considered a potential therapeutic target for different kinds of pathologies including cancer, cardiovascular disease, respiratory disease, and other conditions. Moreover, identification of SIRT modulators and exploring the functions of these different modulators have prompted increased efforts to discover new small molecules, which can modify SIRT activity. Furthermore, several randomized controlled trials have indicated that different interventions might affect the expression of SIRT protein in human samples, and supplementation of SIRT modulators might have diverse impact on physiological function in different participants. In this review, we introduce the history and structure of the SIRT protein family, discuss the molecular mechanisms and biological functions of seven members of the SIRT protein family, elaborate on the regulatory roles of SIRTs in human disease, summarize SIRT inhibitors and activators, and review related clinical studies.

Multi-omics identify falling LRRC15 as a COVID-19 severity marker and persistent pro-thrombotic signals in convalescence

Patients with end-stage kidney disease (ESKD) are at high risk of severe COVID-19. Here, we perform longitudinal blood sampling of ESKD haemodialysis patients with COVID-19, collecting samples pre-infection, serially during infection, and after clinical recovery. Using plasma proteomics, and RNA-sequencing and flow cytometry of immune cells, we identify transcriptomic and proteomic signatures of COVID-19 severity, and find distinct temporal molecular profiles in patients with severe disease. Supervised learning reveals that the plasma proteome is a superior indicator of clinical severity than the PBMC transcriptome. We show that a decreasing trajectory of plasma LRRC15, a proposed co-receptor for SARS-CoV-2, is associated with a more severe clinical course. We observe that two months after the acute infection, patients still display dysregulated gene expression related to vascular, platelet and coagulation pathways, including PF4 (platelet factor 4), which may explain the prolonged thrombotic risk following COVID-19.

Long non-coding RNAs as the regulatory hubs in rice response to salt stress

Salinity seriously constrains growth and fertility of rice worldwide. Long non-coding RNAs (lncRNAs) play crucial roles in plant abiotic stress response. However, salt responsive lncRNAs are poorly understood in rice. Herein, salt responsive lncRNAs (DE-lncRNAs) were identified in FL478 (salt tolerant) compared to its susceptible parent (IR29) using RNA-seq in root tissues at seedling stage. In FL478 and IR29, 8724 and 9235 transcripts with length of > 200 bp were nominated as potential lncRNAs, respectively. Rigorous filtering left four (in FL478) and nine (in IR29) DE-lncRNAs with only 2 DE-lncRNAs in common. ATAC-seq data showed that the genomic regions of all four lncRNAs in FL478 and 6/9 in IR29 are significantly accessible for transcription. Weighted correlation network analysis (WGCNA) revealed that lncRNA.2-FL was highly correlated with 173 mRNAs as trans-targets and a gene encoding pentatricopeptide repeat (PPR) protein was predicted as cis-target of lncRNA.2-FL. In silico mutagenesis analysis proposed the same transcription factor binding sites (TFBSs) in vicinity of the trans- and cis-regulatory target genes of lncRNA.2-FL, which significantly affect their transcription start site (TSS). This study provides new insights into involvement of the DE-lncRNAs in rice response to salt stress. Among them, lncRNA.2-FL may play a significant regulatory role in the salt stress tolerance of FL478.

Spatial transcriptomics landscape of lesions from non-communicable inflammatory skin diseases

Abundant heterogeneous immune cells infiltrate lesions in chronic inflammatory diseases and characterization of these cells is needed to distinguish disease-promoting from bystander immune cells. Here, we investigate the landscape of non-communicable inflammatory skin diseases (ncISD) by spatial transcriptomics resulting in a large repository of 62,000 spatially defined human cutaneous transcriptomes from 31 patients. Despite the expected immune cell infiltration, we observe rather low numbers of pathogenic disease promoting cytokine transcripts (IFNG, IL13 and IL17A), i.e. >125 times less compared to the mean expression of all other genes over lesional skin sections. Nevertheless, cytokine expression is limited to lesional skin and presented in a disease-specific pattern. Leveraging a density-based spatial clustering method, we identify specific responder gene signatures in direct proximity of cytokines, and confirm that detected cytokine transcripts initiate amplification cascades of up to thousands of specific responder transcripts forming localized epidermal clusters. Thus, within the abundant and heterogeneous infiltrates of ncISD, only a low number of cytokine transcripts and their translated proteins promote disease by initiating an inflammatory amplification cascade in their local microenvironment.