Expression Atlas update: gene and protein expression in multiple species

Therapeutic Effects of Exosomal miRNA-4731-5p from Adipose Tissue-Derived Stem Cells on Human Glioblastoma Cells

BACKGROUND AND AIM

Several microRNAs (miRNAs) are differentially expressed and serve as tumor suppressors in glioblastoma (GBM). The present study aimed to elucidate the function of exosomal microRNA-4731-5p (miR-4731-5p) from adipose tissue-derived mesenchymal stem cells (AD-MSCs) in the activity of human GBM cell lines.

METHOD

First, GBM-related miRNAs, their expression, and potential target genes and cytokines of miR-4731-5p were identified using bioinformatic datasets. Subsequently, purified AD-MSCs were transfected with a miRNA-4731-5p expression plasmid, and exosomes were isolated and characterized. Next, the transfection process was confirmed and the 50% inhibitory concentration (IC50) of the overexpressed exosomal miRNA-4731-5p was inhibited for cancer cells. The probable anticancer action of exosomal miRNA-4731-5p on U-87 and U-251 GBM cell lines was verified by flow cytometry, DAPI staining, cell cycle, real-time PCR, and wound healing assays.

RESULTS

A concentration of 50 ng/mL of miRNA-4731-5p-transfected exosomes was the safe dose for anticancer settings. The results showed that the exosomal miR-4731-5p exerted an inhibitory effect on the cell cycle and migration and induced apoptosis in GBM cell lines by regulating the phosphoinositide-3-kinase-AKT (PI3K-AKT) and nuclear factor-kB (NF-kB) signaling pathways.

CONCLUSION

This study reveals that the expression of exosomal miRNA-4731-5p has favorable antitumor properties for the treatment of GBM cell lines and may be a fundamental therapeutic option for this type of brain tumor.

ASACO: Automatic and Serial Analysis of CO-expression to discover gene modifiers with potential use in drug repurposing

Massive gene expression analyses are widely used to find differentially expressed genes under specific conditions. The results of these experiments are often available in public databases that are undergoing a growth similar to that of molecular sequence databases in the past. This now allows novel secondary computational tools to emerge that use such information to gain new knowledge. If several genes have a similar expression profile across heterogeneous transcriptomics experiments, they could be functionally related. These associations are usually useful for the annotation of uncharacterized genes. In addition, the search for genes with opposite expression profiles is useful for finding negative regulators and proposing inhibitory compounds in drug repurposing projects. Here we present a new web application, Automatic and Serial Analysis of CO-expression (ASACO), which has the potential to discover positive and negative correlator genes to a given query gene, based on thousands of public transcriptomics experiments. In addition, examples of use are presented, comparing with previous contrasted knowledge. The results obtained propose ASACO as a useful tool to improve knowledge about genes associated with human diseases and noncoding genes. ASACO is available at http://www.bioinfocabd.upo.es/asaco/.

Dopamine Receptors and TAAR1 Functional Interaction Patterns in the Duodenum Are Impaired in Gastrointestinal Disorders

Currently, there is a growing amount of evidence for the involvement of dopamine receptors and the functionally related trace amine-associated receptor, TAAR1, in upper intestinal function. In the present study, we analyzed their expression in the duodenum using publicly accessible transcriptomic data. We revealed the expression of DRD1, DRD2, DRD4, DRD5, and TAAR1 genes in different available datasets. The results of the gene ontology (GO) enrichment analysis for DRD2 and especially TAAR1 co-expressed genes were consistent with the previously described localization of D2 and TAAR1 in enteric neurons and secretory cells, respectively. Considering that co-expressed genes are more likely to be involved in the same biological processes, we analyzed genes that are co-expressed with TAAR1, DRD2, DRD4, and DRD5 genes in healthy mucosa and duodenal samples from patients with functional dyspepsia (FD) or diabetes-associated gastrointestinal symptoms. Both pathological conditions showed a deregulation of co-expression patterns, with a high discrepancy between DRDs and TAAR1 co-expressed gene sets in normal tissues and patients' samples and a loss of these genes' functional similarity. Meanwhile, we discovered specific changes in co-expression patterns that may suggest the involvement of TAAR1 and D5 receptors in pathologic or compensatory processes in FD or diabetes accordingly. Despite our findings suggesting the possible role of TAAR1 and dopamine receptors in functional diseases of the upper intestine, underlying mechanisms need experimental exploration and validation.

Foundation models in molecular biology

Determining correlations between molecules at various levels is an important topic in molecular biology. Large language models have demonstrated a remarkable ability to capture correlations from large amounts of data in the field of natural language processing as well as image generation, and correlations captured from data using large language models can also be applicable to solving a wide range of specific tasks, hence large language models are also referred to as foundation models. The massive amount of data that exists in the field of molecular biology provides an excellent basis for the development of foundation models, and the recent emergence of foundation models in the field of molecular biology has really pushed the entire field forward. We summarize the foundation models developed based on RNA sequence data, DNA sequence data, protein sequence data, single-cell transcriptome data, and spatial transcriptome data respectively, and further discuss the research directions for the development of foundation models in molecular biology.

Beyond benchmarking and towards predictive models of dataset-specific single-cell RNA-seq pipeline performance

BACKGROUND

The advent of single-cell RNA-sequencing (scRNA-seq) has driven significant computational methods development for all steps in the scRNA-seq data analysis pipeline, including filtering, normalization, and clustering. The large number of methods and their resulting parameter combinations has created a combinatorial set of possible pipelines to analyze scRNA-seq data, which leads to the obvious question: which is best? Several benchmarking studies compare methods but frequently find variable performance depending on dataset and pipeline characteristics. Alternatively, the large number of scRNA-seq datasets along with advances in supervised machine learning raise a tantalizing possibility: could the optimal pipeline be predicted for a given dataset?

RESULTS

Here, we begin to answer this question by applying 288 scRNA-seq analysis pipelines to 86 datasets and quantifying pipeline success via a range of measures evaluating cluster purity and biological plausibility. We build supervised machine learning models to predict pipeline success given a range of dataset and pipeline characteristics. We find that prediction performance is significantly better than random and that in many cases pipelines predicted to perform well provide clustering outputs similar to expert-annotated cell type labels. We identify characteristics of datasets that correlate with strong prediction performance that could guide when such prediction models may be useful.

CONCLUSIONS

Supervised machine learning models have utility for recommending analysis pipelines and therefore the potential to alleviate the burden of choosing from the near-infinite number of possibilities. Different aspects of datasets influence the predictive performance of such models which will further guide users.

Structural variants in linkage disequilibrium with GWAS-significant SNPs

With the recent expansion of structural variant identification in the human genome, understanding the role of these impactful variants in disease architecture is critically important. Currently, a large proportion of genome-wide-significant genome-wide association study (GWAS) single nucleotide polymorphisms (SNPs) are functionally unresolved, raising the possibility that some of these SNPs are associated with disease through linkage disequilibrium with causal structural variants. Hence, understanding the linkage disequilibrium between newly discovered structural variants and statistically significant SNPs may provide a resource for further investigation into disease-associated regions in the genome. Here we present a resource cataloging structural variant-significant SNP pairs in high linkage disequilibrium. The database is composed of (i) SNPs that have exhibited genome-wide significant association with traits, primarily disease phenotypes, (ii) newly released structural variants (SVs), and (iii) linkage disequilibrium values calculated from unphased data. All data files including those detailing SV and GWAS SNP associations and results of GWAS-SNP-SV pairs are available at the SV-SNP LD Database and can be accessed at 'https://github.com/hliang-SchrodiLab/SV_SNPs. Our analysis results represent a useful fine mapping tool for interrogating SVs in linkage disequilibrium with disease-associated SNPs. We anticipate that this resource may play an important role in subsequent studies which investigate incorporating disease causing SVs into disease risk prediction models.

Decoding the tumour-modulatory roles of LIMK2

LIM domains kinase 2 (LIMK2) is a 72 kDa protein that regulates actin and cytoskeleton reorganization. Once phosphorylated by its upstream activator (ROCK1), LIMK2 can phosphorylate cofilin to inactivate it. This relieves the levering stress on actin and allows polymerization to occur. Actin rearrangement is essential in regulating cell cycle progression, apoptosis, and migration. Dysregulation of the ROCK1/LIMK2/cofilin pathway has been reported to link to the development of various solid cancers such as breast, lung, and prostate cancer and liquid cancer like leukemia. This review aims to assess the findings from multiple reported in vitro, in vivo, and clinical studies on the potential tumour-regulatory role of LIMK2 in different human cancers. The findings of the selected literature unraveled that activated AKT, EGF, and TGF-β pathways can upregulate the activities of the ROCK1/LIMK2/cofilin pathway. Besides cofilin, LIMK2 can modulate the cellular levels of other proteins, such as TPPP1, to promote microtubule polymerization. The tumour suppressor protein p53 can transactivate LIMK2b, a splice variant of LIMK2, to induce cell cycle arrest and allow DNA repair to occur before the cell enters the next phase of the cell cycle. Additionally, several non-coding RNAs, such as miR-135a and miR-939-5p, could also epigenetically regulate the expression of LIMK2. Since the expression of LIMK2 is dysregulated in several human cancers, measuring the tissue expression of LIMK2 could potentially help diagnose cancer and predict patient prognosis. As LIMK2 could play tumour-promoting and tumour-inhibiting roles in cancer development, more investigation should be conducted to carefully evaluate whether introducing a LIMK2 inhibitor in cancer patients could slow cancer progression without posing clinical harms.

Integrated Proteomics Analysis of Baseline Protein Expression in Pig Tissues

The availability of an increasingly large amount of public proteomics data sets presents an opportunity for performing combined analyses to generate comprehensive organism-wide protein expression maps across different organisms and biological conditions. Sus scrofa, a domestic pig, is a model organism relevant for food production and for human biomedical research. Here, we reanalyzed 14 public proteomics data sets from the PRIDE database coming from pig tissues to assess baseline (without any biological perturbation) protein abundance in 14 organs, encompassing a total of 20 healthy tissues from 128 samples. The analysis involved the quantification of protein abundance in 599 mass spectrometry runs. We compared protein expression patterns among different pig organs and examined the distribution of proteins across these organs. Then, we studied how protein abundances were compared across different data sets and studied the tissue specificity of the detected proteins. Of particular interest, we conducted a comparative analysis of protein expression between pig and human tissues, revealing a high degree of correlation in protein expression among orthologs, particularly in brain, kidney, heart, and liver samples. We have integrated the protein expression results into the Expression Atlas resource for easy access and visualization of the protein expression data individually or alongside gene expression data.

Characterization of dUTPase expression in mouse postnatal development and adult neurogenesis

The enzyme dUTPase has an essential role in maintaining genomic integrity. In mouse, nuclear and mitochondrial isoforms of the enzyme have been described. Here we present the isoform-specific mRNA expression levels in different murine organs during development using RT-qPCR. In this study, we analyzed organs of 14.5-day embryos and of postnatal 2-, 4-, 10-week- and 13-month-old mice. We demonstrate organ-, sex- and developmental stage-specific differences in the mRNA expression levels of both isoforms. We found high mRNA expression level of the nuclear isoform in the embryo brain, and the expression level remained relatively high in the adult brain as well. This was surprising, since dUTPase is known to play an important role in proliferating cells, and mass production of neural cells is completed by adulthood. Thus, we investigated the pattern of the dUTPase protein expression specifically in the adult brain with immunostaining and found that dUTPase is present in the germinative zones, the subventricular and the subgranular zones, where neurogenesis occurs and in the rostral migratory stream where neuroblasts migrate to the olfactory bulb. These novel findings suggest that dUTPase may have a role in cell differentiation and indicate that accurate dTTP biosynthesis can be vital, especially in neurogenesis.

ReactomeGSA: new features to simplify public data reuse

MOTIVATION

ReactomeGSA is part of the Reactome knowledgebase and one of the leading multi-omics pathway analysis platforms. ReactomeGSA provides access to quantitative pathway analysis methods supporting different 'omics data types. Additionally, ReactomeGSA can process different datasets simultaneously, leading to a comparative pathway analysis that can also be performed across different species.

RESULTS

We present a major update to the ReactomeGSA analysis platforms that greatly simplifies the reuse and direct integration of public data. In order to increase the number of available datasets, we developed the new grein_loader Python application that can directly fetch experiments from the GREIN resource. This enabled us to support both EMBL-EBI's Expression Atlas and GEO RNA-seq Experiments Interactive Navigator within ReactomeGSA. To further increase the visibility and simplify the reuse of public datasets, we integrated a novel search function into ReactomeGSA that enables users to search for public datasets across both supported resources. Finally, we completely re-developed ReactomeGSA's web-frontend and R/Bioconductor package to support the new search and loading features, and greatly simplify the use of ReactomeGSA.

AVAILABILITY AND IMPLEMENTATION

The new ReactomeGSA web frontend is available at https://www.reactome.org/gsa with an built-in, interactive tutorial. The ReactomeGSA R package (https://bioconductor.org/packages/release/bioc/html/ReactomeGSA.html) is available through Bioconductor and shipped with detailed documentation and vignettes. The grein_loader Python application is available through the Python Package Index (pypi). The complete source code for all applications is available on GitHub at https://github.com/grisslab/grein_loader and https://github.com/reactome.

Golgi associated RAB2 interactor protein family contributes to murine male fertility to various extents by assuring correct morphogenesis of sperm heads

Sperm heads contain not only the nucleus but also the acrosome which is a distinctive cap-like structure located anterior to the nucleus and is derived from the Golgi apparatus. The Golgi Associated RAB2 Interactors (GARINs; also known as FAM71) protein family shows predominant expression in the testis and all possess a RAB2-binding domain which confers binding affinity to RAB2, a small GTPase that is responsible for membrane transport and vesicle trafficking. Our previous study showed that GARIN1A and GARIN1B are important for acrosome biogenesis and that GARIN1B is indispensable for male fertility in mice. Here, we generated KO mice of other Garins, namely Garin2, Garin3, Garin4, Garin5a, and Garin5b (Garin2-5b). Using computer-assisted morphological analysis, we found that the loss of each Garin2-5b resulted in aberrant sperm head morphogenesis. While the fertilities of Garin2-/- and Garin4-/- males are normal, Garin5a-/- and Garin5b-/- males are subfertile, and Garin3-/- males are infertile. Further analysis revealed that Garin3-/- males exhibited abnormal acrosomal morphology, but not as severely as Garin1b-/- males; instead, the amounts of membrane proteins, particularly ADAM family proteins, decreased in Garin3 KO spermatozoa. Moreover, only Garin4 KO mice exhibit vacuoles in the sperm head. These results indicate that GARINs assure correct head morphogenesis and some members of the GARIN family function distinctively in male fertility.

Single-cell transcriptomics dissecting the development and evolution of nervous system in insects

Insects can display a vast repertoire of complex and adaptive behaviors crucial for survival and reproduction. Yet, how the neural circuits underlying insect behaviors are assembled throughout development and remodeled during evolution remains largely obscure. The advent of single-cell transcriptomics has opened new paths to illuminate these historically intractable questions. Insect behavior is governed by its brain, whose functional complexity is realized through operations across multiple levels, from the molecular and cellular to the circuit and organ. Single-cell transcriptomics enables dissecting brain functions across all these levels and allows tracking regulatory dynamics throughout development and under perturbation. In this review, we mainly focus on the achievements of single-cell transcriptomics in dissecting the molecular and cellular architectures of nervous systems in representative insects, then discuss its applications in tracking the developmental trajectory and functional evolution of insect brains.

Aplp1 interacts with Lag3 to facilitate transmission of pathologic α-synuclein

Pathologic α-synuclein (α-syn) spreads from cell-to-cell, in part, through binding to the lymphocyte-activation gene 3 (Lag3). Here we report that amyloid β precursor-like protein 1 (Aplp1) interacts with Lag3 that facilitates the binding, internalization, transmission, and toxicity of pathologic α-syn. Deletion of both Aplp1 and Lag3 eliminates the loss of dopaminergic neurons and the accompanying behavioral deficits induced by α-syn preformed fibrils (PFF). Anti-Lag3 prevents the internalization of α-syn PFF by disrupting the interaction of Aplp1 and Lag3, and blocks the neurodegeneration induced by α-syn PFF in vivo. The identification of Aplp1 and the interplay with Lag3 for α-syn PFF induced pathology deepens our insight about molecular mechanisms of cell-to-cell transmission of pathologic α-syn and provides additional targets for therapeutic strategies aimed at preventing neurodegeneration in Parkinson's disease and related α-synucleinopathies.

Modification of Gas6 Protein in the Brain by a Functional Endogenous Tissue Vitamin K Cycle

The TAM receptor ligand Gas6 is known for regulating inflammatory and immune pathways in various organs including the brain. Gas6 becomes fully functional through the post-translational modification of multiple glutamic acid residues into γ-carboxyglutamic in a vitamin K-dependent manner. However, the significance of this mechanism in the brain is not known. We report here the endogenous expression of multiple components of the vitamin K cycle within the mouse brain at various ages as well as in distinct brain glial cells. The brain expression of all genes was increased in the postnatal ages, mirroring their profiles in the liver. In microglia, the proinflammatory agent lipopolysaccharide caused the downregulation of all key vitamin K cycle genes. A secreted Gas6 protein was detected in the medium of both mouse cerebellar slices and brain glial cell cultures. Furthermore, the endogenous Gas6 γ-carboxylation level was abolished through incubation with the vitamin K antagonist warfarin and could be restored through co-incubation with vitamin K1. Finally, the γ-carboxylation level of the Gas6 protein within the brains of warfarin-treated rats was found to be significantly reduced ex vivo compared to the control brains. In conclusion, we demonstrated for the first time the existence of a functional vitamin K cycle within rodent brains, which regulates the functional modification of endogenous brain Gas6. These results indicate that vitamin K is an important nutrient for the brain. Furthermore, the measurement of vitamin K-dependent Gas6 functionality could be an indicator of homeostatic or disease mechanisms in the brain, such as in neurological disorders where Gas6/TAM signalling is impaired.

Detection of circulating tumor cells by means of machine learning using Smart-Seq2 sequencing

Circulating tumor cells (CTCs) are tumor cells that separate from the solid tumor and enter the bloodstream, which can cause metastasis. Detection and enumeration of CTCs show promising potential as a predictor for prognosis in cancer patients. Furthermore, single-cells sequencing is a technique that provides genetic information from individual cells and allows to classify them precisely and reliably. Sequencing data typically comprises thousands of gene expression reads per cell, which artificial intelligence algorithms can accurately analyze. This work presents machine-learning-based classifiers that differentiate CTCs from peripheral blood mononuclear cells (PBMCs) based on single cell RNA sequencing data. We developed four tree-based models and we trained and tested them on a dataset consisting of Smart-Seq2 sequenced data from primary tumor sections of breast cancer patients and PBMCs and on a public dataset with manually annotated CTC expression profiles from 34 metastatic breast patients, including triple-negative breast cancer. Our best models achieved about 95% balanced accuracy on the CTC test set on per cell basis, correctly detecting 133 out of 138 CTCs and CTC-PBMC clusters. Considering the non-invasive character of the liquid biopsy examination and our accurate results, we can conclude that our work has potential application value.

The Hippo kinases control inflammatory Hippo signaling and restrict bacterial infection in phagocytes

The Hippo kinases MST1 and MST2 initiate a highly conserved signaling cascade called the Hippo pathway that limits organ size and tumor formation in animals. Intriguingly, pathogens hijack this host pathway during infection, but the role of MST1/2 in innate immune cells against pathogens is unclear. In this report, we generated Mst1/2 knockout macrophages to investigate the regulatory activities of the Hippo kinases in immunity. Transcriptomic analyses identified differentially expressed genes (DEGs) regulated by MST1/2 that are enriched in biological pathways, such as systemic lupus erythematosus, tuberculosis, and apoptosis. Surprisingly, pharmacological inhibition of the downstream components LATS1/2 in the canonical Hippo pathway did not affect the expression of a set of immune DEGs, suggesting that MST1/2 control these genes via alternative inflammatory Hippo signaling. Moreover, MST1/2 may affect immune communication by influencing the release of cytokines, including TNFα, CXCL10, and IL-1ra. Comparative analyses of the single- and double-knockout macrophages revealed that MST1 and MST2 differentially regulate TNFα release and expression of the immune transcription factor MAF, indicating that the two homologous Hippo kinases individually play a unique role in innate immunity. Notably, both MST1 and MST2 can promote apoptotic cell death in macrophages upon stimulation. Lastly, we demonstrate that the Hippo kinases are critical factors in mammalian macrophages and single-cell amoebae to restrict infection by Legionella pneumophila, Escherichia coli, and Pseudomonas aeruginosa. Together, these results uncover non-canonical inflammatory Hippo signaling in macrophages and the evolutionarily conserved role of the Hippo kinases in the anti-microbial defense of eukaryotic hosts.

IMPORTANCE

Identifying host factors involved in susceptibility to infection is fundamental for understanding host-pathogen interactions. Clinically, individuals with mutations in the MST1 gene which encodes one of the Hippo kinases experience recurrent infection. However, the impact of the Hippo kinases on innate immunity remains largely undetermined. This study uses mammalian macrophages and free-living amoebae with single- and double-knockout in the Hippo kinase genes and reveals that the Hippo kinases are the evolutionarily conserved determinants of host defense against microbes. In macrophages, the Hippo kinases MST1 and MST2 control immune activities at multiple levels, including gene expression, immune cell communication, and programmed cell death. Importantly, these activities controlled by MST1 and MST2 in macrophages are independent of the canonical Hippo cascade that is known to limit tissue growth and tumor formation. Together, these findings unveil a unique inflammatory Hippo signaling pathway that plays an essential role in innate immunity.

Axon-derived PACSIN1 binds to the Schwann cell survival receptor, LRP1, and transactivates TrkC to promote gliatrophic activities

Schwann cells (SCs) undergo phenotypic transformation and then orchestrate nerve repair following PNS injury. The ligands and receptors that activate and sustain SC transformation remain incompletely understood. Proteins released by injured axons represent important candidates for activating the SC Repair Program. The low-density lipoprotein receptor-related protein-1 (LRP1) is acutely up-regulated in SCs in response to injury, activating c-Jun, and promoting SC survival. To identify novel LRP1 ligands released in PNS injury, we applied a discovery-based approach in which extracellular proteins in the injured nerve were captured using Fc-fusion proteins containing the ligand-binding motifs of LRP1 (CCR2 and CCR4). An intracellular neuron-specific protein, Protein Kinase C and Casein Kinase Substrate in Neurons (PACSIN1) was identified and validated as an LRP1 ligand. Recombinant PACSIN1 activated c-Jun and ERK1/2 in cultured SCs. Silencing Lrp1 or inhibiting the LRP1 cell-signaling co-receptor, the NMDA-R, blocked the effects of PACSIN1 on c-Jun and ERK1/2 phosphorylation. Intraneural injection of PACSIN1 into crush-injured sciatic nerves activated c-Jun in wild-type mice, but not in mice in which Lrp1 is conditionally deleted in SCs. Transcriptome profiling of SCs revealed that PACSIN1 mediates gene expression events consistent with transformation to the repair phenotype. PACSIN1 promoted SC migration and viability following the TNFα challenge. When Src family kinases were pharmacologically inhibited or the receptor tyrosine kinase, TrkC, was genetically silenced or pharmacologically inhibited, PACSIN1 failed to induce cell signaling and prevent SC death. Collectively, these studies demonstrate that PACSIN1 is a novel axon-derived LRP1 ligand that activates SC repair signaling by transactivating TrkC.

ITPRIPL1 binds CD3ε to impede T cell activation and enable tumor immune evasion

Cancer immunotherapy has transformed treatment possibilities, but its effectiveness differs significantly among patients, indicating the presence of alternative pathways for immune evasion. Here, we show that ITPRIPL1 functions as an inhibitory ligand of CD3ε, and its expression inhibits T cells in the tumor microenvironment. The binding of ITPRIPL1 extracellular domain to CD3ε on T cells significantly decreased calcium influx and ZAP70 phosphorylation, impeding initial T cell activation. Treatment with a neutralizing antibody against ITPRIPL1 restrained tumor growth and promoted T cell infiltration in mouse models across various solid tumor types. The antibody targeting canine ITPRIPL1 exhibited notable therapeutic efficacy against naturally occurring tumors in pet clinics. These findings highlight the role of ITPRIPL1 (or CD3L1, CD3ε ligand 1) in impeding T cell activation during the critical "signal one" phase. This discovery positions ITPRIPL1 as a promising therapeutic target against multiple tumor types.

Effects of Red Sorghum-Derived Deoxyanthocyanidins and Their O-β-D-Glucosides on E-Cadherin Promoter Activity in PC-3 Prostate Cancer Cells

Although much less common than anthocyanins, 3-Deoxyanthocyanidins (3-DAs) and their glucosides can be found in cereals such as red sorghum. It is speculated that their bioavailability is higher than that of anthocyanins. Thus far, little is known regarding the therapeutic effects of 3-DAs and their O-β-D-glucosides on cancer, including prostate cancer. Thus, we evaluated their potential to decrease cell viability, to modulate the activity of transcription factors such as NFκB, CREB, and SOX, and to regulate the expression of the gene CDH1, encoding E-Cadherin. We found that 4',7-dihydroxyflavylium chloride (P7) and the natural apigeninidin can reduce cell viability, whereas 4',7-dihydroxyflavylium chloride (P7) and 4'-hydroxy-7-O-β-D-glucopyranosyloxyflavylium chloride (P3) increase the activities of NFkB, CREB, and SOX transcription factors, leading to the upregulation of CDH1 promoter activity in PC-3 prostate cancer cells. Thus, these compounds may contribute to the inhibition of the epithelial-to-mesenchymal transition in cancer cells and prevent the metastatic activity of more aggressive forms of androgen-resistant prostate cancer.

Shedding a Light on Dark Genes: A Comparative Expression Study of PRR12 Orthologues during Zebrafish Development

Haploinsufficiency of the PRR12 gene is implicated in a human neuro-ocular syndrome. Although identified as a nuclear protein highly expressed in the embryonic mouse brain, PRR12 molecular function remains elusive. This study explores the spatio-temporal expression of zebrafish PRR12 co-orthologs, prr12a and prr12b, as a first step to elucidate their function. In silico analysis reveals high evolutionary conservation in the DNA-interacting domains for both orthologs, with significant syntenic conservation observed for the prr12b locus. In situ hybridization and RT-qPCR analyses on zebrafish embryos and larvae reveal distinct expression patterns: prr12a is expressed early in zygotic development, mainly in the central nervous system, while prr12b expression initiates during gastrulation, localizing later to dopaminergic telencephalic and diencephalic cell clusters. Both transcripts are enriched in the ganglion cell and inner neural layers of the 72 hpf retina, with prr12b widely distributed in the ciliary marginal zone. In the adult brain, prr12a and prr12b are found in the cerebellum, amygdala and ventral telencephalon, which represent the main areas affected in autistic patients. Overall, this study suggests PRR12's potential involvement in eye and brain development, laying the groundwork for further investigations into PRR12-related neurobehavioral disorders.

Curation, nomenclature, and topological classification of receptor-like kinases from 528 plant species for novel domain discovery and functional inference

Receptor-like kinases (RLKs) are the most numerous signal transduction components in plants and play important roles in determining how different plants adapt to their ecological environments. Research on RLKs has focused mainly on a small number of typical RLK members in a few model plants. There is an urgent need to study the composition, distribution, and evolution of RLKs at the holistic level to increase our understanding of how RLKs assist in the ecological adaptations of different plant species. In this study, we collected the genome assemblies of 528 plant species and constructed an RLK dataset. Using this dataset, we identified and characterized 524 948 RLK family members. Each member underwent systematic topological classification and was assigned a gene ID based on a unified nomenclature system. Furthermore, we identified two novel extracellular domains in some RLKs, designated Xiao and Xiang. Evolutionary analysis of the RLK family revealed that the RLCK-XVII and RLCK-XII-2 classes were present exclusively in dicots, suggesting that diversification of RLKs between monocots and dicots may have led to differences in downstream cytoplasmic responses. We also used an interaction proteome to help empower data mining for inference of new RLK functions from a global perspective, with the ultimate goal of understanding how RLKs shape the adaptation of different plants to the environments/ecosystems. The assembled RLK dataset, together with annotations and analytical tools, forms an integrated foundation of multiomics data that is publicly accessible via the metaRLK web portal (http://metaRLK.biocloud.top).

Comprehensive Genome-Wide Natural Variation and Expression Analysis of Tubby-like Proteins Gene Family in Brachypodium distachyon

The Tubby-like proteins (TLPs) gene family is a group of transcription factors found in both animals and plants. In this study, we identified twelve B. distachyon TLPs, divided into six groups based on conserved domains and evolutionary relationships. We predicted cis-regulatory elements involved in light, hormone, and biotic and abiotic stresses. The expression patterns in response to light and hormones revealed that BdTLP3, 4, 7, and 14 are involved in light responses, and BdTLP1 is involved in ABA responses. Furthermore, BdTLP2, 7, 9, and 13 are expressed throughout vegetative and reproductive stages, whereas BdTLP1, 3, 5, and 14 are expressed at germinating grains and early vegetative development, and BdTLP4, 6, 8, and 10 are expressed at the early reproduction stage. The natural variation in the eleven most diverged B. distachyon lines revealed high conservation levels of BdTLP1-6 to high variation in BdTLP7-14 proteins. Based on diversifying selection, we identified amino acids in BdTLP1, 3, 8, and 13, potentially substantially affecting protein functions. This analysis provided valuable information for further functional studies to understand the regulation, pathways involved, and mechanism of BdTLPs.

Secreted novel AID/APOBEC-like deaminase 1 (SNAD1) - a new important player in fish immunology

The AID/APOBECs are a group of zinc-dependent cytidine deaminases that catalyse the deamination of bases in nucleic acids, resulting in a cytidine to uridine transition. Secreted novel AID/APOBEC-like deaminases (SNADs), characterized by the presence of a signal peptide are unique among all of intracellular classical AID/APOBECs, which are the central part of antibody diversity and antiviral defense. To date, there is no available knowledge on SNADs including protein characterization, biochemical characteristics and catalytic activity. We used various in silico approaches to define the phylogeny of SNADs, their common structural features, and their potential structural variations in fish species. Our analysis provides strong evidence of the universal presence of SNAD1 proteins/transcripts in fish, in which expression commences after hatching and is highest in anatomical organs linked to the immune system. Moreover, we searched published fish data and identified previously, "uncharacterized proteins" and transcripts as SNAD1 sequences. Our review into immunological research suggests SNAD1 role in immune response to infection or immunization, and interactions with the intestinal microbiota. We also noted SNAD1 association with temperature acclimation, environmental pollution and sex-based expression differences, with females showing higher level. To validate in silico predictions we performed expression studies of several SNAD1 gene variants in carp, which revealed distinct patterns of responses under different conditions. Dual sensitivity to environmental and pathogenic stress highlights its importance in the fish and potentially enhancing thermotolerance and immune defense. Revealing the biological roles of SNADs represents an exciting new area of research related to the role of DNA and/or RNA editing in fish biology.

CATD: a reproducible pipeline for selecting cell-type deconvolution methods across tissues

Motivation

Cell-type deconvolution methods aim to infer cell composition from bulk transcriptomic data. The proliferation of developed methods coupled with inconsistent results obtained in many cases, highlights the pressing need for guidance in the selection of appropriate methods. Additionally, the growing accessibility of single-cell RNA sequencing datasets, often accompanied by bulk expression from related samples enable the benchmark of existing methods.

Results

In this study, we conduct a comprehensive assessment of 31 methods, utilizing single-cell RNA-sequencing data from diverse human and mouse tissues. Employing various simulation scenarios, we reveal the efficacy of regression-based deconvolution methods, highlighting their sensitivity to reference choices. We investigate the impact of bulk-reference differences, incorporating variables such as sample, study and technology. We provide validation using a gold standard dataset from mononuclear cells and suggest a consensus prediction of proportions when ground truth is not available. We validated the consensus method on data from the stomach and studied its spillover effect. Importantly, we propose the use of the critical assessment of transcriptomic deconvolution (CATD) pipeline which encompasses functionalities for generating references and pseudo-bulks and running implemented deconvolution methods. CATD streamlines simultaneous deconvolution of numerous bulk samples, providing a practical solution for speeding up the evaluation of newly developed methods.

Availability and implementation

https://github.com/Papatheodorou-Group/CATD_snakemake.

Paternal-effect-genes revealed through sperm cryopreservation in Perca fluviatilis

Knowledge about paternal-effect-genes (PEGs) (genes whose expression in the progeny is influenced by paternal factors present in the sperm) in fish is very limited. To explore this issue, we used milt cryopreservation as a specific challenge test for sperm cells, thus enabling selection amidst cryo-sensitivity. We created two groups of Eurasian perch (Perca fluviatilis) as a model - eggs fertilized either with fresh (Fresh group) or cryopreserved (Cryo group) milt from the same male followed by phenotypic-transcriptomic examination of consequences of cryopreservation in obtained progeny (at larval stages). Most of the phenotypical observations were similar in both groups, except the final weight which was higher in the Cryo group. Milt cryopreservation appeared to act as a "positive selection" factor, upregulating most PEGs in the Cryo group. Transcriptomic profile of freshly hatched larvae sourced genes involved in the development of visual perception and we identified them as PEGs. Consequently, larvae from the Cryo group exhibited enhanced eyesight, potentially contributing to more efficient foraging and weight gain compared to the Fresh group. This study unveils, for the first time, the significant influence of the paternal genome on the development of the visual system in fish, highlighting pde6g, opn1lw1, and rbp4l as novel PEGs.

Identification of genetic and environmental factors influencing aerial root traits that support biological nitrogen fixation in sorghum

Plant breeding and genetics play a major role in the adaptation of plants to meet human needs. The current requirement to make agriculture more sustainable can be partly met by a greater reliance on biological nitrogen fixation by symbiotic diazotrophic microorganisms that provide crop plants with ammonium. Select accessions of the cereal crop sorghum (Sorghum bicolor (L.) Moench) form mucilage-producing aerial roots that harbor nitrogen-fixing bacteria. Breeding programs aimed at developing sorghum varieties that support diazotrophs will benefit from a detailed understanding of the genetic and environmental factors contributing to aerial root formation. A genome-wide association study of the sorghum minicore, a collection of 242 landraces, and 30 accessions from the sorghum association panel was conducted in Florida and Wisconsin and under 2 fertilizer treatments to identify loci associated with the number of nodes with aerial roots and aerial root diameter. Sequence variation in genes encoding transcription factors that control phytohormone signaling and root system architecture showed significant associations with these traits. In addition, the location had a significant effect on the phenotypes. Concurrently, we developed F2 populations from crosses between bioenergy sorghums and a landrace that produced extensive aerial roots to evaluate the mode of inheritance of the loci identified by the genome-wide association study. Furthermore, the mucilage collected from aerial roots contained polysaccharides rich in galactose, arabinose, and fucose, whose composition displayed minimal variation among 10 genotypes and 2 fertilizer treatments. These combined results support the development of sorghums with the ability to acquire nitrogen via biological nitrogen fixation.

Plant genomic resources at National Genomics Data Center: assisting in data-driven breeding applications

Genomic data serve as an invaluable resource for unraveling the intricacies of the higher plant systems, including the constituent elements within and among species. Through various efforts in genomic data archiving, integrative analysis and value-added curation, the National Genomics Data Center (NGDC), which is a part of the China National Center for Bioinformation (CNCB), has successfully established and currently maintains a vast amount of database resources. This dedicated initiative of the NGDC facilitates a data-rich ecosystem that greatly strengthens and supports genomic research efforts. Here, we present a comprehensive overview of central repositories dedicated to archiving, presenting, and sharing plant omics data, introduce knowledgebases focused on variants or gene-based functional insights, highlight species-specific multiple omics database resources, and briefly review the online application tools. We intend that this review can be used as a guide map for plant researchers wishing to select effective data resources from the NGDC for their specific areas of study.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42994-023-00134-4.

The spectrum of pre-mRNA splicing in autism

Disruptions in spatiotemporal gene expression can result in atypical brain function. Specifically, autism spectrum disorder (ASD) is characterized by abnormalities in pre-mRNA splicing. Abnormal splicing patterns have been identified in the brains of individuals with ASD, and mutations in splicing factors have been found to contribute to neurodevelopmental delays associated with ASD. Here we review studies that shed light on the importance of splicing observed in ASD and that explored the intricate relationship between splicing factors and ASD, revealing how disruptions in pre-mRNA splicing may underlie ASD pathogenesis. We provide an overview of the research regarding all splicing factors associated with ASD and place a special emphasis on five specific splicing factors-HNRNPH2, NOVA2, WBP4, SRRM2, and RBFOX1-known to impact the splicing of ASD-related genes. In the discussion of the molecular mechanisms influenced by these splicing factors, we lay the groundwork for a deeper understanding of ASD's complex etiology. Finally, we discuss the potential benefit of unraveling the connection between splicing and ASD for the development of more precise diagnostic tools and targeted therapeutic interventions. This article is categorized under: RNA in Disease and Development > RNA in Disease RNA Evolution and Genomics > RNA and Ribonucleoprotein Evolution RNA Evolution and Genomics > Computational Analyses of RNA RNA-Based Catalysis > RNA Catalysis in Splicing and Translation.

Blood transcriptomics analysis offers insights into variant-specific immune response to SARS-CoV-2

Bulk RNA sequencing (RNA-seq) of blood is typically used for gene expression analysis in biomedical research but is still rarely used in clinical practice. In this study, we propose that RNA-seq should be considered a diagnostic tool, as it offers not only insights into aberrant gene expression and splicing but also delivers additional readouts on immune cell type composition as well as B-cell and T-cell receptor (BCR/TCR) repertoires. We demonstrate that RNA-seq offers insights into a patient's immune status via integrative analysis of RNA-seq data from patients infected with various SARS-CoV-2 variants (in total 196 samples with up to 200 million reads sequencing depth). We compare the results of computational cell-type deconvolution methods (e.g., MCP-counter, xCell, EPIC, quanTIseq) to complete blood count data, the current gold standard in clinical practice. We observe varying levels of lymphocyte depletion and significant differences in neutrophil levels between SARS-CoV-2 variants. Additionally, we identify B and T cell receptor (BCR/TCR) sequences using the tools MiXCR and TRUST4 to show that-combined with sequence alignments and BLASTp-they could be used to classify a patient's disease. Finally, we investigated the sequencing depth required for such analyses and concluded that 10 million reads per sample is sufficient. In conclusion, our study reveals that computational cell-type deconvolution and BCR/TCR methods using bulk RNA-seq analyses can supplement missing CBC data and offer insights into immune responses, disease severity, and pathogen-specific immunity, all achievable with a sequencing depth of 10 million reads per sample.

Progress in single-cell multimodal sequencing and multi-omics data integration

With the rapid advance of single-cell sequencing technology, cell heterogeneity in various biological processes was dissected at different omics levels. However, single-cell mono-omics results in fragmentation of information and could not provide complete cell states. In the past several years, a variety of single-cell multimodal omics technologies have been developed to jointly profile multiple molecular modalities, including genome, transcriptome, epigenome, and proteome, from the same single cell. With the availability of single-cell multimodal omics data, we can simultaneously investigate the effects of genomic mutation or epigenetic modification on transcription and translation, and reveal the potential mechanisms underlying disease pathogenesis. Driven by the massive single-cell omics data, the integration method of single-cell multi-omics data has rapidly developed. Integration of the massive multi-omics single-cell data in public databases in the future will make it possible to construct a cell atlas of multi-omics, enabling us to comprehensively understand cell state and gene regulation at single-cell resolution. In this review, we summarized the experimental methods for single-cell multimodal omics data and computational methods for multi-omics data integration. We also discussed the future development of this field.

In silico characterization of five novel disease-resistance proteins in Oryza sativa sp. japonica against bacterial leaf blight and rice blast diseases

In the current study, gene network analysis revealed five novel disease-resistance proteins against bacterial leaf blight (BB) and rice blast (RB) diseases caused by Xanthomonas oryzae pv. oryzae (Xoo) and Magnaporthe oryzae (M. oryzae), respectively. In silico modeling, refinement, and model quality assessment were performed to predict the best structures of these five proteins and submitted to ModelArchive for future use. An in-silico annotation indicated that the five proteins functioned in signal transduction pathways as kinases, phospholipases, transcription factors, and DNA-modifying enzymes. The proteins were localized in the nucleus and plasma membrane. Phylogenetic analysis showed the evolutionary relation of the five proteins with disease-resistance proteins (XA21, OsTRX1, PLD, and HKD-motif-containing proteins). This indicates similar disease-resistant properties between five unknown proteins and their evolutionary-related proteins. Furthermore, gene expression profiling of these proteins using public microarray data showed their differential expression under Xoo and M. oryzae infection. This study provides an insight into developing disease-resistant rice varieties by predicting novel candidate resistance proteins, which will assist rice breeders in improving crop yield to address future food security through molecular breeding and biotechnology.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-023-03893-5.

Full-length isoform concatenation sequencing to resolve cancer transcriptome complexity

BACKGROUND

Cancers exhibit complex transcriptomes with aberrant splicing that induces isoform-level differential expression compared to non-diseased tissues. Transcriptomic profiling using short-read sequencing has utility in providing a cost-effective approach for evaluating isoform expression, although short-read assembly displays limitations in the accurate inference of full-length transcripts. Long-read RNA sequencing (Iso-Seq), using the Pacific Biosciences (PacBio) platform, can overcome such limitations by providing full-length isoform sequence resolution which requires no read assembly and represents native expressed transcripts. A constraint of the Iso-Seq protocol is due to fewer reads output per instrument run, which, as an example, can consequently affect the detection of lowly expressed transcripts. To address these deficiencies, we developed a concatenation workflow, PacBio Full-Length Isoform Concatemer Sequencing (PB_FLIC-Seq), designed to increase the number of unique, sequenced PacBio long-reads thereby improving overall detection of unique isoforms. In addition, we anticipate that the increase in read depth will help improve the detection of moderate to low-level expressed isoforms.

RESULTS

In sequencing a commercial reference (Spike-In RNA Variants; SIRV) with known isoform complexity we demonstrated a 3.4-fold increase in read output per run and improved SIRV recall when using the PB_FLIC-Seq method compared to the same samples processed with the Iso-Seq protocol. We applied this protocol to a translational cancer case, also demonstrating the utility of the PB_FLIC-Seq method for identifying differential full-length isoform expression in a pediatric diffuse midline glioma compared to its adjacent non-malignant tissue. Our data analysis revealed increased expression of extracellular matrix (ECM) genes within the tumor sample, including an isoform of the Secreted Protein Acidic and Cysteine Rich (SPARC) gene that was expressed 11,676-fold higher than in the adjacent non-malignant tissue. Finally, by using the PB_FLIC-Seq method, we detected several cancer-specific novel isoforms.

CONCLUSION

This work describes a concatenation-based methodology for increasing the number of sequenced full-length isoform reads on the PacBio platform, yielding improved discovery of expressed isoforms. We applied this workflow to profile the transcriptome of a pediatric diffuse midline glioma and adjacent non-malignant tissue. Our findings of cancer-specific novel isoform expression further highlight the importance of long-read sequencing for characterization of complex tumor transcriptomes.

Chromatin accessibility and pioneer factor FOXA1 restrict glucocorticoid receptor action in prostate cancer

Treatment of prostate cancer relies predominantly on the inhibition of androgen receptor (AR) signaling. Despite the initial effectiveness of the antiandrogen therapies, the cancer often develops resistance to the AR blockade. One mechanism of the resistance is glucocorticoid receptor (GR)-mediated replacement of AR function. Nevertheless, the mechanistic ways and means how the GR-mediated antiandrogen resistance occurs have remained elusive. Here, we have discovered several crucial features of GR action in prostate cancer cells through genome-wide techniques. We detected that the replacement of AR by GR in enzalutamide-exposed prostate cancer cells occurs almost exclusively at pre-accessible chromatin sites displaying FOXA1 occupancy. Counterintuitively to the classical pioneer factor model, silencing of FOXA1 potentiated the chromatin binding and transcriptional activity of GR. This was attributed to FOXA1-mediated repression of the NR3C1 (gene encoding GR) expression via the corepressor TLE3. Moreover, the small-molecule inhibition of coactivator p300's enzymatic activity efficiently restricted GR-mediated gene regulation and cell proliferation. Overall, we identified chromatin pre-accessibility and FOXA1-mediated repression as important regulators of GR action in prostate cancer, pointing out new avenues to oppose steroid receptor-mediated antiandrogen resistance.

Comparative analysis of SPL transcription factors from streptophyte algae and embryophytes reveals evolutionary trajectories of SPL family in streptophytes

SQUAMOSA-PROMOTER BINDING PROTEIN-LIKE (SPL) genes encode plant-specific transcription factors which are important regulators of diverse plant developmental processes. We took advantage of available genome sequences of streptophyte algae representatives to investigate the relationships of SPL genes between freshwater green algae and land plants. Our analysis showed that streptophyte algae, hornwort and liverwort genomes encode from one to four SPL genes which is the smallest set, in comparison to other land plants studied to date. Based on the phylogenetic analysis, four major SPL phylogenetic groups were distinguished with Group 3 and 4 being sister to Group 1 and 2. Comparative motif analysis revealed conserved protein motifs within each phylogenetic group and unique bryophyte-specific motifs within Group 1 which suggests lineage-specific protein speciation processes. Moreover, the gene structure analysis also indicated the specificity of each by identifying differences in exon-intron structures between the phylogenetic groups, suggesting their evolutionary divergence. Since current understanding of SPL genes mostly arises from seed plants, the presented comparative and phylogenetic analyzes from freshwater green algae and land plants provide new insights on the evolutionary trajectories of the SPL gene family in different classes of streptophytes.

Discovery of galectin-8 as an LILRB4 ligand driving M-MDSCs defines a class of antibodies to fight solid tumors

LILRB4 is an immunosuppressive receptor, and its targeting drugs are undergoing multiple preclinical and clinical trials. Currently, the absence of a functional LILRB4 ligand in solid tumors not only limits the strategy of early antibody screening but also leads to the lack of companion diagnostic (CDx) criteria, which is critical to the objective response rate in early-stage clinical trials. Here, we show that galectin-8 (Gal-8) is a high-affinity functional ligand of LILRB4, and its ligation induces M-MDSC by activating STAT3 and inhibiting NF-κB. Significantly, Gal-8, but not APOE, can induce MDSC, and both ligands bind LILRB4 noncompetitively. Gal-8 expression promotes in vivo tumor growth in mice, and the knockout of LILRB4 attenuates tumor growth in this context. Antibodies capable of functionally blocking Gal-8 are able to suppress tumor growth in vivo. These results identify Gal-8 as an MDSC-driving ligand of LILRB4, and they redefine a class of antibodies for solid tumors.

SCAN: Spatiotemporal Cloud Atlas for Neural cells

The nervous system is one of the most complicated and enigmatic systems within the animal kingdom. Recently, the emergence and development of spatial transcriptomics (ST) and single-cell RNA sequencing (scRNA-seq) technologies have provided an unprecedented ability to systematically decipher the cellular heterogeneity and spatial locations of the nervous system from multiple unbiased aspects. However, efficiently integrating, presenting and analyzing massive multiomic data remains a huge challenge. Here, we manually collected and comprehensively analyzed high-quality scRNA-seq and ST data from the nervous system, covering 10 679 684 cells. In addition, multi-omic datasets from more than 900 species were included for extensive data mining from an evolutionary perspective. Furthermore, over 100 neurological diseases (e.g. Alzheimer's disease, Parkinson's disease, Down syndrome) were systematically analyzed for high-throughput screening of putative biomarkers. Differential expression patterns across developmental time points, cell types and ST spots were discerned and subsequently subjected to extensive interpretation. To provide researchers with efficient data exploration, we created a new database with interactive interfaces and integrated functions called the Spatiotemporal Cloud Atlas for Neural cells (SCAN), freely accessible at http://47.98.139.124:8799 or http://scanatlas.net. SCAN will benefit the neuroscience research community to better exploit the spatiotemporal atlas of the neural system and promote the development of diagnostic strategies for various neurological disorders.

Plant Reactome Knowledgebase: empowering plant pathway exploration and OMICS data analysis

Plant Reactome (https://plantreactome.gramene.org) is a freely accessible, comprehensive plant pathway knowledgebase. It provides curated reference pathways from rice (Oryza sativa) and gene-orthology-based pathway projections to 129 additional species, spanning single-cell photoautotrophs, non-vascular plants, and higher plants, thus encompassing a wide-ranging taxonomic diversity. Currently, Plant Reactome houses a collection of 339 reference pathways, covering metabolic and transport pathways, hormone signaling, genetic regulations of developmental processes, and intricate transcriptional networks that orchestrate a plant's response to abiotic and biotic stimuli. Beyond being a mere repository, Plant Reactome serves as a dynamic data discovery platform. Users can analyze and visualize omics data, such as gene expression, gene-gene interaction, proteome, and metabolome data, all within the rich context of plant pathways. Plant Reactome is dedicated to fostering data interoperability, upholding global data standards, and embracing the tenets of the Findable, Accessible, Interoperable and Re-usable (FAIR) data policy.

HumanLectome, an update of UniLectin for the annotation and prediction of human lectins

The UniLectin portal (https://unilectin.unige.ch/) was designed in 2019 with the goal of centralising curated and predicted data on carbohydrate-binding proteins known as lectins. UniLectin is also intended as a support for the study of lectomes (full lectin set) of organisms or tissues. The present update describes the inclusion of several new modules and details the latest (https://unilectin.unige.ch/humanLectome/), covering our knowledge of the human lectome and comprising 215 unevenly characterised lectins, particularly in terms of structural information. Each HumanLectome entry is protein-centric and compiles evidence of carbohydrate recognition domain(s), specificity, 3D-structure, tissue-based expression and related genomic data. Other recent improvements regarding interoperability and accessibility are outlined.

CellSTAR: a comprehensive resource for single-cell transcriptomic annotation

Large-scale studies of single-cell sequencing and biological experiments have successfully revealed expression patterns that distinguish different cell types in tissues, emphasizing the importance of studying cellular heterogeneity and accurately annotating cell types. Analysis of gene expression profiles in these experiments provides two essential types of data for cell type annotation: annotated references and canonical markers. In this study, the first comprehensive database of single-cell transcriptomic annotation resource (CellSTAR) was thus developed. It is unique in (a) offering the comprehensive expertly annotated reference data for annotating hundreds of cell types for the first time and (b) enabling the collective consideration of reference data and marker genes by incorporating tens of thousands of markers. Given its unique features, CellSTAR is expected to attract broad research interests from the technological innovations in single-cell transcriptomics, the studies of cellular heterogeneity & dynamics, and so on. It is now publicly accessible without any login requirement at: https://idrblab.org/cellstar.

CellCommuNet: an atlas of cell-cell communication networks from single-cell RNA sequencing of human and mouse tissues in normal and disease states

Cell-cell communication, as a basic feature of multicellular organisms, is crucial for maintaining the biological functions and microenvironmental homeostasis of cells, organs, and whole organisms. Alterations in cell-cell communication contribute to many diseases, including cancers. Single-cell RNA sequencing (scRNA-seq) provides a powerful method for studying cell-cell communication by enabling the analysis of ligand-receptor interactions. Here, we introduce CellCommuNet (http://www.inbirg.com/cellcommunet/), a comprehensive data resource for exploring cell-cell communication networks in scRNA-seq data from human and mouse tissues in normal and disease states. CellCommuNet currently includes 376 single datasets from multiple sources, and 118 comparison datasets between disease and normal samples originating from the same study. CellCommuNet provides information on the strength of communication between cells and related signalling pathways and facilitates the exploration of differences in cell-cell communication between healthy and disease states. Users can also search for specific signalling pathways, ligand-receptor pairs, and cell types of interest. CellCommuNet provides interactive graphics illustrating cell-cell communication in different states, enabling differential analysis of communication strength between disease and control samples. This comprehensive database aims to be a valuable resource for biologists studying cell-cell communication networks.

BloodChIP Xtra: an expanded database of comparative genome-wide transcription factor binding and gene-expression profiles in healthy human stem/progenitor subsets and leukemic cells

The BloodChIP Xtra database (http://bloodchipXtra.vafaeelab.com/) facilitates genome-wide exploration and visualization of transcription factor (TF) occupancy and chromatin configuration in rare primary human hematopoietic stem (HSC-MPP) and progenitor (CMP, GMP, MEP) cells and acute myeloid leukemia (AML) cell lines (KG-1, ME-1, Kasumi1, TSU-1621-MT), along with chromatin accessibility and gene expression data from these and primary patient AMLs. BloodChIP Xtra features significantly more datasets than our earlier database BloodChIP (two primary cell types and two cell lines). Improved methodologies for determining TF occupancy and chromatin accessibility have led to increased availability of data for rare primary cell types across the spectrum of healthy and AML hematopoiesis. However, there is a continuing need for these data to be integrated in an easily accessible manner for gene-based queries and use in downstream applications. Here, we provide a user-friendly database based around genome-wide binding profiles of key hematopoietic TFs and histone marks in healthy stem/progenitor cell types. These are compared with binding profiles and chromatin accessibility derived from primary and cell line AML and integrated with expression data from corresponding cell types. All queries can be exported to construct TF-gene and protein-protein networks and evaluate the association of genes with specific cellular processes.

CancerProteome: a resource to functionally decipher the proteome landscape in cancer

Advancements in mass spectrometry (MS)-based proteomics have greatly facilitated the large-scale quantification of proteins and microproteins, thereby revealing altered signalling pathways across many different cancer types. However, specialized and comprehensive resources are lacking for cancer proteomics. Here, we describe CancerProteome (http://bio-bigdata.hrbmu.edu.cn/CancerProteome), which functionally deciphers and visualizes the proteome landscape in cancer. We manually curated and re-analyzed publicly available MS-based quantification and post-translational modification (PTM) proteomes, including 7406 samples from 21 different cancer types, and also examined protein abundances and PTM levels in 31 120 proteins and 4111 microproteins. Six major analytical modules were developed with a view to describe protein contributions to carcinogenesis using proteome analysis, including conventional analyses of quantitative and the PTM proteome, functional enrichment, protein-protein associations by integrating known interactions with co-expression signatures, drug sensitivity and clinical relevance analyses. Moreover, protein abundances, which correlated with corresponding transcript or PTM levels, were evaluated. CancerProteome is convenient as it allows users to access specific proteins/microproteins of interest using quick searches or query options to generate multiple visualization results. In summary, CancerProteome is an important resource, which functionally deciphers the cancer proteome landscape and provides a novel insight for the identification of tumor protein markers in cancer.

NCBI GEO: archive for gene expression and epigenomics data sets: 23-year update

The Gene Expression Omnibus (GEO) is an international public repository that archives gene expression and epigenomics data sets generated by next-generation sequencing and microarray technologies. Data are typically submitted to GEO by researchers in compliance with widespread journal and funder mandates to make generated data publicly accessible. The resource handles raw data files, processed data files and descriptive metadata for over 200 000 studies and 6.5 million samples, all of which are indexed, searchable and downloadable. Additionally, GEO offers web-based tools that facilitate analysis and visualization of differential gene expression. This article presents the current status and recent advancements in GEO, including the generation of consistently computed gene expression count matrices for thousands of RNA-seq studies, and new interactive graphical plots in GEO2R that help users identify differentially expressed genes and assess data set quality. The GEO repository is built and maintained by the National Center for Biotechnology Information (NCBI), a division of the National Library of Medicine (NLM), and is publicly accessible at https://www.ncbi.nlm.nih.gov/geo/.

The competitive landscape of the dsRNA world

The ability to sense and respond to infection is essential for life. Viral infection produces double-stranded RNAs (dsRNAs) that are sensed by proteins that recognize the structure of dsRNA. This structure-based recognition of viral dsRNA allows dsRNA sensors to recognize infection by many viruses, but it comes at a cost-the dsRNA sensors cannot always distinguish between "self" and "nonself" dsRNAs. "Self" RNAs often contain dsRNA regions, and not surprisingly, mechanisms have evolved to prevent aberrant activation of dsRNA sensors by "self" RNA. Here, we review current knowledge about the life of endogenous dsRNAs in mammals-the biosynthesis and processing of dsRNAs, the proteins they encounter, and their ultimate degradation. We highlight mechanisms that evolved to prevent aberrant dsRNA sensor activation and the importance of competition in the regulation of dsRNA sensors and other dsRNA-binding proteins.

Integrated meta-analysis of colorectal cancer public proteomic datasets for biomarker discovery and validation

The cancer biomarker field has been an object of thorough investigation in the last decades. Despite this, colorectal cancer (CRC) heterogeneity makes it challenging to identify and validate effective prognostic biomarkers for patient classification according to outcome and treatment response. Although a massive amount of proteomics data has been deposited in public data repositories, this rich source of information is vastly underused. Here, we attempted to reuse public proteomics datasets with two main objectives: i) to generate hypotheses (detection of biomarkers) for their posterior/downstream validation, and (ii) to validate, using an orthogonal approach, a previously described biomarker panel. Twelve CRC public proteomics datasets (mostly from the PRIDE database) were re-analysed and integrated to create a landscape of protein expression. Samples from both solid and liquid biopsies were included in the reanalysis. Integrating this data with survival annotation data, we have validated in silico a six-gene signature for CRC classification at the protein level, and identified five new blood-detectable biomarkers (CD14, PPIA, MRC2, PRDX1, and TXNDC5) associated with CRC prognosis. The prognostic value of these blood-derived proteins was confirmed using additional public datasets, supporting their potential clinical value. As a conclusion, this proof-of-the-concept study demonstrates the value of re-using public proteomics datasets as the basis to create a useful resource for biomarker discovery and validation. The protein expression data has been made available in the public resource Expression Atlas.

Function of m5C RNA methyltransferase NOP2 in high-grade serous ovarian cancer

RNA methyltransferase nucleolar protein p120 (NOP2), commonly referred to as NOP2/Sun RNA methyltransferase family member 1 (NSUN1), is involved in cell proliferation and is highly expressed in various cancers. However, its role in high-grade serous ovarian cancer (HGSOC) remains unclear. Our study investigated the expression of NOP2 in HGSOC tissues and normal fimbria tissues, and found that NOP2 was significantly upregulated in HGSOC tissues. Our experiments showed that NOP2 overexpression promoted cell proliferation in vivo and in vitro and increased the migration and invasion ability of HGSOC cells in vitro. Furthermore, we identified Rap guanine nucleotide exchange factor 4 (RAPGEF4) as a potential downstream target of NOP2 in HGSOC. Finally, our findings suggest that the regulation of NOP2 and RAPGEF4 may depend on m5C methylation levels.

Patterns of chromosome evolution in ruminants

Studying when and where gross genomic rearrangements occurred during evolution is key to understanding changes in genome structure with functional consequences that might eventually lead to speciation. Here we identified chromosome rearrangements in ruminants, a clade characterized by large chromosome differences. Using 26 genome assemblies, we reconstructed five ancestral karyotypes and classified the rearrangement events occurring in each lineage. With these reconstructions, we then identified evolutionary breakpoints regions (EBRs) and synteny fragments. Ruminant karyotype evolution is characterized by inversions, while interchromosomal rearrangements occurred preferentially in the oldest ancestor of ruminants. We found that EBRs are depleted of protein coding genes, including housekeeping genes. Similarly, EBRs are not enriched in high GC regions, suggesting that meiotic double strand breaks might not be their origin. Overall, our results characterize at fine detail the location of chromosome rearrangements in ruminant evolution and provide new insights into the formation of EBRs.

Blood transcriptomics analysis offers insights into variant-specific immune response to SARS-CoV-2

Bulk RNA sequencing (RNA-seq) of blood is typically used for gene expression analysis in biomedical research but is still rarely used in clinical practice. In this study, we argue that RNA-seq should be considered a routine diagnostic tool, as it offers not only insights into aberrant gene expression and splicing but also delivers additional readouts on immune cell type composition as well as B-cell and T-cell receptor (BCR/TCR) repertoires. We demonstrate that RNA-seq offers vital insights into a patient's immune status via integrative analysis of RNA-seq data from patients infected with various SARS-CoV-2 variants (in total 240 samples with up to 200 million reads sequencing depth). We compare the results of computational cell-type deconvolution methods (e.g., MCP-counter, xCell, EPIC, quanTIseq) to complete blood count data, the current gold standard in clinical practice. We observe varying levels of lymphocyte depletion and significant differences in neutrophil levels between SARS-CoV-2 variants. Additionally, we identify B and T cell receptor (BCR/TCR) sequences using the tools MiXCR and TRUST4 to show that - combined with sequence alignments and pBLAST - they could be used to classify a patient's disease. Finally, we investigated the sequencing depth required for such analyses and concluded that 10 million reads per sample is sufficient. In conclusion, our study reveals that computational cell-type deconvolution and BCR/TCR methods using bulk RNA-seq analyses can supplement missing CBC data and offer insights into immune responses, disease severity, and pathogen-specific immunity, all achievable with a sequencing depth of 10 million reads per sample.

Functional Tissue Units in the Human Reference Atlas

Functional tissue units (FTUs) form the basic building blocks of organs and are important for understanding and modeling the healthy physiological function of the organ and changes during disease states. In this first comprehensive catalog of FTUs, we document the definition, physical dimensions, vasculature, and cellular composition of 22 anatomically correct, nested functional tissue units (FTUs) in 10 healthy human organs. The catalog includes datasets, illustrations, an interactive online FTU explorer, and a large printable poster. All data and code are freely available. This is part of a larger ongoing international effort to construct a Human Reference Atlas (HRA) of all cells in the human body.