MATTE: a pipeline of transcriptome module alignment for anti-noise phenotype-gene-related analysis.

A phenotype may be associated with multiple genes that interact with each other in the form of a gene module or network. How to identify these relationships is one important aspect of comparative transcriptomics. However, it is still a challenge to align gene modules associated with different phenotypes. Although several studies attempted to address this issue in different aspects, a general framework is still needed. In this study, we introduce Module Alignment of TranscripTomE (MATTE), a novel approach to analyze transcriptomics data and identify differences in a modular manner. MATTE assumes that gene interactions modulate a phenotype and models phenotype differences as gene location changes. Specifically, we first represented genes by a relative differential expression to reduce the influence of noise in omics data. Meanwhile, clustering and aligning are combined to depict gene differences in a modular way robustly. The results show that MATTE outperformed state-of-the-art methods in identifying differentially expressed genes under noise in gene expression. In particular, MATTE could also deal with single-cell ribonucleic acid-seq data to extract the best cell-type marker genes compared to other methods. Additionally, we demonstrate how MATTE supports the discovery of biologically significant genes and modules, and facilitates downstream analyses to gain insight into breast cancer. The source code of MATTE and case analysis are available at https://github.com/zjupgx/MATTE.

Global level of methylation in the sea lamprey (jawless vertebrate) genome is intermediate between invertebrate and jawed vertebrate genomes.

In eukaryotes, cytosine methylation is a primary heritable epigenetic modification of the genome that regulates many cellular processes. In invertebrate, methylated cytosine generally located on specific genomic elements (e.g., gene bodies and silenced repetitive elements) to show a "mosaic" pattern. While in jawed vertebrate (teleost and tetrapod), highly methylated cytosine located genome-wide but only absence at regulatory regions (e.g., promoter and enhancer). Many studies imply that the evolution of DNA methylation reprogramming may have helped the transition from invertebrates to jawed vertebrates, but the detail remains largely elusive. In this study, we used the whole-genome bisulfite-sequencing technology to investigate the genome-wide methylation in three tissues (heart, muscle, and sperm) from the sea lamprey, an extant agnathan (jawless) vertebrate. Strikingly, we found that the methylation level of the sea lamprey is very similar to that in sea urchin (a deuterostome) and sea squirt (a chordate) invertebrates. In sum, the global pattern in sea lamprey is intermediate methylation level (around 30%), that is higher than methylation level in the genomes of pre-bilaterians and protostomes (1%-10%), but lower than methylation level appeared in jawed vertebrates (around 70%, teleost and tetrapod). We anticipate that, in addition to genetic dynamics such as genome duplications, epigenetic dynamics such as global methylation reprograming was also orchestrated toward the emergence and evolution of vertebrates.

Anciently duplicated genes continuously recruited to heart expression in vertebrate evolution are associated with heart chamber increase.

Although gene/genome duplications in the early stage of vertebrates have been thought to provide major resources of raw genetic materials for evolutionary innovations, it is unclear whether they continuously contribute to the evolution of morphological complexity during the course of vertebrate evolution, such as the evolution from two heart chambers (fishes) to four heart chambers (mammals and birds). We addressed this issue by our heart RNA-Seq experiments combined with published data, using 13 vertebrates and one invertebrate (sea squirt, as an outgroup). Our evolutionary transcriptome analysis showed that number of ancient paralogous genes expressed in heart tends to increase with the increase of heart chamber number along the vertebrate phylogeny, in spite that most of them were duplicated at the time near to the origin of vertebrates or even more ancient. Moreover, those paralogs expressed in heart exert considerably different functions from heart-expressed singletons: the former are functionally enriched in cardiac muscle and muscle contraction-related categories, whereas the latter play more basic functions of energy generation like aerobic respiration. These findings together support the notion that recruiting anciently paralogous genes that are expressed in heart is associated with the increase of chamber number in vertebrate evolution.

A fluorescent terbium-metal-organic framework material for high-sensitivity detection of vomitoxin and oxytetracycline hydrochloride in water.

A unique luminescent lanthanide metal-organic framework (LnMOF)-based fluorescence detection platform was utilized to achieve sensitive detection of vomitoxin (VT) and oxytetracycline hydrochloride (OTC-HCL) without the use of antibodies or biomolecular modifications. The sensor had a fluorescence quenching constant of 9.74 × 106 M-1 and a low detection limit of 0.68 nM for vomitoxin. Notably, this is the first example of a Tb-MOF sensor for fluorescence detection of vomitoxin. We further investigated its response to two mycotoxins, aflatoxin B1 and ochratoxin A, and found that their Stern-Volmer fluorescence quenching constants were lower than those of VT. In addition, the fluorescence sensor realized sensitive detection of OTC-HCL with a detection limit of 0.039 µM. In conclusion, the method has great potential as a sensitive and simple technique to detect VT and OTC-HCL in water.

Unraveling the Drivers of Tumorigenesis in the Context of Evolution: Theoretical Models and Bioinformatics Tools.

Cancer originates from somatic cells that have accumulated mutations. These mutations alter the phenotype of the cells, allowing them to escape homeostatic regulation that maintains normal cell numbers. The emergence of malignancies is an evolutionary process in which the random accumulation of somatic mutations and sequential selection of dominant clones cause cancer cells to proliferate. The development of technologies such as high-throughput sequencing has provided a powerful means to measure subclonal evolutionary dynamics across space and time. Here, we review the patterns that may be observed in cancer evolution and the methods available for quantifying the evolutionary dynamics of cancer. An improved understanding of the evolutionary trajectories of cancer will enable us to explore the molecular mechanism of tumorigenesis and to design tailored treatment strategies.

CanDriS: posterior profiling of cancer-driving sites based on two-component evolutionary model.

Current cancer genomics databases have accumulated millions of somatic mutations that remain to be further explored. Due to the over-excess mutations unrelated to cancer, the great challenge is to identify somatic mutations that are cancer-driven. Under the notion that carcinogenesis is a form of somatic-cell evolution, we developed a two-component mixture model: while the ground component corresponds to passenger mutations, the rapidly evolving component corresponds to driver mutations. Then, we implemented an empirical Bayesian procedure to calculate the posterior probability of a site being cancer-driven. Based on these, we developed a software CanDriS (Cancer Driver Sites) to profile the potential cancer-driving sites for thousands of tumor samples from the Cancer Genome Atlas and International Cancer Genome Consortium across tumor types and pan-cancer level. As a result, we identified that approximately 1% of the sites have posterior probabilities larger than 0.90 and listed potential cancer-wide and cancer-specific driver mutations. By comprehensively profiling all potential cancer-driving sites, CanDriS greatly enhances our ability to refine our knowledge of the genetic basis of cancer and might guide clinical medication in the upcoming era of precision medicine. The results were displayed in a database CandrisDB (http://biopharm.zju.edu.cn/candrisdb/).

MODIG: integrating multi-omics and multi-dimensional gene network for cancer driver gene identification based on graph attention network model.

MOTIVATION: Identifying genes that play a causal role in cancer evolution remains one of the biggest challenges in cancer biology. With the accumulation of high-throughput multi-omics data over decades, it becomes a great challenge to effectively integrate these data into the identification of cancer driver genes. RESULTS: Here, we propose MODIG, a graph attention network (GAT)-based framework to identify cancer driver genes by combining multi-omics pan-cancer data (mutations, copy number variants, gene expression and methylation levels) with multi-dimensional gene networks. First, we established diverse types of gene relationship maps based on protein-protein interactions, gene sequence similarity, KEGG pathway co-occurrence, gene co-expression patterns and gene ontology. Then, we constructed a multi-dimensional gene network consisting of approximately 20 000 genes as nodes and five types of gene associations as multiplex edges. We applied a GAT to model within-dimension interactions to generate a gene representation for each dimension based on this graph. Moreover, we introduced a joint learning module to fuse multiple dimension-specific representations to generate general gene representations. Finally, we used the obtained gene representation to perform a semi-supervised driver gene identification task. The experiment results show that MODIG outperforms the baseline models in terms of area under precision-recall curves and area under the receiver operating characteristic curves. AVAILABILITY AND IMPLEMENTATION: The MODIG program is available at https://github.com/zjupgx/modig. The code and data underlying this article are also available on Zenodo, at https://doi.org/10.5281/zenodo.7057241. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genome distance and phylogenetic inference accommodating gene duplication, loss and new gene input.

With the rapid growth of entire genome data, phylogenomics focuses on analyzing evolutionary histories and relationships of species, i.e., the tree of life. For decades it has been realized that the genome-wide phylogenetic inference can be approached based upon the dynamic pattern of gene content (the presence/absence of gene families), or extended gene content (absence, presence as a single-copy, or duplicates). Those methods, conceptually or technically, invoked the birth-and-death process to model the evolutionary process (gene duplication or gene loss. One common drawback is that the mechanism of new gene input, including de novo origin of new genes and the lateral gene transfer, has not been explicitly considered. In this paper, the author developed a new genome distance approach for genome phylogeny inference under the origin-birth-death stochastic process. The model takes gene duplication, gene loss and new gene input into account simultaneously. Computer simulations found that the two-genome approach is statistically difficult to distinguish between two proliferation parameters, i.e., the rate of gene duplication and the rate of new gene input. Nevertheless, it has also demonstrated the statistical feasibility for using the loss-genome distance to infer the genome phylogeny, which can avoid the large sampling problem. The strategy to study the universal tree of life was discussed and exemplified by an example.

Autophagy promotes membrane trafficking of NR2B to alleviate depression by inhibiting AQP4 expression in mice.

Depression is a high-incidence mental illness that seriously affects human health. AQP4 has been reported to be closely associated with depression, while the underlying mechanism is still unclear. This work aimed to investigate the functional role of AQP4 in depression. Depression mouse model was constructed by administration of chronic social defeat stress (CSDS). We found that AQP4 was highly expressed in the hippocampal tissues of CSDS mice. AQP4 knockdown alleviated depression and enhanced the expression of NR2B and PSD95 in CSDS mice. Moreover, primary hippocampal neurons were treated with N-methyl-d-aspartate (NMDA) to induce neuron injury. AQP4 overexpression repressed cell viability and promoted apoptosis of NMDA-treated primary hippocampal neurons. AQP4 up-regulation repressed the expression of NR2B (surface), and enhanced the expression of NR2B (intracellular), P-NR2B, CaMK II and CK2 in the NMDA-treated primary hippocampal neurons. The influence conferred by AQP4 up-regulation was abolished by KN-93 (CaMK II inhibitor) or TBB (CK2 inhibitor) treatment. Rapamycin treatment enhanced the expression of NR2B (surface), and repressed the expression of AQP4, NR2B (intracellular) and P-NR2B in the primary hippocampal neurons by activating autophagy. The activated autophagy alleviated depression in CSDS mice by repressing AQP4 expression. In conclusion, our data demonstrated that autophagy ameliorated depression by repressing AQP4 expression in mice, and AQP4 knockdown promoted membrane trafficking of NR2B and inhibited phosphorylation of NR2B via CaMK II/CK2 pathway. Thus, our work suggests that AQP4 may be a promising molecular target for the development of antidepressant drugs.

The hydrogen bond between tryptophan and the host molecule induced fluorescence enhancement.

Tryptophan is one of important amino acids in the human body, therefore its detection is particularly important. The 3,5-bis(4-pyridyl)-4-amino-1,2,4-triazole (BPAT) organic molecule was designed to be used as fluorescence detectors to detect tryptophan molecules for the interaction between the host and the guest. BPAT shows good sensitivity and selectivity towards tryptophan compared with other amino acid molecules. The limit of detection obtained from formula 3δ/KSV is considered to be 5.43 × 10-7  mol/L. We speculated that this change is mainly caused by the hydrogen bond between tryptophan and the host molecule BPAT. This conjecture was verified by the controlled experiments with other host molecules.

A Simple Evolutionary Model of Genetic Robustness After Gene Duplication.

When a dispensable gene is duplicated (referred to the ancestral dispensability denoted by O+), genetic buffering and duplicate compensation together maintain the duplicate redundancy, whereas duplicate compensation is the only mechanism when an essential gene is duplicated (referred to the ancestral essentiality denoted by O-). To investigate these evolutionary scenarios of genetic robustness, I formulated a simple mixture model for analyzing duplicate pairs with one of the following states: double dispensable (DD), semi-dispensable (one dispensable one essential, DE), or double essential (EE). This model was applied to the yeast duplicate pairs from a whole-genome duplication (WGD) occurred about 100 million years ago (mya), and the mouse duplicate pairs from a WGD occurred about more than 500 mya. Both case studies revealed that the proportion of essentiality for those duplicates with ancestral essentiality [PE(O-)] was much higher than that for those with ancestral dispensability [PE(O+)]. While it was negligible in the yeast duplicate pairs, PE(O+) (about 20%) was shown statistically significant in the mouse duplicate pairs. These findings, together, support the hypothesis that both sub-functionalization and neo-functionalization may play some roles after gene duplication, though the former may be much faster than the later.

dN/dS-H, a New Test to Distinguish Different Selection Modes in Protein Evolution and Cancer Evolution.

One of the most popular measures in the analysis of protein sequence evolution is the ratio of nonsynonymous distance (dN) to synonymous distance (dS). Under the assumption that synonymous substitutions in the coding region are selectively neutral, the dN/dS ratio can be used to statistically detect the adaptive evolution (or purifying selection) if dN/dS > 1 (or dN/dS < 1) significantly. However, due to strong structural constraints and/or variable functional constraints imposed on amino acid sites, most encoding genes in most species have demonstrated dN/dS < 1. Consequently, the statistical power for testing dN/dS = 1 may be insufficient to distinguish between different selection modes. In this paper, we propose a more powerful test, called dN/dS-H, in which a new parameter H, a relative measure of rate variation among sites, was introduced. Given the condition of strong purifying selections at some sites, the dN/dS-H model predicts dN/dS = 1-H for neutral evolution, dN/dS < 1-H for nearly neutral selection, and dN/dS > 1-H for adaptive evolution. The potential of this new method for resolving the neutral-adaptive debates is illustrated by the protein sequence evolution in vertebrates, Drosophila and yeasts, as well as somatic cancer evolution (specialized as the CN/CS-H test).

Ancestral transcriptome inference based on RNA-Seq and ChIP-seq data.

With the help of high-throughput NGS (next-generation sequencing) technologies, ancestral transcriptome reconstruction is helpful to understand the complexity of transcriptional regulatory systems that underlies the evolution of multiple cellular metazoans with sophisticated functions and distinctive morphologies. To this end, we report a new method of ancestral state inference. The new method used Ornstein-Uhlenbeck (OU) model, which is more biologically realistic, to replace the Brownian motion (BM) model and is suitable for multi-transcriptome data. Implemented in the free R package, AnceTran is specially designed for RNA-seq and ChIP-seq data, which is feasible. It should be noticed that our work will be integrated to a unified, statistically-sound phylogenetic framework to study the evolution of many other molecular phenomes such as proteomics, chromatin accessibility, methylation status, and metabolomics. We exemplify our method by a case study, using the ChIP-seq binding data of three liver-specific transcription factors and the RNA-seq liver expression data in four closely related mice species, and some technical issues are discussed.

Estimating the strength of expression conservation from high throughput RNA-seq data.

MOTIVATION: Evolution of gene across species is usually subject to the stabilizing selection to maintain the optimal expression level. While it is generally accepted that the resulting expression conservation may vary considerably among genes, statistically reliable estimation remains challenging, due to few species included in current comparative RNA-seq data with high number of unknown parameters. RESULTS: In this paper, we develop a gamma distribution model to describe how the strength of expression conservation (denoted by W) varies among genes. Given the high throughput RNA-seq datasets from multiple species, we then formulate an empirical Bayesian procedure to estimate W for each gene. Our case studies showed that those W-estimates are useful to study the evolutionary pattern of expression conservation. AVAILABILITY AND IMPLEMENTATION: Our method has been implemented in the R-package software, TreeExp, which is publically available at Github develop site https://github.com/hr1912/TreeExp. It involves three functions: estParaGamma, estParaQ and estParaWBayesian. The manual for software TreeExp is available at https://github.com/hr1912/TreeExp/tree/master/vignettes. For any question, one may contact Dr Hang Ruan (Hang.Ruan@uth.tmc.edu).

Long non-coding RNA uc.80- overexpression promotes M2 polarization of microglias to ameliorate depression in rats.

Microglia polarization is associated with the pathogenesis of depression. A previous study shows that long non-coding RNA uc.80- is down-regulated in the hippocampus of depressed rats. Thus, this article aims to investigate the role of uc.80- in microglia polarization in depression. We first established depression model rats by chronic unpredictable mild stress (CUMS) regiment. We found that hippocampus of depressed rats exhibited an increase of M1 microglias and a decrease of M2 microglias. uc.80- was down-regulated in hippocampus of depressed rats. Furthermore, the detection of behaviouristics of depressed rats showed that uc.80- overexpression alleviated depression of rats. In addition, uc.80- overexpression promoted M2 polarization of microglias in vivo and in vitro. uc.80- overexpression led to a decrease in apoptosis of hippocampal neurons in vivo and in vitro. In conclusion, our study confirms that lncRNA uc.80- overexpression ameliorates depression in rats by promoting M2 polarization of microglias. Thus, our work suggests that uc.80- may be a target gene for depression treatment.

Olfactomedin domain-containing proteins: evolution, functional divergence, expression patterns and damaging SNPs.

Olfactomedin domain-containing proteins appear to facilitate neurodevelopment, cell adhesion, intercellular interactions, and protein-protein interactions, and the disruption of their expression will lead to dramatic developmental perturbations and lethality. The aim of the present work was to study how these genes evolved in metazoans and diverged after their duplication as well as to characterize their expression profiles and detrimental mutations. We conducted an exhaustive survey of olfactomedin domain-containing genes in genomic databases, identifying 235 olfactomedin-like (OLF) proteins in 29 representative species covering all the main metazoan lineages. Phylogenetic analyses allowed us to define nine different subfamilies of OLF genes, and subfamily IX, which specifically includes two immunoglobulin domains, was identified for the first time in arthropods. Functional divergence analysis suggested that the function of this arthropod-specific OLF subfamily might have diverged from that of other subfamilies. Expression pattern analysis of OLF genes in humans and rats showed that human OLF genes tended to be highly expressed in the brain, while rat OLF genes were inclined to be expressed in the ovary and brain. We used the SIFT and PolyPhen servers in dbNSFP to distinguish deleterious mutations from neutral mutations for each member of the OLF gene family. The results showed that OLFML2B contains the most destructive SNPs (up to 61), while none of the mutations in OLFM2, OLFM4 and LPHN2 were predicted to be harmful. Taken together, these findings may not only enhance understanding of the phylogenetic relationships of the OLF family but also aid future studies on OLF protein regulation of nervous system development and immune function.

Model parameters of molecular evolution explain genomic correlations.

One long-standing research focus in evolutionary genomics is trying to resolve how biological variables (expression, essentiality, protein-protein interaction, structural stability, etc.) determine the rate of protein evolution. While these studies have considerably deepened our understanding of molecular evolution, many issues remain unsolved. In this opinion article, after having a brief survey of literatures, we establish relationships between model parameters of molecular evolution and genomic variables, based on which, most-observed genomic correlations and confounds can be explained by model parameter combinations under different conditions, which include the strength of stabilizing selection, mutational variance, expression sufficiency, gene pleiotropy, as well as the effective population size. We suggest that the problem to discern biological variable(s) that may determine the rate of protein evolution can be tackled at two levels. The first level, as discussed here, is to demonstrate how the model of molecular evolution can predict potential genomic correlations under various conditions. And the second level is to estimate genome-wide variations of model parameters (or combinations) that help to identify canonical biological variables that may underlie the rate variation among genes that ranges up to at least three magnitudes.

Understanding tissue expression evolution: from expression phylogeny to phylogenetic network.

Our understanding of tissue expression evolution in multi-cellular model organisms has been considerably advanced with the help of high-throughput technologies from EST, microarray to RNA-seq. Yet, many controversies remained unsolved, ranging from the evolutionary patterns of tissue expressions to expression phylogenetic analysis. Moreover, despite numerous reports published, it is desirable to have a general framework for study of tissue expression evolution. In this article, we first provide an up-to-date and concise review for the study of tissue expression evolution in multi-cellular organisms. While the expression phylogeny of the same tissues sampled from closely or intermediately related species largely reflects the species phylogeny, we demonstrate that phylogenetic network approach may shed some lights for our understanding of the developmental similarity and evolutionary relatedness during the multi-tissue evolution.

Statistical detection of differentially expressed genes based on RNA-seq: from biological to phylogenetic replicates.

RNA-seq has been an increasingly popular high-throughput platform to identify differentially expressed (DE) genes, which is much more reproducible and accurate than the previous microarray technology. Yet, a number of statistical issues remain to be resolved in data analysis, largely due to the high-throughput data volume and over-dispersion of read counts. These problems become more challenging for those biologists who use RNA-seq to measure genome-wide expression profiles in different combinations of sampling resources (species or genotypes) or treatments. In this paper, the author first reviews the statistical methods available for detecting DE genes, which have implemented negative binomial (NB) models and/or quasi-likelihood (QL) approaches to account for the over-dispersion problem in RNA-seq samples. The author then studies how to carry out the DE test in the context of phylogeny, i.e., RNA-seq samples are from a range of species as phylogenetic replicates. The author proposes a computational framework to solve this phylo-DE problem: While an NB model is used to account for data over-dispersion within biological replicates, over-dispersion among phylogenetic replicates is taken into account by QL, plus some special treatments for phylogenetic bias. This work helps to design cost-effective RNA-seq experiments in the field of biodiversity or phenotype plasticity that may involve hundreds of species under a phylogenetic framework.

Identification and characterization of tyrosine kinases in anole lizard indicate the conserved tyrosine kinase repertoire in vertebrates.

The tyrosine kinases (TKs) play principal roles in regulation of multicellular aspects of the organism and are implicated in many cancer types and congenital disorders. The anole lizard has recently been introduced as a model organism for laboratory-based studies of organismal function and field studies of ecology and evolution. However, the TK family of anole lizard has not been systematically identified and characterized yet. In this study, we identified 82 TK-encoding genes in the anole lizard genome and classified them into 28 subfamilies through phylogenetic analysis, with no member from ROS and STYK1 subfamilies identified. Although TK domain sequences and domain organization in each subfamily were conserved, the total number of TKs in different species was much variable. In addition, extensive evolutionary analysis in metazoans indicated that TK repertoire in vertebrates tends to be remarkably stable. Phylogenetic analysis of Eph subfamily indicated that the divergence of EphA and EphB occurred prior to the whole genome duplication (WGD) but after the split of Urochordates and vertebrates. Moreover, the expression pattern analysis of lizard TK genes among 9 different tissues showed that 14 TK genes exhibited tissue-specific expression and 6 TK genes were widely expressed. Comparative analysis of TK expression suggested that the tissue specifically expressed genes showed different expression pattern but the widely expressed genes showed similar pattern between anole lizard and human. These results may provide insights into the evolutionary diversification of animal TK genes and would aid future studies on TK protein regulation of key growth and developmental processes.