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.

Can biomarkers identified from the uterine fluid transcriptome be used to establish a noninvasive endometrial receptivity prediction tool? A proof-of-concept study.

BACKGROUND: Embryo implantation in a receptive endometrium is crucial for successful pregnancy. Endometrial receptivity (ER) prediction tools based on endometrial transcriptome biomarkers by endometrial biopsy have been used to guide successful embryo implantation in in vitro fertilization (IVF) patients. However, no reliable noninvasive ER prediction method has been established, and one is greatly needed. We aimed to identify biomarkers from uterine fluid transcriptomic sequencing data for establishing noninvasive ER prediction tool and to evaluate its clinical application potential in patients undergoing IVF. METHODS: The non-invasive RNA-seq based endometrial receptivity test (nirsERT) was established by analyzing transcriptomic profile of 144 uterine fluid specimens (LH + 5, LH + 7, and LH + 9) at three different receptive status from 48 IVF patients with normal ER in combination with random forest algorithm. Subsequently, 22 IVF patients who underwent frozen-thaw blastocyst transfer were recruited and analyzed the correlation between the predicted results of nirsERT and pregnancy outcomes. RESULTS: A total of 864 ER-associated differentially expressed genes (DEGs) involved in biological processes associated with endometrium-embryo crosstalk, including protein binding, signal reception and transduction, biomacromolecule transport and cell-cell adherens junctions, were selected. Subsequently, a nirsERT model consisting of 87 markers and 3 hub genes was established using a random forest algorithm. 10-fold cross-validation resulted in a mean accuracy of 93.0%. A small cohort (n = 22) retrospective observation shows that 77.8% (14/18) of IVF patients predicted with a normal WOI had successful intrauterine pregnancies, while none of the 3 patients with a displaced WOI had successful pregnancies. One patient failed due to poor sequencing data quality. CONCLUSIONS: NirsERT based on uterine fluid transcriptome biomarkers can predict the WOI period relatively accurately and may serve as a noninvasive, reliable and same cycle test for ER in reproductive clinics. TRIAL REGISTRATION: Chinese Clinical Trial Registry: ChiCTR-DDD-17013375. Registered 14 November 2017, http://www.chictr.org.cn/index.aspx .

Preimplantation genetic testing for human blastocysts with potential parental contamination using a quantitative parental contamination test (qPCT): an evidence-based study.

RESEARCH QUESTION: Is it possible to develop a quantitative method for detecting parental DNA contamination in conventional IVF using preimplantation genetic testing for aneuploidy (PGT-A)? DESIGN: In this study, a quantification method was established for the parental contamination test (qPCT), which ensured more reliable results, and then verified its effectiveness for vitrified conventional IVF embryos. A total of 120 surplus vitrified blastocysts from patients who underwent prior routine IVF cycles were available for study. RESULTS: The results of the prospective clinical study of qPCT-PGT-A showed that the maternal contamination rate was 0.83% (1/120) and that the risk of paternal contamination was negligible. The 24 frozen embryo transfer cycles resulted in 16 clinical pregnancies, including 13 live births, one late inevitable miscarriage and two ongoing pregnancies. CONCLUSIONS: The risk of PGT in embryos with potential parental contamination is relatively low, and PGT-A is applicable for vitrified conventional IVF embryos.

Clinical application of noninvasive chromosomal screening for elective single-blastocyst transfer in frozen-thawed cycles.

BACKGROUND: The objective of this study was to explore the clinical application of noninvasive chromosomal screening (NICS) for elective single-blastocyst transfer (eSBT) in frozen-thawed cycles. METHODS: This study retrospectively analysed the data of 212 frozen-thawed single-blastocyst transfers performed in our centre from January 2019 to July 2019. The frozen embryos were selected based on morphological grades and placed in preincubation for 6 h after warming. Then spent microdroplet culture media of frozen-thawed blastocysts were harvested and subjected to NICS. The clinical outcomes were evaluated and further stratified analysis were performed, especially different fertilization approaches. RESULTS: The clinical pregnancy, ongoing pregnancy, and live birth rates in the euploidy group were significantly higher than those in the aneuploidy group (56.2% versus 29.4%) but were nonsignificantly different from those in the chaotic abnormal/NA embryos group (56.2% versus 60.4%). Compared with day6 (D6) blastocysts, D5 blastocysts had a nonsignificantly different euploidy rate (40.4% versus 48.1%, P = 0.320) but significantly increased clinical pregnancy (57.7% versus 22.2%, P < 0.001), ongoing pregnancy (48.1% versus 14.8%, P < 0.001), and live birth rates (48.1% versus 13.0%, P < 0.001). The percentage of chaotic abnormal/NA embryos group was significantly higher among D5 embryos than among D6 embryos (30.1% versus 11.1%, P = 0.006). The percentage of aneuploid embryos was higher among the embryos with lower morphological quality(21.5% among 'good' embryos versus 34.6% among 'fair' embryos versus 46.0% among 'poor' embryos, P = 0.013); correspondingly, the overall clinical pregnancy, ongoing pregnancy and live birth rate rates showed similar declines. CONCLUSIONS: NICS combined with morphological assessment is an effective tool to guide frozen-thawed SBT. The optimal embryo for SBT is a 'euploid embryo with good morphology', followed sequentially by a 'chaotic abnormal/NA embryo with good morphology', 'euploid embryo with fair morphology', and 'chaotic abnormal/NA embryo with fair morphology'.

Non-invasive preimplantation genetic testing for conventional IVF blastocysts.

BACKGROUND: Previous studies suggested that non-invasive preimplantation genetic testing (niPGT) for intracytoplasmic sperm injection (ICSI) blastocysts can be used to identify chromosomal ploidy and chromosomal abnormalities. Here, we report the feasibility and performance of niPGT for conventional in vitro fertilization (IVF) blastocysts. METHODS: This was a prospective observational study. In the preclinical stage, whole genome amplification and NGS were performed using the sperm spent culture medium (SCM). Then, trophectoderm (TE) biopsies and corresponding SCM derived from 27 conventional IVF monopronuclear embryos were collected. In the clinical stage, samples from 25 conventional IVF cycles and 37 ICSI cycles from April 2020-August 2021 were collected for performance evaluation. RESULTS: Preclinically, we confirmed failed sperm DNA amplification under the current amplification system. Subsequent niPGT from the 27 monopronuclear blastocysts showed 69.2% concordance with PGT results of corresponding TE biopsies. In the clinical stage, no paternal contamination was observed in any of the 161 SCM samples from conventional IVF. While maternal contamination was observed in 29.8% (48/161) SCM samples, only 2.5% (4/161) samples had a contamination ratio ≥ 50%. Compared with that of TE biopsy, the performances of NiPGT from 161 conventional IVF embryos and 122 ICSI embryos were not significantly different (P > 0.05), with ploidy concordance rates of 75% and 74.6% for IVF and ICSI methods, respectively. Finally, evaluation of the euploid probability of embryos with different types of niPGT results showed prediction probabilities of 82.8%, 77.8%, 62.5%, 50.0%, 40.9% and 18.4% for euploidy, sex-chromosome mosaics only, low-level mosaics, multiple abnormal chromosomes, high-level mosaics and aneuploidy, respectively. CONCLUSIONS: Our research results preliminarily confirm that the niPGT approach using SCM from conventional IVF has comparable performance with ICSI and might broadening the application scope of niPGT.

Non-invasive embryo selection strategy for clinical IVF to avoid wastage of potentially competent embryos.

RESEARCH QUESTION: Can a non-invasive embryo transfer strategy provide a reference for embryo selection to be established? DESIGN: Chromosome sequencing of 345 paired blastocyst culture medium and whole blastocyst samples was carried out and a non-invasive embryo grading system was developed based on the random forest machine learning algorithm to predict blastocyst ploidy. The system was validated in 266 patients, and a blinded prospective observational study was conducted to investigate clinical outcomes between machine learning-guided and traditional non-invasive preimplantation genetic testing for aneuploidy (niPGT-A) analyses. Embryos were graded as A, B or C according to their euploidy probability levels predicted by non-invasive chromosomal screening (NICS). RESULTS: Higher live birth rate was observed in A- versus C-grade embryos (50.4% versus 27.1%, P = 0.006) and B- versus C-grade embryos (45.3% versus 27.1%, P = 0.022) and lower miscarriage rate in A- versus C-grade embryos (15.9% versus 33.3%, P = 0.026) and B- versus C-grade embryos (14.3% versus 33.3%, P = 0.021). The embryo utilization rate was significantly higher through the machine learning strategy than the conventional dichotomic judgment of euploidy or aneuploidy in the niPGT-A analysis (78.8% versus 57.9%, P < 0.001). Better outcomes were observed in A- and B-grade embryos versus C-grade embryos and higher embryo utilization rates through the machine learning strategy compared with traditional niPGT-A analysis. CONCLUSION: A machine learning guided embryo grading system can be used to optimize embryo selection and avoid wastage of potential embryos.

The role of transcriptomic biomarkers of endometrial receptivity in personalized embryo transfer for patients with repeated implantation failure.

BACKGROUND: Window of implantation (WOI) displacement is one of the endometrial origins of embryo implantation failure, especially repeated implantation failure (RIF). An accurate prediction tool for endometrial receptivity (ER) is extraordinarily needed to precisely guide successful embryo implantation. We aimed to establish an RNA-Seq-based endometrial receptivity test (rsERT) tool using transcriptomic biomarkers and to evaluate the benefit of personalized embryo transfer (pET) guided by this tool in patients with RIF. METHODS: This was a two-phase strategy comprising tool establishment with retrospective data and benefit evaluation with a prospective, nonrandomized controlled trial. In the first phase, rsERT was established by sequencing and analyzing the RNA of endometrial tissues from 50 IVF patients with normal WOI timing. In the second phase, 142 patients with RIF were recruited and grouped by patient self-selection (experimental group, n = 56; control group, n = 86). pET guided by rsERT was performed in the experimental group and conventional ET in the control group. RESULTS: The rsERT, comprising 175 biomarker genes, showed an average accuracy of 98.4% by using tenfold cross-validation. The intrauterine pregnancy rate (IPR) of the experimental group (50.0%) was significantly improved compared to that (23.7%) of the control group (RR, 2.107; 95% CI 1.159 to 3.830; P = 0.017) when transferring day-3 embryos. Although not significantly different, the IPR of the experimental group (63.6%) was still 20 percentage points higher than that (40.7%) of the control group (RR, 1.562; 95% CI 0.898 to 2.718; P = 0.111) when transferring blastocysts. CONCLUSIONS: The rsERT was developed to accurately predict the WOI period and significantly improve the pregnancy outcomes of patients with RIF, indicating the clinical potential of rsERT-guided pET. Trial registration Chinese Clinical Trial Registry: ChiCTR-DDD-17013375. Registered 14 November 2017, http://www.chictr.org.cn/index.aspx.

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.

Brownian model of transcriptome evolution and phylogenetic network visualization between tissues.

While phylogenetic analysis of transcriptomes of the same tissue is usually congruent with the species tree, the controversy emerges when multiple tissues are included, that is, whether species from the same tissue are clustered together, or different tissues from the same species are clustered together. Recent studies have suggested that phylogenetic network approach may shed some lights on our understanding of multi-tissue transcriptome evolution; yet the underlying evolutionary mechanism remains unclear. In this paper we develop a Brownian-based model of transcriptome evolution under the phylogenetic network that can statistically distinguish between the patterns of species-clustering and tissue-clustering. Our model can be used as a null hypothesis (neutral transcriptome evolution) for testing any correlation in tissue evolution, can be applied to cancer transcriptome evolution to study whether two tumors of an individual appeared independently or via metastasis, and can be useful to detect convergent evolution at the transcriptional level.

Effective estimation of the minimum number of amino acid residues required for functional divergence between duplicate genes.

One of hot research foci has always been predicting amino acid residues underlying functional divergence after gene duplication, as those predicted sites can be used as candidates for further functional experimentations. It is important and interesting to know how many sites, on average, may have been responsible for the functional divergence between duplicate genes. In this article, we studied two basic types of functional divergence (type-I and type-II) in depth in order to give an accurate estimation of functional divergence-related sites. Type-I divergences result from altered functional constraints (i.e., different evolutionary rates) between duplicate genes, whereas type-II divergences refer to residues that are conserved by functional constraints but exhibit different physicochemical properties (e.g., charge or hydrophobicity) between duplicates. An effective site number (NE) strategy was applied in our study, which implements a stepwise regression model to calculate the minimum number of residues responsible for functional divergence without choosing preset threshold. We found that NE-determined cut-off value varies among different duplicate pairs, suggesting that empirical cutoff value is not suitable for every case. Under our standard NE calculation method, we estimated less than 15% of residues that are required for paralogous gene functional divergence. Finally, we established a database, DIVERGE-D, as a public resource for the predicted NE sites between two paralogs in this study, which can be used as candidates for further biological engineering and experimentation.

An update of DIVERGE software for functional divergence analysis of protein family.

DIVERGE is a software system for phylogeny-based analyses of protein family evolution and functional divergence. It provides a suite of statistical tools for selection and prioritization of the amino acid sites that are responsible for the functional divergence of a gene family. The synergistic efforts of DIVERGE and other methods have convincingly demonstrated that the pattern of rate change at a particular amino acid site may contain insightful information about the underlying functional divergence following gene duplication. These predicted sites may be used as candidates for further experiments. We are now releasing an updated version of DIVERGE with the following improvements: 1) a feasible approach to examining functional divergence in nearly complete sequences by including deletions and insertions (indels); 2) the calculation of the false discovery rate of functionally diverging sites; 3) estimation of the effective number of functional divergence-related sites that is reliable and insensitive to cutoffs; 4) a statistical test for asymmetric functional divergence; and 5) a new method to infer functional divergence specific to a given duplicate cluster. In addition, we have made efforts to improve software design and produce a well-written software manual for the general user.

Age distribution patterns of human gene families: divergent for Gene Ontology categories and concordant between different subcellular localizations.

The age distribution of gene duplication events within the human genome exhibits two waves of duplications along with an ancient component. However, because of functional constraint differences, genes in different functional categories might show dissimilar retention patterns after duplication. It is known that genes in some functional categories are highly duplicated in the early stage of vertebrate evolution. However, the correlations of the age distribution pattern of gene duplication between the different functional categories are still unknown. To investigate this issue, we developed a robust pipeline to date the gene duplication events in the human genome. We successfully estimated about three-quarters of the duplication events within the human genome, along with the age distribution pattern in each Gene Ontology (GO) slim category. We found that some GO slim categories show different distribution patterns when compared to the whole genome. Further hierarchical clustering of the GO slim functional categories enabled grouping into two main clusters. We found that human genes located in the duplicated copy number variant regions, whose duplicate genes have not been fixed in the human population, were mainly enriched in the groups with a high proportion of recently duplicated genes. Moreover, we used a phylogenetic tree-based method to date the age of duplications in three signaling-related gene superfamilies: transcription factors, protein kinases and G-protein coupled receptors. These superfamilies were expressed in different subcellular localizations. They showed a similar age distribution as the signaling-related GO slim categories. We also compared the differences between the age distributions of gene duplications in multiple subcellular localizations. We found that the distribution patterns of the major subcellular localizations were similar to that of the whole genome. This study revealed the whole picture of the evolution patterns of gene functional categories in the human genome.

A novel non-invasive embryo evaluation method (NICS-Timelapse) with enhanced predictive precision and clinical impact.

The selection of the finest possible embryo in in-vitro fertilization (IVF) was crucial and revolutionary, particularly when just one embryo is transplanted to lessen the possibility of multiple pregnancies. However, practical usefulness of currently used methodologies may be constrained. Here, we established a novel non-invasive embryo evaluation method that combines non-invasive chromosomal screening (NICS) and Timelapse system along with artificial intelligence algorithms. With an area under the curve (AUC) of 0.94 and an accuracy of 0.88, the NICS-Timelapse model was able to predict blastocyst euploidy. The performance of the model was further evaluated using 75 patients in various clinical settings. The clinical pregnancy and live birth rates of embryos predicted by the NICS-Timelapse model, showing that embryos with higher euploid probabilities were associated with higher clinical pregnancy and live birth rates. These results demonstrated the NICS-Timelapse model's significantly wider application in clinical IVF due to its excellent accuracy and noninvasiveness.

ClinVar and HGMD genomic variant classification accuracy has improved over time, as measured by implied disease burden.

BACKGROUND: Curated databases of genetic variants assist clinicians and researchers in interpreting genetic variation. Yet, these databases contain some misclassified variants. It is unclear whether variant misclassification is abating as these databases rapidly grow and implement new guidelines. METHODS: Using archives of ClinVar and HGMD, we investigated how variant misclassification has changed over 6 years, across different ancestry groups. We considered inborn errors of metabolism (IEMs) screened in newborns as a model system because these disorders are often highly penetrant with neonatal phenotypes. We used samples from the 1000 Genomes Project (1KGP) to identify individuals with genotypes that were classified by the databases as pathogenic. Due to the rarity of IEMs, nearly all such classified pathogenic genotypes indicate likely variant misclassification in ClinVar or HGMD. RESULTS: While the false-positive rates of both ClinVar and HGMD have improved over time, HGMD variants currently imply two orders of magnitude more affected individuals in 1KGP than ClinVar variants. We observed that African ancestry individuals have a significantly increased chance of being incorrectly indicated to be affected by a screened IEM when HGMD variants are used. However, this bias affecting genomes of African ancestry was no longer significant once common variants were removed in accordance with recent variant classification guidelines. We discovered that ClinVar variants classified as Pathogenic or Likely Pathogenic are reclassified sixfold more often than DM or DM? variants in HGMD, which has likely resulted in ClinVar's lower false-positive rate. CONCLUSIONS: Considering misclassified variants that have since been reclassified reveals our increasing understanding of rare genetic variation. We found that variant classification guidelines and allele frequency databases comprising genetically diverse samples are important factors in reclassification. We also discovered that ClinVar variants common in European and South Asian individuals were more likely to be reclassified to a lower confidence category, perhaps due to an increased chance of these variants being classified by multiple submitters. We discuss features for variant classification databases that would support their continued improvement.

Histone modification pattern evolution after yeast gene duplication.

BACKGROUND: Gene duplication and subsequent functional divergence especially expression divergence have been widely considered as main sources for evolutionary innovations. Many studies evidenced that genetic regulatory network evolved rapidly shortly after gene duplication, thus leading to accelerated expression divergence and diversification. However, little is known whether epigenetic factors have mediated the evolution of expression regulation since gene duplication. In this study, we conducted detailed analyses on yeast histone modification (HM), the major epigenetics type in this organism, as well as other available functional genomics data to address this issue. RESULTS: Duplicate genes, on average, share more common HM-code patterns than random singleton pairs in their promoters and open reading frames (ORF). Though HM-code divergence between duplicates in both promoter and ORF regions increase with their sequence divergence, the HM-code in ORF region evolves slower than that in promoter region, probably owing to the functional constraints imposed on protein sequences. After excluding the confounding effect of sequence divergence (or evolutionary time), we found the evidence supporting the notion that in yeast, the HM-code may co-evolve with cis- and trans-regulatory factors. Moreover, we observed that deletion of some yeast HM-related enzymes increases the expression divergence between duplicate genes, yet the effect is lower than the case of transcription factor (TF) deletion or environmental stresses. CONCLUSIONS: Our analyses demonstrate that after gene duplication, yeast histone modification profile between duplicates diverged with evolutionary time, similar to genetic regulatory elements. Moreover, we found the evidence of the co-evolution between genetic and epigenetic elements since gene duplication, together contributing to the expression divergence between duplicate genes.

Predominant gain of promoter TATA box after gene duplication associated with stress responses.

TATA box, the core promoter element, exists in a broad range of eukaryotes, and the expression of TATA-containing genes usually responds to various environmental stresses. Hence, the evolution of TATA-box in duplicate genes may provide some clues for the interrelationship among environmental stress, expression differentiation, and duplicate gene preservation. In the present study, we observed that the TATA box is significantly overrepresented in duplicate genes compared with singletons in human, worm, Arabidopsis, and yeast genomes. We then conducted an extensive functional genomic analysis to investigate the evolution of TATA box along over 700 yeast gene family phylogenies. After reconstructing the ancestral TATA-box states (presence or absence), we found that significantly higher numbers of TATA box gain events than loss events had occurred after yeast gene duplications-the overall gain-loss ratio is about 3-4 to 1. Interestingly, these TATA-gain duplicate genes on average have experienced greater expression divergence from the ancestral expression states than their most closely related TATA-less duplicate partners, but only under environmental stress conditions (asymmetric evolution); indeed, under normal physiological conditions, they have similar expression divergence (symmetric evolution). Moreover, we showed that TATA-gain duplicates are enriched in stress-associated functional categories but that is not the case for TATA-ancestral duplicates (those inherited from their ancestors prior to duplication). Together, we conclude that after the gene duplication, gain of the TATA box in duplicate promoters may have played an important role in yeast duplicate preservation by accelerating expression divergence that may facilitate the adaptive evolution of the organism in response to environmental changes.

The role of exome sequencing in newborn screening for inborn errors of metabolism.

Public health newborn screening (NBS) programs provide population-scale ascertainment of rare, treatable conditions that require urgent intervention. Tandem mass spectrometry (MS/MS) is currently used to screen newborns for a panel of rare inborn errors of metabolism (IEMs)1-4. The NBSeq project evaluated whole-exome sequencing (WES) as an innovative methodology for NBS. We obtained archived residual dried blood spots and data for nearly all IEM cases from the 4.5 million infants born in California between mid-2005 and 2013 and from some infants who screened positive by MS/MS, but were unaffected upon follow-up testing. WES had an overall sensitivity of 88% and specificity of 98.4%, compared to 99.0% and 99.8%, respectively for MS/MS, although effectiveness varied among individual IEMs. Thus, WES alone was insufficiently sensitive or specific to be a primary screen for most NBS IEMs. However, as a secondary test for infants with abnormal MS/MS screens, WES could reduce false-positive results, facilitate timely case resolution and in some instances even suggest more appropriate or specific diagnosis than that initially obtained. This study represents the largest, to date, sequencing effort of an entire population of IEM-affected cases, allowing unbiased assessment of current capabilities of WES as a tool for population screening.

Identifying differentially expressed genes in human acute leukemia and mouse brain microarray datasets utilizing QTModel.

One of the essential issues in microarray data analysis is to identify differentially expressed genes (DEGs) under different experimental treatments. In this article, a statistical procedure was proposed to identify the DEGs for gene expression data with or without missing observations from microarray experiment with one- or two-treatment factors. An F statistic based on Henderson method III was constructed to test the significance of differential expression for each gene under different treatment(s) levels. The cutoff P value was adjusted to control the experimental-wise false discovery rate. A human acute leukemia dataset corrected from 38 leukemia patients was reanalyzed by the proposed method. In comparison to the results from significant analysis of microarray (SAM) and microarray analysis of variance (MAANOVA), it was indicated that the proposed method has similar performance with MAANOVA for data with one-treatment factor, but MAANOVA cannot directly handle missing data. In addition, a mouse brain dataset collected from six brain regions of two inbred strains (two-treatment factors) was reanalyzed to identify genes with distinct regional-specific expression patterns. The results showed that the proposed method could identify more distinct regional-specific expression patterns than the previous analysis of the same dataset. Moreover, a computer program was developed and incorporated in the software QTModel, which is freely available at http://ibi.zju.edu.cn/software/qtmodel .

Uncovering genetic regulatory network divergence between duplicate genes using yeast eQTL landscape.

Genetical genomics, a novel approach combining microarray technology and quantitative genetic analysis, aims to identify the expression quantitative trait loci (eQTLs), which may regulate the genome-wide expression pattern. In this article, we have studied yeast genomic eQTL data to investigate how the genetic eQTL regulation of ancestral gene has diverged since gene duplication. Our findings are as follows: (i) Duplicate genes have higher heritability for gene expression than single-copy genes, but little difference in their epistasis and directional effect. (ii) The divergence of trans-acting eQTLs between duplicate pairs increases with the evolutionary time since gene duplication. (iii) Trans-acting eQTL divergence can explain about 21% of the variation in expression divergence between duplicate pairs with K(S)<2.0, which increases to 27% when the transcription factor (TF)-target interaction divergence is combined. Moreover, under the partial correlation analysis, trans-acting eQTL divergence seems make a bigger contribution to expression divergence than does TF divergence. (iv) Trans-acting eQTL divergence between duplicate pairs is correlated with gene ontology categories "Biological processes" and "Cellular components," but not with "Molecular functions," and is related to fitness defect under treatment conditions, but not with fitness under normal condition. We conclude that eQTL analysis provides a novel approach to explore the effect of gene duplications on the genetic regulatory network.

Comparative Transcriptome Analyses Reveal the Role of Conserved Function in Electric Organ Convergence Across Electric Fishes.

The independent origins of multiple electric organs (EOs) of fish are fascinating examples of convergent evolution. However, comparative transcriptomics of different electric fish lineages are scarce. In this study, we found that the gene expression of EOs and skeletal muscles from three lineages (Mormyroidea, Siluriformes, and Gymnotiformes) tended to cluster together based on the species of origin, irrespective of the organ from which they are derived. A pairwise comparison of differentially expressed genes (DEGs) revealed that no less than half of shared DEGs exhibited parallel expression differentiation, indicating conserved directionality of differential expression either in or between lineages, but only a few shared DEGs were identified across all focal species. Nevertheless, the functional enrichment analysis of DEGs indicated that there were more parallel gene expression changes at the level of pathways and biological functions. Therefore, we may conclude that there is no parallel evolution of the entire transcriptomes of EOs among different lineages. Further, our results support the hypothesis that it is not different genes but conserved biological functions that play a crucial role in the convergence of complex phenotypes. This study provides insight into the genetic basis underlying the EO convergent evolution; however, more studies in different cases will be needed to demonstrate whether this pattern can be extended to other cases to derive a general rule for convergent evolution.