Non-homology-based prediction of gene functions in maize (\u3ci\u3eZea mays\u3c/i\u3e ssp. \u3ci\u3emays\u3c/i\u3e)

Advances in genome sequencing and annotation have eased the difficulty of identifying new gene sequences. Predicting the functions of these newly identified genes remains challenging. Genes descended from a common ancestral sequence are likely to have common functions.As a result, homology is widely used for gene function prediction. This means functional annotation errors also propagate from one species to another. Several approaches based on machine learning classification algorithms were evaluated for their ability to accurately predict gene function from non-homology gene features. Among the eight supervised classification algorithms evaluated, random forest-based prediction consistently provided the most accurate gene function prediction. Non-homology-based functional annotation provides complementary strengths to homology-based annotation, with higher average performance in Biological Process GO terms, the domain where homology-based functional annotation performs the worst, and weaker performance in Molecular Function GO terms, the domain where the accuracy of homology-based functional annotation is highest. GO prediction models trained with homology-based annotations were able to successfully predict annotations from a manually curated “gold standard” GO annotation set. Non-homology-based functional annotation based on machine learning may ultimately prove useful both as a method to assign predicted functions to orphan genes which lack functionally characterized homologs, and to identify and correct functional annotation errors which were propagated through homology-based functional annotations

Salvianolic Acid B Prevents Bone Loss in Prednisone-Treated Rats through Stimulation of Osteogenesis and Bone Marrow Angiogenesis

Glucocorticoid (GC) induced osteoporosis (GIO) is caused by the long-term use of GC for treatment of autoimmune and inflammatory diseases. The GC related disruption of bone marrow microcirculation and increased adipogenesis contribute to GIO development. However, neither currently available anti-osteoporosis agent is completely addressed to microcirculation and bone marrow adipogenesis. Salvianolic acid B (Sal B) is a polyphenolic compound from a Chinese herbal medicine, Salvia miltiorrhiza Bunge. The aim of this study was to determine the effects of Sal B on osteoblast bone formation, angiogenesis and adipogenesis-associated GIO by performing marrow adipogenesis and microcirculation dilation and bone histomorphometry analyses. (1) In vivo study: Bone loss in GC treated rats was confirmed by significantly decreased BMD, bone strength, cancellous bone mass and architecture, osteoblast distribution, bone formation, marrow microvessel density and diameter along with down-regulation of marrow BMPs expression and increased adipogenesis. Daily treatment with Sal B (40 mg/kg/d) for 12 weeks in GC male rats prevented GC-induced cancellous bone loss and increased adipogenesis while increasing cancellous bone formation rate with improved local microcirculation by capillary dilation. Treatment with Sal B at a higher dose (80 mg/kg/d) not only prevented GC-induced osteopenia, but also increased cancellous bone mass and thickness, associated with increase of marrow BMPs expression, inhibited adipogenesis and further increased microvessel diameters. (2) In vitro study: In concentration from 10−6 mol/L to 10−7 mol/L, Sal B stimulated bone marrow stromal cell (MSC) differentiation to osteoblast and increased osteoblast activities, decreased GC associated adipogenic differentiation by down-regulation of PPARγ mRNA expression, increased Runx2 mRNA expression without osteoblast inducement, and, furthermore, Sal B decreased Dickkopf-1 and increased β-catenin mRNA expression with or without adipocyte inducement in MSC. We conclude that Sal B prevented bone loss in GC-treated rats through stimulation of osteogenesis, bone marrow angiogenesis and inhibition of adipogenesis

A compendium of genetic regulatory effects across pig tissues

The Farm Animal Genotype-Tissue Expression (FarmGTEx) project has been established to develop a public resource of genetic regulatory variants in livestock, which is essential for linking genetic polymorphisms to variation in phenotypes, helping fundamental biological discovery and exploitation in animal breeding and human biomedicine. Here we show results from the pilot phase of PigGTEx by processing 5,457 RNA-sequencing and 1,602 whole-genome sequencing samples passing quality control from pigs. We build a pig genotype imputation panel and associate millions of genetic variants with five types of transcriptomic phenotypes in 34 tissues. We evaluate tissue specificity of regulatory effects and elucidate molecular mechanisms of their action using multi-omics data. Leveraging this resource, we decipher regulatory mechanisms underlying 207 pig complex phenotypes and demonstrate the similarity of pigs to humans in gene expression and the genetic regulation behind complex phenotypes, supporting the importance of pigs as a human biomedical model.</p

PigBiobank: a valuable resource for understanding genetic and biological mechanisms of diverse complex traits in pigs

© The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact [email protected] fully unlock the potential of pigs as both agricultural species for animal-based protein food and biomedical models for human biology and disease, a comprehensive understanding of molecular and cellular mechanisms underlying various complex phenotypes in pigs and how the findings can be translated to other species, especially humans, are urgently needed. Here, within the Farm animal Genotype-Tissue Expression (FarmGTEx) project, we build the PigBiobank (http://pigbiobank.farmgtex.org) to systematically investigate the relationships among genomic variants, regulatory elements, genes, molecular networks, tissues and complex traits in pigs. This first version of the PigBiobank curates 71 885 pigs with both genotypes and phenotypes from over 100 pig breeds worldwide, covering 264 distinct complex traits. The PigBiobank has the following functions: (i) imputed sequence-based genotype-phenotype associations via a standardized and uniform pipeline, (ii) molecular and cellular mechanisms underlying trait-associations via integrating multi-omics data, (iii) cross-species gene mapping of complex traits via transcriptome-wide association studies, and (iv) high-quality results display and visualization. The PigBiobank will be updated timely with the development of the FarmGTEx-PigGTEx project, serving as an open-access and easy-to-use resource for genetically and biologically dissecting complex traits in pigs and translating the findings to other species.National Natural Science Foundation of China [32022078]; National Key R&D Program of China [2022YFF1000900]; Local Innovative and Research Teams Project of Guangdong Province [2019BT02N630]; China Agriculture Research System [CARS-35]. Funding for open access charge: National Natural Science Foundation of China [32022078].Peer reviewe

Study of the Interference Rejection Based on ATF in DSSS

A novel, adaptive time-frequency interference rejection for suppressing the interference in Direct Sequence Spread Spectrum communication systems is studied in this paper. For a time-localized interference, excision is performed in the time domain. When a frequency domain excision is decided, an adaptive sub-band transform is employed. The most proper sub-spectral split is defined according to the spectrum of the received signal. This joint treatment of time and frequency domains overcomes the performance limitations of the fixed transform-based excision algorithms. The paper presents the performance evaluation of the rejection technique via the analytical and computer simulation tools. It is found that the analytical performance tools match up with the computer simulation results. It is also shown that the proposed adaptive time-frequency interference rejection consistently outperforms the existing interference excision techniques in the literature

Study of the Interference Rejection Based on ATF in DSSS

A novel, adaptive time-frequency interference rejection for suppressing the interference in Direct Sequence Spread Spectrum communication systems is studied in this paper. For a time-localized interference, excision is performed in the time domain. When a frequency domain excision is decided, an adaptive sub-band transform is employed. The most proper sub-spectral split is defined according to the spectrum of the received signal. This joint treatment of time and frequency domains overcomes the performance limitations of the fixed transform-based excision algorithms. The paper presents the performance evaluation of the rejection technique via the analytical and computer simulation tools. It is found that the analytical performance tools match up with the computer simulation results. It is also shown that the proposed adaptive time-frequency interference rejection consistently outperforms the existing interference excision techniques in the literature

Non-homology-based prediction of gene functions in maize (Zea mays ssp. mays)

Advances in genome sequencing and annotation have eased the difficulty of identifying new gene sequences. Predicting the functions of these newly identified genes remains challenging. Genes descended from a common ancestral sequence are likely to have common functions. As a result, homology is widely used for gene function pre- diction. This means functional annotation errors also propagate from one species to another. Several approaches based on machine learning classification algorithms were evaluated for their ability to accurately predict gene function from non-homology gene features. Among the eight supervised classification algorithms evaluated, random- forest-based prediction consistently provided the most accurate gene function predic- tion. Non-homology-based functional annotation provides complementary strengths to homology-based annotation, with higher average performance in Biological Process GO terms, the domain where homology-based functional annotation performs the worst, and weaker performance in Molecular Function GO terms, the domain where the accuracy of homology-based functional annotation is highest. GO prediction models trained with homology-based annotations were able to successfully predict annotations from a manually curated “gold standard” GO annotation set. Non-homology-based functional annotation based on machine learning may ultimately prove useful both as a method to assign predicted functions to orphan genes which lack functionally characterized homologs, and to identify and correct functional annotation errors which were propagated through homology-based functional annotations