Employing machine learning for reliable miRNA target identification in plants

<p>Abstract</p> <p>Background</p> <p>miRNAs are ~21 nucleotide long small noncoding RNA molecules, formed endogenously in most of the eukaryotes, which mainly control their target genes post transcriptionally by interacting and silencing them. While a lot of tools has been developed for animal miRNA target system, plant miRNA target identification system has witnessed limited development. Most of them have been centered around exact complementarity match. Very few of them considered other factors like multiple target sites and role of flanking regions.</p> <p>Result</p> <p>In the present work, a Support Vector Regression (SVR) approach has been implemented for plant miRNA target identification, utilizing position specific dinucleotide density variation information around the target sites, to yield highly reliable result. It has been named as p-TAREF (plant-Target Refiner). Performance comparison for p-TAREF was done with other prediction tools for plants with utmost rigor and where p-TAREF was found better performing in several aspects. Further, p-TAREF was run over the experimentally validated miRNA targets from species like <it>Arabidopsis</it>, <it>Medicago</it>, Rice and Tomato, and detected them accurately, suggesting gross usability of p-TAREF for plant species. Using p-TAREF, target identification was done for the complete Rice transcriptome, supported by expression and degradome based data. miR156 was found as an important component of the Rice regulatory system, where control of genes associated with growth and transcription looked predominant. The entire methodology has been implemented in a multi-threaded parallel architecture in Java, to enable fast processing for web-server version as well as standalone version. This also makes it to run even on a simple desktop computer in concurrent mode. It also provides a facility to gather experimental support for predictions made, through on the spot expression data analysis, in its web-server version.</p> <p>Conclusion</p> <p>A machine learning multivariate feature tool has been implemented in parallel and locally installable form, for plant miRNA target identification. The performance was assessed and compared through comprehensive testing and benchmarking, suggesting a reliable performance and gross usability for transcriptome wide plant miRNA target identification.</p

Insights from computational modeling in inflammation and acute rejection in limb transplantation

Acute skin rejection in vascularized composite allotransplantation (VCA) is the major obstacle for wider adoption in clinical practice. This study utilized computational modeling to identify biomarkers for diagnosis and targets for treatment of skin rejection. Protein levels of 14 inflammatory mediators in skin and muscle biopsies from syngeneic grafts [n = 10], allogeneic transplants without immunosuppression [n = 10] and allografts treated with tacrolimus [n = 10] were assessed by multiplexed analysis technology. Hierarchical Clustering Analysis, Principal Component Analysis, Random Forest Classification and Multinomial Logistic Regression models were used to segregate experimental groups. Based on Random Forest Classification, Multinomial Logistic Regression and Hierarchical Clustering Analysis models, IL-4, TNF-α and IL-12p70 were the best predictors of skin rejection and identified rejection well in advance of histopathological alterations. TNF-α and IL-12p70 were the best predictors of muscle rejection and also preceded histopathological alterations. Principal Component Analysis identified IL-1α, IL-18, IL-1β, and IL-4 as principal drivers of transplant rejection. Thus, inflammatory patterns associated with rejection are specific for the individual tissue and may be superior for early detection and targeted treatment of rejection. © 2014 Wolfram et al

Lampreys, the jawless vertebrates, contain three Pax6 genes with distinct expression in eye, brain and pancreas

10.1038/s41598-019-56085-8Scientific Reports911955

Toxic Diatom Aldehydes Affect Defence Gene Networks in Sea Urchins.

Marine organisms possess a series of cellular strategies to counteract the negative effects of toxic compounds, including the massive reorganization of gene expression networks. Here we report the modulated dose-dependent response of activated genes by diatom polyunsaturated aldehydes (PUAs) in the sea urchin Paracentrotus lividus. PUAs are secondary metabolites deriving from the oxidation of fatty acids, inducing deleterious effects on the reproduction and development of planktonic and benthic organisms that feed on these unicellular algae and with anti-cancer activity. Our previous results showed that PUAs target several genes, implicated in different functional processes in this sea urchin. Using interactomic Ingenuity Pathway Analysis we now show that the genes targeted by PUAs are correlated with four HUB genes, NF-κB, p53, δ-2-catenin and HIF1A, which have not been previously reported for P. lividus. We propose a working model describing hypothetical pathways potentially involved in toxic aldehyde stress response in sea urchins. This represents the first report on gene networks affected by PUAs, opening new perspectives in understanding the cellular mechanisms underlying the response of benthic organisms to diatom exposure

Expansion and functional diversification of a leucyl aminopeptidase family that encodes the major protein constituents of Drosophila sperm

<p>Abstract</p> <p>Background</p> <p>The evolutionary diversification of gene families through gene creation (and loss) is a dynamic process believed to be critical to the evolution of functional novelty. Previous identification of a closely related family of eight annotated metalloprotease genes of the M17 Merops family in the <it>Drosophila </it>sperm proteome (termed, Sperm-LeucylAminoPeptidases, S-LAPs 1-8) led us to hypothesize that this gene family may have experienced such a diversification during insect evolution.</p> <p>Results</p> <p>To assess putative functional activities of S-LAPs, we (i) demonstrated that all S-LAPs are specifically expressed in the testis, (ii) confirmed their presence in sperm by two-dimensional gel electrophoresis and mass spectrometry, (iii) determined that they represent a major portion of the total protein in sperm and (iv) identified aminopeptidase enzymatic activity in sperm extracts using LAP-specific substrates. Functionally significant divergence at the canonical M17 active site indicates that the largest phylogenetic group of S-LAPs lost catalytic activity and likely acquired novel, as yet undetermined, functions in sperm prior to the expansion of the gene family.</p> <p>Conclusions</p> <p>Comparative genomic and phylogenetic analyses revealed the dramatic expansion of the S-LAP gene family during <it>Drosophila </it>evolution and copy number heterogeneity in the genomes of related insects. This finding, in conjunction with the loss of catalytic activity and potential neofunctionalization amongst some family members, extends empirical support for pervasive "revolving door" turnover in the evolution of reproductive gene family composition and function.</p

Identification of ejaculated proteins in the house mouse (Mus domesticus) via isotopic labeling

<p>Abstract</p> <p>Background</p> <p>Seminal fluid plays an important role in successful fertilization, but knowledge of the full suite of proteins transferred from males to females during copulation is incomplete. The list of ejaculated proteins remains particularly scant in one of the best-studied mammalian systems, the house mouse (<it>Mus domesticus</it>), where artificial ejaculation techniques have proven inadequate. Here we investigate an alternative method for identifying ejaculated proteins, by isotopically labeling females with ¹⁵N and then mating them to unlabeled, vasectomized males. Proteins were then isolated from mated females and identified using mass spectrometry. In addition to gaining insights into possible functions and fates of ejaculated proteins, our study serves as proof of concept that isotopic labeling is a powerful means to study reproductive proteins.</p> <p>Results</p> <p>We identified 69 male-derived proteins from the female reproductive tract following copulation. More than a third of all spectra detected mapped to just seven genes known to be structurally important in the formation of the copulatory plug, a hard coagulum that forms shortly after mating. Seminal fluid is significantly enriched for proteins that function in protection from oxidative stress and endopeptidase inhibition. Females, on the other hand, produce endopeptidases in response to mating. The 69 ejaculated proteins evolve significantly more rapidly than other proteins that we previously identified directly from dissection of the male reproductive tract.</p> <p>Conclusion</p> <p>Our study attempts to comprehensively identify the proteins transferred from males to females during mating, expanding the application of isotopic labeling to mammalian reproductive genomics. This technique opens the way to the targeted monitoring of the fate of ejaculated proteins as they incubate in the female reproductive tract.</p