Highlights d A deep learning model for AD prediction was derived from a large set of synthetic ADs d The predictor (ADpred) identifies sequence features important for acidic AD function d AD sequence features explain the basis for the fuzzy binding mechanism of acidic ADs d Acidic ADs are enriched in yeast but not in Drosophila or human transcription factors

MotivationWord-based or ‘alignment-free’ algorithms are increasingly used for phylogeny reconstruction and genome comparison, since they are much faster than traditional approaches that are based on full sequence alignments. Existing alignment-free programs, however, are less accurate than alignment-based methods.ResultsWe propose Filtered Spaced Word Matches (FSWM), a fast alignment-free approach to estimate phylogenetic distances between large genomic sequences. For a pre-defined binary pattern of match and don’t-care positions, FSWM rapidly identifies spaced word-matches between input sequences, i.e. gap-free local alignments with matching nucleotides at the match positions and with mismatches allowed at the don’t-care positions. We then estimate the number of nucleotide substitutions per site by considering the nucleotides aligned at the don’t-care positions of the identified spaced-word matches. To reduce the noise from spurious random matches, we use a filtering procedure where we discard all spaced-word matches for which the overall similarity between the aligned segments is below a threshold. We show that our approach can accurately estimate substitution frequencies even for distantly related sequences that cannot be analyzed with existing alignment-free methods; phylogenetic trees constructed with FSWM distances are of high quality. A program run on a pair of eukaryotic genomes of a few hundred Mb each takes a few minutes.Availability and ImplementationThe program source code for FSWM including a documentation, as well as the software that we used to generate artificial genome sequences are freely available at http://fswm.gobics.de/Supplementary information Supplementary data are available at Bioinformatics online.

Liquid-liquid phase separation is a key organizational principle in eukaryotic cells, on par with intracellular membranes. It allows cells to concentrate specific proteins into condensates, increasing reaction rates and achieving switch-like regulation. However, it is unclear how cells trigger condensate formation or dissolution and regulate their sizes. We predict from first principles two mechanisms of active regulation by post-translational modifications such as phosphorylation: In enrichment-inhibition, the regulating modifying enzyme enriches in condensates and the modifications of proteins inhibit their interactions. Stress granules, Cajal bodies, P granules, splicing speckles, and synapsin condensates obey this model. In localization-induction, condensates form around an immobilized modifying enzyme, whose modifications strengthen protein interactions. Spatially targeted condensates formed during transmembrane signaling, microtubule assembly, and actin polymerization conform to this model. The two models make testable predictions that can guide studies into the many emerging roles of biomolecular condensates.Eukaryotic cells contain numerous types of membraneless organelles, which contain between a few and thousands of protein and RNA species that are highly enriched in comparison to the surrounding nucleoplasm or cytoplasm. These biomolecular condensates are held together by weak, multivalent and highly collaborative interactions, often between intrinsically disordered regions of their constituent proteins (Banani et al., 2017;Shin and Brangwynne, 2017).In contrast to membrane-bound organelles, cells can regulate the formation and size of condensates by posttranslational modifications of one or a few key proteins, most prominently by phosphorylation. The modifications usually lie within intrinsically disordered regions and modulate the strength of attractive interactions with other condensate components (Bah and Forman-Kay, 2016; Fung et al., 2018). Due to the highly cooperative nature of phase transitions, small changes in interaction strengths can result in the formation or dissolution of condensates, and this switch-like, dynamic nature makes them ideal for regulation.For instance the nucleolus, Cajal bodies, splicing speckles, paraspeckles, and PML bodies in the nucleus and P-bodies in the cytoplasm have to be dissolved during mitosis and reformed afterwards to ensure a balanced distribution of their content to daughter cells (Rai et al., 2018;Dundr and Misteli, 2010). Stress granules form upon cellular stress and are dissolved when the stress ceases (Wippich et al., 2013).Whereas these long-known, floating droplet or-ganelles are large enough to be visible using simpler 31 light microscopic techniques, in the past years liquid-32 liquid phase separation has been implicated in mul-33 tifarious processes in which -often sub-micrometer-34 sized -condensates are formed at particular sites in the 35 cell: at sites of DNA repair foci (Altmeyer et al., 2015), 36 Polycomb-mediated chromatin silencing (Tatavo...

Current tissue regenerative strategies rely mainly on tissue repair by transplantation of the synthetic/natural implants. However, limitations of the existing strategies have increased the demand for tissue engineering approaches. Appropriate cell source, effective cell modification, and proper supportive matrices are three bases of tissue engineering. Selection of appropriate methods for cell stimulation, scaffold synthesis, and tissue transplantation play a definitive role in successful tissue engineering. Although the variety of the players are available, but proper combination and functional synergism determine the practical efficacy. Hence, in this review, a comprehensive view of tissue engineering and its different aspects are investigated.

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Liquid-liquid phase separation is a key organizational principle in eukaryotic cells, on par with intracellular membranes. It allows cells to concentrate specific proteins into condensates, increasing reaction rates and achieving switch-like regulation. However, it is unclear how cells trigger condensate formation or dissolution and regulate their sizes. We predict from first principles two mechanisms of active regulation by post-translational modifications such as phosphorylation: In enrichment-inhibition, the regulating modifying enzyme enriches in condensates and the modifications of proteins inhibit their interactions. Stress granules, Cajal bodies, P granules, splicing speckles, and synapsin condensates obey this model. In localization-induction, condensates form around an immobilized modifying enzyme, whose modifications strengthen protein interactions. Spatially targeted condensates formed during transmembrane signaling, microtubule assembly, and actin polymerization conform to this model. The two models make testable predictions that can guide studies into the many emerging roles of biomolecular condensates. Eukaryotic cells contain numerous types of mem-1 braneless organelles, which contain between a few 2 and thousands of protein and RNA species that are 3 highly enriched in comparison to the surrounding nu-4 cleoplasm or cytoplasm. These biomolecular conden-5 sates are held together by weak, multivalent and highly 6 collaborative interactions, often between intrinsically 7 54 active promoters. Here, we propose two active mecha-55 nisms used by cells for these purposes.56 Phase separation and condensate size behaviour 57 To keep the model simple, we consider only one type of 58 condensate protein. In the dilute regime below the sat-59 1 Cellular control of liquid droplet formation, size, and localization • July 5, 2019 Figure 1: Phase separation and condensate droplet size behaviour. A When protein-protein and solvent-solventinteractions are more favorable than protein-solvent interactions, demixing into two phases can occur, a dilute phase with low protein concentration c out and a dense phase with high concentration c in . This happens when the sum of free energies of the two phases is lower (tip of blue arrow) than the energy of the single phase (base of blue arrow) . B c out is the limiting concentration for infinite condensate droplet radius R. The concentration on the outside of a condensate of radius R is larger the smaller the condensate is (green double-headed arrows), as it cannot hold on to its proteins as well as large ones. This leads to a concentration gradient (∇concentration), which fuels a diffusive flux from small to large condensates (wiggly arrows). (l c is a measure of interaction strength between proteins in comparison to the solvent.) C As a result, condensates below a radius R crit will shrink and larger ones will grow. uration protein concentration c out , condensate droplets 60 cannot form ( Figure 1A). Above c out , in the phase sepa-61 ration regime, condensates can be stable...

RNA degradation pathways enable RNA processing, the regulation of RNA levels, and the surveillance of aberrant or poorly functional RNAs in cells. Here we provide transcriptome-wide RNA-binding profiles of 30 general RNA degradation factors in the yeast Saccharomyces cerevisiae. The profiles reveal the distribution of degradation factors between different RNA classes. They are consistent with the canonical degradation pathway for closed-loop forming mRNAs after deadenylation. Modeling based on mRNA half-lives suggests that most degradation factors bind intact mRNAs, whereas decapping factors are recruited only for mRNA degradation, consistent with decapping being a rate-limiting step. Decapping factors preferentially bind mRNAs with non-optimal codons, consistent with rapid degradation of inefficiently translated mRNAs. Global analysis suggests that the nuclear surveillance machinery, including the complexes Nrd1/Nab3 and TRAMP4, targets aberrant nuclear RNAs and processes snoRNAs.

Focal Segmental Glomerulosclerosis (FSGS) is a type of nephrotic syndrome which accounts for 20 and 40 % of such cases in children and adults, respectively. The high prevalence of FSGS makes it the most common primary glomerular disorder causing end-stage renal disease. Although the pathogenesis of this disorder has been widely investigated, the exact mechanism underlying this disease is still to be discovered. Current therapies seek to stop the progression of FSGS and often fail to cure the patients since progression to end-stage renal failure is usually inevitable. In the present work, we use a kidney-specific metabolic network model to study FSGS. The model was obtained by merging two previously published kidney-specific metabolic network models. The validity of the new model was checked by comparing the inactivating reaction genes identified in silico to the list of kidney disease implicated genes. To model the disease state, we used a complete list of FSGS metabolic biomarkers extracted from transcriptome and proteome profiling of patients as well as genetic deficiencies known to cause FSGS. We observed that some specific pathways including chondroitin sulfate degradation, eicosanoid metabolism, keratan sulfate biosynthesis, vitamin B6 metabolism, and amino acid metabolism tend to show variations in FSGS model compared to healthy kidney. Furthermore, we computationally searched for the potential drug targets that can revert the diseased metabolic state to the healthy state. Interestingly, only one drug target, N-acetylgalactosaminidase, was found whose inhibition could alter cellular metabolism towards healthy state.