Estimating the total number of phosphoproteins and phosphorylation sites in eukaryotic proteomes

Landscape of the gut mycobiome dynamics during pregnancy and its relationship with host metabolism and pregnancy health

OBJECTIVE

The remodelling of gut mycobiome (ie, fungi) during pregnancy and its potential influence on host metabolism and pregnancy health remains largely unexplored. Here, we aim to examine the characteristics of gut fungi in pregnant women, and reveal the associations between gut mycobiome, host metabolome and pregnancy health.

DESIGN

Based on a prospective birth cohort in central China (2017 to 2020): Tongji-Huaxi-Shuangliu Birth Cohort, we included 4800 participants who had available ITS2 sequencing data, dietary information and clinical records during their pregnancy. Additionally, we established a subcohort of 1059 participants, which included 514 women who gave birth to preterm, low birthweight or macrosomia infants, as well as 545 randomly selected controls. In this subcohort, a total of 750, 748 and 709 participants had ITS2 sequencing data, 16S sequencing data and serum metabolome data available, respectively, across all trimesters.

RESULTS

The composition of gut fungi changes dramatically from early to late pregnancy, exhibiting a greater degree of variability and individuality compared with changes observed in gut bacteria. The multiomics data provide a landscape of the networks among gut mycobiome, biological functionality, serum metabolites and pregnancy health, pinpointing the link between Mucor and adverse pregnancy outcomes. The prepregnancy overweight status is a key factor influencing both gut mycobiome compositional alteration and the pattern of metabolic remodelling during pregnancy.

CONCLUSION

This study provides a landscape of gut mycobiome dynamics during pregnancy and its relationship with host metabolism and pregnancy health, which lays the foundation of the future gut mycobiome investigation for healthy pregnancy.

Simulation of nanopore sequencing signal data with tunable parameters

In silico simulation of high-throughput sequencing data is a technique used widely in the genomics field. However, there is currently a lack of effective tools for creating simulated data from nanopore sequencing devices, which measure DNA or RNA molecules in the form of time-series current signal data. Here, we introduce Squigulator, a fast and simple tool for simulation of realistic nanopore signal data. Squigulator takes a reference genome, a transcriptome, or read sequences, and generates corresponding raw nanopore signal data. This is compatible with basecalling software from Oxford Nanopore Technologies (ONT) and other third-party tools, thereby providing a useful substrate for development, testing, debugging, validation, and optimization at every stage of a nanopore analysis workflow. The user may generate data with preset parameters emulating specific ONT protocols or noise-free "ideal" data, or they may deterministically modify a range of experimental variables and/or noise parameters to shape the data to their needs. We present a brief example of Squigulator's use, creating simulated data to model the degree to which different parameters impact the accuracy of ONT basecalling and downstream variant detection. This analysis reveals new insights into the nature of ONT data and basecalling algorithms. We provide Squigulator as an open-source tool for the nanopore community.

Integrated Platform for Large-Scale Quantitative Profiling of Phosphotyrosine Signaling Complexes Based on Cofractionation/Mass Spectrometry and Complex-Centric Algorithm

The scarcity and dynamic nature of phosphotyrosine (pTyr)-modified proteins pose a challenge for researching protein complexes with pTyr modification, which are assembled through multiple protein-protein interactions. We developed an integrated complex-centric platform for large-scale quantitative profiling of pTyr signaling complexes based on cofractionation/mass spectrometry (CoFrac-MS) and a complex-centric algorithm. We initially constructed a trifunctional probe based on pTyr superbinder (SH2-S) for specifically binding and isolation of intact pTyr protein complexes. Then, the CoFrac-MS strategy was employed for the identification of pTyr protein complexes by integrating ion exchange chromatography in conjunction with data independent acquisition mass spectrometry. Furthermore, we developed a novel complex-centric algorithm for quantifying protein complexes based on the protein complex elution curve. Utilizing this algorithm, we effectively quantified 216 putative protein complexes. We further screened 21 regulated pTyr protein complexes related to the epidermal growth factor signal. Our study engenders a comprehensive framework for the intricate examination of pTyr protein complexes and presents, for the foremost occasion, a quantitative landscape delineating the composition of pTyr protein complexes in HeLa cells.

A Decade in a Systematic Review: The Evolution and Impact of Cell Painting

High-content image-based assays have fueled significant discoveries in the life sciences in the past decade (2013-2023), including novel insights into disease etiology, mechanism of action, new therapeutics, and toxicology predictions. Here, we systematically review the substantial methodological advancements and applications of Cell Painting. Advancements include improvements in the Cell Painting protocol, assay adaptations for different types of perturbations and applications, and improved methodologies for feature extraction, quality control, and batch effect correction. Moreover, machine learning methods recently surpassed classical approaches in their ability to extract biologically useful information from Cell Painting images. Cell Painting data have been used alone or in combination with other - omics data to decipher the mechanism of action of a compound, its toxicity profile, and many other biological effects. Overall, key methodological advances have expanded Cell Painting's ability to capture cellular responses to various perturbations. Future advances will likely lie in advancing computational and experimental techniques, developing new publicly available datasets, and integrating them with other high-content data types.

Characterization of Phosphorylated Peptides by Electron-Activated and Ultraviolet Dissociation Mass Spectrometry: A Comparative Study with Collision-Induced Dissociation

Mass-spectrometry-based methods have made significant progress in the characterization of post-translational modifications (PTMs) in peptides and proteins; however, room remains to improve fragmentation methods. Ideal MS/MS methods are expected to simultaneously provide extensive sequence information and localization of PTM sites and retain labile PTM groups. This collection of criteria is difficult to meet, and the various activation methods available today offer different capabilities. In order to examine the specific case of phosphorylation on peptides, we investigate electron transfer dissociation (ETD), electron-activated dissociation (EAD), and 193 nm ultraviolet photodissociation (UVPD) and compare all three methods with classical collision-induced dissociation (CID). EAD and UVPD show extensive backbone fragmentation, comparable in scope to that of CID. These methods provide diverse backbone fragmentation, producing a/x, b/y, and c/z ions with substantial sequence coverages. EAD displays a high retention efficiency of the phosphate modification, attributed to its electron-mediated fragmentation mechanisms, as observed in ETD. UVPD offers reasonable retention efficiency, also allowing localization of the PTM site. EAD experiments were also performed in an LC-MS/MS workflow by analyzing phosphopeptides spiked in human plasma, and spectra allow accurate identification of the modified sites and discrimination of isomers. Based on the overall performance, EAD and 193 nm UVPD offer alternative options to CID and ETD for phosphoproteomics.

Post-translational modifications: emerging directors of cell-fate decisions during endoplasmic reticulum stress in Arabidopsis thaliana

Homeostasis of the endoplasmic reticulum (ER) is critical for growth, development, and stress responses. Perturbations causing an imbalance in ER proteostasis lead to a potentially lethal condition known as ER stress. In ER stress situations, cell-fate decisions either activate pro-life pathways that reestablish homeostasis or initiate pro-death pathways to prevent further damage to the organism. Understanding the mechanisms underpinning cell-fate decisions in ER stress is critical for crop development and has the potential to enable translation of conserved components to ER stress-related diseases in metazoans. Post-translational modifications (PTMs) of proteins are emerging as key players in cell-fate decisions in situations of imbalanced ER proteostasis. In this review, we address PTMs orchestrating cell-fate decisions in ER stress in plants and provide evidence-based perspectives for where future studies may focus to identify additional PTMs involved in ER stress management.

SPACe (Swift Phenotypic Analysis of Cells): an open-source, single cell analysis of Cell Painting data

Phenotypic profiling by high throughput microscopy has become one of the leading tools for screening large sets of perturbations in cellular models. Of the numerous methods used over the years, the flexible and economical Cell Painting (CP) assay has been central in the field, allowing for large screening campaigns leading to a vast number of data-rich images. Currently, to analyze data of this scale, available open-source software ( i.e. , CellProfiler) requires computational resources that are not available to most laboratories worldwide. In addition, the image-embedded cell-to-cell variation of responses within a population, while collected and analyzed, is usually averaged and unused. Here we introduce SPACe ( S wift P henotypic A nalysis of Ce lls), an open source, Python-based platform for the analysis of single cell image-based morphological profiles produced by CP experiments. SPACe can process a typical dataset approximately ten times faster than CellProfiler on common desktop computers without loss in mechanism of action (MOA) recognition accuracy. It also computes directional distribution-based distances (Earth Mover's Distance - EMD) of morphological features for quality control and hit calling. We highlight several advantages of SPACe analysis on CP assays, including reproducibility across multiple biological replicates, easy applicability to multiple (∼20) cell lines, sensitivity to variable cell-to-cell responses, and biological interpretability to explain image-based features. We ultimately illustrate the advantages of SPACe in a screening campaign of cell metabolism small molecule inhibitors which we performed in seven cell lines to highlight the importance of testing perturbations across models.

A meta-analysis of the gut microbiome in inflammatory bowel disease patients identifies disease-associated small molecules

Changes in the gut microbiome have been associated with several human diseases, but the molecular and functional details underlying these associations remain largely unknown. Here, we performed a multi-cohort analysis of small molecule biosynthetic gene clusters (BGCs) in 5,306 metagenomic samples of the gut microbiome from 2,033 Inflammatory Bowel Disease (IBD) patients and 833 matched healthy subjects and identified a group of Clostridia-derived BGCs that are significantly associated with IBD. Using synthetic biology, we discovered and solved the structures of six fatty acid amides as the products of the IBD-enriched BGCs. Using two mouse models of colitis, we show that the discovered small molecules disrupt gut permeability and exacerbate inflammation in chemically and genetically susceptible mice. These findings suggest that microbiome-derived small molecules may play a role in the etiology of IBD and represent a generalizable approach for discovering molecular mediators of microbiome-host interactions in the context of microbiome-associated diseases.

Multilayer Network Analysis across Cortical Depths in Resting-State 7T fMRI

In graph theory, "multilayer networks" represent systems involving several interconnected topological levels. A neuroscience example is the hierarchy of connections between different cortical depths or "lamina". This hierarchy is becoming non-invasively accessible in humans using ultra-high-resolution functional MRI (fMRI). Here, we applied multilayer graph theory to examine functional connectivity across different cortical depths in humans, using 7T fMRI (1-mm3 voxels; 30 participants). Blood oxygenation level dependent (BOLD) signals were derived from five depths between the white matter and pial surface. We then compared networks where the inter-regional connections were limited to a single cortical depth only ("layer-by-layer matrices") to those considering all possible connections between regions and cortical depths ("multilayer matrix"). We utilized global and local graph theory features that quantitatively characterize network attributes such as network composition, nodal centrality, path-based measures, and hub segregation. Detecting functional differences between cortical depths was improved using multilayer connectomics compared to the layer-by-layer versions. Superficial aspects of the cortex dominated information transfer and deeper aspects clustering. These differences were largest in frontotemporal and limbic brain regions. fMRI functional connectivity across different cortical depths may contain neurophysiologically relevant information. Multilayer connectomics could provide a methodological framework for studies on how information flows across this hierarchy.

Horizontal transfer and recombination fuel Ty4 retrotransposon evolution in Saccharomyces

Horizontal transposon transfer (HTT) plays an important role in the evolution of eukaryotic genomes, however the detailed evolutionary history and impact of most HTT events remain to be elucidated. To better understand the process of HTT in closely-related microbial eukaryotes, we studied Ty4 retrotransposon subfamily content and sequence evolution across the genus Saccharomyces using short- and long-read whole genome sequence data, including new PacBio genome assemblies for two S. mikatae strains. We find evidence for multiple independent HTT events introducing the Tsu4 subfamily into specific lineages of S. paradoxus, S. cerevisiae, S. eubayanus, S. kudriavzevii and the ancestor of the S. mikatae/S. jurei species pair. In both S. mikatae and S. kudriavzevii, we identified novel Ty4 clades that were independently generated through recombination between resident and horizontally-transferred subfamilies. Our results reveal that recurrent HTT and lineage-specific extinction events lead to a complex pattern of Ty4 subfamily content across the genus Saccharomyces. Moreover, our results demonstrate how HTT can lead to coexistence of related retrotransposon subfamilies in the same genome that can fuel evolution of new retrotransposon clades via recombination.

Small molecules targeting molecular chaperones for tau regulation: Achievements and challenges

Abnormal post-translational modification of microtubule-associated protein Tau (MAPT) is a prominent pathological feature in Alzheimer's disease (AD). Previous research has focused on designing small molecules to target Tau modification, aiming to restore microtubule stability and regulate Tau levels in vivo. However, progress has been hindered, and no effective Tau-targeted drugs have been successfully marketed, which urgently requires more strategies. Heat shock proteins (HSPs), especially Hsp90 and Hsp70, have been found to play a crucial role in Tau maturation and degradation. This review explores innovative approaches using small molecules that interact with the chaperone system to regulate Tau levels. We provide a comprehensive overview of the mechanisms involving HSPs and their co-chaperones in the Tau regulation cycle. Additionally, we analyze small molecules targeting these chaperone systems to modulate Tau function. By understanding the characteristics of the molecular chaperone system and its specific impact on Tau, we aim to provide a perspective that seeks to regulate Tau levels through the manipulation of the molecular chaperone system and ultimately develop effective treatments for AD.

A chromosome-level genome for the nudibranch gastropod Berghia stephanieae helps parse clade-specific gene expression in novel and conserved phenotypes

How novel phenotypes originate from conserved genes, processes, and tissues remains a major question in biology. Research that sets out to answer this question often focuses on the conserved genes and processes involved, an approach that explicitly excludes the impact of genetic elements that may be classified as clade-specific, even though many of these genes are known to be important for many novel, or clade-restricted, phenotypes. This is especially true for understudied phyla such as mollusks, where limited genomic and functional biology resources for members of this phylum has long hindered assessments of genetic homology and function. To address this gap, we constructed a chromosome-level genome for the gastropod Berghia stephanieae (Valdés, 2005) to investigate the expression of clade-specific genes across both novel and conserved tissue types in this species. The final assembled and filtered Berghia genome is comparable to other high quality mollusk genomes in terms of size (1.05 Gb) and number of predicted genes (24,960 genes), and is highly contiguous. The proportion of upregulated, clade-specific genes varied across tissues, but with no clear trend between the proportion of clade-specific genes and the novelty of the tissue. However, more complex tissue like the brain had the highest total number of upregulated, clade-specific genes, though the ratio of upregulated clade-specific genes to the total number of upregulated genes was low. Our results, when combined with previous research on the impact of novel genes on phenotypic evolution, highlight the fact that the complexity of the novel tissue or behavior, the type of novelty, and the developmental timing of evolutionary modifications will all influence how novel and conserved genes interact to generate diversity.

Insights into Drug Cardiotoxicity from Biological and Chemical Data: The First Public Classifiers for FDA DICTrank

Drug-induced cardiotoxicity (DICT) is a major concern in drug development, accounting for 10-14% of postmarket withdrawals. In this study, we explored the capabilities of various chemical and biological data to predict cardiotoxicity, using the recently released Drug-Induced Cardiotoxicity Rank (DICTrank) dataset from the United States FDA. We analyzed a diverse set of data sources, including physicochemical properties, annotated mechanisms of action (MOA), Cell Painting, Gene Expression, and more, to identify indications of cardiotoxicity. We found that such data, including protein targets, especially those related to ion channels (such as hERG), physicochemical properties (such as electrotopological state) as well as peak concentration in plasma offer strong predictive ability as well as valuable insights into DICT. We also found compounds annotated with particular mechanisms of action, such as cyclooxygenase inhibition, could distinguish between most-concern and no-concern DICT compounds. Cell Painting features related to ER stress discern the most-concern cardiotoxic compounds from non-toxic compounds. While models based on physicochemical properties currently provide substantial predictive accuracy (AUCPR = 0.93), this study also underscores the potential benefits of incorporating more comprehensive biological data in future DICT predictive models. With the availability of - omics data in the future, using biological data promises enhanced predictability and delivers deeper mechanistic insights, paving the way for safer therapeutic drug development. All models and data used in this study are publicly released at https://broad.io/DICTrank_Predictor.

Nutrient Deficiencies Impact on the Cellular and Metabolic Responses of Saxitoxin Producing Alexandrium minutum: A Transcriptomic Perspective

Dinoflagellate Alexandrium minutum Halim is commonly associated with harmful algal blooms (HABs) in tropical marine waters due to its saxitoxin production. However, limited information is available regarding the cellular and metabolic changes of A. minutum in nutrient-deficient environments. To fill this gap, our study aimed to investigate the transcriptomic responses of A. minutum under nitrogen and phosphorus deficiency. The induction of nitrogen and phosphorus deficiency resulted in the identification of 1049 and 763 differently expressed genes (DEGs), respectively. Further analysis using gene set enrichment analysis (GSEA) revealed 702 and 1251 enriched gene ontology (GO) terms associated with nitrogen and phosphorus deficiency, respectively. Our results indicate that in laboratory cultures, nitrogen deficiency primarily affects meiosis, carbohydrate catabolism, ammonium assimilation, ion homeostasis, and protein kinase activity. On the other hand, phosphorus deficiency primarily affects the carbon metabolic response, cellular ion transfer, actin-dependent cell movement, signalling pathways, and protein recycling. Our study provides valuable insights into biological processes and genes regulating A. minutum's response to nutrient deficiencies, furthering our understanding of the ecophysiological response of HABs to environmental change.

Intragenic DNA inversions expand bacterial coding capacity

Bacterial populations that originate from a single bacterium are not strictly clonal. Often, they contain subgroups with distinct phenotypes. Bacteria can generate heterogeneity through phase variation: a preprogrammed, reversible mechanism that alters gene expression levels across a population. One well studied type of phase variation involves enzyme-mediated inversion of specific intergenic regions of genomic DNA. Frequently, these DNA inversions flip the orientation of promoters, turning ON or OFF adjacent coding regions within otherwise isogenic populations. Through this mechanism, inversion can affect fitness, survival, or group dynamics. Here, we develop and apply bioinformatic approaches to discover thousands of previously undescribed phase-variable regions in prokaryotes using long-read datasets. We identify 'intragenic invertons', a surprising new class of invertible elements found entirely within genes, in bacteria and archaea. To date, inversions within single genes have not been described. Intragenic invertons allow a gene to encode two or more versions of a protein by flipping a DNA sequence within the coding region, thereby increasing coding capacity without increasing genome size. We experimentally characterize specific intragenic invertons in the gut commensal Bacteroides thetaiotaomicron, presenting a 'roadmap' for investigating this new gene-diversifying phenomenon.

Proximity-inducing modalities: the past, present, and future

Living systems use proximity to regulate biochemical processes. Inspired by this phenomenon, bifunctional modalities that induce proximity have been developed to redirect cellular processes. An emerging example of this class is molecules that induce ubiquitin-dependent proteasomal degradation of a protein of interest, and their initial development sparked a flurry of discovery for other bifunctional modalities. Recent advances in this area include modalities that can change protein phosphorylation, glycosylation, and acetylation states, modulate gene expression, and recruit components of the immune system. In this review, we highlight bifunctional modalities that perform functions other than degradation and have great potential to revolutionize disease treatment, while also serving as important tools in basic research to explore new aspects of biology.

Systematic assessment of long-read RNA-seq methods for transcript identification and quantification

The Long-read RNA-Seq Genome Annotation Assessment Project (LRGASP) Consortium was formed to evaluate the effectiveness of long-read approaches for transcriptome analysis. The consortium generated over 427 million long-read sequences from cDNA and direct RNA datasets, encompassing human, mouse, and manatee species, using different protocols and sequencing platforms. These data were utilized by developers to address challenges in transcript isoform detection and quantification, as well as de novo transcript isoform identification. The study revealed that libraries with longer, more accurate sequences produce more accurate transcripts than those with increased read depth, whereas greater read depth improved quantification accuracy. In well-annotated genomes, tools based on reference sequences demonstrated the best performance. When aiming to detect rare and novel transcripts or when using reference-free approaches, incorporating additional orthogonal data and replicate samples are advised. This collaborative study offers a benchmark for current practices and provides direction for future method development in transcriptome analysis.

Recent progress in quantitative phosphoproteomics

INTRODUCTION

Protein phosphorylation is a critical post-translational modification involved in the regulation of numerous cellular processes from signal transduction to modulation of enzyme activities. Knowledge of dynamic changes of phosphorylation levels during biological processes, under various treatments or between healthy and disease models is fundamental for understanding the role of each phosphorylation event. Thereby, LC-MS/MS based technologies in combination with quantitative proteomics strategies evolved as a powerful strategy to investigate the function of individual protein phosphorylation events.

AREAS COVERED

State-of-the-art labeling techniques including stable isotope and isobaric labeling provide precise and accurate quantification of phosphorylation events. Here, we review the strengths and limitations of recent quantification methods and provide examples based on current studies, how quantitative phosphoproteomics can be further optimized for enhanced analytic depth, dynamic range, site localization, and data integrity. Specifically, reducing the input material demands is key to a broader implementation of quantitative phosphoproteomics, not least for clinical samples.

EXPERT OPINION

Despite quantitative phosphoproteomics is one of the most thriving fields in the proteomics world, many challenges still have to be overcome to facilitate even deeper and more comprehensive analyses as required in the current research, especially at single cell levels and in clinical diagnostics.

Unbiased single-cell morphology with self-supervised vision transformers

Accurately quantifying cellular morphology at scale could substantially empower existing single-cell approaches. However, measuring cell morphology remains an active field of research, which has inspired multiple computer vision algorithms over the years. Here, we show that DINO, a vision-transformer based, self-supervised algorithm, has a remarkable ability for learning rich representations of cellular morphology without manual annotations or any other type of supervision. We evaluate DINO on a wide variety of tasks across three publicly available imaging datasets of diverse specifications and biological focus. We find that DINO encodes meaningful features of cellular morphology at multiple scales, from subcellular and single-cell resolution, to multi-cellular and aggregated experimental groups. Importantly, DINO successfully uncovers a hierarchy of biological and technical factors of variation in imaging datasets. The results show that DINO can support the study of unknown biological variation, including single-cell heterogeneity and relationships between samples, making it an excellent tool for image-based biological discovery.

PP2Acα-B'/PR61 Holoenzyme of Toxoplasma gondii Is Required for the Amylopectin Metabolism and Proliferation of Tachyzoites

Here, we report that the inhibition of the PP2A subfamily by okadaic acid results in an accumulation of polysaccharides in the acute infection stage (tachyzoites) of Toxoplasma gondii, which is a protozoan of global zoonotic importance and a model for the apicomplexan parasites. The loss of the catalytic subunit α of PP2A (ΔPP2Acα) in RHΔku80 leads to the polysaccharide accumulation phenotype in the base of tachyzoites as well as residual bodies and significantly compromises the intracellular growth in vitro and the virulence in vivo. A metabolomic analysis revealed that the accumulated polysaccharides in ΔPP2Acα are derived from interrupted glucose metabolism, which affects the production of ATP and energy homeostasis in the T. gondii knockout. The assembly of the PP2Acα holoenzyme complex involved in the amylopectin metabolism in tachyzoites is possibly not regulated by LCMT1 or PME1, and this finding contributes to the identification of the regulatory B subunit (B'/PR61). The loss of B'/PR61 results in the accumulation of polysaccharide granules in the tachyzoites as well as reduced plaque formation ability, exactly the same as ΔPP2Acα. Taken together, we have identified a PP2Acα-B'/PR61 holoenzyme complex that plays a crucial role in the carbohydrate metabolism and viability in T. gondii, and its deficiency in function remarkably suppresses the growth and virulence of this important zoonotic parasite both in vitro and in vivo. Hence, rendering the PP2Acα-B'/PR61 holoenzyme functionless should be a promising strategy for the intervention of Toxoplasma acute infection and toxoplasmosis. IMPORTANCE Toxoplasma gondii switches back and forth between acute and chronic infections, mainly in response to host immunologic status, which is characterized by flexible but specific energy metabolism. Polysaccharide granules are accumulated in the acute infection stage of T. gondii that have been exposed to a chemical inhibitor of the PP2A subfamily. The genetic depletion of the catalytic subunit α of PP2A leads to this phenotype and significantly affects the cell metabolism, energy production, and viability. Further, a regulatory B subunit PR61 is necessary for the PP2A holoenzyme to function in glucose metabolism and in the intracellular growth of T. gondii tachyzoites. A deficiency of this PP2A holoenzyme complex (PP2Acα-B'/PR61) in T. gondii knockouts results in the abnormal accumulation of polysaccharides and the disruption of energy metabolism, suppressing their growth and virulence. These findings provide novel insights into cell metabolism and identify a potential target for an intervention against a T. gondii acute infection.

Individual differences in time-varying and stationary brain connectivity during movie watching from childhood to early adulthood: age, sex, and behavioral associations

Spatially remote brain regions exhibit dynamic functional interactions across various task conditions. While time-varying functional connectivity during movie watching shows sensitivity to movie content, stationary functional connectivity remains relatively stable across videos. These findings suggest that dynamic and stationary functional interactions may represent different aspects of brain function. However, the relationship between individual differences in time-varying and stationary connectivity and behavioral phenotypes remains elusive. To address this gap, we analyzed an open-access functional MRI dataset comprising participants aged 5 to 22 years, who watched two cartoon movie clips. We calculated regional brain activity, time-varying connectivity, and stationary connectivity, examining associations with age, sex, and behavioral assessments. Model comparison revealed that time-varying connectivity was more sensitive to age and sex effects compared with stationary connectivity. The preferred age models exhibited quadratic log age or quadratic age effects, indicative of inverted-U shaped developmental patterns. In addition, females showed higher consistency in regional brain activity and time-varying connectivity than males. However, in terms of behavioral predictions, only stationary connectivity demonstrated the ability to predict full-scale intelligence quotient. These findings suggest that individual differences in time-varying and stationary connectivity may capture distinct aspects of behavioral phenotypes.

On-Slide Lambda Protein Phosphatase-Mediated Dephosphorylation of Fixed Samples

Protein phosphorylation is a ubiquitous post-translational modification that regulates a plethora of intracellular processes, making its analysis crucial for understanding intracellular dynamics. The commonly used methods, such as radioactive labeling and gel electrophoresis, do not provide information about subcellular localization. Immunofluorescence using phospho-specific antibodies and subsequent analysis via microscopy allows researchers to assess subcellular localization, but it typically lacks validation whether the observed fluorescent signal is phosphorylation specific. In this study, an on-slide dephosphorylation assay coupled with immunofluorescence staining using phospho-specific antibodies on fixed samples is proposed as a fast and simple approach to validate phosphorylated proteins in their native subcellular context. The assay was validated using antibodies against two different phosphorylated target proteins, connexin 43 phosphorylated at serine 373, and phosphorylated substrates of protein kinase A, with a dramatic reduction in the signal upon dephosphorylation. The proposed approach provides a convenient way to validate phosphorylated proteins without the need for additional sample preparation steps, reducing the time and effort required for analysis, while minimizing the risk of protein loss or alteration.

Effects of Phosphorylation on the Activity, Inhibition and Stability of Carbonic Anhydrases

Carbonic anhydrases (CAs) are a metalloenzyme family that have important roles in cellular processes including pH homeostasis and have been implicated in multiple pathological conditions. Small molecule inhibitors have been developed to target carbonic anhydrases, but the effects of post-translational modifications (PTMs) on the activity and inhibition profiles of these enzymes remain unclear. Here, we investigate the effects of phosphorylation, the most prevalent carbonic anhydrase PTM, on the activities and drug-binding affinities of human CAI and CAII, two heavily modified active isozymes. Using serine to glutamic acid (S > E) mutations to mimic the effect of phosphorylation, we demonstrate that phosphomimics at a single site can significantly increase or decrease the catalytic efficiencies of CAs, depending on both the position of the modification and the CA isoform. We also show that the S > E mutation at Ser50 of hCAII decreases the binding affinities of hCAII with well-characterized sulphonamide inhibitors including by over 800-fold for acetazolamide. Our findings suggest that CA phosphorylation may serve as a regulatory mechanism for enzymatic activity, and affect the binding affinity and specificity of small, drug and drug-like molecules. This work should motivate future studies examining the PTM-modification forms of CAs and their distributions, which should provide insights into CA physiopathological functions and facilitate the development of 'modform-specific' carbonic anhydrase inhibitors.

Barcode-free multiplex plasmid sequencing using Bayesian analysis and nanopore sequencing

Plasmid construction is central to life science research, and sequence verification is arguably its costliest step. Long-read sequencing has emerged as a competitor to Sanger sequencing, with the principal benefit that whole plasmids can be sequenced in a single run. Nevertheless, the current cost of nanopore sequencing is still prohibitive for routine sequencing during plasmid construction. We develop a computational approach termed Simple Algorithm for Very Efficient Multiplexing of Oxford Nanopore Experiments for You (SAVEMONEY) that guides researchers to mix multiple plasmids and subsequently computationally de-mixes the resultant sequences. SAVEMONEY defines optimal mixtures in a pre-survey step, and following sequencing, executes a post-analysis workflow involving sequence classification, alignment, and consensus determination. By using Bayesian analysis with prior probability of expected plasmid construction error rate, high-confidence sequences can be obtained for each plasmid in the mixture. Plasmids differing by as little as two bases can be mixed for submission as a single sample for nanopore sequencing, and routine multiplexing of even six plasmids can still maintain high accuracy of consensus sequencing. SAVEMONEY should further democratize whole-plasmid sequencing by nanopore and related technologies, driving down the effective cost of whole-plasmid sequencing to lower than that of a single Sanger sequencing run.

Transsynaptic labeling and transcriptional control of zebrafish neural circuits

Deciphering the connectome, the ensemble of synaptic connections that underlie brain function is a central goal of neuroscience research. The trans-Tango genetic approach, initially developed for anterograde transsynaptic tracing in Drosophila, can be used to map connections between presynaptic and postsynaptic partners and to drive gene expression in target neurons. Here, we describe the successful adaptation of trans-Tango to visualize neural connections in a living vertebrate nervous system, that of the zebrafish. Connections were validated between synaptic partners in the larval retina and brain. Results were corroborated by functional experiments in which optogenetic activation of retinal ganglion cells elicited responses in neurons of the optic tectum, as measured by trans-Tango-dependent expression of a genetically encoded calcium indicator. Transsynaptic signaling through trans-Tango reveals predicted as well as previously undescribed synaptic connections, providing a valuable in vivo tool to monitor and interrogate neural circuits over time.

Principles of phosphoproteomics and applications in cancer research

Phosphorylation constitutes the most common and best-studied regulatory post-translational modification in biological systems and archetypal signalling pathways driven by protein and lipid kinases are disrupted in essentially all cancer types. Thus, the study of the phosphoproteome stands to provide unique biological information on signalling pathway activity and on kinase network circuitry that is not captured by genetic or transcriptomic technologies. Here, we discuss the methods and tools used in phosphoproteomics and highlight how this technique has been used, and can be used in the future, for cancer research. Challenges still exist in mass spectrometry phosphoproteomics and in the software required to provide biological information from these datasets. Nevertheless, improvements in mass spectrometers with enhanced scan rates, separation capabilities and sensitivity, in biochemical methods for sample preparation and in computational pipelines are enabling an increasingly deep analysis of the phosphoproteome, where previous bottlenecks in data acquisition, processing and interpretation are being relieved. These powerful hardware and algorithmic innovations are not only providing exciting new mechanistic insights into tumour biology, from where new drug targets may be derived, but are also leading to the discovery of phosphoproteins as mediators of drug sensitivity and resistance and as classifiers of disease subtypes. These studies are, therefore, uncovering phosphoproteins as a new generation of disruptive biomarkers to improve personalised anti-cancer therapies.

Crosstalkr: An open-source R package to facilitate drug target identification

In the last few decades, interest in graph-based analysis of biological networks has grown substantially. Protein-protein interaction networks are one of the most common biological networks, and represent the molecular relationships between every known protein and every other known protein. Integration of these interactomic data into bioinformatic pipelines may increase the translational potential of discoveries made through analysis of multi-omic datasets. Crosstalkr provides a unified toolkit for drug target and disease subnetwork identification, two of the most common uses of protein protein interaction networks. First, crosstalkr enables users to download and leverage high-quality protein-protein interaction networks from online repositories. Users can then filter these large networks into manageable subnetworks using a variety of methods. For example, network filtration can be done using random walks with restarts, starting at the user-provided seed proteins. Affinity scores from a given random walk with restarts are compared to a bootstrapped null distribution to assess statistical significance. Random walks are implemented using sparse matrix multiplication to facilitate fast execution. Next, users can perform in-silico repression experiments to assess the relative importance of nodes in their network. At this step, users can supply protein or gene expression data to make node rankings more meaningful. The default behavior evaluates the human interactome. However, users can evaluate more than 1000 non-human protein-protein interaction networks as a result of integration with StringDB. It is a free, open-source R package designed to allow users to integrate functional analysis using the protein-protein interaction network into existing bioinformatic pipelines. A beta version of crosstalkr available on CRAN (https://cran.rstudio.com/web/packages/crosstalkr/index.html).

EpiMix: an integrative tool for epigenomic subtyping using DNA methylation

DNA methylation (DNAme) is a major epigenetic factor influencing gene expression with alterations leading to cancer, immunological, and cardiovascular diseases. Recent technological advances enable genome-wide quantification of DNAme in large human cohorts. So far, existing methods have not been evaluated to identify differential DNAme present in large and heterogeneous patient cohorts. We developed an end-to-end analytical framework named "EpiMix" for population-level analysis of DNAme and gene expression. Compared to existing methods, EpiMix showed higher sensitivity in detecting abnormal DNAme that was present in only small patient subsets. We extended the model-based analyses of EpiMix to cis-regulatory elements within protein-coding genes, distal enhancers, and genes encoding microRNAs and lncRNAs. Using cell-type specific data from two separate studies, we discovered novel epigenetic mechanisms underlying childhood food allergy and survival-associated, methylation-driven non-coding RNAs in non-small cell lung cancer.

Protein phosphatases and their targets: Comprehending the interactions in plant signaling pathways

Protein phosphorylation is a vital reversible post-translational modification. This process is established by two classes of enzymes: protein kinases and protein phosphatases. Protein kinases phosphorylate proteins while protein phosphatases dephosphorylate phosphorylated proteins, thus, functioning as 'critical regulators' in signaling pathways. The eukaryotic protein phosphatases are classified as phosphoprotein phosphatases (PPP), metallo-dependent protein phosphatases (PPM), protein tyrosine (Tyr) phosphatases (PTP), and aspartate (Asp)-dependent phosphatases. The PPP and PPM families are serine (Ser)/threonine (Thr) specific phosphatases (STPs) that dephosphorylate Ser and Thr residues. The PTP family dephosphorylates Tyr residues while dual-specificity phosphatases (DsPTPs/DSPs) dephosphorylate Ser, Thr, and Tyr residues. The composition of these enzymes as well as their substrate specificity are important determinants of their functional significance in a number of cellular processes and stress responses. Their role in animal systems is well-understood and characterized. The functional characterization of protein phosphatases has been extensively covered in plants, although the comprehension of their mechanistic basis is an ongoing pursuit. The nature of their interactions with other key players in the signaling process is vital to our understanding. The substrates or targets determine their potential as well as magnitude of the impact they have on signaling pathways. In this article, we exclusively overview the various substrates of protein phosphatases in plant signaling pathways, which are a critical determinant of the outcome of various developmental and stress stimuli.

The second intracellular loop of the yeast Trk1 potassium transporter is involved in regulation of activity, and interaction with 14-3-3 proteins

Potassium is an essential intracellular ion, and a sufficient intracellular concentration of it is crucial for many processes; therefore it is fundamental for cells to precisely regulate K⁺ uptake and efflux through the plasma membrane. The uniporter Trk1 is a key player in K⁺ acquisition in yeasts. The TRK1 gene is expressed at a low and stable level; thus the activity of the transporter needs to be regulated at a posttranslational level. S. cerevisiae Trk1 changes its activity and affinity for potassium ion quickly and according to both internal and external concentrations of K⁺, as well as the membrane potential. The molecular basis of these changes has not been elucidated, though phosphorylation is thought to play an important role. In this study, we examined the role of the second, short, and highly conserved intracellular hydrophilic loop of Trk1 (IL2), and identified two phosphorylable residues (Ser882 and Thr900) as very important for 1) the structure of the loop and consequently for the targeting of Trk1 to the plasma membrane, and 2) the upregulation of the transporter's activity reaching maximal affinity under low external K⁺ conditions. Moreover, we identified three residues (Thr155, Ser414, and Thr900) within the Trk1 protein as strong candidates for interaction with 14-3-3 regulatory proteins, and showed, in an in vitro experiment, that phosphorylated Thr900 of the IL2 indeed binds to both isoforms of yeast 14-3-3 proteins, Bmh1 and Bmh2.

FDA-Approved Kinase Inhibitors in Preclinical and Clinical Trials for Neurological Disorders

Cancers and neurological disorders are two major types of diseases. We previously developed a new concept termed "Aberrant Cell Cycle Diseases" (ACCD), revealing that these two diseases share a common mechanism of aberrant cell cycle re-entry. The aberrant cell cycle re-entry is manifested as kinase/oncogene activation and tumor suppressor inactivation, which are hallmarks of both tumor growth in cancers and neuronal death in neurological disorders. Therefore, some cancer therapies (e.g., kinase inhibition, tumor suppressor elevation) can be leveraged for neurological treatments. The United States Food and Drug Administration (US FDA) has so far approved 74 kinase inhibitors, with numerous other kinase inhibitors in clinical trials, mostly for the treatment of cancers. In contrast, there are dire unmet needs of FDA-approved drugs for neurological treatments, such as Alzheimer's disease (AD), intracerebral hemorrhage (ICH), ischemic stroke (IS), traumatic brain injury (TBI), and others. In this review, we list these 74 FDA-approved kinase-targeted drugs and identify those that have been reported in preclinical and/or clinical trials for neurological disorders, with a purpose of discussing the feasibility and applicability of leveraging these cancer drugs (FDA-approved kinase inhibitors) for neurological treatments.

Evolutionary Divergence of Phosphorylation to Regulate Interactive Protein Networks in Lower and Higher Species

The phosphorylation of proteins affects their functions in extensively documented circumstances. However, the role of phosphorylation in many interactive networks of proteins remains very elusive due to the experimental limits of exploring the transient interaction in a large complex of assembled proteins induced by stimulation. Previous studies have suggested that phosphorylation is a recent evolutionary process that differently regulates ortholog proteins in numerous lineages of living organisms to create new functions. Despite the fact that numerous phospho-proteins have been compared between species, little is known about the organization of the full phospho-proteome, the role of phosphorylation to orchestrate large interactive networks of proteins, and the intertwined phospho-landscape in these networks. In this report, we aimed to investigate the acquired role of phosphate addition in the phenomenon of protein networking in different orders of living organisms. Our data highlighted the acquired status of phosphorylation in organizing large, connected assemblages in Homo sapiens. The protein networking guided by phosphorylation turned out to be prominent in humans, chaotic in yeast, and weak in flies. Furthermore, the molecular functions of GO annotation enrichment regulated by phosphorylation were found to be drastically different between flies, yeast, and humans, suggesting an evolutionary drift specific to each species.

Genomic analyses of wild argali, domestic sheep, and their hybrids provide insights into chromosome evolution, phenotypic variation, and germplasm innovation

Understanding the genetic mechanisms of phenotypic variation in hybrids between domestic animals and their wild relatives may aid germplasm innovation. Here, we report the high-quality genome assemblies of a male Pamir argali (O ammon polii, 2n = 56), a female Tibetan sheep (O aries, 2n = 54), and a male hybrid of Pamir argali and domestic sheep, and the high-throughput sequencing of 425 ovine animals, including the hybrids of argali and domestic sheep. We detected genomic synteny between Chromosome 2 of sheep and two acrocentric chromosomes of argali. We revealed consistent satellite repeats around the chromosome breakpoints, which could have resulted in chromosome fusion. We observed many more hybrids with karyotype 2n = 54 than with 2n = 55, which could be explained by the selfish centromeres, the possible decreased rate of normal/balanced sperm, and the increased incidence of early pregnancy loss in the aneuploid ewes or rams. We identified genes and variants associated with important morphological and production traits (e.g., body weight, cannon circumference, hip height, and tail length) that show significant variations. We revealed a strong selective signature at the mutation (c.334C > A, p.G112W) in TBXT and confirmed its association with tail length among sheep populations of wide geographic and genetic origins. We produced an intercross population of 110 F2 offspring with varied number of vertebrae and validated the causal mutation by whole-genome association analysis. We verified its function using CRISPR-Cas9 genome editing. Our results provide insights into chromosomal speciation and phenotypic evolution and a foundation of genetic variants for the breeding of sheep and other animals.

Mini-review: Recent advances in post-translational modification site prediction based on deep learning

Post-translational modifications (PTMs) are closely linked to numerous diseases, playing a significant role in regulating protein structures, activities, and functions. Therefore, the identification of PTMs is crucial for understanding the mechanisms of cell biology and diseases therapy. Compared to traditional machine learning methods, the deep learning approaches for PTM prediction provide accurate and rapid screening, guiding the downstream wet experiments to leverage the screened information for focused studies. In this paper, we reviewed the recent works in deep learning to identify phosphorylation, acetylation, ubiquitination, and other PTM types. In addition, we summarized PTM databases and discussed future directions with critical insights.

Acylcarnitines: Nomenclature, Biomarkers, Therapeutic Potential, Drug Targets, and Clinical Trials

Acylcarnitines are fatty acid metabolites that play important roles in many cellular energy metabolism pathways. They have historically been used as important diagnostic markers for inborn errors of fatty acid oxidation and are being intensively studied as markers of energy metabolism, deficits in mitochondrial and peroxisomal β -oxidation activity, insulin resistance, and physical activity. Acylcarnitines are increasingly being identified as important indicators in metabolic studies of many diseases, including metabolic disorders, cardiovascular diseases, diabetes, depression, neurologic disorders, and certain cancers. The US Food and Drug Administration-approved drug L-carnitine, along with short-chain acylcarnitines (acetylcarnitine and propionylcarnitine), is now widely used as a dietary supplement. In light of their growing importance, we have undertaken an extensive review of acylcarnitines and provided a detailed description of their identity, nomenclature, classification, biochemistry, pathophysiology, supplementary use, potential drug targets, and clinical trials. We also summarize these updates in the Human Metabolome Database, which now includes information on the structures, chemical formulae, chemical/spectral properties, descriptions, and pathways for 1240 acylcarnitines. This work lays a solid foundation for identifying, characterizing, and understanding acylcarnitines in human biosamples. We also discuss the emerging opportunities for using acylcarnitines as biomarkers and as dietary interventions or supplements for many wide-ranging indications. The opportunity to identify new drug targets involved in controlling acylcarnitine levels is also discussed. SIGNIFICANCE STATEMENT: This review provides a comprehensive overview of acylcarnitines, including their nomenclature, structure and biochemistry, and use as disease biomarkers and pharmaceutical agents. We present updated information contained in the Human Metabolome Database website as well as substantial mapping of the known biochemical pathways associated with acylcarnitines, thereby providing a strong foundation for further clarification of their physiological roles.

KinasePhos 3.0: Redesign and Expansion of the Prediction on Kinase-specific Phosphorylation Sites

The purpose of this work is to enhance KinasePhos, a machine learning-based kinase-specific phosphorylation site prediction tool. Experimentally verified kinase-specific phosphorylation data were collected from PhosphoSitePlus, UniProtKB, the Group-based Prediction System 5.0, and Phospho.ELM. In total, 41,421 experimentally verified kinase-specific phosphorylation sites were identified. A total of 1380 unique kinases were identified, including 753 with existing classification information from KinBase and the remaining 627 annotated by building a phylogenetic tree. Based on this kinase classification, a total of 771 predictive models were built at the individual, family, and group levels, using at least 15 experimentally verified substrate sites in positive training datasets. The improved models demonstrated their effectiveness compared with other prediction tools. For example, the prediction of sites phosphorylated by the protein kinase B, casein kinase 2, and protein kinase A families had accuracies of 94.5%, 92.5%, and 90.0%, respectively. The average prediction accuracy for all 771 models was 87.2%. For enhancing interpretability, the SHapley Additive exPlanations (SHAP) method was employed to assess feature importance. The web interface of KinasePhos 3.0 has been redesigned to provide comprehensive annotations of kinase-specific phosphorylation sites on multiple proteins. Additionally, considering the large scale of phosphoproteomic data, a downloadable prediction tool is available at https://awi.cuhk.edu.cn/KinasePhos/download.html or https://github.com/tom-209/KinasePhos-3.0-executable-file.

A faecal microbiota signature with high specificity for pancreatic cancer

BACKGROUND

Recent evidence suggests a role for the microbiome in pancreatic ductal adenocarcinoma (PDAC) aetiology and progression.

OBJECTIVE

To explore the faecal and salivary microbiota as potential diagnostic biomarkers.

METHODS

We applied shotgun metagenomic and 16S rRNA amplicon sequencing to samples from a Spanish case-control study (n=136), including 57 cases, 50 controls, and 29 patients with chronic pancreatitis in the discovery phase, and from a German case-control study (n=76), in the validation phase.

RESULTS

Faecal metagenomic classifiers performed much better than saliva-based classifiers and identified patients with PDAC with an accuracy of up to 0.84 area under the receiver operating characteristic curve (AUROC) based on a set of 27 microbial species, with consistent accuracy across early and late disease stages. Performance further improved to up to 0.94 AUROC when we combined our microbiome-based predictions with serum levels of carbohydrate antigen (CA) 19-9, the only current non-invasive, Food and Drug Administration approved, low specificity PDAC diagnostic biomarker. Furthermore, a microbiota-based classification model confined to PDAC-enriched species was highly disease-specific when validated against 25 publicly available metagenomic study populations for various health conditions (n=5792). Both microbiome-based models had a high prediction accuracy on a German validation population (n=76). Several faecal PDAC marker species were detectable in pancreatic tumour and non-tumour tissue using 16S rRNA sequencing and fluorescence in situ hybridisation.

CONCLUSION

Taken together, our results indicate that non-invasive, robust and specific faecal microbiota-based screening for the early detection of PDAC is feasible.

Beyond Proteolysis-Targeting Chimeric Molecules: Designing Heterobifunctional Molecules Based on Functional Effectors

In recent years, with the successful development of proteolysis-targeting chimeric molecules (PROTACs), the potential of heterobifunctional molecules to contribute to reenvisioning drug design, especially small-molecule drugs, has been increasingly recognized. Inspired by PROTACs, diverse heterobifunctional molecules have been reported to simultaneously bind two or more molecules and bring them into proximity to interaction, such as ribonuclease-recruiting, autophagy-recruiting, lysosome-recruiting, kinase-recruiting, phosphatase-recruiting, glycosyltransferase-recruiting, and acetyltransferase-recruiting chimeras. On the basis of the heterobifunctional principle, more opportunities for advancing drug design by linking potential effectors to a protein of interest (POI) have emerged. Herein, we introduce heterobifunctional molecules other than PROTACs, summarize the limitations of existing molecules, list the main challenges, and propose perspectives for future research directions, providing insight into alternative design strategies based on substrate-proximity-based targeting.

Sbk2, a Newly Discovered Atrium-Enriched Regulator of Sarcomere Integrity

BACKGROUND

Heart development relies on tight spatiotemporal control of cardiac gene expression. Genes involved in this intricate process have been identified using animals and pluripotent stem cell-based models of cardio(myo)genesis. Recently, the repertoire of cardiomyocyte differentiation models has been expanded with iAM-1, a monoclonal line of conditionally immortalized neonatal rat atrial myocytes (NRAMs), which allows toggling between proliferative and differentiated (ie, excitable and contractile) phenotypes in a synchronized and homogenous manner.

METHODS

In this study, the unique properties of conditionally immortalized NRAMs (iAMs) were exploited to identify and characterize (lowly expressed) genes with an as-of-yet uncharacterized role in cardiomyocyte differentiation.

RESULTS

Transcriptome analysis of iAM-1 cells at different stages during one cycle of differentiation and subsequent dedifferentiation identified ≈13 000 transcripts, of which the dynamic changes in expression upon cardiomyogenic differentiation mostly opposed those during dedifferentiation. Among the genes whose expression increased during differentiation and decreased during dedifferentiation were many with known (lineage-specific) functions in cardiac muscle formation. Filtering for cardiac-enriched low-abundance transcripts, identified multiple genes with an uncharacterized role during cardio(myo)genesis including Sbk2 (SH3 domain binding kinase family member 2). Sbk2 encodes an evolutionarily conserved putative serine/threonine protein kinase, whose expression is strongly up- and downregulated during iAM-1 cell differentiation and dedifferentiation, respectively. In neonatal and adult rats, the protein is muscle-specific, highly atrium-enriched, and localized around the A-band of cardiac sarcomeres. Knockdown of Sbk2 expression caused loss of sarcomeric organization in NRAMs, iAMs and their human counterparts, consistent with a decrease in sarcomeric gene expression as evinced by transcriptome and proteome analyses. Interestingly, co-immunoprecipitation using Sbk2 as bait identified possible interaction partners with diverse cellular functions (translation, intracellular trafficking, cytoskeletal organization, chromatin modification, sarcomere formation).

CONCLUSIONS

iAM-1 cells are a relevant and suitable model to identify (lowly expressed) genes with a hitherto unidentified role in cardiomyocyte differentiation as exemplified by Sbk2: a regulator of atrial sarcomerogenesis.

EMBER: multi-label prediction of kinase-substrate phosphorylation events through deep learning

MOTIVATION

Kinase-catalyzed phosphorylation of proteins forms the backbone of signal transduction within the cell, enabling the coordination of numerous processes such as the cell cycle, apoptosis, and differentiation. Although on the order of 105 phosphorylation events have been described, we know the specific kinase performing these functions for <5% of cases. The ability to predict which kinases initiate specific individual phosphorylation events has the potential to greatly enhance the design of downstream experimental studies, while simultaneously creating a preliminary map of the broader phosphorylation network that controls cellular signaling.

RESULTS

We describe Embedding-based multi-label prediction of phosphorylation events (EMBER), a deep learning method that integrates kinase phylogenetic information and motif-dissimilarity information into a multi-label classification model for the prediction of kinase-motif phosphorylation events. Unlike previous deep learning methods that perform single-label classification, we restate the task of kinase-motif phosphorylation prediction as a multi-label problem, allowing us to train a single unified model rather than a separate model for each of the 134 kinase families. We utilize a Siamese neural network to generate novel vector representations, or an embedding, of peptide motif sequences, and we compare our novel embedding to a previously proposed peptide embedding. Our motif vector representations are used, along with one-hot encoded motif sequences, as input to a classification neural network while also leveraging kinase phylogenetic relationships into our model via a kinase phylogeny-weighted loss function. Results suggest that this approach holds significant promise for improving the known map of phosphorylation relationships that underlie kinome signaling.

AVAILABILITY AND IMPLEMENTATION

The data and code underlying this article are available in a GitHub repository at https://github.com/gomezlab/EMBER.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Pex30 undergoes phosphorylation and regulates peroxisome number in Saccharomyces cerevisiae

Pex30 is a dysferlin domain-containing protein whose role in peroxisome biogenesis has been studied by several research groups. Notably, recent studies have linked this protein to peroxisomes, endoplasmic reticulum and lipid bodies in Saccharomyces cerevisiae. Phosphoproteome studies of S. cerevisiae have identified several phosphorylation sites in Pex30. In this study we expressed and purified Pex30 from its native host. Analysis of the purified protein by circular dichroism spectroscopy showed that it retained its secondary structure and revealed primarily a helical structure. Further phosphorylation of Pex30 at three residues, Threonine 60, Serine 61 and Serine 511 was identified by mass spectrometry in this study. To understand the importance of this post-translational modification in peroxisome biogenesis, the identified residues were mutated to both non-phosphorylatable (alanine) and phosphomimetic (aspartic acid) variants. Upon analysis of the mutant variants by fluorescence microscopy, no alteration in the localization of the protein to ER and peroxisomes was observed. Interestingly, reduced number of peroxisomes were observed in cells expressing phosphomimetic mutations when cultured in peroxisome-inducing conditions. Our data suggest that phosphorylation and dephosphorylation of Pex30 may promote distinct interactions essential in regulating peroxisome number in a cell.

Butterfly eyespots evolved via cooption of an ancestral gene-regulatory network that also patterns antennae, legs, and wings

Where do butterfly eyespots come from? One of the long-standing questions in the field of evolution concerns addressing where novel complex traits come from. Here we show that butterfly eyespots, a novel complex trait, likely originated from the redeployment of a preexisting gene-regulatory network regulating antennae, legs, and wings, to novel locations on the wing.

Butterfly eyespots are beautiful novel traits with an unknown developmental origin. Here we show that eyespots likely originated via cooption of parts of an ancestral appendage gene-regulatory network (GRN) to novel locations on the wing. Using comparative transcriptome analysis, we show that eyespots cluster most closely with antennae, relative to multiple other tissues. Furthermore, three genes essential for eyespot development, Distal-less (Dll), spalt (sal), and Antennapedia (Antp), share similar regulatory connections as those observed in the antennal GRN. CRISPR knockout of cis-regulatory elements (CREs) for Dll and sal led to the loss of eyespots, antennae, legs, and also wings, demonstrating that these CREs are highly pleiotropic. We conclude that eyespots likely reused an ancient GRN for their development, a network also previously implicated in the development of antennae, legs, and wings.

Prediction of serine phosphorylation sites mapping on Schizosaccharomyces Pombe by fusing three encoding schemes with the random forest classifier

Serine phosphorylation is one type of protein post-translational modifications (PTMs), which plays an essential role in various cellular processes and disease pathogenesis. Numerous methods are used for the prediction of phosphorylation sites. However, the traditional wet-lab based experimental approaches are time-consuming, laborious, and expensive. In this work, a computational predictor was proposed to predict serine phosphorylation sites mapping on Schizosaccharomyces pombe (SP) by the fusion of three encoding schemes namely k-spaced amino acid pair composition (CKSAAP), binary and amino acid composition (AAC) with the random forest (RF) classifier. So far, the proposed method is firstly developed to predict serine phosphorylation sites for SP. Both the training and independent test performance scores were used to investigate the success of the proposed RF based fusion prediction model compared to others. We also investigated their performances by 5-fold cross-validation (CV). In all cases, it was observed that the recommended predictor achieves the largest scores of true positive rate (TPR), true negative rate (TNR), accuracy (ACC), Mathew coefficient of correlation (MCC), Area under the ROC curve (AUC) and pAUC (partial AUC) at false positive rate (FPR) = 0.20. Thus, the prediction performance as discussed in this paper indicates that the proposed approach may be a beneficial and motivating computational resource for predicting serine phosphorylation sites in the case of Fungi. The online interface of the software for the proposed prediction model is publicly available at http://mollah-bioinformaticslab-stat.ru.ac.bd/PredSPS/ .

Systems biomarker characteristics of circulating alkaline phosphatase activities for 48 types of human diseases

BACKGROUND

Most human diseases are accompanied by systems changes. Systems biomarkers should reflect such changes. The phosphorylation and dephosphorylation of biomolecules maintain human homeostasis. However, the systems biomarker characteristics of circulating alkaline phosphatase, a routine blood test conducted for many human diseases, have never been investigated.

METHOD

This study retrieved the circulating alkaline phosphatase (ALP) activities from patients with 48 clinically confirmed diseases and healthy individuals from the database of our hospital during the past five years. A detailed analysis of the statistical characteristics of ALP was conducted, including quantiles, receiving operator curve (ROC), and principal component analysis.

RESULTS

Among the 48 diseases, 45 had increased, and three had decreased median levels of ALP activities compared to the healthy control. Preeclampsia, hepatic encephalopathy, pancreatic cancer, and liver cancer had the highest median values, whereas nephrotic syndrome, lupus erythematosus, and nephritis had decreased median values compared to the healthy control. Further, area under curve (AUC) values were ranged between 0.61 and 0.87 for 19 diseases, and the ALP activities were the best systems biomarker for preeclampsia (AUC 0.87), hepatic encephalopathy (AUC 0.87), liver cancer (AUC 0.81), and pancreatic cancer (AUC 0.81).

CONCLUSIONS

Alkaline phosphatase was a decent systems biomarker for 19 different types of human diseases. Understanding the molecular mechanisms of over-up-and-down-regulation of ALP activities might be the key to understanding the whole-body systems' reactions during specific disease progression.

IL-17/IL-17 Receptor Pathway-Mediated Inflammatory Response in Apostichopus japonicus Supports the Conserved Functions of Cytokines in Invertebrates

Inflammation participates in host defenses against infectious agents and contributes to the pathophysiology of many diseases. IL-17 is a well-known proinflammatory cytokine that contributes to various aspects of inflammation in vertebrates. However, the functional role of invertebrate IL-17 in inflammatory regulation is not well understood. In this study, we first established an inflammatory model in the Vibrio splendidus-challenged sea cucumber Apostichopus japonicus (Echinodermata). Typical inflammatory symptoms, such as increased coelomocyte infiltration, tissue vacuoles, and tissue fractures, were observed in the V. splendidus-infected and diseased tissue of the body wall. Interestingly, A. japonicus IL-17 (AjIL-17) expression in the body wall and coelomocytes was positively correlated with the development of inflammation. The administration of purified recombinant AjIL-17 protein also directly promoted inflammation in A. japonicus Through genome searches and ZDOCK prediction, a novel IL-17R counterpart containing FNIII and hypothetical TIR domains was identified in the sea cucumber genome. Coimmunoprecipitation, far-Western blotting, and laser confocal microscopy confirmed that AjIL-17R could bind AjIL-17. A subsequent cross-linking assay revealed that the AjIL-17 dimer mediates the inflammatory response by the specific binding of dimeric AjIL-17R upon pathogen infection. Moreover, silencing AjIL-17R significantly attenuated the LPS- or exogenous AjIL-17-mediated inflammatory response. Functional analysis revealed that AjIL-17/AjIL-17R modulated inflammatory responses by promoting A. japonicus TRAF6 ubiquitination and p65 nuclear translocation and evenly mediated coelomocyte proliferation and migration. Taken together, our results provide functional evidence that IL-17 is a conserved cytokine in invertebrates and vertebrates associated with inflammatory regulation via the IL-17-IL-17R-TRAF6 axis.

Modulation of Phosphoprotein Activity by Phosphorylation Targeting Chimeras (PhosTACs)

Protein phosphorylation, which regulates many critical aspects of cell biology, is dynamically governed by kinases and phosphatases. Many diseases are associated with dysregulated hyperphosphorylation of critical proteins, such as retinoblastoma protein in cancer. Although kinase inhibitors have been widely applied in the clinic, growing evidence of off-target effects and increasing drug resistance prompts the need to develop a new generation of drugs. Here, we propose a proof-of-concept study of phosphorylation targeting chimeras (PhosTACs). Similar to PROTACs in their ability to induce ternary complexes, PhosTACs focus on recruiting a Ser/Thr phosphatase to a phosphosubstrate to mediate its dephosphorylation. However, distinct from PROTACs, PhosTACs can uniquely provide target gain-of-function opportunities to manipulate protein activity. In this study, we applied a chemical biology approach to evaluate the feasibility of PhosTACs by recruiting the scaffold and catalytic subunits of the PP2A holoenzyme to protein substrates such as PDCD4 and FOXO3a for targeted protein dephosphorylation. For FOXO3a, this dephosphorylation resulted in the transcriptional activation of a FOXO3a-responsive reporter gene.

CNVs with adaptive potential in Rangifer tarandus: genome architecture and new annotated assembly

Rangifer tarandus has experienced recent drastic population size reductions throughout its circumpolar distribution and preserving the species implies genetic diversity conservation. To facilitate genomic studies of the species populations, we improved the genome assembly by combining long read and linked read and obtained a new highly accurate and contiguous genome assembly made of 13,994 scaffolds (L90 = 131 scaffolds). Using de novo transcriptome assembly of RNA-sequencing reads and similarity with annotated human gene sequences, 17,394 robust gene models were identified. As copy number variations (CNVs) likely play a role in adaptation, we additionally investigated these variations among 20 genomes representing three caribou ecotypes (migratory, boreal and mountain). A total of 1,698 large CNVs (length > 1 kb) showing a genome distribution including hotspots were identified. 43 large CNVs were particularly distinctive of the migratory and sedentary ecotypes and included genes annotated for functions likely related to the expected adaptations. This work includes the first publicly available annotation of the caribou genome and the first assembly allowing genome architecture analyses, including the likely adaptive CNVs reported here.

Protein modifications in Hedgehog signaling: Cross talk and feedback regulation confer divergent Hedgehog signaling activity

The complexity of the Hedgehog (Hh) signaling cascade has increased over the course of evolution; however, it does not suffice to accommodate the dynamic yet robust requirements of differential Hh signaling activity needed for embryonic development and adult homeostatic maintenance. One solution to solve this dilemma is to apply multiple forms of post-translational modifications (PTMs) to the core Hh signaling components, modulating their abundance, localization, and signaling activity. This review summarizes various forms of protein modifications utilized to regulate Hh signaling, with a special emphasis on crosstalk between different forms of PTMs and their feedback regulation by Hh signaling.