PaxDb, a database of protein abundance averages across all three domains of life

ECMpy 2.0: A Python package for automated construction and analysis of enzyme-constrained models

Genome-scale metabolic models (GEMs) have been widely employed to predict microorganism behaviors. However, GEMs only consider stoichiometric constraints, leading to a linear increase in simulated growth and product yields as substrate uptake rates rise. This divergence from experimental measurements prompted the creation of enzyme-constrained models (ecModels) for various species, successfully enhancing chemical production. Building upon studies that allocate macromolecule resources, we developed a Python-based workflow (ECMpy) that constructs an enzyme-constrained model. This involves directly imposing an enzyme amount constraint in GEM and accounting for protein subunit composition in reactions. However, this procedure demands manual collection of enzyme kinetic parameter information and subunit composition details, making it rather user-unfriendly. In this work, we've enhanced the ECMpy toolbox to version 2.0, broadening its scope to automatically generate ecGEMs for a wider array of organisms. ECMpy 2.0 automates the retrieval of enzyme kinetic parameters and employs machine learning for predicting these parameters, which significantly enhances parameter coverage. Additionally, ECMpy 2.0 introduces common analytical and visualization features for ecModels, rendering computational results more user accessible. Furthermore, ECMpy 2.0 seamlessly integrates three published algorithms that exploit ecModels to uncover potential targets for metabolic engineering. ECMpy 2.0 is available at https://github.com/tibbdc/ECMpy or as a pip package (https://pypi.org/project/ECMpy/).

The substrate quality of CK2 target sites has a determinant role on their function and evolution

Most biological processes are regulated by signaling modules that bind to short linear motifs. For protein kinases, substrates may have full or only partial matches to the kinase recognition motif, a property known as "substrate quality." However, it is not clear whether differences in substrate quality represent neutral variation or if they have functional consequences. We examine this question for the kinase CK2, which has many fundamental functions. We show that optimal CK2 sites are phosphorylated at maximal stoichiometries and found in many conditions, whereas minimal substrates are more weakly phosphorylated and have regulatory functions. Optimal CK2 sites tend to be more conserved, and substrate quality is often tuned by selection. For intermediate sites, increases or decreases in substrate quality may be deleterious, as we demonstrate for a CK2 substrate at the kinetochore. The results together suggest a strong role for substrate quality in phosphosite function and evolution. A record of this paper's transparent peer review process is included in the supplemental information.

Genome-scale models of metabolism and expression predict the metabolic burden of recombinant protein expression

The production of recombinant proteins in a host using synthetic constructs such as plasmids comes at the cost of detrimental effects such as reduced growth, energetic inefficiencies, and other stress responses, collectively known as metabolic burden. Increasing the number of copies of the foreign gene increases the metabolic load but increases the expression of the foreign protein. Thus, there is a trade-off between biomass and product yield in response to changes in heterologous gene copy number. This work proposes a computational method, rETFL (recombinant Expression and Thermodynamic Flux), for analyzing and predicting the responses of recombinant organisms to the introduction of synthetic constructs. rETFL is an extension to the ETFL formulations designed to reconstruct models of metabolism and expression (ME-models). We have illustrated the capabilities of the method in four studies to (i) capture the growth reduction in plasmid-containing E. coli and recombinant protein production; (ii) explore the trade-off between biomass and product yield as plasmid copy number is varied; (iii) predict the emergence of overflow metabolism in recombinant E. coli in agreement with experimental data; and (iv) investigate the individual pathways and enzymes affected by the presence of the plasmid. We anticipate that rETFL will serve as a comprehensive platform for integrating available omics data for recombinant organisms and making context-specific predictions that can help optimize recombinant expression systems for biopharmaceutical production and gene therapy.

An adaptable in silico ensemble model of the arachidonic acid cascade

Eicosanoids are a family of bioactive lipids, including derivatives of the ubiquitous fatty acid arachidonic acid (AA). The intimate involvement of eicosanoids in inflammation motivates the development of predictive in silico models for a systems-level exploration of disease mechanisms, drug development and replacement of animal models. Using an ensemble modelling strategy, we developed a computational model of the AA cascade. This approach allows the visualisation of plausible and thermodynamically feasible predictions, overcoming the limitations of fixed-parameter modelling. A quality scoring method was developed to quantify the accuracy of ensemble predictions relative to experimental data, measuring the overall uncertainty of the process. Monte Carlo ensemble modelling was used to quantify the prediction confidence levels. Model applicability was demonstrated using mass spectrometry mediator lipidomics to measure eicosanoids produced by HaCaT epidermal keratinocytes and 46BR.1N dermal fibroblasts, treated with stimuli (calcium ionophore A23187), (ultraviolet radiation, adenosine triphosphate) and a cyclooxygenase inhibitor (indomethacin). Experimentation and predictions were in good qualitative agreement, demonstrating the ability of the model to be adapted to cell types exhibiting differences in AA release and enzyme concentration profiles. The quantitative agreement between experimental and predicted outputs could be improved by expanding network topology to include additional reactions. Overall, our approach generated an adaptable, tuneable ensemble model of the AA cascade that can be tailored to represent different cell types and demonstrated that the integration of in silico and in vitro methods can facilitate a greater understanding of complex biological networks such as the AA cascade.

Single-molecule imaging reveals the mechanism of bidirectional replication initiation in metazoa

Metazoan genomes are copied bidirectionally from thousands of replication origins. Replication initiation entails the assembly and activation of two CMG helicases (Cdc45⋅Mcm2-7⋅GINS) at each origin. This requires several replication firing factors (including TopBP1, RecQL4, and DONSON) whose exact roles are still under debate. How two helicases are correctly assembled and activated at each origin is a long-standing question. By visualizing the recruitment of GINS, Cdc45, TopBP1, RecQL4, and DONSON in real time, we uncovered that replication initiation is surprisingly dynamic. First, TopBP1 transiently binds to the origin and dissociates before the start of DNA synthesis. Second, two Cdc45 are recruited together, even though Cdc45 alone cannot dimerize. Next, two copies of DONSON and two GINS simultaneously arrive at the origin, completing the assembly of two CMG helicases. Finally, RecQL4 is recruited to the CMG⋅DONSON⋅DONSON⋅CMG complex and promotes DONSON dissociation and CMG activation via its ATPase activity.

DNP-assisted solid-state NMR enables detection of proteins at nanomolar concentrations in fully protonated cellular milieu

With the sensitivity enhancements conferred by dynamic nuclear polarization (DNP), magic angle spinning (MAS) solid state NMR spectroscopy experiments can attain the necessary sensitivity to detect very low concentrations of proteins. This potentially enables structural investigations of proteins at their endogenous levels in their biological contexts where their native stoichiometries with potential interactors is maintained. Yet, even with DNP, experiments are still sensitivity limited. Moreover, when an isotopically-enriched target protein is present at physiological levels, which typically range from low micromolar to nanomolar concentrations, the isotope content from the natural abundance isotopes in the cellular milieu can outnumber the isotope content of the target protein. Using isotopically enriched yeast prion protein, Sup35NM, diluted into natural abundance yeast lysates, we optimized sample composition. We found that modest cryoprotectant concentrations and fully protonated environments support efficient DNP. We experimentally validated theoretical calculations of the limit of specificity for an isotopically enriched protein in natural abundance cellular milieu. We establish that, using pulse sequences that are selective for adjacent NMR-active nuclei, proteins can be specifically detected in cellular milieu at concentrations in the hundreds of nanomolar. Finally, we find that maintaining native stoichiometries of the protein of interest to the components of the cellular environment may be important for proteins that make specific interactions with cellular constituents.

UPF1 ATPase autoinhibition and activation modulate RNA binding kinetics and NMD efficiency

The RNA helicase UPF1 interacts with mRNAs, mRNA decay machinery, and the terminating ribosome to promote nonsense-mediated mRNA decay (NMD). Structural and biochemical data have revealed that UPF1 exists in an enzymatically autoinhibited 'closed' state. Upon binding the NMD protein UPF2, UPF1 undergoes an extensive conformational change into a more enzymatically active 'open' state, which exhibits enhanced ATPase and helicase activity. However, mechanically deficient UPF1 mutants (i.e. poorly processive, slow, and mechanochemically uncoupled) can support efficient NMD, bringing into question the roles of UPF1 enzymatic autoinhibition and activation in NMD. Here, we identify two additional important features of the activated open state: slower RNA binding kinetics and enhanced ATP-stimulated RNA dissociation kinetics. Computational modeling based on empirical measurements of UPF1, UPF2 and RNA interaction kinetics predicts that the majority of UPF1-RNA binding and dissociation events in cells occur independently of UPF2 binding. We find that UPF1 mutants with either reduced or accelerated dissociation from RNA have NMD defects, whereas UPF1 mutants that are more dependent on UPF2 for catalytic activity remain active on well-established NMD targets. These findings support a model in which the kinetics of UPF1-mRNA interactions are important determinants of cellular NMD efficiency.

Application of Machine Learning in the Quantitative Analysis of the Surface Characteristics of Highly Abundant Cytoplasmic Proteins: Toward AI-Based Biomimetics

Proteins in the crowded environment of human cells have often been studied regarding nonspecific interactions, misfolding, and aggregation, which may cause cellular malfunction and disease. Specifically, proteins with high abundance are more susceptible to these issues due to the law of mass action. Therefore, the surfaces of highly abundant cytoplasmic (HAC) proteins directly exposed to the environment can exhibit specific physicochemical, structural, and geometrical characteristics that reduce nonspecific interactions and adapt to the environment. However, the quantitative relationships between the overall surface descriptors still need clarification. Here, we used machine learning to identify HAC proteins using hydrophobicity, charge, roughness, secondary structures, and B-factor from the protein surfaces and quantified the contribution of each descriptor. First, several supervised learning algorithms were compared to solve binary classification problems for the surfaces of HAC and extracellular proteins. Then, logistic regression was used for the feature importance analysis of descriptors considering model performance (80.2% accuracy and 87.6% AUC) and interpretability. The HAC proteins showed positive correlations with negatively and positively charged areas but negative correlations with hydrophobicity, the B-factor, the proportion of beta structures, roughness, and the proportion of disordered regions. Finally, the details of each descriptor could be explained concerning adaptative surface strategies of HAC proteins to regulate nonspecific interactions, protein folding, flexibility, stability, and adsorption. This study presented a novel approach using various surface descriptors to identify HAC proteins and provided quantitative design rules for the surfaces well-suited to human cellular crowded environments.

Vo N, Nguyen BT, Huynh ST, Nguyen HD. Analyzing the correlation between protein expression and sequence-related features of mRNA and protein in Escherichia coli K-12 MG1655 model

It was necessary to have a tool that could predict the amount of protein and optimize the gene sequences to produce recombinant proteins efficiently. The Transim model published by Tuller et al. in 2018 can calculate the translation rate in E. coli using features on the mRNA sequence, achieving a Spearman correlation with the amount of protein per mRNA of 0.36 when tested on the dataset of operons' first genes in E. coli K-12 MG1655 genome. However, this Spearman correlation was not high, and the model did not fully consider the features of mRNA and protein sequences. Therefore, to enhance the prediction capability, our study firstly tried expanding the testing dataset, adding genes inside the operon, and using the microarray of the mRNA expression data set, thereby helping to improve the correlation of translation rate with the amount of protein with more than 0.42. Next, the applicability of 6 traditional machine learning models to calculate a "new translation rate" was examined using initiation rate and elongation rate as inputs. The result showed that the SVR algorithm had the most correlated new translation rates, with Spearman correlation improving to R = 0.6699 with protein level output and to R = 0.6536 with protein level per mRNA. Finally, the study investigated the degree of improvement when combining more features with the new translation rates. The results showed that the model's predictive ability to produce a protein per mRNA reached R = 0.6660 when using six features, while the correlation of this model's final translation rate to protein level was up to R = 0.6729. This demonstrated the model's capability to predict protein expression of a gene, rather than being limited to predicting expression by an mRNA and showed the model's potential for development into gene expression predicting tools.

Collision-Induced Unfolding, Tandem MS, Bottom-up Proteomics, and Interactomics for Identification of Protein Complexes in Native Surface Mass Spectrometry

Endogenously occurring salts and nonvolatile matrix components in untreated biological surfaces can suppress protein ionization and promote adduct formation, challenging protein identification. Characterization of labile proteins within biological specimens is particularly demanding because additional purification or sample treatment steps can be time-intensive and can disrupt noncovalent interactions. It is demonstrated that the combined use of collision-induced unfolding, tandem mass spectrometry, and bottom-up proteomics improves protein characterization in native surface mass spectrometry (NSMS). This multiprong analysis is achieved by acquiring NSMS, MS/MS, ion mobility (IM), and bottom-up proteomics data from a single surface extracted sample. The validity of this multiprong approach was confirmed by the successful characterization of nine surface-deposited proteins, with molecular weights ranging from 8 to 147 kDa, in two separate mixtures. Bottom-up proteomics provided a list of proteins to match against observed proteins in NSMS and their detected subunits in tandem MS. The method was applied to characterize endogenous proteins from untreated chicken liver samples. The subcapsular liver sampling for NSMS analysis allowed for the detection of endogenous proteins with molecular weights of up to ∼220 kDa. Moreover, using IM-MS, collision cross sections and collision-induced unfolding pathways of enzymatic proteins and protein complexes of up to 145 kDa were obtained.

DNAzyme-based faithful probing and pulldown to identify candidate biomarkers of low abundance

Biomarker discovery is essential for the understanding, diagnosis, targeted therapy and prognosis assessment of malignant diseases. However, it remains a huge challenge due to the lack of sensitive methods to identify disease-specific rare molecules. Here we present MORAC, molecular recognition based on affinity and catalysis, which enables the effective identification of candidate biomarkers with low abundance. MORAC relies on a class of DNAzymes, each cleaving a sole RNA linkage embedded in their DNA chain upon specifically sensing a complex system with no prior knowledge of the system's molecular content. We show that signal amplification from catalysis ensures the DNAzymes high sensitivity (for target probing); meanwhile, a simple RNA-to-DNA mutation can shut down their RNA cleavage ability and turn them into a pure affinity tool (for target pulldown). Using MORAC, we identify previously unknown, low-abundance candidate biomarkers with clear clinical value, including apolipoprotein L6 in breast cancer and seryl-tRNA synthetase 1 in polyps preceding colon cancer.

Application of quantitative protein mass spectrometric data in the early predictive analysis of membrane-bound target engagement by monoclonal antibodies

Model-informed drug discovery advocates the use of mathematical modeling and simulation for improved efficacy in drug discovery. In the case of monoclonal antibodies (mAbs) against cell membrane antigens, this requires quantitative insight into the target tissue concentration levels. Protein mass spectrometry data are often available but the values are expressed in relative, rather than in molar concentration units that are easier to incorporate into pharmacokinetic models. Here, we present an empirical correlation that converts the parts per million (ppm) concentrations in the PaxDb database to their molar equivalents that are more suitable for pharmacokinetic modeling. We evaluate the insight afforded to target tissue distribution by analyzing the likely tumor-targeting accuracy of mAbs recognizing either epidermal growth factor receptor or its homolog HER2. Surprisingly, the predicted tissue concentrations of both these targets exceed the Kd values of their respective therapeutic mAbs. Physiologically based pharmacokinetic (PBPK) modeling indicates that in these conditions only about 0.05% of the dosed mAb is likely to reach the solid tumor target cells. The rest of the dose is eliminated in healthy tissues via both nonspecific and target-mediated processes. The presented approach allows evaluation of the interplay between the target expression level in different tissues that determines the overall pharmacokinetic properties of the drug and the fraction that reaches the cells of interest. This methodology can help to evaluate the efficacy and safety properties of novel drugs, especially if the off-target cell degradation has cytotoxic outcomes, as in the case of antibody-drug conjugates.

UPF1 ATPase autoinhibition and activation modulate RNA binding kinetics and NMD efficiency

The RNA helicase UPF1 interacts with mRNAs, mRNA decay machinery, and the terminating ribosome to promote nonsense-mediated mRNA decay (NMD). Structural and biochemical data have revealed that UPF1 exists in an enzymatically autoinhibited "closed" state. Upon binding the NMD protein UPF2, UPF1 undergoes an extensive conformational change into a more enzymatically active "open" state, which exhibits enhanced ATPase and helicase activity. However, mechanically deficient UPF1 mutants can support efficient NMD, bringing into question the roles of UPF1 enzymatic autoinhibition and activation in NMD. Here, we identify two additional important features of the activated open state: slower nucleic acid binding kinetics and enhanced ATP-stimulated nucleic acid dissociation kinetics. Computational modeling based on empirical measurements of UPF1, UPF2, and RNA interaction kinetics predicts that the majority of UPF1-RNA binding and dissociation events in cells occur independently of UPF2 binding. We find that UPF1 mutants with either reduced or accelerated dissociation from RNA have NMD defects, whereas UPF1 mutants that are more dependent on UPF2 for catalytic activity remain active on well-established NMD targets. These findings support a model in which the kinetics of UPF1-mRNA interactions are important determinants of cellular NMD efficiency.

PaxDb 5.0: Curated Protein Quantification Data Suggests Adaptive Proteome Changes in Yeasts

The "Protein Abundances Across Organisms" database (PaxDb) is an integrative metaresource dedicated to protein abundance levels, in tissue-specific or whole-organism proteomes. PaxDb focuses on computing best-estimate abundances for proteins in normal/healthy contexts and expresses abundance values for each protein in "parts per million" in relation to all other protein molecules in the cell. The uniform data reprocessing, quality scoring, and integrated orthology relations have made PaxDb one of the preferred tools for comparisons between individual datasets, tissues, or organisms. In describing the latest version 5.0 of PaxDb, we particularly emphasize the data integration from various types of raw data and how we expanded the number of organisms and tissue groups as well as the proteome coverage. The current collection of PaxDb includes 831 original datasets from 170 species, including 22 Archaea, 81 Bacteria, and 67 Eukaryota. Apart from detailing the data update, we also present a comparative analysis of the human proteome subset of PaxDb against the two most widely used human proteome data resources: Human Protein Atlas and Genotype-Tissue Expression. Lastly, through our protein abundance data, we reveal an evolutionary trend in the usage of sulfur-containing amino acids in the proteomes of Fungi.

A network of DZF proteins controls alternative splicing regulation and fidelity

Proteins containing DZF (domain associated with zinc fingers) modules play important roles throughout gene expression, from transcription to translation. Derived from nucleotidyltransferases but lacking catalytic residues, DZF domains serve as heterodimerization surfaces between DZF protein pairs. Three DZF proteins are widely expressed in mammalian tissues, ILF2, ILF3 and ZFR, which form mutually exclusive ILF2-ILF3 and ILF2-ZFR heterodimers. Using eCLIP-Seq, we find that ZFR binds across broad intronic regions to regulate the alternative splicing of cassette and mutually exclusive exons. ZFR preferentially binds dsRNA in vitro and is enriched on introns containing conserved dsRNA elements in cells. Many splicing events are similarly altered upon depletion of any of the three DZF proteins; however, we also identify independent and opposing roles for ZFR and ILF3 in alternative splicing regulation. Along with widespread involvement in cassette exon splicing, the DZF proteins control the fidelity and regulation of over a dozen highly validated mutually exclusive splicing events. Our findings indicate that the DZF proteins form a complex regulatory network that leverages dsRNA binding by ILF3 and ZFR to modulate splicing regulation and fidelity.

How an overlooked gene in coenzyme a synthesis solved an enzyme mechanism predicament

Coenzyme A (CoA) is an essential cofactor throughout biology. The first committed step in the CoA synthetic pathway is synthesis of β-alanine from aspartate. In Escherichia coli and Salmonella enterica panD encodes the responsible enzyme, aspartate-1-decarboxylase, as a proenzyme. To become active, the E. coli and S. enterica PanD proenzymes must undergo an autocatalytic cleavage to form the pyruvyl cofactor that catalyzes decarboxylation. A problem was that the autocatalytic cleavage was too slow to support growth. A long-neglected gene (now called panZ) was belatedly found to encode the protein that increases autocatalytic cleavage of the PanD proenzyme to a physiologically relevant rate. PanZ must bind CoA or acetyl-CoA to interact with the PanD proenzyme and accelerate cleavage. The CoA/acetyl-CoA dependence has led to proposals that the PanD-PanZ CoA/acetyl-CoA interaction regulates CoA synthesis. Unfortunately, regulation of β-alanine synthesis is very weak or absent. However, the PanD-PanZ interaction provides an explanation for the toxicity of the CoA anti-metabolite, N5-pentyl pantothenamide.

Extending the Range of SLIM-Labeling Applications: From Human Cell Lines in Culture to Caenorhabditis elegans Whole-Organism Labeling

The simple light isotope metabolic-labeling technique relies on the in vivo biosynthesis of amino acids from U-[¹²C]-labeled molecules provided as the sole carbon source. The incorporation of the resulting U-[¹²C]-amino acids into proteins presents several key advantages for mass-spectrometry-based proteomics analysis, as it results in more intense monoisotopic ions, with a better signal-to-noise ratio in bottom-up analysis. In our initial studies, we developed the simple light isotope metabolic (SLIM)-labeling strategy using prototrophic eukaryotic microorganisms, the yeasts Candida albicans and Saccharomyces cerevisiae, as well as strains with genetic markers that lead to amino-acid auxotrophy. To extend the range of SLIM-labeling applications, we evaluated (i) the incorporation of U-[¹²C]-glucose into proteins of human cells grown in a complex RPMI-based medium containing the labeled molecule, considering that human cell lines require a large number of essential amino-acids to support their growth, and (ii) an indirect labeling strategy in which the nematode Caenorhabditis elegans grown on plates was fed U-[¹²C]-labeled bacteria (Escherichia coli) and the worm proteome analyzed for ¹²C incorporation into proteins. In both cases, we were able to demonstrate efficient incorporation of ¹²C into the newly synthesized proteins, opening the way for original approaches in quantitative proteomics.

Mass Spectrometry-Based Atlas of Extracellular Matrix Proteins across 25 Mouse Organs

The extracellular matrix (ECM) is a critical non-cellular component of multicellular organisms containing a variety of proteins, glycoproteins, and proteoglycans which have been implicated in a wide variety of essential biological processes, including development, wound healing, and aging. Due to low solubility, many ECM proteins have been underrepresented in previous proteomic datasets. Using an optimized three-step decellularization and ECM extraction method involving chaotrope extraction and digestion via hydroxylamine hydrochloride, we have generated coverage of the matrisome across 25 organs. We observe that the top 100 most abundant proteins from the ECM fractions of all tissues are generally present in all tissues, indicating that tissue matrices are principally composed of a shared set of ECM proteins. However, these proteins vary up to 4000-fold between tissues, resulting in highly unique matrix profiles even with the same primary set of proteins. A data reduction approach was used to reveal related networks of expressed ECM proteins across varying tissues, including basement membrane and collagen subtypes.

Capture, Release, and Identification of Newly Synthesized Proteins for Improved Profiling of Functional Translatomes

New protein synthesis is regulated both at the level of mRNA transcription and translation. RNA-Seq is effective at measuring levels of mRNA expression, but techniques to monitor mRNA translation are much more limited. Previously, we reported results from O-propargyl-puromycin (OPP) labeling of proteins undergoing active translation in a 2-h time frame, followed by biotinylation using click chemistry, affinity purification, and on-bead digestion to identify nascent proteins by mass spectrometry (OPP-ID). As with any on-bead digestion protocol, the problem of nonspecific binders complicated the rigorous categorization of nascent proteins by OPP-ID. Here, we incorporate a chemically cleavable linker, Dde biotin-azide, into the protocol (OPP-ID_CL) to provide specific release of modified proteins from the streptavidin beads. Following capture, the Dde moiety is readily cleaved with 2% hydrazine, releasing nascent polypeptides bearing OPP plus a residual C₃H₈N₄ tag. When results are compared side by side with the original OPP-ID method, change to a cleavable linker led to a dramatic reduction in the number of background proteins detected in controls and a concomitant increase in the number of proteins that could be characterized as newly synthesized. We evaluated the method's ability to detect nascent proteins at various submilligram protein input levels and showed that, when starting with only 100 μg of protein, ∼1500 nascent proteins could be identified with low background. Upon treatment of K562 cells with MLN128, a potent inhibitor of the mammalian target of rapamycin, prior to OPP treatment, we identified 1915 nascent proteins, the majority of which were downregulated upon inhibitor treatment. Repressed proteins with log2 FC <-1 revealed a complex network of functionally interacting proteins, with the largest cluster associated with translational initiation. Overall, incorporation of the Dde biotin-azide cleavable linker into our protocol has increased the depth and accuracy of profiling of nascent protein networks.

Good things come to those who bait: the peroxisomal docking complex

Peroxisomal integrity and function are highly dependent on its membrane and soluble (matrix) components. Matrix enzymes are imported post-translationally in a folded or even oligomeric state, via a still mysterious protein translocation mechanism. They are guided to peroxisomes via the Peroxisomal Targeting Signal (PTS) sequences which are recognized by specific cytosolic receptors, Pex5, Pex7 and Pex9. Subsequently, cargo-loaded receptors bind to the docking complex in an initial step, followed by channel formation, cargo-release, receptor-recycling and -quality control. The docking complexes of different species share Pex14 as their core component but differ in composition and oligomeric state of Pex14. Here we review and highlight the latest insights on the structure and function of the peroxisomal docking complex. We summarize differences between yeast and mammals and then we integrate this knowledge into our current understanding of the import machinery.

A dynamical stochastic model of yeast translation across the cell cycle

Translation is a central step in gene expression, however its quantitative and time-resolved regulation is poorly understood. We developed a discrete, stochastic model for protein translation in S. cerevisiae in a whole-transcriptome, single-cell context. A "base case" scenario representing an average cell highlights translation initiation rates as the main co-translational regulatory parameters. Codon usage bias emerges as a secondary regulatory mechanism through ribosome stalling. Demand for anticodons with low abundancy is shown to cause above-average ribosome dwelling times. Codon usage bias correlates strongly both with protein synthesis rates and elongation rates. Applying the model to a time-resolved transcriptome estimated by combining data from FISH and RNA-Seq experiments, it could be shown that increased total transcript abundance during the cell cycle decreases translation efficiency at single transcript level. Translation efficiency grouped by gene function shows highest values for ribosomal and glycolytic genes. Ribosomal proteins peak in S phase while glycolytic proteins rank highest in later cell cycle phases.

Application of single-cell RNA sequencing on human testicular samples: a comprehensive review

So far there has been no comprehensive review using systematic literature search strategies to show the application of single-cell RNA sequencing (scRNA-seq) in the human testis of the whole life cycle (from embryos to aging males). Here, we summarized the application of scRNA-seq analyses on various human testicular biological samples. A systematic search was conducted in PubMed and Gene Expression Omnibus (GEO), focusing on English researches published after 2009. Articles related to GEO data-series were also retrieved in PubMed or BioRxiv. 81 full-length studies were finally included in the review. ScRNA-seq has been widely used on different human testicular samples with various library strategies, and new cell subtypes such as State 0 spermatogonial stem cells (SSC) and stage_a/b/c Sertoli cells (SC) were identified. For the development of normal testes, scRNA-seq-based evidence showed dynamic transcriptional changes of both germ cells and somatic cells from embryos to adults. And dysregulated metabolic signaling or hedgehog signaling were revealed by scRNA-seq in aged SC or Leydig cells (LC), respectively. For infertile males, scRNA-seq studies revealed profound changes of testes, such as the increased proportion of immature SC/LC of Klinefelter syndrome, the somatic immaturity and altered germline autophagy of patients with non-obstructive azoospermia, and the repressed differentiation of SSC in trans-females receiving testosterone inhibition therapy. Besides, the re-analyzing of public scRNA-seq data made further discoveries such as the potential vulnerability of testicular SARS-CoV-2 infection, and both evolutionary conservatism and divergence among species. ScRNA-seq analyses would unveil mechanisms of testes' development and changes so as to help developing novel treatments for male infertility.

Evolutionary analysis of KED-rich proteins in plants

During the course of evolution, organisms have developed genetic mechanisms in response to various environmental stresses including wounding from mechanical damage or herbivory-caused injury. A previous study of wounding response in the plant tobacco identified a unique wound-induced gene, aptly named KED due to its coding for a protein that has an unusually high content of amino acids lysine (K), glutamic acid (E) and aspartic acid (D). However, by far little is known about this intriguing gene. In this study, we investigated the evolutionary aspects of the KED-rich coding genes. We found that a consistent pattern of wound-induced KED gene expression is maintained across representative species of angiosperm and gymnosperm. KED genes can be identified in species from all groups of land plants (Embryophyta). All the KED proteins from vascular plants (Tracheophyta) including angiosperm, gymnosperm, fern and lycophyte share a conserved 19-amino acid domain near the C-terminus, whereas bryophytes (moss, liverwort and hornwort) possess KED-rich, multi-direct-repeat sequences that are distinct from the vascular plant KEDs. We detected KED-rich sequences in Charophyta species but not in Chlorophyta wherever genome sequences are available. Our studies suggest diverse and complex evolution pathways for land plant KED genes. Vascular plant KEDs exhibit high evolutionary conservation, implicating their shared function in response to wounding stress. The extraordinary enrichment of amino acids K, E and D in these groups of distinct and widely distributed proteins may reflect the structural and functional requirement for these three residues during some 600 million years of land plant evolution.

Architecture of the outbred brown fat proteome defines regulators of metabolic physiology

Brown adipose tissue (BAT) regulates metabolic physiology. However, nearly all mechanistic studies of BAT protein function occur in a single inbred mouse strain, which has limited the understanding of generalizable mechanisms of BAT regulation over physiology. Here, we perform deep quantitative proteomics of BAT across a cohort of 163 genetically defined diversity outbred mice, a model that parallels the genetic and phenotypic variation found in humans. We leverage this diversity to define the functional architecture of the outbred BAT proteome, comprising 10,479 proteins. We assign co-operative functions to 2,578 proteins, enabling systematic discovery of regulators of BAT. We also identify 638 proteins that correlate with protection from, or sensitivity to, at least one parameter of metabolic disease. We use these findings to uncover SFXN5, LETMD1, and ATP1A2 as modulators of BAT thermogenesis or adiposity, and provide OPABAT as a resource for understanding the conserved mechanisms of BAT regulation over metabolic physiology.

Grabody B, an IGF1 receptor-based shuttle, mediates efficient delivery of biologics across the blood-brain barrier

Effective delivery of therapeutics to the brain is challenging. Molecular shuttles use receptors expressed on brain endothelial cells to deliver therapeutics. Antibodies targeting transferrin receptor (TfR) have been widely developed as molecular shuttles. However, the TfR-based approach raises concerns about safety and developmental burden. Here, we report insulin-like growth factor 1 receptor (IGF1R) as an ideal target for the molecular shuttle. We also describe Grabody B, an antibody against IGF1R, as a molecular shuttle. Grabody B has broad cross-species reactivity and does not interfere with IGF1R-mediated signaling. We demonstrate that administration of Grabody B-fused anti-alpha-synuclein (α-Syn) antibody induces better improvement in neuropathology and behavior in a Parkinson's disease animal model than the therapeutic antibody alone due to its superior serum pharmacokinetics and enhanced brain exposure. The results indicate that IGF1R is an ideal shuttle target and Grabody B is a safe and efficient molecular shuttle.

Follistatin-like 1 and its paralogs in heart development and cardiovascular disease

Cardiovascular diseases (CVDs) are a group of disorders affecting the heart and blood vessels and a leading cause of death worldwide. Thus, there is a need to identify new cardiokines that may protect the heart from damage as reported in GBD 2017 Causes of Death Collaborators (2018) (The Lancet 392:1736-1788). Follistatin-like 1 (FSTL1) is a cardiokine that is highly expressed in the heart and released to the serum after cardiac injury where it is associated with CVD and predicts poor outcome. The action of FSTL1 likely depends not only on the tissue source but also post-translation modifications that are target tissue- and cell-specific. Animal studies examining the effect of FSTL1 in various models of heart disease have exploded over the past 15 years and primarily report a protective effect spanning from inhibiting inflammation via transforming growth factor, preventing remodeling and fibrosis to promoting angiogenesis and hypertrophy. A better understanding of FSTL1 and its homologs is needed to determine whether this protein could be a useful novel biomarker to predict poor outcome and death and whether it has therapeutic potential. The aim of this review is to provide a comprehensive description of the literature for this family of proteins in order to better understand their role in normal physiology and CVD.

The protein aggregation inhibitor YAT2150 has potent antimalarial activity in Plasmodium falciparum in vitro cultures

Background

By 2016, signs of emergence of Plasmodium falciparum resistance to artemisinin and partner drugs were detected in the Greater Mekong Subregion. Recently, the independent evolution of artemisinin resistance has also been reported in Africa and South America. This alarming scenario calls for the urgent development of new antimalarials with novel modes of action. We investigated the interference with protein aggregation, which is potentially toxic for the cell and occurs abundantly in all Plasmodium stages, as a hitherto unexplored drug target in the pathogen.

Results

Attempts to exacerbate the P. falciparum proteome’s propensity to aggregation by delivering endogenous aggregative peptides to in vitro cultures of this parasite did not significantly affect their growth. In contrast, protein aggregation inhibitors clearly reduced the pathogen’s viability. One such compound, the bis(styrylpyridinium) salt YAT2150, exhibited potent antiplasmodial activity with an in vitro IC₅₀ of 90 nM for chloroquine- and artemisinin-resistant lines, arresting asexual blood parasites at the trophozoite stage, as well as interfering with the development of both sexual and hepatic forms of Plasmodium. At its IC₅₀, this compound is a powerful inhibitor of the aggregation of the model amyloid β peptide fragment 1-40, and it reduces the amount of aggregated proteins in P. falciparum cultures, suggesting that the underlying antimalarial mechanism consists in a generalized impairment of proteostasis in the pathogen. YAT2150 has an easy, rapid, and inexpensive synthesis, and because it fluoresces when it accumulates in its main localization in the Plasmodium cytosol, it is a theranostic agent.

Conclusions

Inhibiting protein aggregation in Plasmodium significantly reduces the parasite’s viability in vitro. Since YAT2150 belongs to a novel structural class of antiplasmodials with a mode of action that potentially targets multiple gene products, rapid evolution of resistance to this drug is unlikely to occur, making it a promising compound for the post-artemisinin era.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12915-022-01374-4.

Identification of a novel missense mutation in non-syndromic familial multiple supernumerary teeth

OBJECTIVE

This study intended to evaluate the involvement of genetic factors in the etiology of non-syndromic multiple supernumerary teeth.

DESIGN

We filtered the single nucleotide polymorphisms (SNPs) of the proband and his mother with similar phenotypes through whole-genome sequencing (WGS). By integrating multiple databases related to human genome mutations and disease information for mutation annotation, we excluded the SNPs of people without supernumerary teeth. Subsequently, the bioinformatics analysis tools (Sorting Intolerant From Tolerant (SIFT) < 0.05, Polymorphism Phenotyping (PolyPhen) > 0.90) were used to screen out the most correlated SNPs of the disease, besides, Gene Ontology (GO) analysis (P<0.05, FDR<0.05) and Sanger sequencing was applied to further verify the candidate pathogenic mutation point.

RESULTS

A novel heterozygous variant in fer-1 like family member 6 (FER1L6) gene likely denoted pathogenicity in non-syndromic familial multiple supernumerary teeth. We identified a cohort of 3499 non-synonymous SNPs (nsSNPs), and only 142 nsSNPs with the score of SIFT < 0.05 and PolyPhen > 0.90 were retained. Then we got 54 nsSNPs from 31 candidate genes through GO analysis. Sanger sequencing revealed a missense variant in exon 31 of the FER1L6 gene, causing a transition from guanine to adenine in position 1447 of protein kinase C conserved region 2.

CONCLUSIONS

We identified a novel heterozygous chromosome 8q24.13 mutation of FER1L6, which was a new mutation site identified in non-syndromic familial multiple supernumerary teeth through genetic analysis of a Chinese family.

Vulnerability of the spinal motor neuron presynaptic terminal sub-proteome in ALS

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder, characterised by the loss of motor neurons and subsequent paralysis. Evidence indicates that synaptic alterations are associated with the early stages of ALS pathogenesis. A hallmark of ALS postmortem tissue is the presence of proteinaceous inclusions, indicative of disturbed protein homeostasis, particularly in spinal cord motor neurons. We recently demonstrated that spinal cord motor neurons contain a supersaturated proteome, as they possess proteins at concentrations that exceed their solubility limits, resulting in a metastable proteome conducive to protein misfolding and aggregation. Recent evidence indicates metastable sub-proteomes within neuronal compartments, such as the synapse, may be particularly vulnerable and underlie their involvement in the initial stages of neurodegenerative diseases. To investigate if the motor neuron presynaptic terminal possesses a metastable sub-proteome, we used human and mouse spinal cord motor neuron expression data to calculate supersaturation scores. Here, we found that both the human and mouse presynaptic terminal sub-proteomes have higher supersaturation scores than the entire motor neuron proteome. In addition, we observed that proteins down-regulated in ALS were over-represented in the synapse. These results provide support for the notion that the metastability of the sub-proteome within the motor neuron presynaptic terminal may be particularly susceptible to protein homeostasis disturbances in ALS, and may contribute to explaining the observed synaptic dysfunction in ALS.

Low complexity regions in mammalian proteins are associated with low protein abundance and high transcript abundance

Low Complexity Regions (LCRs) are present in a surprisingly large number of eukaryotic proteins. These highly repetitive and compositionally biased sequences are often structurally disordered, bind promiscuously, and evolve rapidly. Frequently studied in terms of evolutionary dynamics, little is known about how LCRs affect the expression of the proteins which contain them. It would be expected that rapidly evolving LCRs are unlikely to be tolerated in strongly conserved, highly abundant proteins, leading to lower overall abundance in proteins which contain LCRs. To test this hypothesis and examine the associations of protein abundance and transcript abundance with the presence of LCRs, we have integrated high-throughput data from across mammals. We have found that LCRs are indeed associated with reduced protein abundance, but are also associated with elevated transcript abundance. These associations are qualitatively consistent across 12 human tissues and nine mammalian species. The differential impacts of LCRs on abundance at the protein and transcript level are not explained by differences in either protein degradation rates or the inefficiency of translation for LCR containing proteins. We suggest that rapidly evolving LCRs are a source of selective pressure on the regulatory mechanisms which maintain steady-state protein abundance levels.

Ionizing radiation and chemical oxidant exposure impacts on Cryptococcus neoformans transfer RNAs

Cryptococcus neoformans is a fungus that is able to survive abnormally high levels of ionizing radiation (IR). The radiolysis of water by IR generates reactive oxygen species (ROS) such as H₂O₂ and OH^-. C. neoformans withstands the damage caused by IR and ROS through antioxidant production and enzyme-catalyzed breakdown of ROS. Given these particular cellular protein needs, questions arise whether transfer ribonucleic acids molecules (tRNAs) undergo unique chemical modifications to maintain their structure, stability, and/or function under such environmental conditions. Here, we investigated the effects of IR and H₂O₂ exposure on tRNAs in C. neoformans. We experimentally identified the modified nucleosides present in C. neoformans tRNAs and quantified changes in those modifications upon exposure to oxidative conditions. To better understand these modified nucleoside results, we also evaluated tRNA pool composition in response to the oxidative conditions. We found that regardless of environmental conditions, tRNA modifications and transcripts were minimally affected. A rationale for the stability of the tRNA pool and its concomitant profile of modified nucleosides is proposed based on the lack of codon bias throughout the C. neoformans genome and in particular for oxidative response transcripts. Our findings suggest that C. neoformans can rapidly adapt to oxidative environments as mRNA translation/protein synthesis are minimally impacted by codon bias.

Inhibitors of PARP: Number crunching and structure gazing

PARP is an important target in the treatment of cancers, particularly in patients with breast, ovarian, or prostate cancer that have compromised homologous recombination repair (i.e., BRCA^−/−). This review about inhibitors of PARP (PARPi) is for readers interested in the development of next-generation drugs for the treatment of cancer, providing insights into structure–activity relationships, in vitro vs. in vivo potency, PARP trapping, and synthetic lethality.

Selective inhibitors of PARP1 and PARP2 (PARP1/2) are used to treat cancer patients with deficiencies in the repair of DNA via homologous recombination. Here we provide a perspective on the reported potencies of the most studied of these inhibitors (olaparib, talazoparib, niraparib, rucaparib, and veliparib) in vitro and in vivo and how these numbers relate to the known structures of these inhibitors bound to the active sites of PARP1 and PARP2. We suggest that the phenomenon of PARP trapping is primarily due to the inhibition of the catalytic activity of PARP1 and that the basis for the higher potency of talazoparib compared to the other inhibitors lies in its more extensive network of interactions with conserved residues in the active site. We also consider the potential role of the recently characterized protein “Histone PARylation Factor 1” (HPF1), which interacts with PARP1/2 to form a shared active site, for the design of the next generation of inhibitors of PARP1/2.

Subcellular Proteomics as a Unified Approach of Experimental Localizations and Computed Prediction Data for Arabidopsis and Crop Plants

In eukaryotic organisms, subcellular protein location is critical in defining protein function and understanding sub-functionalization of gene families. Some proteins have defined locations, whereas others have low specificity targeting and complex accumulation patterns. There is no single approach that can be considered entirely adequate for defining the in vivo location of all proteins. By combining evidence from different approaches, the strengths and weaknesses of different technologies can be estimated, and a location consensus can be built. The Subcellular Location of Proteins in Arabidopsis database ( http://suba.live/ ) combines experimental data sets that have been reported in the literature and is analyzing these data to provide useful tools for biologists to interpret their own data. Foremost among these tools is a consensus classifier (SUBAcon) that computes a proposed location for all proteins based on balancing the experimental evidence and predictions. Further tools analyze sets of proteins to define the abundance of cellular structures. Extending these types of resources to plant crop species has been complex due to polyploidy, gene family expansion and contraction, and the movement of pathways and processes within cells across the plant kingdom. The Crop Proteins of Annotated Location database ( http://crop-pal.org/ ) has developed a range of subcellular location resources including a species-specific voting consensus for 12 plant crop species that offers collated evidence and filters for current crop proteomes akin to SUBA. Comprehensive cross-species comparison of these data shows that the sub-cellular proteomes (subcellulomes) depend only to some degree on phylogenetic relationship and are more conserved in major biosynthesis than in metabolic pathways. Together SUBA and cropPAL created reference subcellulomes for plants as well as species-specific subcellulomes for cross-species data mining. These data collections are increasingly used by the research community to provide a subcellular protein location layer, inform models of compartmented cell function and protein-protein interaction network, guide future molecular crop breeding strategies, or simply answer a specific question-where is my protein of interest inside the cell?

Integrative OMICS Data-Driven Procedure Using a Derivatized Meta-Analysis Approach

The wealth of high-throughput data has opened up new opportunities to analyze and describe biological processes at higher resolution, ultimately leading to a significant acceleration of scientific output using high-throughput data from the different omics layers and the generation of databases to store and report raw datasets. The great variability among the techniques and the heterogeneous methodologies used to produce this data have placed meta-analysis methods as one of the approaches of choice to correlate the resultant large-scale datasets from different research groups. Through multi-study meta-analyses, it is possible to generate results with greater statistical power compared to individual analyses. Gene signatures, biomarkers and pathways that provide new insights of a phenotype of interest have been identified by the analysis of large-scale datasets in several fields of science. However, despite all the efforts, a standardized regulation to report large-scale data and to identify the molecular targets and signaling networks is still lacking. Integrative analyses have also been introduced as complementation and augmentation for meta-analysis methodologies to generate novel hypotheses. Currently, there is no universal method established and the different methods available follow different purposes. Herein we describe a new unifying, scalable and straightforward methodology to meta-analyze different omics outputs, but also to integrate the significant outcomes into novel pathways describing biological processes of interest. The significance of using proper molecular identifiers is highlighted as well as the potential to further correlate molecules from different regulatory levels. To show the methodology’s potential, a set of transcriptomic datasets are meta-analyzed as an example.

ECMpy, a Simplified Workflow for Constructing Enzymatic Constrained Metabolic Network Model

Genome-scale metabolic models (GEMs) have been widely used for the phenotypic prediction of microorganisms. However, the lack of other constraints in the stoichiometric model often leads to a large metabolic solution space being inaccessible. Inspired by previous studies that take an allocation of macromolecule resources into account, we developed a simplified Python-based workflow for constructing enzymatic constrained metabolic network model (ECMpy) and constructed an enzyme-constrained model for Escherichia coli (eciML1515) by directly adding a total enzyme amount constraint in the latest version of GEM for E. coli (iML1515), considering the protein subunit composition in the reaction, and automated calibration of enzyme kinetic parameters. Using eciML1515, we predicted the overflow metabolism of E. coli and revealed that redox balance was the key reason for the difference between E. coli and Saccharomyces cerevisiae in overflow metabolism. The growth rate predictions on 24 single-carbon sources were improved significantly when compared with other enzyme-constrained models of E. coli. Finally, we revealed the tradeoff between enzyme usage efficiency and biomass yield by exploring the metabolic behaviours under different substrate consumption rates. Enzyme-constrained models can improve simulation accuracy and thus can predict cellular phenotypes under various genetic perturbations more precisely, providing reliable guidance for metabolic engineering.

Genome-wide gene expression noise in Escherichia coli is condition-dependent and determined by propagation of noise through the regulatory network

Although it is well appreciated that gene expression is inherently noisy and that transcriptional noise is encoded in a promoter’s sequence, little is known about the extent to which noise levels of individual promoters vary across growth conditions. Using flow cytometry, we here quantify transcriptional noise in Escherichia coli genome-wide across 8 growth conditions and find that noise levels systematically decrease with growth rate, with a condition-dependent lower bound on noise. Whereas constitutive promoters consistently exhibit low noise in all conditions, regulated promoters are both more noisy on average and more variable in noise across conditions. Moreover, individual promoters show highly distinct variation in noise across conditions. We show that a simple model of noise propagation from regulators to their targets can explain a significant fraction of the variation in relative noise levels and identifies TFs that most contribute to both condition-specific and condition-independent noise propagation. In addition, analysis of the genome-wide correlation structure of various gene properties shows that gene regulation, expression noise, and noise plasticity are all positively correlated genome-wide and vary independently of variations in absolute expression, codon bias, and evolutionary rate. Together, our results show that while absolute expression noise tends to decrease with growth rate, relative noise levels of genes are highly condition-dependent and determined by the propagation of noise through the gene regulatory network.

Genome-wide flow cytometry measurements reveal that gene expression noise in bacteria is highly condition-dependent; while absolute noise levels of all genes decrease with growth-rate, theoretical modeling shows that the relative noise levels of different genes are determined by the propagation of noise through the gene regulatory network (GRN). Thus GRN structure controls not only mean expression but also noise levels.

A Standardized Brain Molecular Atlas: A Resource for Systems Modeling and Simulation

Accurate molecular concentrations are essential for reliable analyses of biochemical networks and the creation of predictive models for molecular and systems biology, yet protein and metabolite concentrations used in such models are often poorly constrained or irreproducible. Challenges of using data from different sources include conflicts in nomenclature and units, as well as discrepancies in experimental procedures, data processing and implementation of the model. To obtain a consistent estimate of protein and metabolite levels, we integrated and normalized data from a large variety of sources to calculate Adjusted Molecular Concentrations. We found a high degree of reproducibility and consistency of many molecular species across brain regions and cell types, consistent with tight homeostatic regulation. We demonstrated the value of this normalization with differential protein expression analyses related to neurodegenerative diseases, brain regions and cell types. We also used the results in proof-of-concept simulations of brain energy metabolism. The standardized Brain Molecular Atlas overcomes the obstacles of missing or inconsistent data to support systems biology research and is provided as a resource for biomolecular modeling.

Evaluation of an integrative Bayesian peptide detection approach on a combinatorial peptide library

Detection of peptides lies at the core of bottom-up proteomics analyses. We examined a Bayesian approach to peptide detection, integrating match-based models (fragments, retention time, isotopic distribution, and precursor mass) and peptide prior probability models under a unified probabilistic framework. To assess the relevance of these models and their various combinations, we employed a complete- and a tail-complete search of a low-precursor-mass synthetic peptide library based on oncogenic KRAS peptides. The fragment match was by far the most informative match-based model, while the retention time match was the only remaining such model with an appreciable impact--increasing correct detections by around 8 %. A peptide prior probability model built from a reference proteome greatly improved the detection over a uniform prior, essentially transforming de novo sequencing into a reference-guided search. The knowledge of a correct sequence tag in advance to peptide-spectrum matching had only a moderate impact on peptide detection unless the tag was long and of high certainty. The approach also derived more precise error rates on the analyzed combinatorial peptide library than those estimated using PeptideProphet and Percolator, showing its potential applicability for the detection of homologous peptides. Although the approach requires further computational developments for routine data analysis, it illustrates the value of peptide prior probabilities and presents a Bayesian approach for their incorporation into peptide detection.

Intergenic ORFs as elementary structural modules of de novo gene birth and protein evolution

The noncoding genome plays an important role in de novo gene birth and in the emergence of genetic novelty. Nevertheless, how noncoding sequences' properties could promote the birth of novel genes and shape the evolution and the structural diversity of proteins remains unclear. Therefore, by combining different bioinformatic approaches, we characterized the fold potential diversity of the amino acid sequences encoded by all intergenic open reading frames (ORFs) of S. cerevisiae with the aim of (1) exploring whether the structural states' diversity of proteomes is already present in noncoding sequences, and (2) estimating the potential of the noncoding genome to produce novel protein bricks that could either give rise to novel genes or be integrated into pre-existing proteins, thus participating in protein structure diversity and evolution. We showed that amino acid sequences encoded by most yeast intergenic ORFs contain the elementary building blocks of protein structures. Moreover, they encompass the large structural state diversity of canonical proteins, with the majority predicted as foldable. Then, we investigated the early stages of de novo gene birth by reconstructing the ancestral sequences of 70 yeast de novo genes and characterized the sequence and structural properties of intergenic ORFs with a strong translation signal. This enabled us to highlight sequence and structural factors determining de novo gene emergence. Finally, we showed a strong correlation between the fold potential of de novo proteins and one of their ancestral amino acid sequences, reflecting the relationship between the noncoding genome and the protein structure universe.

HPF1 dynamically controls the PARP1/2 balance between initiating and elongating ADP-ribose modifications

PARP1 and PARP2 produce poly(ADP-ribose) in response to DNA breaks. HPF1 regulates PARP1/2 catalytic output, most notably permitting serine modification with ADP-ribose. However, PARP1 is substantially more abundant in cells than HPF1, challenging whether HPF1 can pervasively modulate PARP1. Here, we show biochemically that HPF1 efficiently regulates PARP1/2 catalytic output at sub-stoichiometric ratios matching their relative cellular abundances. HPF1 rapidly associates/dissociates from multiple PARP1 molecules, initiating serine modification before modification initiates on glutamate/aspartate, and accelerating initiation to be more comparable to elongation reactions forming poly(ADP-ribose). This “hit and run” mechanism ensures HPF1 contributions to PARP1/2 during initiation do not persist and interfere with PAR chain elongation. We provide structural insights into HPF1/PARP1 assembled on a DNA break, and assess HPF1 impact on PARP1 retention on DNA. Our data support the prevalence of serine-ADP-ribose modification in cells and the efficiency of serine-ADP-ribose modification required for an acute DNA damage response.

HPF1 controls the ADP-ribosylation activity of PARP1/2 in response to DNA breaks. Here, the authors show that HPF1 regulates the balance between ADP-ribose initiation and elongation through a dynamic interaction that accelerates the initiation rate on serine residues.

Multistability maintains redox homeostasis in human cells

Cells metabolize nutrients through a complex metabolic and signaling network that governs redox homeostasis. At the core of this, redox regulatory network is a mutually inhibitory relationship between reduced glutathione and reactive oxygen species (ROS)-two opposing metabolites that are linked to upstream nutrient metabolic pathways (glucose, cysteine, and glutamine) and downstream feedback loops of signaling pathways (calcium and NADPH oxidase). We developed a nutrient-redox model of human cells to understand system-level properties of this network. Combining in silico modeling and ROS measurements in individual cells, we show that ROS dynamics follow a switch-like, all-or-none response upon glucose deprivation at a threshold that is approximately two orders of magnitude lower than its physiological concentration. We also confirm that this ROS switch can be irreversible and exhibits hysteresis, a hallmark of bistability. Our findings evidence that bistability modulates redox homeostasis in human cells and provide a general framework for quantitative investigations of redox regulation in humans.

Analysis of the Repertoires of Circulating Autoantibodies' Specificities as a Tool for Identification of the Tumor-Associated Antigens: Current Problems and Solutions

Circulating autoantibodies against tumor-associated autoantigens (TAA) may serve as valuable biomarkers for a wide range of diagnostic purposes. Modern immunology offers a large variety of methods for in-depth comparative analysis of the repertoires of circulating antibodies' antigenic specificities in health and disease. Nevertheless, this research field so far has met somewhat limited clinical success, while numerous data on the repertoires of circulating autoantibodies' specificities in cancer patients are poorly integrated into the contemporary picture of the immunological and molecular landscapes of human tumors. This review is an attempt to identify and systematize the key and essentially universal conceptual and methodological limitations of analyses of the repertoires of circulating antibodies' antigenic specificities in cancer (expression bias, redundancy of TAA repertoires, identification of natural IgG, the absence of the pathogenetically relevant context in the experimental systems used to detect TAA), as well as to discuss potential and already known methodological improvements that may significantly increase the detectability of the pathogenetically relevant and diagnostically significant bona fide TAA.

Cryptic amyloidogenic regions in intrinsically disordered proteins: Function and disease association

The amyloid conformation is considered a fundamental state of proteins and the propensity to populate it a generic property of polypeptides. Multiple proteome-wide analyses addressed the presence of amyloidogenic regions in proteins, nurturing our understanding of their nature and biological implications. However, these analyses focused on highly aggregation-prone and hydrophobic stretches that are only marginally found in intrinsically disordered regions (IDRs). Here, we explore the prevalence of cryptic amyloidogenic regions (CARs) of polar nature in IDRs. CARs are widespread in IDRs and associated with IDPs function, with particular involvement in protein–protein interactions, but their presence is also connected to a risk of malfunction. By exploring this function/malfunction dichotomy, we speculate that ancestral CARs might have evolved into functional interacting regions playing a significant role in protein evolution at the origins of life.

A genome-scale metabolic model of Saccharomyces cerevisiae that integrates expression constraints and reaction thermodynamics

Eukaryotic organisms play an important role in industrial biotechnology, from the production of fuels and commodity chemicals to therapeutic proteins. To optimize these industrial systems, a mathematical approach can be used to integrate the description of multiple biological networks into a single model for cell analysis and engineering. One of the most accurate models of biological systems include Expression and Thermodynamics FLux (ETFL), which efficiently integrates RNA and protein synthesis with traditional genome-scale metabolic models. However, ETFL is so far only applicable for E. coli. To adapt this model for Saccharomyces cerevisiae, we developed yETFL, in which we augmented the original formulation with additional considerations for biomass composition, the compartmentalized cellular expression system, and the energetic costs of biological processes. We demonstrated the ability of yETFL to predict maximum growth rate, essential genes, and the phenotype of overflow metabolism. We envision that the presented formulation can be extended to a wide range of eukaryotic organisms to the benefit of academic and industrial research.

Ultrasensitivity and bistability in covalent-modification cycles with positive autoregulation

Switch-like behaviours in biochemical networks are of fundamental significance in biological signal processing, and exist as two distinct types: ultra-sensitivity and bistability. Here we propose two new models of a reversible covalent-modification cycle with positive autoregulation (PAR), a motif structure that is thought to be capable of both ultrasensitivity and bistability in different parameter regimes. These new models appeal to a modelling framework that we call complex-complete, which accounts fully for the molecular complexities of the underlying signalling mechanisms. Each of the two new models encodes a specific molecular mechanism for PAR. We demonstrate that the modelling simplifications for PAR models that have been used in previous work, which rely on Michaelian approximations, are unable to accurately recapitulate the qualitative signalling responses supported by our detailed models. Strikingly, we show that complex-complete PAR models are capable of new qualitative responses such as one-way switches and a 'prozone' effect, depending on the specific PAR-encoding mechanism, which are not supported by Michaelian simplifications. Our results highlight the critical importance of accurately representing the molecular details of biochemical signalling mechanisms, and strongly suggest that the Michaelian approximation is inadequate for predictive models of enzyme-mediated chemical reactions with added regulations such as PAR.

Occurrence of Calcium Oscillations in Human Spermatozoa Is Based on Spatial Signaling Enzymes Distribution

In human spermatozoa, calcium dynamics control most of fertilization events. Progesterone, present in the female reproductive system, can trigger several types of calcium responses, such as low-frequency oscillations. Here we aimed to identify the mechanisms of progesterone-induced calcium signaling in human spermatozoa. Progesterone-induced activation of fluorophore-loaded spermatozoa was studied by fluorescent microscopy. Two computational models were developed to describe the spermatozoa calcium responses: a homogeneous one based on a system of ordinary differential equations and a three-dimensional one with added space dimensions and diffusion for the cytosolic species. In response to progesterone, three types of calcium responses were observed in human spermatozoa: a single transient rise of calcium concentration in cytosol, a steady elevation, or low-frequency oscillations. The homogenous model provided qualitative description of the oscillatory and the single spike responses, while the three-dimensional model captured the calcium peak shape and the frequency of calcium oscillations. The model analysis demonstrated that an increase in the calcium diffusion coefficient resulted in the disappearance of the calcium oscillations. Additionally, in silico analysis suggested that the spatial distribution of calcium signaling enzymes governs the appearance of calcium oscillations in progesterone-activated human spermatozoa.

Chaperonin Abundance Enhances Bacterial Fitness

The ability of chaperonins to buffer mutations that affect protein folding pathways suggests that their abundance should be evolutionarily advantageous. Here, we investigate the effect of chaperonin overproduction on cellular fitness in Escherichia coli. We demonstrate that chaperonin abundance confers 1) an ability to tolerate higher temperatures, 2) improved cellular fitness, and 3) enhanced folding of metabolic enzymes, which is expected to lead to enhanced energy harvesting potential.

Oxygen-dependent changes in binding partners and post-translational modifications regulate the abundance and activity of HIF-1α/2α

Cellular adaptation to low-oxygen environments is mediated in part by the hypoxia-inducible factors (HIFs). Like other transcription factors, the stability and transcriptional activity of HIFs-and consequently, the hypoxic response-are regulated by post-translational modifications (PTMs) and changes in protein-protein interactions. Our current understanding of PTM-mediated regulation of HIFs is primarily based on in vitro protein fragment-based studies typically validated in fragment-expressing cells treated with hypoxia-mimicking compounds. Here, we used immunoprecipitation-based mass spectrometry to characterize the PTMs and binding partners for full-length HIF-1α and HIF-2α under normoxic (21% oxygen) and hypoxic (1% oxygen) conditions. Hypoxia substantially altered the complexity and composition of the HIFα protein interaction networks, particularly for HIF-2α, with the hypoxic networks of both isoforms being enriched for mitochondrial proteins. Moreover, both HIFα isoforms were heavily covalently modified. We identified ~40 PTM sites composed of 13 different types of modification on both HIFα isoforms, including multiple cysteine modifications and an unusual phosphocysteine. More than 80% of the PTMs identified were not previously known and about half exhibited oxygen dependency. We further characterized an evolutionarily conserved phosphorylation of Ser31 in HIF-1α as a regulator of its transcriptional function, and we propose functional roles for Thr406, Thr528, and Ser581 in HIF-2α. These data will help to delineate the different physiological roles of these closely related isoforms in fine-tuning the hypoxic response.

Integrating thermodynamic and enzymatic constraints into genome-scale metabolic models

Stoichiometric genome-scale metabolic network models (GEMs) have been widely used to predict metabolic phenotypes. In addition to stoichiometric ratios, other constraints such as enzyme availability and thermodynamic feasibility can also limit the phenotype solution space. Extended GEM models considering either enzymatic or thermodynamic constraints have been shown to improve prediction accuracy. In this paper, we propose a novel method that integrates both enzymatic and thermodynamic constraints in a single Pyomo modeling framework (ETGEMs). We applied this method to construct the EcoETM (E. coli metabolic model with enzymatic and thermodynamic constraints). Using this model, we calculated the optimal pathways for cellular growth and the production of 22 metabolites. When comparing the results with those of iML1515 and models with one of the two constraints, we observed that many thermodynamically unfavorable and/or high enzyme cost pathways were excluded from EcoETM. For example, the synthesis pathway of carbamoyl-phosphate (Cbp) from iML1515 is both thermodynamically unfavorable and enzymatically costly. After introducing the new constraints, the production pathways and yields of several Cbp-derived products (e.g. L-arginine, orotate) calculated using EcoETM were more realistic. The results of this study demonstrate the great application potential of metabolic models with multiple constraints for pathway analysis and phenotype prediction.