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Abstract Network controllability is unifying the traditional control theory with the structural network information rooted in many large-scale biological systems of interest, from intracellular networks in molecular biology to brain neuronal networks. In controllability approaches, the set of minimum driver nodes is not unique, and critical nodes are the most important control elements because they appear in all possible solution sets. On the other hand, a common but largely unexplored feature in network control approaches is the probabilistic failure of edges or the uncertainty in the determination of interactions between molecules. This is particularly true when directed probabilistic interactions are considered. Until now, no efficient algorithm existed to determine critical nodes in probabilistic directed networks. Here we present a probabilistic control model based on a minimum dominating set framework that integrates the probabilistic nature of directed edges between molecules and determines the critical control nodes that drive the entire network functionality. The proposed algorithm, combined with the developed mathematical tools, offers practical efficiency in determining critical control nodes in large probabilistic networks. The method is then applied to the human intracellular signal transduction network revealing that critical control nodes are associated with important biological features and perturbed sets of genes in human diseases, including SARS-CoV-2 target proteins and rare disorders. We believe that the proposed methodology can be useful to investigate multiple biological systems in which directed edges are probabilistic in nature, both in natural systems or when determined with large uncertainties in-silico.

Abstract Ovarian cancer is an aggressive, heterogeneous disease, burdened with late diagnosis and resistance to chemotherapy. Clinical features of ovarian cancer could be explained by investigating its metabolism, and how the regulation of specific pathways links to individual phenotypes. Ovarian cancer is of particular interest for metabolic research due to its heterogeneous nature, with five distinct subtypes having been identified, each of which may display a unique metabolic signature. To elucidate metabolic differences, constraint-based modelling (CBM) represents a powerful technology, inviting the integration of ‘omics’ data, such as transcriptomics. However, many CBM methods have not prioritised accurate growth rate predictions, and there are very few ovarian cancer genome-scale studies. Here, a novel method for CBM has been developed, employing the genome-scale model Human1 and flux balance analysis, enabling the integration of in vitro growth rates, transcriptomics data and media conditions to predict the metabolic behaviour of cells. Using low- and high-grade ovarian cancer, subtype-specific metabolic differences have been predicted, which have been supported by publicly available CRISPR-Cas9 data from the Cancer Cell Line Encyclopaedia and an extensive literature review. Metabolic drivers of aggressive, invasive phenotypes, as well as pathways responsible for increased chemoresistance in low-grade cell lines have been suggested. Experimental gene dependency data has been used to validate areas of the pentose phosphate pathway as essential for low-grade cellular growth, highlighting potential vulnerabilities for this ovarian cancer subtype.

Abstract In the battle of the host against lentiviral pathogenesis, the immune response is crucial. However, several questions remain unanswered about the interaction with different viruses and their influence on disease progression. The simian immunodeficiency virus (SIV) infecting nonhuman primates (NHP) is widely used as a model for the study of the human immunodeficiency virus (HIV) both because they are evolutionarily linked and because they share physiological and anatomical similarities that are largely explored to understand the disease progression. The HIHISIV database was developed to support researchers to integrate and evaluate the large number of transcriptional data associated with the presence/absence of the pathogen (SIV or HIV) and the host response (NHP and human). The datasets are composed of microarray and RNA-Seq gene expression data that were selected, curated, analyzed, enriched, and stored in a relational database. Six query templates comprise the main data analysis functions and the resulting information can be downloaded. The HIHISIV database, available at https://hihisiv.github.io , provides accurate resources for browsing and visualizing results and for more robust analyses of pre-existing data in transcriptome repositories.

ABSTRACT In silico tools such as genome-scale metabolic models have shown to be powerful for metabolic engineering of microorganisms. Saccharomyces pastorianus is a complex aneuploid hybrid between the mesophilic Saccharomyces cerevisiae and the cold-tolerant Saccharomyces eubayanus. This species is of biotechnological importance because it is the primary yeast used in lager beer fermentation and is also a key model for studying the evolution of hybrid genomes, including expression pattern of ortholog genes, composition of protein complexes, and phenotypic plasticity. Here, we created the iSP_1513 GSMM for S. pastorianus CBS1513 to allow top-down computational approaches to predict the evolution of metabolic pathways and to aid strain optimization in production processes. The iSP_1513 comprises 4,062 reactions, 1,808 alleles, and 2,747 metabolites, and takes into account the functional redundancy in the gene-protein-reaction rule caused by the presence of orthologous genes. Moreover, a universal algorithm to constrain GSMM reactions using transcriptome data was developed as a python library and enabled the integration of temperature as parameter. Essentiality data sets, growth data on various carbohydrates and volatile metabolites secretion were used to validate the model and showed the potential of media engineering to improve specific flavor compounds. The iSP_1513 also highlighted the different contributions of the parental sub-genomes to the oxidative and non-oxidative parts of the pentose phosphate pathway. Overall, the iSP_1513 GSMM represent an important step toward understanding the metabolic capabilities, evolutionary trajectories, and adaptation potential of S. pastorianus in different industrial settings.IMPORTANCEGenome-scale metabolic models (GSMM) have been successfully applied to predict cellular behavior and design cell factories in several model organisms, but no models to date are currently available for hybrid species due to their more complex genetics and general lack of molecular data. In this study, we generated a bespoke GSMM, iSP_1513, for this industrial aneuploid hybrid Saccharomyces pastorianus, which takes into account the aneuploidy and functional redundancy from orthologous parental alleles. This model will (i) help understand the metabolic capabilities and adaptive potential of S. pastorianus (domestication processes), (ii) aid top-down predictions for strain development (industrial biotechnology), and (iii) allow predictions of evolutionary trajectories of metabolic pathways in aneuploid hybrids (evolutionary genetics).

Abstract Recent controllability analyses have demonstrated that driver nodes tend to be associated to genes related to important biological functions as well as human diseases. While researchers have focused on identifying critical nodes, intermittent nodes have received much less attention. Here, we propose a new efficient algorithm based on the Hamming distance for computing the importance of intermittent nodes using a Minimum Dominating Set (MDS)-based control model. We refer to this metric as criticality. The application of the proposed algorithm to compute criticality under the MDS control framework allows us to unveil the biological importance and roles of the intermittent nodes in different network systems, from cellular level such as signaling pathways and cell-cell interactions such as cytokine networks, to the complete nervous system of the nematode worm C. elegans. Taken together, the developed computational tools may open new avenues for investigating the role of intermittent nodes in many biological systems of interest in the context of network control.

Abstract Genome-scale metabolic models are widely used to enhance our understanding of metabolic features of organisms, host-pathogen interactions and to identify therapeutics for diseases. Here we present iTMU798, the genome-scale metabolic model of the mouse whipworm Trichuris muris. The model demonstrates the metabolic features of T. muris and allows the prediction of metabolic steps essential for its survival. Specifically, that Thioredoxin Reductase (TrxR) enzyme is essential, a prediction we validate in vitro with the drug auranofin. Furthermore, our observation that the T. muris genome lacks gsr-1 encoding Glutathione Reductase (GR) but has GR activity that can be inhibited by auranofin indicates a mechanism for the reduction of glutathione by the TrxR enzyme in T. muris. In addition, iTMU798 predicts seven essential amino acids that cannot be synthesised by T. muris, a prediction we validate for the amino acid tryptophan. Overall, iTMU798 is as a powerful tool to study not only the T. muris metabolism but also other Trichuris spp. in understanding host parasite interactions and the rationale design of new intervention strategies.

SYNGAP1 haploinsufficiency results in a developmental and epileptic encephalopathy (DEE) causing generalized epilepsies accompanied by a spectrum of neurodevelopmental symptoms. Concerning interictal epileptiform discharges (IEDs) in electroencephalograms (EEG), potential biomarkers have been postulated, including changes in background activity, fixation-off sensitivity (FOS) or eye closure sensitivity (ECS). In this study we clinically evaluate a new cohort of 36 SYNGAP1-DEE individuals. Standardized questionnaires were employed to collect clinical, electroencephalographic and genetic data. We investigated electroencephalographic findings, focusing on the cortical distribution of interictal abnormalities and their changes with age. Among the 36 SYNGAP1-DEE cases 18 presented variants in the SYNGAP1 gene that had never been previously reported. The mean age of diagnosis was 8 years and 8 months, ranging from 2 to 17 years, with 55.9% being male. All subjects had global neurodevelopmental/language delay and behavioral abnormalities; 83.3% had moderate to profound intellectual disability (ID), 91.7% displayed autistic traits, 73% experienced sleep disorders and 86.1% suffered from epileptic seizures, mainly eyelid myoclonia with absences (55.3%). A total of 63 VEEGs were revised, observing a worsening of certain EEG findings with increasing age. A disorganized background was observed in all age ranges, yet this was more common among older cases. The main IEDs were bilateral synchronous and asynchronous posterior discharges, accounting for ≥50% in all age ranges. Generalized alterations with maximum amplitude in the anterior region showed as the second most frequent IED (≥15% in all age ranges) and were also more common with increasing age. Finally, diffuse fast activity was much more prevalent in cases with 6 years or older. To the best of our knowledge, this is the first study to analyze EEG features across different age groups, revealing an increase in interictal abnormalities over infancy and adolescence. Our findings suggest that SYNGAP1 haploinsufficiency has complex effects in human brain development, some of which might unravel at different developmental stages. Furthermore, they highlight the potential of baseline EEG to identify candidate biomarkers and the importance of natural history studies to develop specialized therapies and clinical trials.

Functional selectivity refers to the activation of differential signalling and cellular outputs downstream of the same membrane-bound receptor when activated by two or more different ligands. Functional selectivity has been described and extensively studied for G-protein Coupled Receptors (GPCRs), leading to specific therapeutic options for dysregulated GPCRs functions. However, studies regarding the functional selectivity of Receptor Tyrosine Kinases (RTKs) remain sparse. Here, we will summarize recent data about RTK functional selectivity focusing on how the nature and the amount of RTK ligands and the crosstalk of RTKs with other membrane proteins regulate the specificity of RTK signalling. In addition, we will discuss how structural changes in RTKs upon ligand binding affects selective signalling pathways. Much remains to be known about the integration of different signals affecting RTK signalling specificity to orchestrate long-term cellular outcomes. Recent advancements in omics, specifically quantitative phosphoproteomics, and in systems biology methods to study, model and integrate different types of large-scale omics data have increased our ability to compare several signals affecting RTK functional selectivity in a global, system-wide fashion. We will discuss how such methods facilitate the exploration of important signalling hubs and enable data-driven predictions aiming at improving the efficacy of therapeutics for diseases like cancer, where redundant RTK signalling pathways often compromise treatment efficacy.

Abstract The kidney plays a critical role in excreting ammonia during metabolic acidosis and liver failure. The mechanisms behind this process have been poorly explored. The present study combines results of in vivo experiments of increased total ammoniagenesis with systems biology modeling, in which eight rats were fed an amino acid-rich diet (HD group) and eight a normal chow diet (AL group). We developed a method based on elementary mode analysis to study changes in amino acid flux occurring across the kidney in increased ammoniagenesis. Elementary modes represent minimal feasible metabolic paths in steady state. The model was used to predict amino acid fluxes in healthy and pre-hyperammonemic conditions, which were compared to experimental fluxes in rats. First, we found that total renal ammoniagenesis increased from 264 ± 68 to 612 ± 87 nmol (100 g body weight)−1 min−1 in the HD group (P = 0.021) and a concomitated upregulation of NKCC2 ammonia and other transporters in the kidney. In the kidney metabolic model, the best predictions were obtained with ammonia transport as an objective. Other objectives resulting in a fair correlation with the measured fluxes (correlation coefficient >0.5) were growth, protein uptake, urea excretion, and lysine and phenylalanine transport. These predictions were improved when specific gene expression data were considered in HD conditions, suggesting a role for the mitochondrial glycine pathway. Further studies are needed to determine if regulation through the mitochondrial glycine pathway and ammonia transporters can be modulated and how to use the kidney as a therapeutic target in hyperammonemia.

Recruitment of Receptor Tyrosine Kinase signalling partners during endocytosis, specifically during recycling to the plasma membrane, is crucial to signal propagation and regulation. Here, the authors reveal FGFR signalling partners proximal to recycling endosomes with a spatially resolved phosphoproteomics approach.

Abstract Sarcomas include cancer stem cells, but how these cells contribute to local and metastatic relapse is largely unknown. We previously showed the pro-tumor functions of calpain-6 in sarcoma stem cells. Here, we use an osteosarcoma cell model, osteosarcoma tissues and transcriptomic data from human tumors to study gene patterns associated with calpain-6 expression or suppression. Calpain-6 modulates the expression of Hippo pathway genes and stabilizes the hippo effector YAP. It also modulates the vesicular trafficking of β-catenin degradation complexes. Calpain-6 expression is associated with genes of the G2M phase of the cell cycle, supports G2M-related YAP activities and up-regulated genes controlling mitosis in sarcoma stem cells and tissues. In mouse models of bone sarcoma, most tumor cells expressed calpain-6 during the early steps of tumor out-growth. YAP inhibition prevented the neoformation of primary tumors and metastases but had no effect on already developed tumors. It could even accelerate lung metastasis associated with large bone tumors by affecting tumor-associated inflammation in the host tissues. Our results highlight a specific mechanism involving YAP transcriptional activity in cancer stem cells that is crucial during the early steps of tumor and metastasis outgrowth and that could be targeted to prevent sarcoma relapse.

Abstract Since the onset of the coronavirus disease 2019 (COVID-19) pandemic, different mitigation and management strategies limiting economic and social activities have been implemented across many countries. Despite these strategies, the virus continues to spread and mutate. As a result, vaccinations are now administered to suppress the pandemic. Current COVID-19 epidemic models need to be expanded to account for the change in behaviour of new strains, such as an increased virulence and higher transmission rate. Furthermore, models need to account for an increasingly vaccinated population. We present a network model of COVID-19 transmission accounting for different immunity and vaccination scenarios. We conduct a parameter sensitivity analysis and find the average immunity length after an infection to be one of the most critical parameters that define the spread of the disease. Furthermore, we simulate different vaccination strategies and show that vaccinating highly connected individuals first is the quickest strategy for controlling the disease.

Abstract Severe skeletal alterations are common symptoms in patients with mucolipidosis type II (MLII), a rare lysosomal storage disorder of childhood. We have previously reported that progressive bone loss in a mouse model for MLII is caused by an increased number of bone-resorbing osteoclasts, which is accompanied by elevated expression of the cytokine interleukin-6 (IL-6) in the bone microenvironment. In the present study we addressed the question, if pharmacological blockade of IL-6 can prevent the low bone mass phenotype of MLII mice. Since the cellular IL-6 response can be mediated by either the membrane-bound (classic signaling) or the soluble IL-6 receptor (trans-signaling), we first performed cell culture assays and found that both pathways can increase osteoclastogenesis. We then crossed MLII mice with transgenic mice expressing the recombinant soluble fusion protein sgp130Fc, which represents a natural inhibitor of IL-6 trans-signaling. By undecalcified histology and bone-specific histomorphometry we found that high circulating sgp130Fc levels do not affect skeletal growth or remodeling in wild-type mice. Most importantly, blockade of IL-6 trans-signaling did neither reduce osteoclastogenesis, nor increase bone mass in MLII mice. Therefore, our data clearly demonstrate that the bone phenotype of MLII mice cannot be corrected by blocking the IL-6 trans-signaling.

IntroductionThe Cervical Dystonia Patient Registry for Observation of OnabotulinumtoxinA Efficacy (CD PROBE) study (ClinicalTrials.gov identifier: NCT00836017), a multicenter, prospective, observational registry, was designed to identify real-world practices and outcomes for patients with cervical dystonia (CD) treated with onabotulinumtoxinA (onabotA). This secondary analysis from CD PROBE aims to determine the impact of presentation subtype on onabotA utilization and CD severity.Materials and MethodsThe study cohort includes those who completed all 3 treatments, 4 office visits, and had data recorded for all assessments. Patient outcomes were assessed with the Cervical Dystonia Impact Profile (CDIP-58), Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS), and determination of CD severity. Treatment interval, dose, and adverse events (AEs) were also recorded. Data were stratified according to prior exposure to botulinum toxins (BoNTs) and analyzed with descriptive statistics.ResultsTorticollis was the most common presentation subtype in the study cohort (N = 350); the proportion of patients with torticollis was highest in those with severe disease. At each treatment, between 40.7 and 65.2% of those categorized as severe shifted to moderate or mild severity after treatment. Sustained improvements in CDIP-58 and TWSTRS were observed regardless of prior exposure to BoNTs. Dosing of onabotA generally increased from injection 1 to injection 3 and tended to be lower for patients naïve to BoNT. Median time interval between injections for the study cohort was 94.0 to 97.5 days. The most common AEs (dysphagia, muscular weakness) and injection intervals were similar between naïve vs. non-naïve patients; there were no serious treatment-related AEs.ConclusionsThis secondary cohort analysis from CD PROBE demonstrates that three repeat treatments with onabotA at intervals consistent with labeling attenuated disease severity and neck pain, resulting in sustained improvements in physician- and patient-reported outcomes. No new safety signals were identified.

Metabolic rewiring is a feature of many cancers. Here, the authors combine control theory and flux correlation analysis to study the transition of healthy metabolic networks to cancer states, and find that cancer metabolism is characterized by more streamlined flux distributions.

In a population-based case control study of testicular germ cell tumors (TGCT), we reported a strong positive association between serum levels of Wolff's Group 1 (potentially estrogenic) polychlorinated biphenyl (PCBs) and risk of TGCT, and the observed associations were similar for both seminoma and non-seminoma. While the observed specific associations between TGCT and Wolff's Group 1 PCBs cannot be easily explained by bias or confounding, a question can still be asked, that is, could the relationship between PCBs and TGCT differ by age at diagnosis? PCBs tend to bioaccumulate, with more heavily chlorinated PCB congeners tending to have longer half-lives. Half-lives of PCB congeners were reported ranging from 4.6 years for PCB-28 to 41.0 years for PCB-156. The half-life for the heavy PCB congeners (17.8 years) was found to be approximately twice that for the light PCBs (9.6 years) in early studies. Therefore, the same PCB concentration measured in a 20-year-old vs. a 55-year-old is unlikely to represent the same lifetime PCB exposure or type of PCB exposure. In this analysis, we stratified the data by median age of diagnosis of TGCT and further stratified by histologic type of TGCT (seminoma vs non-seminoma) to explore if the risk of TGCT associated with PCB exposures differs by age.

Plants in natural environments receive light through sunflecks, the duration and distribution of these being highly variable across the day. Consequently, plants need to adjust their photosynthetic processes to avoid photoinhibition and maximize yield. Changes in the composition of the photosynthetic apparatus in response to sustained changes in the environment are referred to as photosynthetic acclimation, a process that involves changes in protein content and composition. Considering this definition, acclimation differs from regulation, which involves processes that alter the activity of individual proteins over short-time periods, without changing the abundance of those proteins. The interconnection and overlapping of the short- and long-term photosynthetic responses, which can occur simultaneously or/and sequentially over time, make the study of long-term acclimation to fluctuating light in plants challenging. In this review we identify short-term responses of plants to fluctuating light that could act as sensors and signals for acclimation responses, with the aim of understanding how plants integrate environmental fluctuations over time and tailor their responses accordingly. Mathematical modeling has the potential to integrate physiological processes over different timescales and to help disentangle short-term regulatory responses from long-term acclimation responses. We review existing mathematical modeling techniques for studying photosynthetic responses to fluctuating light and propose new methods for addressing the topic from a holistic point of view.

Abstract Background Malignant pleural mesothelioma (MPM) is an orphan disease that is difficult to treat using traditional chemotherapy, an approach which has been effective in other types of cancer. Most chemotherapeutics cause DNA damage leading to cell death. Recent discoveries have highlighted a potential role for the p53 tumor suppressor in this disease. Given the pivotal role of p53 in the DNA damage response, here we investigated the predictive power of the p53 interactome model for MPM patients’ stratification. Methods We used bioinformatics approaches including omics type analysis of data from MPM cells and from MPM patients in order to predict which pathways are crucial for patients’ survival. Analysis of the PKT206 model of the p53 network was validated by microarrays from the Mero-14 MPM cell line and RNA-seq data from 71 MPM patients, whilst statistical analysis was used to identify the deregulated pathways and predict therapeutic schemes by linking the affected pathway with the patients’ clinical state. Results In silico simulations demonstrated successful predictions ranging from 52 to 85% depending on the drug, algorithm or sample used for validation. Clinical outcomes of individual patients stratified in three groups and simulation comparisons identified 30 genes that correlated with survival. In patients carrying wild-type p53 either treated or not treated with chemotherapy, FEN1 and MMP2 exhibited the highest inverse correlation, whereas in untreated patients bearing mutated p53, SIAH1 negatively correlated with survival. Numerous repositioned and experimental drugs targeting FEN1 and MMP2 were identified and selected drugs tested. Epinephrine and myricetin, which target FEN1, have shown cytotoxic effect on Mero-14 cells whereas marimastat and batimastat, which target MMP2 demonstrated a modest but significant inhibitory effect on MPM cell migration. Finally, 8 genes displayed correlation with disease stage, which may have diagnostic implications. Conclusions Clinical decisions related to MPM personalized therapy based on individual patients’ genetic profile and previous chemotherapeutic treatment could be reached using computational tools and the predictions reported in this study upon further testing in animal models.

Abstract Background The consolidation of pathway databases, such as KEGG, Reactome and ConsensusPathDB, has generated widespread biological interest, however the issue of pathway redundancy impedes the use of these consolidated datasets. Attempts to reduce this redundancy have focused on visualizing pathway overlap or merging pathways, but the resulting pathways may be of heterogeneous sizes and cover multiple biological functions. Efforts have also been made to deal with redundancy in pathway data by consolidating enriched pathways into a number of clusters or concepts. We present an alternative approach, which generates pathway subsets capable of covering all of genes presented within either pathway databases or enrichment results, generating substantial reductions in redundancy. Results We propose a method that uses set cover to reduce pathway redundancy, without merging pathways. The proposed approach considers three objectives: removal of pathway redundancy, controlling pathway size and coverage of the gene set. By applying set cover to the ConsensusPathDB dataset we were able to produce a reduced set of pathways, representing 100% of the genes in the original data set with 74% less redundancy, or 95% of the genes with 88% less redundancy. We also developed an algorithm to simplify enrichment data and applied it to a set of enriched osteoarthritis pathways, revealing that within the top ten pathways, five were redundant subsets of more enriched pathways. Applying set cover to the enrichment results removed these redundant pathways allowing more informative pathways to take their place. Conclusion Our method provides an alternative approach for handling pathway redundancy, while ensuring that the pathways are of homogeneous size and gene coverage is maximised. Pathways are not altered from their original form, allowing biological knowledge regarding the data set to be directly applicable. We demonstrate the ability of the algorithms to prioritise redundancy reduction, pathway size control or gene set coverage. The application of set cover to pathway enrichment results produces an optimised summary of the pathways that best represent the differentially regulated gene set.

Tisochrysis lutea is a haptophyte microalga important in aquaculture. The 105,837-bp chloroplast genome encodes 113 proteins, 27 different transfer-RNAs and ribosomal-RNAs. Phylogenetic analysis on currently available chloroplast sequences showed T. lutea was most closely related to Emiliania huxleyi. Unlike E. huxleyi chloroplast DNA, T. lutea contains directly-repeated ribosomal repeats following multiple genome rearrangements during evolution. The non-identical 4.9-kb ribosomal direct-repeats are distinguished by 42 nucleotide polymorphisms and three single- or double-nucleotide INDELs. This is unlike land plant and green algal chloroplast genomes in which efficient copy correction by homologous recombination ensures identical long inverted repeats containing the rRNA operons. Imperfect rDNA repeats are also found in the chloroplast genomes of cryptophytes, rhodophytes and other haptophytes. Reduced copy correction rates in these algal lineages raise questions on the mechanism(s) by which genetic uniformity of multi-copy chloroplast genomes is maintained and has implications for developing chloroplast transformation in these species.

The unfolded protein response (UPR) is a conserved cellular response to the accumulation of proteinaceous material in endoplasmic reticulum (ER), active both in health and disease to alleviate cellular stress and improve protein folding. Multiple epiphyseal dysplasia (EDM5) is a genetic skeletal condition and a classic example of an intracellular protein aggregation disease, whereby mutant matrilin-3 forms large insoluble aggregates in the ER lumen, resulting in a specific 'disease signature' of increased expression of chaperones and foldases, and alternative splicing of the UPR effector XBP1. Matrilin-3 is expressed exclusively by chondrocytes thereby making EDM5 a perfect model system to study the role of protein aggregation in disease. In order to dissect the role of XBP1 signalling in aggregation-related conditions we crossed a p.V194D Matn3 knock-in mouse model of EDM5 with a mouse line carrying a cartilage specific deletion of XBP1 and analysed the resulting phenotype. Interestingly, the growth of mice carrying the Matn3 p.V194D mutation compounded with the cartilage specific deletion of XBP1 was severely retarded. Further phenotyping revealed increased intracellular retention of amyloid-like aggregates of mutant matrilin-3 coupled with dramatically decreased cell proliferation and increased apoptosis, suggesting a role of XBP1 signalling in protein accumulation and/or degradation. Transcriptomic analysis of chondrocytes extracted from wild type, EDM5, Xbp1-null and compound mutant lines revealed that the alternative splicing of Xbp1 is crucial in modulating levels of protein aggregation. Moreover, through detailed transcriptomic comparison with a model of metaphyseal chondrodysplasia type Schmid (MCDS), an UPR-related skeletal condition in which XBP1 was removed without overt consequences, we show for the first time that the differentiation-state of cells within the cartilage growth plate influences the UPR resulting from retention of a misfolded mutant protein and postulate that modulation of XBP1 signalling pathway presents a therapeutic target for aggregation related conditions in cells undergoing proliferation.

Genome-scale computational approaches are opening opportunities to model and predict favorable combination of traits for strain development. However, mining the genome of complex hybrids is not currently an easy task, due to the high level of redundancy and presence of homologous. For example, Saccharomyces pastorianus is an allopolyploid sterile yeast hybrid used in brewing to produce lager-style beers. The development of new yeast strains with valuable industrial traits such as improved maltose utilization or balanced flavor profiles are now a major ambition and challenge in craft brewing and distilling industries. Moreover, no genome annotation for most of these industrial strains have been published. Here, we developed HybridMine, a new user-friendly, open-source tool for functional annotation of hybrid aneuploid genomes of any species by predicting parental alleles including paralogs. Our benchmark studies showed that HybridMine produced biologically accurate results for hybrid genomes compared to other well-established software. As proof of principle, we carried out a comprehensive structural and functional annotation of complex yeast hybrids to enable system biology prediction studies. HybridMine is developed in Python, Perl, and Bash programming languages and is available in GitHub.