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Summary: The major lysine methyltransferase (KMT) Setdb1 is essential for self-renewal and viability of mouse embryonic stem cells (mESCs). Setdb1 was primarily known to methylate the lysine 9 of histone 3 (H3K9) in the nucleus, where it regulates chromatin functions. However, Setdb1 is also massively localized in the cytoplasm, including in mESCs, where its role remains elusive. Here, we show that the cytoplasmic Setdb1 (cSetdb1) is essential for the survival of mESCs. Yeast two-hybrid analysis revealed that cSetdb1 interacts with several regulators of mRNA stability and protein translation machinery, such as the ESCs-specific E3 ubiquitin ligase and mRNA silencer Trim71/Lin41. We found that cSetdb1 is required for the integrity of Trim71 complex(es) involved in mRNA metabolism and translation. cSetdb1 modulates the abundance of mRNAs and the rate of newly synthesized proteins. Altogether, our data uncovered the cytoplasmic post-transcriptional regulation of gene expression mediated by a key epigenetic regulator.

Abstract Genomic profiles of cancer cells provide valuable information on genetic alterations in cancer. Several recent studies employed these data to predict the response of cancer cell lines to drug treatment. Nonetheless, due to the multifactorial phenotypes and intricate mechanisms of cancer, the accurate prediction of the effect of pharmacotherapy on a specific cell line based on the genetic information alone is problematic. Emphasizing on the system-level complexity of cancer, we devised a procedure to integrate multiple heterogeneous data, including biological networks, genomics, inhibitor profiling, and gene-disease associations, into a unified graph structure. In order to construct compact, yet information-rich cancer-specific networks, we developed a novel graph reduction algorithm. Driven by not only the topological information, but also the biological knowledge, the graph reduction increases the feature-only entropy while preserving the valuable graph-feature information. Subsequent comparative benchmarking simulations employing a tissue level cross-validation protocol demonstrate that the accuracy of a graph-based predictor of the drug efficacy is 0.68, which is notably higher than those measured for more traditional, matrix-based techniques on the same data. Overall, the non-Euclidean representation of the cancer-specific data improves the performance of machine learning to predict the response of cancer to pharmacotherapy. The generated data are freely available to the academic community at https://osf.io/dzx7b/ .

Environmental pollution is a growing threat to natural ecosystems and one of the world’s most pressing concerns. The increasing worldwide use of pharmaceuticals has elevated their status as significant emerging contaminants. Pharmaceuticals enter aquatic environments through multiple pathways related to anthropogenic activity. Their high consumption, insufficient waste treatment, and the incapacity of organisms to completely metabolize them contribute to their accumulation in aquatic environments, posing a threat to all life forms. Various analytical methods have been used to quantify pharmaceuticals. Biotechnology advancements based on next-generation sequencing (NGS) techniques, like eDNA metabarcoding, have enabled the development of new methods for assessing and monitoring the ecotoxicological effects of pharmaceuticals. eDNA metabarcoding is a valuable biomonitoring tool for pharmaceutical pollution because it (a) provides an efficient method to assess and predict pollution status, (b) identifies pollution sources, (c) tracks changes in pharmaceutical pollution levels over time, (d) assesses the ecological impact of pharmaceutical pollution, (e) helps prioritize cleanup and mitigation efforts, and (f) offers insights into the diversity and composition of microbial and other bioindicator communities. This review highlights the issue of aquatic pharmaceutical pollution while emphasizing the importance of using modern NGS-based biomonitoring actions to assess its environmental effects more consistently and effectively.

Tsetse flies (Glossina spp.; Diptera: Glossinidae) are viviparous flies that feed on blood and are found exclusively in sub-Saharan Africa. They are the only cyclic vectors of African trypanosomes, responsible for human African trypanosomiasis (HAT) and animal African trypanosomiasis (AAT). In this study, we employed high throughput sequencing of the 16S rRNA gene to unravel the diversity of symbiotic bacteria in five wild and three laboratory populations of tsetse species (Glossina pallidipes, G. morsitans, G. swynnertoni, and G. austeni). The aim was to assess the dynamics of bacterial diversity both within each laboratory and wild population in relation to the developmental stage, insect age, gender, and location. Our results indicated that the bacterial communities associated with the four studied Glossina species were significantly influenced by their region of origin, with wild samples being more diverse compared to the laboratory samples. We also observed that the larval microbiota was significantly different than the adults. Furthermore, the sex and the species did not significantly influence the formation of the bacterial profile of the laboratory colonies once these populations were kept under the same rearing conditions. In addition, Wigglesworthia, Acinetobacter, and Sodalis were the most abundant bacterial genera in all the samples, while Wolbachia was significantly abundant in G. morsitans compared to the other studied species. The operational taxonomic unit (OTU) co-occurrence network for each location (VVBD insectary, Doma, Makao, and Msubugwe) indicated a high variability between G. pallidipes and the other species in terms of the number of mutual exclusion and copresence interactions. In particular, some bacterial genera, like Wigglesworthia and Sodalis, with high relative abundance, were also characterized by a high degree of interactions.

Radio bursts provide important diagnostics of energetic phenomena of the Sun. In particular, bursts in decimetric and metric wavelengths probe the physical conditions and the energy release processes in the low corona as well as their association with heliospheric phenomena. The advent of spectral radio data with high time and high frequency resolution has provided a wealth of information on phenomena of short duration and narrow bandwidth. Of particular value are spectral data combined with imaging observations at specific frequencies. In this work we briefly review the results of a series of observations comprised from high-sensitivity, low-noise dynamic spectra obtained with the acousto-optic analyzer (SAO) of the ARTEMIS-IV/JLS solar radiospectrograph, in conjunction with high time-resolution images from the Nançay Radioheliograph (NRH). Our studies include fine structures embedded in type-IV burst continua (mostly narrow-band “spikes” and intermediate drift “fiber” bursts) and spike-like structures detected near the front of type-II bursts. The implications of the observational results to theoretical models are discussed.

The meagre, Argyrosomus regius, has recently become a species of increasing economic interest for the Mediterranean aquaculture and there is ongoing work to boost production efficiency through selective breeding. Access to the complete genomic sequence will provide an essential resource for studying quantitative trait-associated loci and exploring the genetic diversity of different wild populations and aquaculture stocks in more detail. Here, we present the first complete genome for A. regius, produced through a combination of long and short read technologies and an efficient in-house developed pipeline for assembly and polishing. Scaffolding using previous linkage map data allowed us to reconstruct a chromosome level assembly with high completeness, complemented with gene annotation and repeat masking. The 696 Mb long assembly has an N50 = 27.87 Mb and an L50 = 12, with 92.85% of its length placed in 24 chromosomes. We use this new resource to study the evolution of the meagre genome and other Sciaenids, via a comparative analysis of 25 high-quality teleost genomes. Combining a rigorous investigation of gene duplications with base-wise conservation analysis, we identify candidate loci related to immune, fat metabolism and growth adaptations in the meagre. Following phylogenomic reconstruction, we show highly conserved synteny within Sciaenidae. In contrast, we report rapidly evolving syntenic rearrangements and gene copy changes in the sex-related dmrt1 neighbourhood in meagre and other members of the family. These novel genomic datasets and findings will add important new tools for aquaculture studies and greatly facilitate husbandry and breeding work in the species.

Abstract Background Fabry disease (FD) is a rare genetic disorder characterized by glycosphingolipid accumulation and progressive damage across multiple organ systems. Due to its heterogeneous presentation, the condition is likely significantly underdiagnosed. Several approaches, including provider education efforts and newborn screening, have attempted to address underdiagnosis of FD across the age spectrum, with limited success. Artificial intelligence (AI) methods present another option for improving diagnosis. These methods isolate common health history patterns among patients using longitudinal real-world data, and can be particularly useful when patients experience nonspecific, heterogeneous symptoms over time. In this study, the performance of an AI tool in identifying patients with FD was analyzed. The tool was calibrated using de-identified health record data from a large cohort of nearly 5000 FD patients, and extracted phenotypic patterns from these records. The tool then used this FD pattern information to make individual-level estimates of FD in a testing dataset. Patterns were reviewed and confirmed with medical experts. Results The AI tool demonstrated strong analytic performance in identifying FD patients. In out-of-sample testing, it achieved an area under the receiver operating characteristic curve (AUROC) of 0.82. Strong performance was maintained when testing on male-only and female-only cohorts, with AUROCs of 0.83 and 0.82 respectively. The tool identified small segments of the population with greatly increased prevalence of FD: in the 1% of the population identified by the tool as at highest risk, FD was 23.9 times more prevalent than in the population overall. The AI algorithm used hundreds of phenotypic signals to make predictions and included both familiar symptoms associated with FD (e.g. renal manifestations) as well as less well-studied characteristics. Conclusions The AI tool analyzed in this study performed very well in identifying Fabry disease patients using structured medical history data. Performance was maintained in all-male and all-female cohorts, and the phenotypic manifestations of FD highlighted by the tool were reviewed and confirmed by clinical experts in the condition. The platform’s analytic performance, transparency, and ability to generate predictions based on existing real-world health data may allow it to contribute to reducing persistent underdiagnosis of Fabry disease.

Mediterranean finfish aquaculture is mainly represented by the production of the gilthead sea bream (Sparus aurata) and sea bass (Dicentrarchus labrax) growing in marine cage farms. Despite the numerous benefits of fish farming, the ecological risk potentially caused by fish escapes to the wild populations is occasionally high. In the current study, an integrated review of fish escapes is presented regarding S. aurata and D. labrax escapes and their potential effect on the genetic composition of wild populations. The collected data from the literature are combined and discussed along with recorded escapes in a fish farm located in the Aegean Sea, Greece. According to these records, fish escapes present a generally stable rate, ranging between 0 and 2% in each escape event, which occur 5–8 times per year. Although for other farmed fish species worldwide, the ecological risk may be higher, this risk is probably lower in Mediterranean finfish populations, which are characterized by generally low levels of genetic differentiation. Nevertheless, the risk of genetic introgression still exists. Particularly in sea bream and sea bass farming, genetic structure and differentiation in wild populations seem to be unaffected for now. Nevertheless, suitable management measurements would be a useful strategy to avoid future negative effects of fish escapes. These strategies should be focused on further efforts towards recapture program development, reduced escapees’ survival, and proper legislation. Furthermore, more data on escapees’ survival percentage, their migration pathways, and the way these pathways relate to mortality, the type of escape event, and the season of release would be also valuable.

The need for a more energy-efficient future is now more evident than ever. Energy disagreggation (NILM) methodologies have been proposed as an effective solution for the reduction in energy consumption. However, there is a wide range of challenges that NILM faces that still have not been addressed. Herein, we propose HeartDIS, a generalizable energy disaggregation pipeline backed by an extensive set of experiments, whose aim is to tackle the performance and efficiency of NILM models with respect to the available data. Our research (i) shows that personalized machine learning models can outperform more generic models; (ii) evaluates the generalization capabilities of these models through a wide range of experiments, highlighting the fact that the combination of synthetic data, the decreased volume of real data, and fine-tuning can provide comparable results; (iii) introduces a more realistic synthetic data generation pipeline based on other state-of-the-art methods; and, finally, (iv) facilitates further research in the field by publicly sharing synthetic and real data for the energy consumption of two households and their appliances.

Abstract The phenotype of juvenile fish is closely associated with the adult phenotype, thus consisting an important quality trait for reared fish stocks. In this study, we estimated the correlation between the juvenile and adult body-shape in Gilthead seabream, and examined the genetic basis of the ontogenetic trajectories. The body shape of 959 pit-tagged fish was periodically examined during the juvenile-to-adult period. Individual shape ontogenetic trajectories were studied in respect to the initial (juvenile) and final (adult) phenotypes, as well as to the rate that adult phenotype is attained (phenotypic integration rate). We found that the juvenile body-shape presented a rapid change up to 192.7 ± 1.9 mm standard length, followed by a phenotypically stable period (plateau). Depending on the shape component considered, body-shape correlations between juvenile and adult stages ranged from 0.22 to 0.76. Heritability estimates (h2) of the final phenotype ranged from 0.370 ± 0.077 to 0.511 ± 0.089, whereas h2 for the phenotypic integration rate was 0.173 ± 0.062. To our knowledge, this is the first study demonstrating that the variance of the ontogenetic trajectories has a substantial additive genetic component. Results are discussed in respect to their potential use in selective breeding programs of Gilthead seabream.

Pancreatic beta cell response to glucose is critical for the maintenance of normoglycemia. A strong transcriptional response was classically described in rodent models but, interestingly, not in human cells. In this study, we exposed human pancreatic beta cells to an increased concentration of glucose and analysed at a global level the mRNAs steady state levels and their translationalability. Polysome profiling analysis showed an early acute increase in protein synthesis and a specific translation regulation of more than 400 mRNAs, independently of their transcriptional regulation. We clustered the co-regulated mRNAs according to their behaviour in translation in response to glucose and discovered common structural and sequence mRNA features. Among them mTOR- and eIF2-sensitive elements have a predominant role to increase mostly the translation of mRNAs encoding for proteins of the translational machinery. Furthermore, we show that mTOR and eIF2α pathways are independently regulated in response to glucose, participating to a translational reshaping to adapt beta cell metabolism. The early acute increase in the translation machinery components prepare the beta cell for further protein demand due to glucose-mediated metabolism changes.

Reliable stock identification constitutes an integral component of effective fishery management. Current methods for the identification of putative stock units comprise the analysis of both phenotypic and genetic variability. The present study examined the spatial variation in otolith morphology (shape and asymmetry) and genetic composition of 395 wild-caught Gilthead seabream (Sparus aurata) specimens, collected from the Aegean and Ionian Seas (eastern Mediterranean) between 2014–2018. The degree of scale regeneration (SRD, % of regenerated scales) was used as an indicator to assess the potential presence of aquaculture escapees in the wild-caught samples. Otolith shape and asymmetry analyses showed a phenotypic discrimination between northwestern Aegean and Ionian Gilthead seabream populations. Genetic analyses of nine microsatellite markers revealed higher levels of genetic variation in the wild compared with samples obtained from aquaculture farms. Despite the absence of genetic structure among the wild-caught seabream populations, a low but statistically significant genetic differentiation was found between reared fish and fish collected in the field. The SRD was considered effective in detecting the presence of aquaculture escapees that may have escaped in either early or late rearing phases.