miR126-5p Downregulation Facilitates Axon Degeneration and NMJ Disruption via a Non-Cell-Autonomous Mechanism in ALS.

Axon degeneration and disruption of neuromuscular junctions (NMJs) are key events in amyotrophic lateral sclerosis (ALS) pathology. Although the disease\u27s etiology is not fully understood, it is thought to involve a non-cell-autonomous mechanism and alterations in RNA metabolism. Here, we identified reduced levels of miR126-5p in presymptomatic ALS male mice models, and an increase in its targets: axon destabilizing Type 3 Semaphorins and their coreceptor Neuropilins. Using compartmentalize

QuasiMotiFinder: protein annotation by searching for evolutionarily conserved motif-like patterns

Sequence signature databases such as PROSITE, which include amino acid segments that are indicative of a protein's function, are useful for protein annotation. Lamentably, the annotation is not always accurate. A signature may be falsely detected in a protein that does not carry out the associated function (false positive prediction, FP) or may be overlooked in a protein that does carry out the function (false negative prediction, FN). A new approach has emerged in which a signature is replaced with a sequence profile, calculated based on multiple sequence alignment (MSA) of homologous proteins that share the same function. This approach, which is superior to the simple pattern search, essentially searches with the sequence of the query protein against an MSA library. We suggest here an alternative approach, implemented in the QuasiMotiFinder web server (), which is based on a search with an MSA of homologous query proteins against the original PROSITE signatures. The explicit use of the average evolutionary conservation of the signature in the query proteins significantly reduces the rate of FP prediction compared with the simple pattern search. QuasiMotiFinder also has a reduced rate of FN prediction compared with simple pattern searches, since the traditional search for precise signatures has been replaced by a permissive search for signature-like patterns that are physicochemically similar to known signatures. Overall, QuasiMotiFinder and the profile search are comparable to each other in terms of performance. They are also complementary to each other in that signatures that are falsely detected in (or overlooked by) one may be correctly detected by the other

HydraScreen: A Generalizable Structure-Based Deep Learning Approach to Drug Discovery

We propose HydraScreen, a deep-learning approach that aims to provide a framework for more robust machine-learning-accelerated drug discovery. HydraScreen utilizes a state-of-the-art 3D convolutional neural network, designed for the effective representation of molecular structures and interactions in protein-ligand binding. We design an end-to-end pipeline for high-throughput screening and lead optimization, targeting applications in structure-based drug design. We assess our approach using established public benchmarks based on the CASF 2016 core set, achieving top-tier results in affinity and pose prediction (Pearson's r = 0.86, RMSE = 1.15, Top-1 = 0.95). Furthermore, we utilize a novel interaction profiling approach to identify potential biases in the model and dataset to boost interpretability and support the unbiased nature of our method. Finally, we showcase HydraScreen's capacity to generalize across unseen proteins and ligands, offering directions for future development of robust machine learning scoring functions. HydraScreen (accessible at https://hydrascreen.ro5.ai) provides a user-friendly GUI and a public API, facilitating easy assessment of individual protein-ligand complexes

Stepwise RNP assembly at the site of H/ACA RNA transcription in human cells

Mammalian H/ACA RNPs are essential for ribosome biogenesis, premessenger RNA splicing, and telomere maintenance. These RNPs consist of four core proteins and one RNA, but it is not known how they assemble. By interrogating the site of H/ACA RNA transcription, we dissected their biogenesis in single cells and delineated the role of the non-core protein NAF1 in the process. NAF1 and all of the core proteins except GAR1 are recruited to the site of transcription. NAF1 binds one of the core proteins, NAP57, and shuttles between nucleus and cytoplasm. Both proteins are essential for stable H/ACA RNA accumulation. NAF1 and GAR1 bind NAP57 competitively, suggesting a sequential interaction. Our analyses indicate that NAF1 binds NAP57 and escorts it to the nascent H/ACA RNA and that GAR1 then replaces NAF1 to yield mature H/ACA RNPs in Cajal bodies and nucleoli

Algorithm Engineering in Robust Optimization

Robust optimization is a young and emerging field of research having received a considerable increase of interest over the last decade. In this paper, we argue that the the algorithm engineering methodology fits very well to the field of robust optimization and yields a rewarding new perspective on both the current state of research and open research directions. To this end we go through the algorithm engineering cycle of design and analysis of concepts, development and implementation of algorithms, and theoretical and experimental evaluation. We show that many ideas of algorithm engineering have already been applied in publications on robust optimization. Most work on robust optimization is devoted to analysis of the concepts and the development of algorithms, some papers deal with the evaluation of a particular concept in case studies, and work on comparison of concepts just starts. What is still a drawback in many papers on robustness is the missing link to include the results of the experiments again in the design

A Novel Eukaryotic Denitrification Pathway in Foraminifera

Benthic foraminifera are unicellular eukaryotes inhabiting sediments of aquatic environments. Several species were shown to store and use nitrate for complete denitrification, a unique energy metabolism among eukaryotes. The population of benthic foraminifera reaches high densities in oxygen-depleted marine habitats, where they play a key role in the marine nitrogen cycle. However, the mechanisms of denitrification in foraminifera are still unknown, and the possibility of a contribution of associated bacteria is debated. Here, we present evidence for a novel eukaryotic denitrification pathway that is encoded in foraminiferal genomes. Large-scale genome and transcriptomes analyses reveal the presence of a denitrification pathway in foraminifera species of the genus Globobulimina. This includes the enzymes nitrite reductase (NirK) and nitric oxide reductase (Nor) as well as a wide range of nitrate transporters (Nrt). A phylogenetic reconstruction of the enzymes' evolutionary history uncovers evidence for an ancient acquisition of the foraminiferal denitrification pathway from prokaryotes. We propose a model for denitrification in foraminifera, where a common electron transport chain is used for anaerobic and aerobic respiration. The evolution of hybrid respiration in foraminifera likely contributed to their ecological success, which is well documented in palaeontological records since the Cambrian period

Denitrification in foraminifera has ancient origins and is complemented by associated bacteria

Benthic foraminifera are unicellular eukaryotes that inhabit sediments of aquatic environments. Several foraminifera of the order Rotaliida are known to store and use nitrate for denitrification, a unique energy metabolism among eukaryotes. The rotaliid Globobulimina spp. has been shown to encode an incomplete denitrification pathway of bacterial origins. However, the prevalence of denitrification genes in foraminifera remains unknown and the missing denitrification pathway components are elusive. Analysing transcriptomes and metagenomes of ten foraminifera species from the Peruvian oxygen minimum zone, we show that denitrification genes are highly conserved in foraminifera. We infer of the last common ancestor of denitrifying foraminifera, which enables us to predict further denitrifying species. Additionally, an examination of the foraminifera microbiota reveals evidence for a stable interaction with Desulfobacteracea , which harbour genes that complement the foraminifera denitrification pathway. Our results provide evidence that foraminiferal denitrification is complemented by the foraminifera microbiome. The interaction of Foraminifera with their resident bacteria is at the basis of foraminifera adaptation to anaerobic environments that manifested in ecological success within oxygen depleted habitats

Intramolecular Structural Heterogeneity altered by Long-range Contacts in an Intrinsically Disordered Protein

Short-range interactions and long-range contacts drive the 3D folding of structured proteins. The proteins' structure has a direct impact on their biological function. However, nearly 40% of the eukaryotes proteome is composed of intrinsically disordered proteins (IDPs) and protein regions that fluctuate between ensembles of numerous conformations. Therefore, to understand their biological function, it is critical to depict how the structural ensemble statistics correlate to the IDPs' amino acid sequence. Here, using small-angle x-ray scattering (SAXS) and time-resolved F\"orster resonance energy transfer (trFRET), we study the intra-molecular structural heterogeneity of the neurofilament low intrinsically disordered tail domain (NFLt). Using theoretical results of polymer physics, we find that the Flory scaling exponent of NFLt sub-segments correlates linearly with their net charge, ranging from statistics of ideal to self-avoiding chains. Surprisingly, measuring the same segments in the context of the whole NFLt protein, we find that regardless of the peptide sequence, the segments' structural statistics are more expanded than when measured independently. Our findings show that while polymer physics can, to some level, relate the IDP's sequence to its ensemble conformations, long-range contacts between distant amino acids play a crucial role in determining intra-molecular structures. This emphasizes the necessity of advanced polymer theories to fully describe IDPs ensembles with the hope it will allow us to model their biological function

The <i>Arabidopsis</i> NPF3 protein is a GA transporter

Gibberellins (GAs) are plant hormones that promote a wide range of developmental processes. While GA signalling is well understood, little is known about how GA is transported or how GA distribution is regulated. Here we utilize fluorescently labelled GAs (GA-Fl) to screen for Arabidopsis mutants deficient in GA transport. We show that the NPF3 transporter efficiently transports GA across cell membranes in vitro and GA-Fl in vivo. NPF3 is expressed in root endodermis and repressed by GA. NPF3 is targeted to the plasma membrane and subject to rapid BFA-dependent recycling. We show that abscisic acid (ABA), an antagonist of GA, is also transported by NPF3 in vitro. ABA promotes NPF3 expression and GA-Fl uptake in plants. On the basis of these results, we propose that GA distribution and activity in Arabidopsis is partly regulated by NPF3 acting as an influx carrier and that GA–ABA interaction may occur at the level of transport

A reference map of potential determinants for the human serum metabolome

The serum metabolome contains a plethora of biomarkers and causative agents of various diseases, some of which are endogenously produced and some that have been taken up from the environment(1). The origins of specific compounds are known, including metabolites that are highly heritable(2,3), or those that are influenced by the gut microbiome(4), by lifestyle choices such as smoking(5), or by diet(6). However, the key determinants of most metabolites are still poorly understood. Here we measured the levels of 1,251 metabolites in serum samples from a unique and deeply phenotyped healthy human cohort of 491 individuals. We applied machine-learning algorithms to predict metabolite levels in held-out individuals on the basis of host genetics, gut microbiome, clinical parameters, diet, lifestyle and anthropometric measurements, and obtained statistically significant predictions for more than 76% of the profiled metabolites. Diet and microbiome had the strongest predictive power, and each explained hundreds of metabolites-in some cases, explaining more than 50% of the observed variance. We further validated microbiome-related predictions by showing a high replication rate in two geographically independent cohorts(7,8) that were not available to us when we trained the algorithms. We used feature attribution analysis(9) to reveal specific dietary and bacterial interactions. We further demonstrate that some of these interactions might be causal, as some metabolites that we predicted to be positively associated with bread were found to increase after a randomized clinical trial of bread intervention. Overall, our results reveal potential determinants of more than 800 metabolites, paving the way towards a mechanistic understanding of alterations in metabolites under different conditions and to designing interventions for manipulating the levels of circulating metabolites.The levels of 1,251 metabolites are measured in 475 phenotyped individuals, and machine-learning algorithms reveal that diet and the microbiome are the determinants with the strongest predictive power for the levels of these metabolites