bluuebunny/crossref_metadata_2025_split huggingface dataset:
Two-Dimensional-Dwell-Time Analysis of Ion Channel Gating using High Performance Computing Clusters
Authors: Oikonomou Efthymios , Gruber Thomas , Chandra Achanta Ravi , Höller Sarina , Alzheimer Christian , Wellein Gerhard , Huth Tobias 
Published: September 2022

AbstractThe power of single-channel patch-clamp recordings is widely acknowledged among ion channel enthusiasts. The method allows observing the action of a single protein complex in real time and hence the deduction of the underlying conformational changes in the ion channel protein. Commonly, recordings are modeled using hidden Markov chains, connecting open and closed states in the experimental data with protein conformations. The rates between states denote transition probabilities that, for instance, could be modified by membrane voltage or ligand binding. Preferably, the time resolution of recordings should be in the range of microseconds or below, potentially bridging Molecular Dynamic simulations and experimental patch-clamp data. Modeling algorithms have to deal with limited recording bandwidth and a very noisy background. It was previously shown that the fit of 2-Dimensional-Dwell-Time histograms (2D-fit) with simulations is very robust in that regard. Errors introduced by the low-pass filter or noise cancel out to a certain degree when comparing experimental and simulated data. In addition, the topology of models, that is, the chain of open and closed states could be inferred from 2D-histograms. However, the 2D-fit was never applied to its full potential. The reason was the extremely time-consuming and unreliable fitting process, due to the stochastic variability in the simulations. We have now solved both issues by introducing a Message Passing Interface (MPI) allowing massive parallel computing on a High Performance Computing (HPC) cluster and obtaining ensemble solutions. With the ensembles, we have optimized the fit algorithm and demonstrated how important the ranked solutions are for difficult tasks related to a noisy background, fast gating events beyond the corner frequency of the low-pass filter and topology estimation of the underlying Markov model. The fit can reliably extract events down to a signal-to-noise ratio of one and rates up to ten times higher than the filter frequency. It is even possible to identify equivalent Markov topologies. Finally, we have shown that, by combining the objective function of the 2D-fit with the deviation of the current amplitude distributions automatic determination of the current level of the conducting state is possible. It is even possible to infer the level with an apparent current reduction due to the application of the low-pass filter. Making use of an HPC cluster, the power of 2D-Dwell-Time analysis can be used to its fullest, allowing extraction of the matching Markov model from a time series with minor input of the experimenter. Additionally, we add the benefit of estimating the reliability of the results by generating ensemble solutions.
Epistatic effects between amino acid insertions and substitutions mediate toxin-resistance of vertebrate Na+,K+-ATPases
Authors: Mohammadi Shabnam , Özdemir Halil İbrahim , Ozbek Pemra , Sumbul Fidan , Stiller Josefin , Deng Yuan , Crawford Andrew J. , Rowland Hannah M. , Storz Jay F. , Andolfatto Peter , Dobler Susanne 
Published: September 2022

AbstractThe recurrent evolution of resistance to cardiotonic steroids (CTS) across diverse animals most frequently involves convergent amino-acid substitutions to the H1-H2 extracellular loop of Na+,K+-ATPase (NKA). Previous work established that hystricognath rodents (e.g. chinchilla) and pterocliform birds (sandgrouse) have convergently evolved amino-acid insertions in the H1-H2 loop, but their functional significance is not known. Using protein engineering, we show that these insertions have distinct effects on CTS resistance of NKA in the two lineages that strongly depend on intramolecular interactions with other residues. Removing the insertion in the chinchilla lineage unexpectedly increases CTS resistance and decreases NKA activity. In the sandgrouse lineage, the insertion works in concert with the substitution Q111R to increase CTS resistance while maintaining wild-type ATPase activity levels. Molecular docking simulations provide additional insight into the biophysical mechanisms responsible for the context-specific CTS insensitivity of the enzyme. Our results highlight the diversity of genetic substrates that underlie CTS insensitivity in vertebrate NKA and reveal how amino-acid insertions can alter the phenotypic effects of point mutations at key sites in the same protein domain.
Epistasis decreases with increasing antibiotic pressure but not temperature
Authors: Ghenu Ana-Hermina , Amado André , Gordo Isabel , Bank Claudia 
Published: September 2022

AbstractPredicting mutational effects is essential for the control of antibiotic resistance (ABR). Predictions are difficult when there are strong genotype-by-environment (G×E), gene-by-gene (G×G or epistatic), or gene- by-gene-by-environment (G×G×E) interactions. We quantified G×G×E effects inEscherichia coliacross environmental gradients. We created intergenic fitness landscapes using gene knock-outs and single nucleotide ABR mutations previously identified to vary in the extent of G×E effects in our environments of interest. Then, we measured competitive fitness across a complete combinatorial set of temperature and antibiotic dosage gradients. In this way, we assessed the predictability of 15 fitness landscapes across 12 different but related environments. We found G×G interactions and rugged fitness landscapes in the absence of antibiotic, but as antibiotic concentration increased, the fitness effects of ABR genotypes quickly overshadowed those of gene knock-outs, and the landscapes became smoother. Our work reiterates that some single mutants, like those conferring resistance or susceptibility to antibiotics, have consistent effects across genetic backgrounds in stressful environments. Thus, although epistasis may reduce the predictability of evolution in benign environments, evolution may be more predictable in adverse environments.
Treatment-mediated selection of lethal prostate cancer clones defined by copy number architectures
Authors: Hasan A M Mahedi , Cremaschi Paolo , Wetterskog Daniel , Jayaram Anuradha , Wong Stephen Q , Williams Scott , Pasam Anupama , Trigos Anna , Trujillo Blanca , Grist Emily , Friedrich Stefanie , Vainauskas Osvaldas , Parry Marina , Ismail Mazlina , Devlies Wout , Wingate Anna , Lise Stefano , Sandhu Shahneen , Attard Gerhardt 
Published: September 2022

AbstractDespite initial responses to hormone treatment, metastatic prostate cancer invariably evolves to a lethal state. To characterize the intra-patient relationships of metastases that evade treatment, we performed genomewide copy number profiling and bespoke approaches targeting the androgen receptor (AR) on 142 metastatic regions from 10 organs harvested post-mortem from nine men who died from prostate cancer. We identified diverse and patient-unique alterations clustering around the AR in metastases from every patient with evidence of independent acquisition of related genomic changes within an individual and, in some patients, the co-existence of AR-neutral clones. Using the genomic boundaries of pan-autosome copy number change, we confirmed a common clone of origin across metastases and diagnostic biopsies; and identified in individual patients, clusters of metastases occupied by dominant clones with diverged autosomal copy number alterations. Autosome-defined clusters were characterized by cluster-specific AR gene architectures that in two index cases were topologically more congruent than by chance (p-values 0.03, 3.07×10-8). Integration with anatomical site suggested patterns of spread and points of genomic divergence. Copy number boundaries identified treatment-selected clones with putatively distinct lethal trajectories.Statement of significanceLethal prostate cancer evolves from a single clone of origin and upon a treatment-mediated selection, progresses to lethal disease via a limited number of related clones harboring patient-unique androgen receptor gene architectures.
Novel Laser Capture Microdissection-Proteomic Analysis Identifies Spatially Distinct Extracellular Matrix Signatures in the Core and Infiltrating Edge of Human Glioblastoma
Authors: Pedley Robert , Prescott Danielah T. , Appleton Ellen , Dingle Lewis , Minshull James , D’Urso Pietro Ivo , Djoukhadar Ibrahim , Gilmore Andrew P. , Roncaroli Federico , Swift Joe 
Published: September 2022

AbstractBackgroundGlioblastoma is the most common and aggressive primary brain tumour in adults. Hallmarks of glioblastoma include its intra-tumoural heterogeneity and extensive invasion of the surrounding brain. Glioblastoma is known to remodel the extracellular matrix (ECM) of the brain, resulting in altered mechanical properties and the establishment of a tumour-promoting microenvironment. How changes in the expression and spatial distribution of ECM constituents within glioblastoma contribute to invasion and disease progression is still unclear.MethodsHere we report on a protocol for laser-capture microdissection coupled with mass spectrometry (LCM-proteomics) that allowed a spatially resolved and unbiased analysis of the regional ECM proteome (matrisome) in formalin-fixed and paraffin-embedded (FFPE) samples of human glioblastoma. We investigated five molecularly characterised hemispheric adult glioblastomas where the brain/tumour interface and tumour epicentre were represented in the surgical specimens and snap-frozen tissue was available. LCM-proteomic analysis was validated with immunohistochemistry.ResultsLCM-proteomics identified 53 matrisome proteins in FFPE tissue, demonstrating comparable performance with conventional analysis of snap-frozen tissue. The analysis revealed distinct matrisome components in the brain/tumour interface versus the tumour epicentre. Guided by data from LCM-proteomic analysis, immunostaining for tenascin-R confirmed greater staining in the brain/tumour interface, whilst expression of fibronectin was higher in the tumour epicentre.ConclusionThe protocol described in this work allowed for accurate, spatially resolved analysis of ECM in FFPE tissues, with performance comparable to analysis of snap-frozen tissue. While the focus for this work was on the regional ECM composition of glioblastoma, we found that the LCM-proteomics protocol is also applicable to the study of the wider proteome, and represents a valuable tool for investigating tumour/tissue heterogeneity. This protocol opens the possibility to apply LCM-proteomics to retrospective studies with the advantage of accessing clinical history and follow-up information, providing a valuable resource for translational research in glioblastoma.
FAS: Assessing the similarity between proteins using multi-layered feature architectures
Authors: Dosch Julian , Bergmann Holger , Tran Vinh , Ebersberger Ingo 
Published: September 2022

AbstractMotivationExpert curation to differentiate between functionally diverged homologs and those that may still share a similar function routinely relies on the visual interpretation of domain architecture changes. However, the size of contemporary data sets integrating homologs from hundreds to thousands of species calls for alternate solutions. Scoring schemes to evaluate domain architecture similarities can help to automatize this procedure, in principle. But existing schemes are often too simplistic in the similarity assessment, many require an a-priori resolution of overlapping domain annotations, and those that allow overlaps to extend the set of annotations sources cannot account for redundant annotations. As a consequence, the gap between the automated similarity scoring and the similarity assessment based on visual architecture comparison is still too wide to make the integration of both approaches meaningful.ResultsHere, we present FAS, a scoring system for the comparison of multi-layered feature architectures integrating information from a broad spectrum of annotation sources. Feature architectures are represented as directed acyclic graphs, and redundancies are resolved in the course of comparison using a score maximization algorithm. A benchmark using more than 10,000 human-yeast ortholog pairs reveals that FAS consistently outperforms existing scoring schemes. Using three examples, we show how automated architecture similarity assessments can be routinely applied in the benchmarking of orthology assignment software, in the identification of functionally diverged orthologs, and in the identification of entries in protein collections that most likely stem from a faulty gene prediction.Availability and implementationFAS is available as python package: https://pypi.org/project/greedyFAS/
Antibiotic effects on microbial communities are modulated by resource competition
Authors: Newton Daniel Philip , Ho Po-Yi , Huang Kerwyn Casey 
Published: September 2022

ABSTRACTAntibiotic treatment significantly impacts the human gut microbiota, but quantitative understanding of how antibiotics affect community diversity is lacking. Here, we build on classical ecological models of resource competition to investigate community responses to antibiotic-induced species-specific death rates. Our analyses highlight the complex dependence of species coexistence that can arise from the interplay of resource competition and antibiotic activity, independent of other biological mechanisms. We show that resource competition can cause richness to change non-monotonically as antibiotic concentrations are increased. We identified resource competition structures that cause richness to depend on the order of sequential application of antibiotics (non-transitivity), and the emergence of synergistic and antagonistic effects under simultaneous application of multiple antibiotics (non-additivity). These complex behaviors can be prevalent, especially when generalist consumers are targeted. Communities can be prone to either synergism or antagonism, but typically not both, and antagonism is more common. Furthermore, we identify a striking overlap in competition structures that lead to non-transitivity during antibiotic sequences and those that lead to non-additivity during antibiotic combination, suggesting that our analysis is broadly applicable across a wide range of clinically relevant antibiotic treatment schemes. In sum, our results will facilitate the engineering of community dynamics via deleterious agents.
Tailored machine learning models for functional RNA detection in genome-wide screens
Authors: Klapproth Christopher , Zöztsche Siegfried , Kühnl Felix , Fallmann Jörg , Stadler Peter F. , Findeiß Sven 
Published: September 2022

AbstractThe in silico prediction of non-coding and protein-coding genetic loci is an area of research that has gathered large attention in the field of comparative genomics. In the last decade, much effort has been made to investigate numerous properties of nucleotide sequences that hint at their biological role in the cell. We present here a software framework for the alignment-based training, evaluation and application of machine learning models with user-defined parameters. Instead of focusing on the one-size-fits-all approach of pervasive in silico annotation pipelines, we offer a framework for the structured generation and evaluation of models based on arbitrary features and input data, focusing on stable and explainable results. Furthermore, we showcase the usage of our software package in a full-genome screen of Drosophila melanogaster and evaluate our results against the well-known but much less flexible program RNAz.
Quantitative proteomics and lipidomics of TFG-deficient B cells provide insights into mechanisms of autophagic flux and plasma cell biology
Authors: Steinmetz Tobit D. , Reimann Lena , Schulz Sebastian R. , Urbanczyk Sophia , Thomas Jana , Himmelreich Ann-Kathrin , Golombek Florian , Castiglione Kathrin , Brodesser Susanne , Warscheid Bettina , Mielenz Dirk 
Published: September 2022

AbstractThe autophagy-flux-promoting protein TFG (Trk-fused gene) is up-regulated during B cell differentiation into plasma cells and supports survival of CH12 B cells. We hypothesized that quantitative proteomics analysis of CH12tfgKO B cells with intact or blocked autophagy-lysosome flux (via NH4Cl) will identify mechanisms of TFG-dependent autophagy, plasma cell biology and B cell survival. Analysis of CH12WT B cells in the presence of NH4Cl will identify proteins whose presence is continuously regulated by lysosomes independent of TFG. We determined hundreds of proteins to be controlled by TFG and/or NH4Cl. Notably, NH4Cl treatment alone increased the abundance of a cluster of cytosolic and mitochondrial translational proteins while it also reduced a number of proteins. Within the B cell relevant protein pool, BCL10 was reduced, while JCHAIN was increased in CH12tfgKO B cells. Furthermore, TFG regulated the abundance of transcription factors, such as JUNB, metabolic enzymes, such as the short-chain fatty acid activating enzyme ACOT9 or the glycolytic enzyme ALDOC. Gene ontology enrichment analysis revealed that TFG-regulated proteins localized to mitochondria and membrane-bounded organelles. Due to these findings we performed shotgun lipidomics of glycerophospholipids, uncovering that a particular phosphatidylethanolamine (PE) species, 32:0 PE, which lipidates LC3 most efficiently, was less abundant while phosphatidylglycerol (PG) was more abundant in CH12tfgKO B cells. In line with the role of PG as precursor for Cardiolipin (CL), the CL content was higher in CH12tfgKO B cells and addition of PG liposomes to B cells increased the amount of CL. We propose a role for TFG in B cell activation and plasma cell biology via regulation of proteins involved in germinal center and plasma cell development, such as BCL10 or JCHAIN, as well as in lipid homeostasis, mitochondria and metabolism.
Outcome of Crash Course Training on Protein Structure Prediction with Artificial Intelligence
Authors: Balamurugan D. , Dougherty Maureen , Lubin Joseph , Arias Paul , Chang Janet , Dalenberg Kenneth , Kholodovych Vlad , Zelzion Ehud , Khare Sagar D. , von Oehsen James Barr , Zwick Michael E. , Burley Stephen K. 
Published: September 2022

Protein structure predictions have broad impact on several science disciplines such as biology, bioengineering, and medical science. AlphaFold2[1] and RoseTTAFold[2] are the current state-of-the-art AI methods to predict the structures of proteins with an accuracy comparable to lower-resolution experimental methods. In its 2021 year review, both these methods were recognized as “breakthrough of the year” by Science magazine[3] and “method of the year” by Nature magazine [4]. It is timely and important to provide training and support on these emerging methods. Our crash course “Enabling Protein Structure Prediction with Artificial Intelligence “was conducted in collaboration with domain experts and research computing professionals. The crash course was well received by the community as there were 750 registrants from all over the world. Here we provide the summary of the crash course, describe our findings in organizing the crash course, and explain what preparation steps helped us with the hands-on training.CCS CONCEPTSComputing methodologies à Machine learning à Machine learning approaches à Bio-inspired approaches
Intra-islet glucagon signalling regulates pulsatile insulin secretion and glucose homeostasis
Authors: Suba K , Patel Y , Martin-Alonso A , Roberts A , Hansen B , Norton M , Shrewsbury J , Kwok R , Kalogianni V , Chen S , Liu X , Rutter GA , Jones B , Minnion J , Owen BM , Distaso W , Drucker DJ , Tan TM , Bloom SR , Murphy KG , Salem V 
Published: September 2022

ABSTRACTBackgroundType 2 diabetes (T2D) is characterised by the loss of pulsatile insulin secretion. We studied mice with β-cell specific loss of the glucagon receptor (Gcgr fl/fl X Ins-1Cre), to investigate the role of intra-islet glucagon receptor signalling on pan-islet calcium oscillations and insulin pulsatility.MethodsFrequently sampled intravenous glucose tolerance tests were conducted on Gcgr β-cell-/- and littermate controls. Crossing with GCaMP6f (STOP flox) animals further allowed for β-cell specific expression of a fluorescent calcium indicator. These islets were functionally imaged in vitro and in vivo. Wild-type mice were transplanted with islets expressing GCaMP6f in β-cells into the anterior eye chamber and placed on a high fat diet. Part of the cohort received a glucagon analogue (GCG-analogue) for 40 days and the control group were fed to achieve weight matching. Calcium imaging was performed regularly during the development of hyperglycaemia and in response to GCG-analogue treatment.ResultsGcgr β-cell-/- mice exhibited impaired glucose tolerance following intraperitoneal glucose challenge (control 12.7mmol/L ±0.6 vs. Gcgr β-cell-/- 15.4mmol/L ±0.0 at 15 min, p=0.002); fasting glycaemia was not different to controls. In vitro, Gcgr β-cell-/- islets showed profound loss of synchronised calcium waves in response to glucose which was only partially rescued in vivo. First-phase insulin pulsatility on peripheral blood sampling (n=5) was significantly disordered in Gcgr β-cell-/- mice (burst mass Gcgr β-cell-/- 0.30 ±0.03 versus 0.84 ±0.23 for controls p=0.04). Diet induced obesity and hyperglycaemia resulted in a loss of co-ordinated [Ca2+]I waves in transplanted islets. This was reversed with GCG-analogue treatment, independently of weight-loss (n=8).ConclusionThese data provide novel evidence for the role of intra-islet GCGR signalling in sustaining synchronised calcium oscillations and support a possible therapeutic role for glucagonergic agents to restore the insulin pulsatility lost in T2D.
Sophorolipid Candidates Demonstrate Cytotoxic Efficacy Against 2D And 3D Breast Cancer Models
Authors: Roberge Cassandra L. , Miceli Rebecca T. , Murphy Lillian R. , Kingsley David M. , Gross Richard A. , Corr David T. 
Published: September 2022

ABSTRACTSophorolipids are biosurfactants derived from the non-pathogenic yeast Starmerella bombicola, with potential efficacy in anti-cancer applications. Simple and cost-effective synthesis of these drugs makes them a promising alternative to traditional chemotherapeutics, pending their success in preliminary drugscreening. Drug screening typically utilizes 2D cell monolayers due to their simplicity and potential for high-throughput assessment. However, 2D assays fail to capture the complexity and 3D context of the tumor microenvironment, and have consequently been implicated in the high percentage of drugs investigated in vitro that later fail in clinical trials. We screened two sophorolipid candidates and clinically-used chemotherapeutic, doxorubicin, on in vitro breast cancer models ranging from 2D monolayers to 3D spheroids, employing Optical Coherence Tomography to confirm these morphologies. We calculated corresponding IC50 values for these drugs and found one of the sophorolipids to have comparable toxicities to the chemotherapeutic control. Our findings show increased drug resistance associated with model dimensionality, such that all drugs tested showed that 3D spheroids exhibited higher IC50 values than their 2D counterparts. These findings demonstrate promising preliminary data to support the use of sophorolipids as a more affordable alternative to traditional clinical interventions and demonstrate the importance of 3D tumor models in assessing drug response.