bluuebunny/crossref_metadata_2025_split huggingface dataset:
Design of anArabidopsis thalianareporter line to detect heat-sensing and signaling mutants
Authors: Guihur Anthony , Bourgine Baptiste , Rebeaud Mathieu E. , Goloubinoff Pierre 
Published: March 2023

AbstractBackgroundGlobal warming is a major challenge for plant survival and growth. Understanding the molecular mechanisms by which higher plants sense and adapt to upsurges in the ambient temperature, is essential for developing strategies to enhance plant tolerance to heat stress. Here, we designed a special heat-responsiveArabidopsis thalianareporter line that allowed an in-depth investigation of the mechanisms underlying the accumulation of protective heat-shock proteins (HSPs) in response to high temperature.MethodsA transgenicArabidopsis thalianareporter line named “Heat-Inducible Bioluminescence And Toxicity” (HIBAT) was designed to express from a conditional heat-inducible promoter, a fusion gene encoding for nanoluciferase and D-amino acid oxidase, whose expression was found to be toxic only in the presence of D-valine. HIBAT seedlings were exposed to different heat treatments in presence or absence of D-valine and analyzed for survival rate, bioluminescence and HSP gene expression.ResultsWhereas at 22°C, HIBAT seedlings grew unaffected by D-valine, and all survived following iterative heat treatments without D-valine, 98% died following heat treatments on D-valine. The HSP17.3B promoter was highly specific to heat, as it remained unresponsive to various plant hormones, Flagellin, H2O2, osmotic stress and high salt. Confirming that HIBAT does not significantly differ from its Col-0 parent, RNAseq analysis of heat-treated seedlings showed a strong correlation between the two lines. Using HIBAT, a forward genetic screen revealed candidate loss-of-function mutants defective either at accumulating HSPs at high temperature or at repressing HSP accumulation at low, non-heat-shock temperatures.ConclusionThis study adds insights into the molecular mechanisms by which higher plants sense and adapt to rapid elevations of ambient temperatures. HIBAT was a valuable tool to identifyArabidopsismutants defective in the response to high temperature stress. Our findings open new avenues for future research on the regulation of HSP expression and understanding their role in the onset of plant acquired thermotolerance.
Thymidine kinase-independent click chemistry DNADetect™ probes for DNA proliferation assessment in malaria parasites
Authors: Hilko David H. , Fisher Gillian M. , Addison Russell S. , Andrews Katherine T. , Poulsen Sally-Ann 
Published: March 2023

AbstractMetabolic chemical probes are small molecule reagents that utilise naturally occurring biosynthetic enzymes forin situincorporation into biomolecules of interest. These reagents can be used to label, detect, and track important biological processes within living cells including protein synthesis, protein glycosylation and nucleic acid proliferation. A limitation of current chemical probes, which have largely focused on mammalian cells, is that they often cannot be applied to other organisms due to metabolic differences. For example, the thymidine derivative 5-ethynyl-2’-deoxyuridine (EdU) is a gold standard metabolic chemical probe for assessing DNA proliferation in mammalian cells however is unsuitable for the study of malaria parasites due toPlasmodiumspecies lacking the thymidine kinase enzyme that is essential for metabolism of EdU. Herein we report the design and synthesis of new thymidine-based probes that sidestep the requirement for a thymidine kinase enzyme inPlasmodium. Two of these DNADetect™ probes exhibit robust labelling of replicating asexual intraerythrocyticP. falciparumparasites, as determined by flow cytometry using copper catalysed azide-alkyne cycloaddition (CuAAC) to a fluorescent azide. The DNADetect™ chemical probes are synthetically accessible and thus can be made widely available to researchers as tools to further understand the biology of differentPlasmodiumspecies, including laboratory lines and clinical isolates.
Comprehensive Approach to Simulating Large Scale Conformational Changes in Biological Systems Utilizing a Path Collective Variable and New Barrier Restraint
Authors: Kolossváry István , Sherman Woody 
Published: March 2023

AbstractConformational sampling of complex biomolecules is an emerging frontier in drug discovery. Advances in lab-based structural biology and related computational approaches like AlphaFold have made great strides in obtaining static protein structures for biologically relevant targets. However, biology is in constant motion and many important biological processes rely on conformationally-driven events. Conventional molecular dynamics (MD) simulations run on standard hardware, are impractical for many drug design projects, where conformationally-driven biological events can take microseconds to milliseconds or longer. An alternative approach is to focus the search on a limited region of conformational space defined by a putative reaction coordinate (i.e. path collective variable). The search space is typically limited by applying restraints, which can be guided by insights about the underlying biological process of interest. The challenge is striking a balance between the degree to which the system is constrained while still allowing for natural motions along the path. A plethora of restraints exist to limit the size of conformational search space, although each has drawbacks when simulating complex biological motions. In this work, we present a three-stage procedure to construct realistic path collective variables (PCV), and introduce a new kind of barrier restraint that is particularly well suited for complex conformationally-driven biological events, such as allosteric modulations and conformational signalling. The PCV presented here is all-atom (as opposed to C-alpha or backbone only) and is derived from all-atom MD trajectory frames. The new restraint relies on a barrier function (specifically, the scaled reciprocal function), which we show is particularly beneficial in the context of molecular dynamics, where near-hard-wall restraints are needed with zero tolerance to restraint violation. We have implemented our PCV and barrier restraint within a hybrid sampling framework that combines well-tempered meta-dynamics and extended-Lagrangian adaptive biasing force (meta-eABF). We use three particular examples of high pharmaceutical interest to demonstrate the value of this approach: (1) sampling the distance from ubiquitin to a protein of interest within the supramolecular Cullin-RING ligase complex, (2) stabilizing the wild-type conformation of the oncogenic mutant JAK2-V617F pseudokinase domain, and (3) inducing an activated state of the stimulator of interferon genes (STING) protein observed upon ligand binding. For (2) and (3), we present statistical analysis of meta-eABF free energy estimates and for each case, code for reproducing this work.
Dev’s Formulae for Diagnostic Test Accuracy Meta-analysis
Authors: Desai Dev 
Published: March 2023

AbstractCalculating the sensitivity and specificity values from True Positive, True Negative, False Positive, and False Negative is easy. But estimating the True and False values back is not possible. All the formulae present do that need the value of the disease’s prevalence in the population and hospital-based studies are usually biased in the prevalence. We have tried to create formulae and have been successful along with its proof on formulae that uses Sensitivity, Specificity, PPV and Total participants of the study to calculate True and False values.
Prevalence and risk factors of adverse effects after the first COVID-19 booster dose: evidence from Greece
Authors: Galanis Petros , Katsiroumpa Aglaia , Vraka Irene , Chrysagi Vanessa , Siskou Olga , Konstantakopoulou Olympia , Katsoulas Theodoros , Gallos Parisis , Kaitelidou Daphne 
Published: March 2023

AbstractBackgroundIn general, COVID-19 vaccines are safe and effective, but minor adverse effects are common.ObjectiveTo estimate the prevalence of adverse effects after the first COVID-19 booster dose, and to identify possible risk factors.Material and methodsWe conducted a cross-sectional study with a convenience sample in Greece during November 2022. We measured several adverse effects after the booster dose, such as pain at the injection site, swelling at the injection site, fatigue, muscle pain, headaches, fever, chills, nausea, etc. We considered gender, age, chronic disease, self-assessment of health status, COVID-19 diagnosis, and self-assessment of COVID-19 course as possible predictors of adverse effects.ResultsIn our sample, 96% developed at least one adverse effect. Half of the participants (50.2%) developed one to five adverse effects, 35.9% developed six to ten adverse effects, and 9.5% developed 11 to 16 adverse effects. Mean number of adverse effects was 5.5. The most frequent adverse effects were pain at the injection site (84.3%), fatigue (70.8%), muscle pain (61%), swelling at the injection site (55.2%), headache (49.8%), fever (42.9%), and chills (41%). Females developed more adverse effects than males (p<0.001). Also, we found a positive relationship between severity of COVID-19 symptoms and adverse effects of COVID-19 vaccines (p=0.005). Moreover, younger age was associated with increased adverse effects (p<0.001).ConclusionsAlmost all participants in our study developed minor adverse effects after the booster dose. Female gender, worse clinical course of COVID-19, and decreased age were associated with increased adverse effects.
A single-cell atlas of the miracidium larva of the human blood flukeSchistosoma mansoni: cell types, developmental pathways and tissue architecture
Authors: Attenborough Teresa , Rawlinson Kate A. , Soria Carmen L. Diaz , Ambridge Kirsty , Sankaranarayanan Geetha , Graham Jennie , Cotton James A. , Doyle Stephen R. , Rinaldi Gabriel , Berriman Matthew 
Published: March 2023

AbstractSchistosomes are parasitic flatworms that cause the water-borne disease schistosomiasis, affecting millions of people worldwide. The miracidium larva of schistosomes represents the first post-embryonic stage of development and is critical to transmission. After hatching, a miracidium infects a freshwater snail and transforms into a mother sporocyst, where its stem cells generate daughter sporocysts that give rise to many human-infective cercariae larvae. To understand this important life cycle stage at the cellular and molecular levels, we have used single-cell RNA sequencing,in situhybridisation and image analysis to create a whole-body cell atlas of the miracidium larva ofSchistosoma mansoni. Our atlas shows that each miracidium is composed of ∼365 cells and 19 transcriptionally distinct cell types. We show that 93% of miracidium cells are somatic (57% neural, 19% muscle, 13% epidermal (tegument), 2% parenchyma, 2% protonephridia), and the remaining 7% are stem cells. Cellular diversity within tissue types is revealed, and is highest in neurons. There are two stem cell populations, and they show different activation and potency states. Trajectory analysis indicates that one stem cell population is the origin of the tegument lineage and the other likely contains pluripotent cells. Furthermore, each of these stem populations is transcriptionally distinct based on sex-linked gene expression in male and female larvae. Through single cell transcriptomics andin-situhybridisation we identified every cell in the whole organism revealing the organisation of the miracidium. This single cell atlas provides the foundation to understand the development and interaction of cell types and tissues as they change over a life cycle that is characterised by complex morphological changes.
Cleavage kinetics of human mitochondrial RNase P and contribution of its non-nuclease subunits
Authors: Vilardo Elisa , Toth Ursula , Hazisllari Enxhi , Rossmanith Walter 
Published: March 2023

ABSTRACTRNase P is the endonuclease responsible for the 5’-end processing of tRNA precursors (pre-tRNAs). Unlike the single-subunit protein-only RNase P (PRORP) found in plants or protists, human mitochondrial RNase P is a multi-enzyme assembly that in addition to the homologous PRORP subunit comprises a methyltransferase (TRMT10C) and a dehydrogenase (SDR5C1) subunit; these proteins, but not their enzymatic activities, are required for efficient pre-tRNA cleavage. Here we report a detailed kinetic analysis of the cleavage reaction by human PRORP and its interplay with TRMT10C-SDR5C1 using a comprehensive set of mitochondrial pre-tRNAs. Surprisingly, we found that PRORP alone binds pre-tRNAs with nanomolar affinity and can even cleave some of them at reduced efficiency without the other subunits. Thus, the ancient binding mode, involving the tRNA elbow and PRORP’s PPR domain, seems retained by human PRORP, and its metallonuclease domain is in principle correctly folded and functional. Our findings support a model according to which the main function of TRMT10C-SDR5C1 is to direct PRORP’s nuclease domain to the cleavage site, thereby increasing the rate and accuracy of cleavage. Human PRORP’s dependence on the extra tRNA binder appears to have evolved to secure specificity in the cleavage of the structurally degenerating mitochondrial tRNAs.
Anticancer efficacy of KRASG12C inhibitors is potentiated by PAK4 inhibitor KPT9274 in preclinical models of KRASG12C mutant pancreatic and lung cancers
Authors: Khan Husain Yar , Nagasaka Misako , Aboukameel Amro , Alkhalili Osama , Uddin Md. Hafiz , Bannoura Sahar , Mzannar Yousef , Azar Ibrahim , Beal Eliza , Tobon Miguel , Kim Steve , Beydoun Rafic , Baloglu Erkan , Senapedis William , El-Rayes Bassel , Philip Philip A. , Mohammad Ramzi M. , Shields Anthony F. , Al-Hallak Mohammed Najeeb , Azmi Asfar S. 
Published: March 2023

ABSTRACTKRASG12C inhibitors have revolutionized the treatment landscape for cancer patients harboring the G12C mutant isoform of KRAS. With the recent FDA approval of sotorasib and adagrasib, patients now have access to more promising treatment options. However, patients who receive these agents as a monotherapy usually develop drug resistance. Thus, there is a need to develop logical combination strategies that can delay or prevent the onset of resistance and simultaneously enhance the antitumor effectiveness of the treatment regimen. In this study, we aimed at pharmacologically targeting PAK4 by KPT9274 in combination with KRASG12C inhibitors in KRASG12C mutant pancreatic ductal adenocarcinoma (PDAC) and non–small cell lung cancer (NSCLC) preclinical models. PAK4 is a hub molecule that links several major signaling pathways and is known for its tumorigenic role in mutant Ras-driven cancers. We assessed the cytotoxicity of PAK4 and KRASG12C inhibitors combination in KRASG12C mutant 2D and 3D cellular models. KPT9274 synergized with both sotorasib and adagrasib in inhibiting the growth of KRASG12C mutant cancer cells. The combination was able to reduce the clonogenic potential of KRASG12C mutant PDAC cells. We also evaluated the antitumor activity of the combination in a KRASG12C mutant PDAC cell line-derived xenograft (CDX) model. Oral administration of a sub-optimal dose of KPT9274 in combination with sotorasib (at one-fourth of MTD) demonstrated significant inhibition of the tumor burden (p= 0.002). Similarly, potent antitumor efficacy was observed in an NSCLC CDX model where KPT9274, acting as an adjuvant, prevented tumor relapse following the discontinuation of sotorasib treatment (p= 0.0001). KPT9274 and sotorasib combination also resulted in enhanced survival. This is the first study showing that KRASG12C inhibitors can synergize with PAK4 inhibitor KPT9274 bothin vitroandin vivoresulting in remarkably enhanced antitumor activity and survival outcomes.SignificanceKRASG12C inhibitors demonstrate limited durable response in patients with KRASG12C mutations. In this study, combining PAK4 inhibitor KPT9274 with KRASG12C inhibitors has resulted in potent antitumor effects in preclinical cancer models of PDAC and NSCLC. Our results bring forward a novel combination therapy for cancer patients that do not respond or develop resistance to KRASG12C inhibitor treatment.
Small molecule machine learning: All models are wrong, some may not even be useful
Authors: Kretschmer Fleming , Seipp Jan , Ludwig Marcus , Klau Gunnar W. , Böcker Sebastian 
Published: March 2023

AbstractSmall molecule machine learning tries to predict chemical, biochemical or biological properties from the structure of a molecule. Applications include prediction of toxicity, ligand binding or retention time. A recent trend is to develop end-to-end models that avoid the explicit integration of domain knowledge via inductive bias. A central assumption in doing so, is that there is no coverage bias in the training and evaluation data, meaning that these data are a representative subset of the true distribution we want to learn. Usually, the domain of applicability is neither considered nor analyzed for such large-scale end-to-end models.Here, we investigate how well certain large-scale datasets from the field cover the space of all known biomolecular structures. Investigation of coverage requires a sensible distance measure between molecular structures. We use a well-known distance measure based on solving the Maximum Common Edge Subgraph (MCES) problem, which agrees well with the chemical intuition of similarity between compounds. Unfortunately, this computational problem is provably hard, severely restricting the use of the corresponding distance measure in large-scale studies. We introduce an exact approach that combines Integer Linear Programming and intricate heuristic bounds to ensure efficient computations and dependable results.We find that several large-scale datasets frequently used in this domain of machine learning are far from a uniform coverage of known biomolecular structures. This severely confines the predictive power of models trained on this data. Next, we propose two further approaches to check if a training dataset differs substantially from the distribution of known biomolecular structures. On the positive side, our methods may allow creators of large-scale datasets to identify regions in molecular structure space where it is advisable to provide additional training data.
Nascent evolution of recombination rate differences as a consequence of chromosomal rearrangements
Authors: Näsvall K. , Boman J. , Höök L. , Vila R. , Wiklund C. , Backström N. 
Published: March 2023

AbstractReshuffling of genetic variation occurs both by independent assortment of chromosomes and by homologous recombination. Such reshuffling can generate novel allele combinations and break linkage between advantageous and deleterious variants which increases both the potential and the efficacy of natural selection. Here we used high-density linkage maps to characterize global and regional recombination rate variation in two populations of the wood white butterfly (Leptidea sinapis) with distinct karyotypes. The recombination data were compared to estimates of genetic diversity and measures of selection to assess the relationship between chromosomal rearrangements, crossing over, maintenance of genetic diversity and adaptation. Our data show that the recombination rate is influenced by both chromosome size and number, but that the difference in recombination rate between karyotypes is reduced as a consequence of a higher frequency of double crossovers in larger chromosomes. As expected from effects of selection on linked sites, we observed an overall positive association between recombination rate and genetic diversity in both populations. Our results also revealed a significant effect of chromosomal rearrangements on the rate of intergenic diversity change between populations, but limited effects on polymorphisms in coding sequence. We conclude that chromosomal rearrangements can have considerable effects on the recombination landscape and consequently influence both maintenance of genetic diversity and efficiency of selection in natural populations.Author summaryReshuffling genetic variation is fundamental for maintaining genetic diversity and creating novel allelic combinations. The two main processes involved are the independent assortment of chromosomes and homologous recombination. The number and size of chromosomes can influence the amount of pairwise reshuffling and local recombination patterns. However, studying this in natural populations is challenging. In this study, we used the wood white butterfly, which exhibits an extreme within-species karyotype difference. Extensive fusions and fissions have resulted in almost twice as many chromosomes in the southern populations compared to the northeast populations. This unique system allowed us to assess the relationship between karyotype differences, pairwise reshuffling, recombination rate variation and subsequent effects on diversity and linked selection. We found that a higher number of chromosomes result in a higher recombination rate, although the difference was less than expected due to multiple recombination events occuring on longer chromosomes. Both populations showed an association between recombination rate and genome-wide patterns of genetic diversity and efficacy of selection. We provide evidence that chromosomal rearrangements have considerable effects on the recombination landscape and thereby influence the maintenance of genetic diversity in populations.
Flattening the curve - How to get better results with small deep-mutational-scanning datasets
Authors: Wirnsberger Gregor , Pritišanac Iva , Oberdorfer Gustav , Gruber Karl 
Published: March 2023

AbstractProteins are utilized in various biotechnological applications, often requiring the optimization of protein properties by introducing specific amino acid exchanges. Deep mutational scanning (DMS) is an effective high-throughput method for evaluating the effects of these exchanges on protein function. DMS data can then inform the training of a neural network to predict the impact of mutations. Most approaches employ some representation of the protein sequence for training and prediction. As proteins are characterized by complex structures and intricate residue interaction networks, directly providing structural information as input reduces the need to learn these features from the data.We introduce a method for encoding protein structures as stacked 2D contact maps, which capture residue interactions, their evolutionary conservation, and mutation-induced interaction changes. Furthermore, we explored techniques to augment neural network training performance on smaller DMS datasets. To validate our approach, we trained three neural network architectures originally used for image analysis on three DMS datasets, and we compared their performances with networks trained solely on protein sequences. The results confirm the effectiveness of the protein structure encoding in machine learning efforts on DMS data. Using structural representations as direct input to the networks, along with data augmentation and pre-training, significantly reduced demands on training data size and improved prediction performance, especially on smaller datasets, while performance on large datasets was on par with state-of-the-art sequence convolutional neural networks.The methods presented here have the potential to provide the same workflow as DMS without the experimental and financial burden of testing thousands of mutants. Additionally, we present an open-source, user-friendly software tool to make these data analysis techniques accessible, particularly to biotechnology and protein engineering researchers who wish to apply them to their mutagenesis data.