bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comparison of classification accuracy and feature selection between sparse and non-sparse modeling of metabolomics data
Authors: Toda Arisa , Goudo Misa , Sugimoto Masahiro , Hiwa Satoru , Hiroyasu Tomoyuki 
Published: April 2023

AbstractMachine learnings such as multivariate analyses and clustering have been frequently used for metabolomics data analyses. In metabolomics data analyses, how much difference there is between the results calculated by supervised and unsupervised learning models is an interesting topic. Since metabolomics data include hundreds to thousands of metabolites greater than the sample numbers, only a small fraction of metabolites is relevant to the phenotype of interest. For this reason, sparse mechanisms have been introduced into many machine learning models. However, its explanatory power decreases when the number of explanatory variables is reduced to an extreme level. In this paper, serum lipidomic data of breast cancer patients (1) pre/post-menopause and (2) before/after neoadjuvant chemotherapy was chosen as one of metabolomics data. Here, this data was analyzed by partial least squares (PLS) for regression and K-means and hierarchical clustering for clustering. Results were also compare with the sparse modeling. Between the non-sparse and sparse modeling accuracy, there is no significant difference. Metabolite subsets selected by sparse modeling were almost identical to the PLS-selected features. At the same time, several metabolites were consistently selected regardless of the algorithm used. These results contribute to exploring biomarkers in high-dimensional metabolomics datasets.
The nitrogenase G-subunit is an ancient orphan protein that drove the ecological expansion of nitrogen fixation
Authors: Cuevas-Zuviría Bruno , Garcia Amanda K. , Carruthers Brooke M. , Kaçar Betül 
Published: April 2023

ABSTRACTNitrogenase metalloenzymes have catalyzed biological nitrogen fixation for billions of years and revolutionized planet Earth by supplying essential nitrogen to the biosphere. How these enzymes were built and distributed by microbial and evolutionary processes in a shifting geochemical landscape remains an open question. Here, we probe the birth and evolution of the G-subunit protein, an integral, Precambrian-age structural component of certain nitrogenase isozymes that makes its appearance midway through nitrogenase evolutionary history. We establish that the G-subunit is an orphan protein, with no homologs detected across wider protein diversity. We find that G-subunit emergence accompanied both the diversification of nitrogenase metal usage and an ecological expansion of nitrogen-fixing microbes during the transition in enviromental metal availabilities triggered by Earth surface oxygenation ∼2.5 billion years ago. Further, analyses of ancestral nitrogenase structures implicate a role for the G-subunit in novel metal incorporation, which would have primed nitrogenases and their hosts to exploit these newly diversified geochemical environments. However, permanent recruitment of the G-subunit into the nitrogenase complex was likely only enabled by tuning preexisting, protein interaction features that were selected prior to Earth oxygenation. Our results showcase how contingent evolutionary novelties shape microbial ecological responses and their global consequences.
Mechanism of delayed cell death following simultaneous CRISPR-Cas9 targeting in pancreatic cancers
Authors: Teh Selina Shiqing K. , Halper-Stromberg Eitan , Morsberger Laura , Bennett Alexis , Bowland Kirsten , Skaist Alyza , Cai Fidel , Liang Hong , Hruban Ralph H. , Roberts Nicholas J. , Scharpf Robert B. , Zou Ying S. , Eshleman James R. 
Published: April 2023

AbstractWhen we transduced pancreatic cancers with sgRNAs that targeted 2-16 target sites in the human genome, we found that increasing the number of CRISPR-Cas9 target sites produced greater cytotoxicity, with >99% growth inhibition observed by targeting only 12 sites. However, cell death was delayed by 2-3 weeks after sgRNA transduction, in contrast to the repair of double strand DNA breaks (DSBs) that happened within 3 days after transduction. To explain this discrepancy, we used both cytogenetics and whole genome sequencing to interrogate the genome. We first detected chromatid and chromosome breaks, followed by radial formations, dicentric, ring chromosomes, and other chromosomal aberrations that peaked at 14 days after transduction. Structural variants (SVs) were detected at sites that were directly targeted by CRISPR-Cas9, including SVs generated from two sites that were targeted, but the vast majority of SVs (89.4%) were detected elsewhere in the genome that arose later than those directly targeted. Cells also underwent polyploidization that peaked at day 10 as detected by XY FISH assay, and ultimately died via apoptosis. Overall, we found that the simultaneous DSBs induced by CRISPR-Cas9 in pancreatic cancers caused chromosomal instability and polyploidization that ultimately led to delayed cell death.Statement of significanceUsing whole genome sequencing and conventional cytogenetics, we discovered that CRISPR-Cas9 cuts led to genomic instability, including chromosomal rearrangements and polyploidization, and ultimately to delayed cell death, 2-3 weeks after the induction of CRISPR-Cas9 DSBs.
Synergistic Integration of Deep Neural Networks and Finite Element Method with Applications for Biomechanical Analysis of Human Aorta
Authors: Liang Liang , Liu Minliang , Elefteriades John , Sun Wei 
Published: April 2023

AbstractMotivation: Patient-specific finite element analysis (FEA) has the potential to aid in the prognosis of cardiovascular diseases by providing accurate stress and deformation analysis in various scenarios. It is known that patient-specific FEA is time-consuming and unsuitable for time-sensitive clinical applications. To mitigate this challenge, machine learning (ML) techniques, including deep neural networks (DNNs), have been developed to construct fast FEA surrogates. However, due to the data-driven nature of these ML models, they may not generalize well on new data, leading to unacceptable errors.MethodsWe propose a synergistic integration of DNNs and finite element method (FEM) to overcome each other’s limitations. We demonstrated this novel integrative strategy in forward and inverse problems. For the forward problem, we developed DNNs using state-of-the-art architectures, and DNN outputs were then refined by FEM to ensure accuracy. For the inverse problem of heterogeneous material parameter identification, our method employs a DNN as regularization for the inverse analysis process to avoid erroneous material parameter distribution.ResultsWe tested our methods on biomechanical analysis of the human aorta. For the forward problem, the DNN-only models yielded acceptable stress errors in majority of test cases; yet, for some test cases that could be out of the training distribution (OOD), the peak stress errors were larger than 50%. The DNN-FEM integration eliminated the large errors for these OOD cases. Moreover, the DNN-FEM integration was magnitudes faster than the FEM-only approach. For the inverse problem, the FEM-only inverse method led to errors larger than 50%, and our DNN-FEM integration significantly improved performance on the inverse problem with errors less than 1%.
Asparagine availability controls B cell homeostasis
Authors: Yazicioglu Yavuz F. , Marin Eros , Andrew Hana F. , Bentkowska Karolina , Johnstone Julia C. , Mitchell Robert , Wong Zhi Yi , Zec Kristina , Fergusson Joannah , Borsa Mariana , Raza Iwan G. A. , Attar Moustafa , Ali Mohammad , Kronsteiner Barbara , Furlani Izadora L. , MacRae James I. , Devine Michael J. , Coles Mark , Buckley Christopher D. , Dunachie Susanna J. , Clarke Alexander J. 
Published: April 2023

AbstractGerminal centre (GC) B cells proliferate at some of the highest rates of any mammalian cell, yet the metabolic processes which enable this are poorly understood. We performed integrated metabolomic and transcriptomic profiling of GC B cells, and found that metabolism of the non-essential amino acid asparagine (Asn) was highly upregulated. Asn was conditionally essential to B cells, and its synthetic enzyme, asparagine synthetase (ASNS) was upregulated following their activation, particularly more markedly in the absence of Asn, through the integrated stress response sensor general control non-derepressible 2 (GCN2). WhenAsnsis deleted B cell survival and proliferation in low Asn conditions were strongly impaired, and removal of environmental Asn by asparaginase or dietary restriction markedly compromised the GC reaction, impairing affinity maturation and the humoral response to influenza infection. Using stable isotope tracing and single cell RNA sequencing, we found that metabolic adaptation to the absence of Asn requires ASNS, and that oxidative phosphorylation, mitochondrial homeostasis, and synthesis of nucleotides was particularly sensitive to Asn deprivation. Altogether, we reveal that Asn metabolism acts as a key regulator of B cell function and GC homeostasis.The one sentence summaryAsparagine metabolism is a critical regulator of B cell function, maintaining the germinal centre reaction.
Immune Profiling of Dermatologic Adverse Events from Checkpoint Blockade using Tissue Cyclic Immunofluorescence
Authors: Maliga Zoltan , Kim Daniel Y. , Bui Ai-Tram N. , Lin Jia-Ren , Dewan Anna K. , Murphy George F. , Nirmal Ajit J. , Lian Christine G. , Sorger Peter K. , LeBoeuf Nicole R. 
Published: April 2023

AbstractIn this study, we demonstrate the utility of whole-slide CyCIF (tissue-based cyclic immunofluorescence) imaging for characterizing immune cell infiltrates in immune checkpoint inhibitor (ICI)-induced dermatologic adverse events (dAEs). We analyzed six cases of ICI-induced dAEs, including lichenoid, bullous pemphigoid, psoriasis, and eczematous eruptions, comparing immune profiling results obtained using both standard immunohistochemistry (IHC) and CyCIF. Our findings indicate that CyCIF provides more detailed and precise single-cell characterization of immune cell infiltrates than IHC, which relies on semi-quantitative scoring by pathologists. This pilot study highlights the potential of CyCIF to advance our understanding of the immune environment in dAEs by revealing tissue-level spatial patterns of immune cell infiltrates, allowing for more precise phenotypic distinctions and deeper exploration of disease mechanisms. By demonstrating that CyCIF can be performed on friable tissues, such as bullous pemphigoid, we provide a foundation for future studies to examine the drivers of specific dAEs using larger cohorts of phenotyped toxicity and suggest a broader role for highly multiplexed tissue imaging in phenotyping the immune mediated disease that they resemble.
asmbPLS: Adaptive Sparse Multi-block Partial Least Square for Survival Prediction using Multi-Omics Data
Authors: Zhang Runzhi , Datta Susmita 
Published: April 2023

AbstractBackgroundAs high-throughput studies advance, more and more high-dimensional multi-omics data are available and collected from the same patient cohort. Using multi-omics data as predictors to predict survival outcomes is challenging due to the complex structure of such data.ResultsIn this article, we introduce an adaptive sparse multi-block partial least square (asmbPLS) regression method by assigning different penalty factors to different blocks in different PLS components for feature selection and prediction. We compared the proposed method with several competitive algorithms in many aspects including prediction performance, feature selection and computation efficiency. The performance and the efficiency of our method were demonstrated using both the simulated and the real data.ConclusionsIn summary, asmbPLS achieved a competitive performance in prediction, feature selection, and computation efficiency. We anticipate asmbPLS to be a valuable tool for multi-omics research. An R package calledasmbPLSimplementing this method is made publicly available on GitHub.
BNP-Track: A framework for superresolved tracking
Authors: Sgouralis Ioannis , Xu Lance W.Q. , Jalihal Ameya P. , Walter Nils G. , Pressé Steve 
Published: April 2023

AbstractAssessing dynamic processes at single molecule scales is key toward capturing life at the level of its molecular actors. Widefield superresolution methods, such as STORM, PALM, and PAINT, provide nanoscale localization accuracy, even when distances between fluorescently labeled single molecules (“emitters”) fall below light’s diffraction limit. However, as these superresolution methods rely on rare photophysical events to distinguish emitters from both each other and background, they are largely limited to static samples. In contrast, here we leverage spatiotemporal correlations of dynamic widefield imaging data to extend superresolution to simultaneous multiple emitter tracking without relying on photodynamics even as emitter distances from one another fall below the diffraction limit. We simultaneously determine emitter numbers and their tracks (localization and linking) with the same localization accuracy per frame as widefield superresolution does for immobilized emitters under similar imaging conditions (≈50 nm). We demonstrate our results for bothin cellulodata and, for benchmarking purposes, on synthetic data. To this end, we avoid the existing tracking paradigm relying on completely or partially separating the tasks of emitter number determination, localization of each emitter, and linking emitter positions across frames. Instead, we develop a fully joint posterior distribution over the quantities of interest, including emitter tracks and their total, otherwise unknown, number within the Bayesian nonparametric paradigm. Our posterior quantifies the full uncertainty over emitter numbers and their associated tracks propagated from origins including shot noise and camera artefacts, pixelation, stochastic background, and out-of-focus motion. Finally, it remains accurate in more crowded regimes where alternative tracking tools cannot be applied.
Fluorescence lifetime analysis of smFRET with contribution of PIFE on donor and acceptor
Authors: Jazani Sina , Ha Taekjip 
Published: April 2023

AbstractSingle-molecule fluorescence resonance energy transfer (FRET) is a powerful technique based on dipole-dipole interaction between donor and acceptor fluorophores to observe inter- and intra-molecular dynamics in realtime with sensitivity to macro-molecular distances (∼ 2.5-10 nm). That said, some fluorophores have an inherent characteristic known as protein induced fluorescence enhancement (PIFE). PIFE is a photo-physical feature of dyes undergoing cis-trans transitions and occurs for protein-dye interactions closer than 3 nm. Here, the challenge is uncoupling the PIFE effect in the FRET data. Ignoring the PIFE effect in the analysis of the FRET data may lead to misinterpretation of the system under investigation. As a solution to this problem, we develop a computational framework based on Bayesian statistics to analyze the fluorescence lifetime signals of the donor and acceptor channels which allows us to uncouple the PIFE effects from the FRET. Our framework can extract any changes in the FRET efficiency simultaneously with any changes in the fluorescence lifetimes of the donor and acceptor due to the PIFE effect. In addition, our framework can provide other parameters, such as the donor and acceptor excitation rates, background photon rates, and detectors’ cross-talk ratios. Our framework extracts all these parameters by analyzing a single photon arrival time trace with only a few thousand photons.
Improving deep models of protein-coding potential with a Fourier-transform architecture and machine translation task
Authors: Valencia Joseph D. , Hendrix David A. 
Published: April 2023

AbstractRibosomes are information-processing macromolecular machines that integrate complex sequence patterns in messenger RNA (mRNA) transcripts to synthesize proteins. Studies of the sequence features that distinguish mRNAs from long noncoding RNAs (lncRNAs) may yield insight into the information that directs and regulates translation. Computational methods for calculating protein-coding potential are important for distinguishing mRNAs from lncRNAs during genome annotation, but most machine learning methods for this task rely on previously known rules to define features. Sequence-to-sequence (seq2seq) models, particularly ones using transformer networks, have proven capable of learning complex grammatical relationships between words to perform natural language translation. Seeking to leverage these advancements in the biological domain, we present a seq2seq formulation for predicting protein-coding potential with deep neural networks and demonstrate that simultaneously learning translation from RNA to protein improves classification performance relative to a classification-only training objective. Inspired by classical signal processing methods for gene discovery and Fourier-based image-processing neural networks, we introduce LocalFilterNet (LFNet). LFNet is a network architecture with an inductive bias for modeling the three-nucleotide periodicity apparent in coding sequences. We incorporate LFNet within an encoder-decoder framework to test whether the translation task improves the classification of transcripts and the interpretation of their sequence features. We use the resulting model to compute nucleotide-resolution importance scores, revealing sequence patterns that could assist the cellular machinery in distinguishing mRNAs and lncRNAs. Finally, we develop a novel approach for estimating mutation effects from Integrated Gradients, a backpropagation-based feature attribution, and characterize the difficulty of efficient approximations in this setting.
One-carbon metabolism is required for epigenetic stability in the mouse placenta
Authors: Senner Claire E. , Dong Ziqi , Branco Miguel R. , Watson Erica D. 
Published: April 2023

AbstractOne-carbon metabolism, including the folate cycle, has a crucial role in fetal development though its molecular function is complex and unclear. The hypomorphicMtrrgtallele is known to disrupt one-carbon metabolism, and thus methyl group availability, leading to several developmental phenotypes (e.g., neural tube closure defects, fetal growth anomalies). Remarkably, previous studies showed that some of the phenotypes were transgenerationally inherited. Here, we explored the genome-wide epigenetic impact of one-carbon metabolism in placentas associated with fetal growth phenotypes and determined whether specific DNA methylation changes were inherited. Firstly, methylome analysis ofMtrrgt/gthomozygous placentas revealed genome-wide epigenetic instability. Several DMRs were identified including at theCxcl1gene promoter and at theEn2gene locus, which may have phenotypic implications. Importantly, we discovered hypomethylation and ectopic expression of a subset of ERV elements throughout the genome ofMtrrgt/gtplacentas with broad implications for genomic stability. Next, we determined that sperm DMRs in males from theMtrrgtmodel were reprogrammed in the placenta with little evidence of direct or transgenerational germline DMR inheritance. However, some sperm DMRs were associated with placental gene misexpression despite normalisation of DNA methylation, suggesting the inheritance of an alternative epigenetic mechanism. Integration of published histone ChIP-seq datasets with sperm methylome and placenta transcriptome data from theMtrrgtmodel point towards H3K4me3 deposition at key loci suggesting that histone modifications might play a role in epigenetic inheritance in this context. This study sheds light on the mechanistic complexities of one-carbon metabolism in development and epigenetic inheritance.
Massively parallel single-cell profiling of transcriptome and multiple epigenetic proteins in cell fate regulation
Authors: Xiong Haiqing , Wang Qianhao , Li Chen C. , He Aibin 
Published: April 2023

ABSTRACTSculpting the epigenome with a combination of histone modifications and transcription factor (TF) occupancy determines gene transcription and cell fate specification. Here we first develop uCoTarget, utilizing a split-pool barcoding strategy for realizing ultra-high throughput single-cell joint profiling of multiple epigenetic proteins. Through extensive optimization for sensitivity and multimodality resolution, we demonstrate that uCoTarget enables simultaneous detection of five histone modifications (H3K27ac, H3K4me3, H3K4me1, H3K36me3 and H3K27me3) in 19,860 single cells. We applied uCoTarget to thein vitrogeneration of hematopoietic stem/progenitor cells (HSPCs) from human embryonic stem cells, presenting multimodal epigenomic profiles in 26,418 single cells. uCoTarget with high sensitivity per modality reveals establishment of pairing of HSPC enhancers (H3K27ac) and promoters (H3K4me3) along the differentiation trajectory and RUNX1 engagement priming for the H3K27ac activation along the HSPC path. We then develop uCoTargetX, an expansion of uCoTarget to simultaneously measure transcriptome and multiple epigenome targets. Together, our methods enable generalizable, versatile multi-modal profiles for reconstructing comprehensive epigenome and transcriptome landscapes and analyzing the regulatory interplay at single-cell level.
Sensitivity Analysis of Biochemical Systems Using Bond Graphs
Authors: Gawthrop Peter J. , Pan Michael 
Published: April 2023

AbstractThe sensitivity of systems biology models to parameter variation can give insights into which parameters are most important for physiological function, and also direct efforts to estimate parameters. However, in general, kinetic models of biochemical systems do not remain thermodynamically consistent after perturbing parameters. To address this issue, we analyse the sensitivity of biological reaction networks in the context of a bond graph representation. We find that the parameter sensitivities can themselves be represented as bond graph components, mirroring potential mechanisms for controlling biochemistry. In particular asensitivity systemis derived which re-expresses parameter variation as additional system inputs. The sensitivity system is then linearised with respect to these new inputs to derive a linear system which can be used to give local sensitivity to parameters in terms of linear system properties such as gain and time constant. This linear system can also be used to find so-called sloppy parameters in biological models. We verify our approach using a model of the Pentose Phosphate Pathway, confirming the reactions and metabolites most essential to maintaining the function of the pathway.