Graph Network-Based Simulation of Multicellular Dynamics Driven by Concentrated Polymer Brush-Modified Cellulose Nanofibers.

Manipulating the three-dimensional (3D) structures of cells is important for facilitating to repair or regenerate tissues. A self-assembly system of cells with cellulose nanofibers (CNFs) and concentrated polymer brushes (CPBs) has been developed to fabricate various cell 3D structures. To further generate tissues at an implantable level, it is necessary to carry out a large number of experiments using different cell culture conditions and material properties; however this is practically intractable. To address this issue, we present a graph-neural network-based simulator (GNS) that can be trained by using assembly process images to predict the assembly status of future time steps. A total of 24 (25 steps) time-series images were recorded (four repeats for each of six different conditions), and each image was transformed into a graph by regarding the cells as nodes and the connecting neighboring cells as edges. Using the obtained data, the performances of the GNS were examined under three scenarios (i.e., changing a pair of the training and testing data) to verify the possibility of using the GNS as a predictor for further time steps. It was confirmed that the GNS could reasonably reproduce the assembly process, even under the toughest scenario, in which the experimental conditions differed between the training and testing data. Practically, this means that the GNS trained by the first 24 h images could predict the cell types obtained 3 weeks later. This result could reduce the number of experiments required to find the optimal conditions for generating cells with desired 3D structures. Ultimately, our approach could accelerate progress in regenerative medicine.

Machine Learning Refinement of In Situ Images Acquired by Low Electron Dose LC-TEM.

We have studied a machine learning (ML) technique for refining images acquired during in situ observation using liquid-cell transmission electron microscopy. Our model is constructed using a U-Net architecture and a ResNet encoder. For training our ML model, we prepared an original image dataset that contained pairs of images of samples acquired with and without a solution present. The former images were used as noisy images, and the latter images were used as corresponding ground truth images. The number of pairs of image sets was 1,204, and the image sets included images acquired at several different magnifications and electron doses. The trained model converted a noisy image into a clear image. The time necessary for the conversion was on the order of 10 ms, and we applied the model to in situ observations using the software Gatan DigitalMicrograph (DM). Even if a nanoparticle was not visible in a view window in the DM software because of the low electron dose, it was visible in a successive refined image generated by our ML model.

Gait video-based prediction of unified Parkinson's disease rating scale score: a retrospective study.

BACKGROUND: The diagnosis of Parkinson's disease (PD) and evaluation of its symptoms require in-person clinical examination. Remote evaluation of PD symptoms is desirable, especially during a pandemic such as the coronavirus disease 2019 pandemic. One potential method to remotely evaluate PD motor impairments is video-based analysis. In this study, we aimed to assess the feasibility of predicting the Unified Parkinson's Disease Rating Scale (UPDRS) score from gait videos using a convolutional neural network (CNN) model. METHODS: We retrospectively obtained 737 consecutive gait videos of 74 patients with PD and their corresponding neurologist-rated UPDRS scores. We utilized a CNN model for predicting the total UPDRS part III score and four subscores of axial symptoms (items 27, 28, 29, and 30), bradykinesia (items 23, 24, 25, 26, and 31), rigidity (item 22) and tremor (items 20 and 21). We trained the model on 80% of the gait videos and used 10% of the videos as a validation dataset. We evaluated the predictive performance of the trained model by comparing the model-predicted score with the neurologist-rated score for the remaining 10% of videos (test dataset). We calculated the coefficient of determination (R2) between those scores to evaluate the model's goodness of fit. RESULTS: In the test dataset, the R2 values between the model-predicted and neurologist-rated values for the total UPDRS part III score and subscores of axial symptoms, bradykinesia, rigidity, and tremor were 0.59, 0.77, 0.56, 0.46, and 0.0, respectively. The performance was relatively low for videos from patients with severe symptoms. CONCLUSIONS: Despite the low predictive performance of the model for the total UPDRS part III score, it demonstrated relatively high performance in predicting subscores of axial symptoms. The model approximately predicted the total UPDRS part III scores of patients with moderate symptoms, but the performance was low for patients with severe symptoms owing to limited data. A larger dataset is needed to improve the model's performance in clinical settings.

Accelerated discovery of multi-elemental reverse water-gas shift catalysts using extrapolative machine learning approach.

Designing novel catalysts is key to solving many energy and environmental challenges. Despite the promise that data science approaches, including machine learning (ML), can accelerate the development of catalysts, truly novel catalysts have rarely been discovered through ML approaches because of one of its most common limitations and criticisms-the assumed inability to extrapolate and identify extraordinary materials. Herein, we demonstrate an extrapolative ML approach to develop new multi-elemental reverse water-gas shift catalysts. Using 45 catalysts as the initial data points and performing 44 cycles of the closed loop discovery system (ML prediction + experiment), we experimentally tested a total of 300 catalysts and identified more than 100 catalysts with superior activity compared to those of the previously reported high-performance catalysts. The composition of the optimal catalyst discovered was Pt(3)/Rb(1)-Ba(1)-Mo(0.6)-Nb(0.2)/TiO2. Notably, niobium (Nb) was not included in the original dataset, and the catalyst composition identified was not predictable even by human experts.

Calcium sparks enhance the tissue fluidity within epithelial layers and promote apical extrusion of transformed cells.

In vertebrates, newly emerging transformed cells are often apically extruded from epithelial layers through cell competition with surrounding normal epithelial cells. However, the underlying molecular mechanism remains elusive. Here, using phospho-SILAC screening, we show that phosphorylation of AHNAK2 is elevated in normal cells neighboring RasV12 cells soon after the induction of RasV12 expression, which is mediated by calcium-dependent protein kinase C. In addition, transient upsurges of intracellular calcium, which we call calcium sparks, frequently occur in normal cells neighboring RasV12 cells, which are mediated by mechanosensitive calcium channel TRPC1 upon membrane stretching. Calcium sparks then enhance cell movements of both normal and RasV12 cells through phosphorylation of AHNAK2 and promote apical extrusion. Moreover, comparable calcium sparks positively regulate apical extrusion of RasV12-transformed cells in zebrafish larvae as well. Hence, calcium sparks play a crucial role in the elimination of transformed cells at the early phase of cell competition.

Fast Improvement of TEM Images with Low-Dose Electrons by Deep Learning.

Low electron dose observation is indispensable for observing various samples using a transmission electron microscope; consequently, image processing has been used to improve transmission electron microscopy (TEM) images. To apply such image processing to in situ observations, we here apply a convolutional neural network to TEM imaging. Using a dataset that includes short-exposure images and long-exposure images, we develop a pipeline for processed short-exposure images, based on end-to-end training. The quality of images acquired with a total dose of approximately $5$$e^{-}$ per pixel becomes comparable to that of images acquired with a total dose of approximately $1{,}000$$e^{-}$ per pixel. Because the conversion time is approximately 8 ms, in situ observation at 125 fps is possible. This imaging technique enables in situ observation of electron-beam-sensitive specimens.

Early Detection of Nucleation Events From Solution in LC-TEM by Machine Learning.

To support the detection, recording, and analysis of nucleation events during in situ observations, we developed an early detection system for nucleation events observed using a liquid-cell transmission electron microscope. Detectability was achieved using the machine learning equivalent of detection by humans watching a video numerous times. The detection system was applied to the nucleation of sodium chloride crystals from a saturated acetone solution of sodium chlorate. Nanoparticles with a radius of more greater than 150 nm were detected in a viewing area of 12 µm × 12 µm by the detection system. The analysis of the change in the size of the growing particles as a function of time suggested that the crystal phase of the particles with a radius smaller than 400 nm differed from that of the crystals larger than 400 nm. Moreover, the use of machine learning enabled the detection of numerous nanometer sized nuclei. The nucleation rate estimated from the machine-learning-based detection was of the same order as that estimated from the detection using manual procedures.

Dual graph convolutional neural network for predicting chemical networks.

BACKGROUND: Predicting of chemical compounds is one of the fundamental tasks in bioinformatics and chemoinformatics, because it contributes to various applications in metabolic engineering and drug discovery. The recent rapid growth of the amount of available data has enabled applications of computational approaches such as statistical modeling and machine learning method. Both a set of chemical interactions and chemical compound structures are represented as graphs, and various graph-based approaches including graph convolutional neural networks have been successfully applied to chemical network prediction. However, there was no efficient method that can consider the two different types of graphs in an end-to-end manner. RESULTS: We give a new formulation of the chemical network prediction problem as a link prediction problem in a graph of graphs (GoG) which can represent the hierarchical structure consisting of compound graphs and an inter-compound graph. We propose a new graph convolutional neural network architecture called dual graph convolutional network that learns compound representations from both the compound graphs and the inter-compound network in an end-to-end manner. CONCLUSIONS: Experiments using four chemical networks with different sparsity levels and degree distributions shows that our dual graph convolution approach achieves high prediction performance in relatively dense networks, while the performance becomes inferior on extremely-sparse networks.

The role of Mediator and Little Elongation Complex in transcription termination.

Mediator is a coregulatory complex that regulates transcription of Pol II-dependent genes. Previously, we showed that human Mediator subunit MED26 plays a role in the recruitment of Super Elongation Complex (SEC) or Little Elongation Complex (LEC) to regulate the expression of certain genes. MED26 plays a role in recruiting SEC to protein-coding genes including c-myc and LEC to small nuclear RNA (snRNA) genes. However, how MED26 engages SEC or LEC to regulate distinct genes is unclear. Here, we provide evidence that MED26 recruits LEC to modulate transcription termination of non-polyadenylated transcripts including snRNAs and mRNAs encoding replication-dependent histone (RDH) at Cajal bodies. Our findings indicate that LEC recruited by MED26 promotes efficient transcription termination by Pol II through interaction with CBC-ARS2 and NELF/DSIF, and promotes 3' end processing by enhancing recruitment of Integrator or Heat Labile Factor to snRNA or RDH genes, respectively.

SiBIC: A Tool for Generating a Network of Biclusters Captured by Maximal Frequent Itemset Mining.

Biclustering extracts coexpressed genes under certain experimental conditions, providing more precise insight into the genetic behaviors than one-dimensional clustering. For understanding the biological features of genes in a single bicluster, visualizations such as heatmaps or parallel coordinate plots and tools for enrichment analysis are widely used. However, simultaneously handling many biclusters still remains a challenge. Thus, we developed a web service named SiBIC, which, using maximal frequent itemset mining, exhaustively discovers significant biclusters, which turn into networks of overlapping biclusters, where nodes are gene sets and edges show their overlaps in the detected biclusters. SiBIC provides a graphical user interface for manipulating a gene set network, where users can find target gene sets based on the enriched network. This chapter provides a user guide/instruction of SiBIC with background of having developed this software. SiBIC is available at http://utrecht.kuicr.kyoto-u.ac.jp:8080/sibic/faces/index.jsp .

Obesity Suppresses Cell-Competition-Mediated Apical Elimination of RasV12-Transformed Cells from Epithelial Tissues.

Recent studies have revealed that newly emerging transformed cells are often eliminated from epithelial tissues via cell competition with the surrounding normal epithelial cells. This cancer preventive phenomenon is termed epithelial defense against cancer (EDAC). However, it remains largely unknown whether and how EDAC is diminished during carcinogenesis. In this study, using a cell competition mouse model, we show that high-fat diet (HFD) feeding substantially attenuates the frequency of apical elimination of RasV12-transformed cells from intestinal and pancreatic epithelia. This process involves both lipid metabolism and chronic inflammation. Furthermore, aspirin treatment significantly facilitates eradication of transformed cells from the epithelial tissues in HFD-fed mice. Thus, our work demonstrates that obesity can profoundly influence competitive interaction between normal and transformed cells, providing insights into cell competition and cancer preventive medicine.

Genomic copy number variation analysis in multiple system atrophy.

Genomic variation includes single-nucleotide variants, small insertions or deletions (indels), and copy number variants (CNVs). CNVs affect gene expression by altering the genome structure and transposable elements within a region. CNVs are greater than 1 kb in size; hence, CNVs can produce more variation than can individual single-nucleotide variations that are detected by next-generation sequencing. Multiple system atrophy (MSA) is an α-synucleinopathy adult-onset disorder. Pathologically, it is characterized by insoluble aggregation of filamentous α-synuclein in brain oligodendrocytes. Generally, MSA is sporadic, although there are rare cases of familial MSA. In addition, the frequencies of the clinical phenotypes differ considerably among countries. Reports indicate that genetic factors play roles in the mechanisms involved in the pathology and onset of MSA. To evaluate the genetic background of this disorder, we attempted to determine whether there are differences in CNVs between patients with MSA and normal control subjects. We found that the number of CNVs on chromosomes 5, 22, and 4 was increased in MSA; 3 CNVs in non-coding regions were considered risk factors for MSA. Our results show that CNVs in non-coding regions influence the expression of genes through transcription-related mechanisms and potentially increase subsequent structural alterations of chromosomes. Therefore, these CNVs likely play roles in the molecular mechanisms underlying MSA.

Machine learning reveals orbital interaction in materials.

We propose a novel representation of materials named an 'orbital-field matrix (OFM)', which is based on the distribution of valence shell electrons. We demonstrate that this new representation can be highly useful in mining material data. Experimental investigation shows that the formation energies of crystalline materials, atomization energies of molecular materials, and local magnetic moments of the constituent atoms in bimetal alloys of lanthanide metal and transition-metal can be predicted with high accuracy using the OFM. Knowledge regarding the role of the coordination numbers of the transition-metal and lanthanide elements in determining the local magnetic moments of the transition-metal sites can be acquired directly from decision tree regression analyses using the OFM.

Exploring phenotype patterns of breast cancer within somatic mutations: a modicum in the intrinsic code.

Triple-negative (TN) breast cancer (BC) patients have limited treatment options and poor prognosis even after extant treatments and standard chemotherapeutic regimens. Linking TN patients to clinically known phenotypes with appropriate treatments is vital. Location-specific sequence variants are expected to be useful for this purpose by identifying subgroups within a disease population. Single gene mutational signatures have been widely reported, with related phenotypes in literature. We thoroughly survey currently available mutations (and mutated genes), linked to BC phenotypes, to demonstrate their limited performance as sole predictors/biomarkers to assign phenotypes to patients. We then explore mutational combinations, as a pilot study, using The Cancer Genome Atlas Research Network mutational data of BC and three machine learning methods: association rules (limitless arity multiple procedure), decision tree and hierarchical disjoint clustering. The study results in a patient classification scheme through combinatorial mutations in Phosphatidylinositol-4,5-Bisphosphate 3-Kinase and tumor protein 53, being consistent with all three methods, implying its validity from a diverse viewpoint. However, it would warrant further research to select multi-gene signatures to identify phenotypes specifically and be clinically used routinely.

Generalized Sparse Learning of Linear Models Over the Complete Subgraph Feature Set.

Supervised learning over graphs is an intrinsically difficult problem: simultaneous learning of relevant features from the complete subgraph feature set, in which enumerating all subgraph features occurring in given graphs is practically intractable due to combinatorial explosion. We show that 1) existing graph supervised learning studies, such as Adaboost, LPBoost, and LARS/LASSO, can be viewed as variations of a branch-and-bound algorithm with simple bounds, which we call Morishita-Kudo bounds; 2) We present a direct sparse optimization algorithm for generalized problems with arbitrary twice-differentiable loss functions, to which Morishita-Kudo bounds cannot be directly applied; 3) We experimentally showed that i) our direct optimization method improves the convergence rate and stability, and ii) L1-penalized logistic regression (L1-LogReg) by our method identifies a smaller subgraph set, keeping the competitive performance, iii) the learned subgraphs by L1-LogReg are more size-balanced than competing methods, which are biased to small-sized subgraphs.

Predictions of Cleavability of Calpain Proteolysis by Quantitative Structure-Activity Relationship Analysis Using Newly Determined Cleavage Sites and Catalytic Efficiencies of an Oligopeptide Array.

Calpains are intracellular Ca(2+)-regulated cysteine proteases that are essential for various cellular functions. Mammalian conventional calpains (calpain-1 and calpain-2) modulate the structure and function of their substrates by limited proteolysis. Thus, it is critically important to determine the site(s) in proteins at which calpains cleave. However, the calpains' substrate specificity remains unclear, because the amino acid (aa) sequences around their cleavage sites are very diverse. To clarify calpains' substrate specificities, 84 20-mer oligopeptides, corresponding to P10-P10' of reported cleavage site sequences, were proteolyzed by calpains, and the catalytic efficiencies (kcat/Km) were globally determined by LC/MS. This analysis revealed 483 cleavage site sequences, including 360 novel ones. Thekcat/Kms for 119 sites ranged from 12.5-1,710 M(-1)s(-1) Although most sites were cleaved by both calpain-1 and -2 with a similarkcat/Km, sequence comparisons revealed distinct aa preferences at P9-P7/P2/P5'. The aa compositions of the novel sites were not statistically different from those of previously reported sites as a whole, suggesting calpains have a strict implicit rule for sequence specificity, and that the limited proteolysis of intact substrates is because of substrates' higher-order structures. Cleavage position frequencies indicated that longer sequences N-terminal to the cleavage site (P-sites) were preferred for proteolysis over C-terminal (P'-sites). Quantitative structure-activity relationship (QSAR) analyses using partial least-squares regression and >1,300 aa descriptors achievedkcat/Kmprediction withr= 0.834, and binary-QSAR modeling attained an 87.5% positive prediction value for 132 reported calpain cleavage sites independent of our model construction. These results outperformed previous calpain cleavage predictors, and revealed the importance of the P2, P3', and P4' sites, and P1-P2 cooperativity. Furthermore, using our binary-QSAR model, novel cleavage sites in myoglobin were identified, verifying our predictor. This study increases our understanding of calpain substrate specificities, and opens calpains to "next-generation,"i.e.activity-related quantitative and cooperativity-dependent analyses.

The cell competition-based high-throughput screening identifies small compounds that promote the elimination of RasV12-transformed cells from epithelia.

Recent studies have revealed that cell competition can occur between normal and transformed epithelial cells; normal epithelial cells recognize the presence of the neighboring transformed cells and actively eliminate them from epithelial tissues. Here, we have established a brand-new high-throughput screening platform that targets cell competition. By using this platform, we have identified Rebeccamycin as a hit compound that specifically promotes elimination of RasV12-transformed cells from the epithelium, though after longer treatment it shows substantial cytotoxic effect against normal epithelial cells. Among several Rebeccamycin-derivative compounds, we have found that VC1-8 has least cytotoxicity against normal cells but shows the comparable effect on the elimination of transformed cells. This cell competition-promoting activity of VC1-8 is observed both in vitro and ex vivo. These data demonstrate that the cell competition-based screening is a promising tool for the establishment of a novel type of cancer preventive medicine.

MED26 regulates the transcription of snRNA genes through the recruitment of little elongation complex.

Regulation of transcription elongation by RNA polymerase II (Pol II) is a key regulatory step in gene transcription. Recently, the little elongation complex (LEC)-which contains the transcription elongation factor ELL/EAF-was found to be required for the transcription of Pol II-dependent small nuclear RNA (snRNA) genes. Here we show that the human Mediator subunit MED26 plays a role in the recruitment of LEC to a subset of snRNA genes through direct interaction of EAF and the N-terminal domain (NTD) of MED26. Loss of MED26 in cells decreases the occupancy of LEC at a subset of snRNA genes and results in a reduction in their transcription. Our results suggest that the MED26-NTD functions as a molecular switch in the exchange of TBP-associated factor 7 (TAF7) for LEC to facilitate the transition from initiation to elongation during transcription of a subset of snRNA genes.

Ribosomes in a stacked array: elucidation of the step in translation elongation at which they are stalled during S-adenosyl-L-methionine-induced translation arrest of CGS1 mRNA.

Expression of CGS1, which codes for an enzyme of methionine biosynthesis, is feedback-regulated by mRNA degradation in response to S-adenosyl-L-methionine (AdoMet). In vitro studies revealed that AdoMet induces translation arrest at Ser-94, upon which several ribosomes stack behind the arrested one, and mRNA degradation occurs at multiple sites that presumably correspond to individual ribosomes in a stacked array. Despite the significant contribution of stacked ribosomes to inducing mRNA degradation, little is known about the ribosomes in the stacked array. Here, we assigned the peptidyl-tRNA species of the stacked second and third ribosomes to their respective codons and showed that they are arranged at nine-codon intervals behind the Ser-94 codon, indicating tight stacking. Puromycin reacts with peptidyl-tRNA in the P-site, releasing the nascent peptide as peptidyl-puromycin. This reaction is used to monitor the activity of the peptidyltransferase center (PTC) in arrested ribosomes. Puromycin reaction of peptidyl-tRNA on the AdoMet-arrested ribosome, which is stalled at the pre-translocation step, was slow. This limited reactivity can be attributed to the peptidyl-tRNA occupying the A-site at this step rather than to suppression of PTC activity. In contrast, puromycin reactions of peptidyl-tRNA with the stacked second and third ribosomes were slow but were not as slow as pre-translocation step ribosomes. We propose that the anticodon end of peptidyl-tRNA resides in the A-site of the stacked ribosomes and that the stacked ribosomes are stalled at an early step of translocation, possibly at the P/E hybrid state.

Similarity-based machine learning methods for predicting drug-target interactions: a brief review.

Computationally predicting drug-target interactions is useful to select possible drug (or target) candidates for further biochemical verification. We focus on machine learning-based approaches, particularly similarity-based methods that use drug and target similarities, which show relationships among drugs and those among targets, respectively. These two similarities represent two emerging concepts, the chemical space and the genomic space. Typically, the methods combine these two types of similarities to generate models for predicting new drug-target interactions. This process is also closely related to a lot of work in pharmacogenomics or chemical biology that attempt to understand the relationships between the chemical and genomic spaces. This background makes the similarity-based approaches attractive and promising. This article reviews the similarity-based machine learning methods for predicting drug-target interactions, which are state-of-the-art and have aroused great interest in bioinformatics. We describe each of these methods briefly, and empirically compare these methods under a uniform experimental setting to explore their advantages and limitations.