Conditional random field approach to prediction of protein-protein interactions using domain information

For understanding cellular systems and biological networks, it is important to analyze functions and interactions of proteins and domains. Many methods for predicting protein-protein interactions have been developed. It is known that mutual information between residues at interacting sites can be higher than that at non-interacting sites. It is based on the thought that amino acid residues at interacting sites have coevolved with those at the corresponding residues in the partner proteins. Several studies have shown that such mutual information is useful for identifying contact residues in interacting proteins

Prediction using step-wise L1, L2 regularization and feature selection for small data sets with large number of features

<p>Abstract</p> <p>Background</p> <p>Machine learning methods are nowadays used for many biological prediction problems involving drugs, ligands or polypeptide segments of a protein. In order to build a prediction model a so called training data set of molecules with measured target properties is needed. For many such problems the size of the training data set is limited as measurements have to be performed in a wet lab. Furthermore, the considered problems are often complex, such that it is not clear which molecular descriptors (features) may be suitable to establish a strong correlation with the target property. In many applications all available descriptors are used. This can lead to difficult machine learning problems, when thousands of descriptors are considered and only few (e.g. below hundred) molecules are available for training.</p> <p>Results</p> <p>The CoEPrA contest provides four data sets, which are typical for biological regression problems (few molecules in the training data set and thousands of descriptors). We applied the same two-step training procedure for all four regression tasks. In the first stage, we used optimized L1 regularization to select the most relevant features. Thus, the initial set of more than 6,000 features was reduced to about 50. In the second stage, we used only the selected features from the preceding stage applying a milder L2 regularization, which generally yielded further improvement of prediction performance. Our linear model employed a soft loss function which minimizes the influence of outliers.</p> <p>Conclusions</p> <p>The proposed two-step method showed good results on all four CoEPrA regression tasks. Thus, it may be useful for many other biological prediction problems where for training only a small number of molecules are available, which are described by thousands of descriptors.</p

GraphIX: Graph-based In silico XAI(explainable artificial intelligence) for drug repositioning from biopharmaceutical network

Drug repositioning holds great promise because it can reduce the time and cost of new drug development. While drug repositioning can omit various R&D processes, confirming pharmacological effects on biomolecules is essential for application to new diseases. Biomedical explainability in a drug repositioning model can support appropriate insights in subsequent in-depth studies. However, the validity of the XAI methodology is still under debate, and the effectiveness of XAI in drug repositioning prediction applications remains unclear. In this study, we propose GraphIX, an explainable drug repositioning framework using biological networks, and quantitatively evaluate its explainability. GraphIX first learns the network weights and node features using a graph neural network from known drug indication and knowledge graph that consists of three types of nodes (but not given node type information): disease, drug, and protein. Analysis of the post-learning features showed that node types that were not known to the model beforehand are distinguished through the learning process based on the graph structure. From the learned weights and features, GraphIX then predicts the disease-drug association and calculates the contribution values of the nodes located in the neighborhood of the predicted disease and drug. We hypothesized that the neighboring protein node to which the model gave a high contribution is important in understanding the actual pharmacological effects. Quantitative evaluation of the validity of protein nodes' contribution using a real-world database showed that the high contribution proteins shown by GraphIX are reasonable as a mechanism of drug action. GraphIX is a framework for evidence-based drug discovery that can present to users new disease-drug associations and identify the protein important for understanding its pharmacological effects from a large and complex knowledge base.Comment: add supplementary material

タンパク質分子の構造ダイナミクス：ウェーブレット変換による解析

要旨あり生体高分子の揺らぎとダイナミクス－シミュレーションと実験の統計解析－研究詳

Callus Formation and Plant Regeneration of Herbs in Perilla Family

Effective methods of callus culture of herbs were studied to establish basic techniques for cell fusion and gene engineering. Eight basil cultivars, five species of Perilla family and a sweet basil were used, and following results were obtained. 1: Effects of phytohormones on callus formation. Callus formed effectively from hypocotyls and cotyledons of sterile seedlings cultured on MS medium supplemented with 0.1mg/12,4-D and BA. Plantlets succeeded in regenerating from callus cultured on MS medium supplemented with 0.1mg/I NAA and BA, but callus formation on a similar medium was inferior to that on MS medium supplemented with 2,4-D and BA. Callus formed best on MS medium supplemented with 2ip, among other cytokinins, but only BA actually induced regeneration of plantlets from callus. 2: Effects of age and different organs of explants on callus formation. Callus with similar weight formed from hypocotyls of young seedlings about 1-3 weeks after germination. The heaviest callus formed from cotyledons, followed by hypocotyls and roots. 3: Callus formation and regeneration of adventitious buds in eight basil cultivars. Calli formed cotyledons of lettuce basil, Anise basil, lemon basil, bush basil, sweet basil, purple raffles basil, dasil opal basil and cinnamon basil, in descending orderof weight, on MS medium supplemented with 2,4-D and 2ip. Callus from lettuce basil was three times as heavy as that from cinnamon basil. Callus formed on MS medium supplemented NAA and BA from cotyledone of all cultivars, but adventitious buds regenerated only from sweet basil, dark opal basil and bush basil. 4: Callus formation from six Perilla herbs. Callus fromed from hypocotyls and cotyledons of sweet basil, red perilla, green perilla, lemon balm, peppermint and sweet majoram in descending order of weight.ハーブの新しい植物育成のための細胞融合や遺伝子導入の基礎技術として、カルス培養の方法を検討した。材料として、カルス培養の為に、8品種のバジルおよび6種類のシソ科ハーブを用いて実験した。得られた結果は次の様であった。1:植物ホルモンの効果　カルスは、無菌実生の胚軸および子葉をMS培地に0.1mg/ℓの2,4-DとBAを添加した培地で培養することにより効果的に形成され、また同じ組成の培地または3mg/ℓ 2,4-Dと0.1mg/ℓ BA添加培地継代することにより高い増殖率を示したが、植物体は再分化しなかった。植物体の再分化は上記の材料を0.1mg/ℓ　のNAAと0.1または1.0mg/ℓのBAを添加した培地で培養することにより、カルスおよび不定根または不定芽を再分化することができたが、カルス形成は2,4-D添加培地に及ばさなかった。培地添加サイトカイニンとしては、カルス形成のためには2ipが大きなカルスを形成したが、植物体の再分化のためにはBAしか効果がなかった。2:植え付け外植体の差　無菌培養した実生の発芽1から3週間後の胚軸を外植体とした時、カルス形成には大きな差は見られなかった。無菌実生の1週間後の胚軸、根、子葉を外植体とした時、子葉が最も重いカルス形成し、次いて胚軸であった。3:8品種のバジルのカルス形成と不定芽再生2,4-Dと2ip添加培地でカルスの形成が最もよかったのはレタスバジルで次いでアニスバジル、レモンバジル、プッシュバジル、スイートバジル、パープルラフレスバジル、ダークオパールバジル、シナモンバジルの順で、レタスバジルはシナモンバジルの3.3倍の重さがあった。NAAとBA添加培地では、カルスはいずれの品種でも形成されたが、不定芽が再生されたのはスイートバジル、ダークオパールバジル、ブッシュバジルであった。4:6種のシソ科ハーブのカルス形成いずれの種類もカルスは形成されたが、スイートバジルに比較して生体重は軽く、アカジソがかろうじて匹敵するくらいで、アオジソ、レモンバーム、ペパーミント、スイートマジョラムの順に軽くなり、特にスイートマジョラムは形成外植体率も低かった。

Machine learning-based prediction of relapse in rheumatoid arthritis patients using data on ultrasound examination and blood test

Recent effective therapies enable most rheumatoid arthritis (RA) patients to achieve remission; however, some patients experience relapse. We aimed to predict relapse in RA patients through machine learning (ML) using data on ultrasound (US) examination and blood test. Overall, 210 patients with RA in remission at baseline were dichotomized into remission (n = 150) and relapse (n = 60) based on the disease activity at 2-year follow-up. Three ML classifiers [Logistic Regression, Random Forest, and extreme gradient boosting (XGBoost)] and data on 73 features (14 US examination data, 54 blood test data, and five data on patient information) at baseline were used for predicting relapse. The best performance was obtained using the XGBoost classifier (area under the receiver operator characteristic curve (AUC) = 0.747), compared with Random Forest and Logistic Regression (AUC = 0.719 and 0.701, respectively). In the XGBoost classifier prediction, ten important features, including wrist/metatarsophalangeal superb microvascular imaging scores, were selected using the recursive feature elimination method. The performance was superior to that predicted by researcher-selected features, which are conventional prognostic markers. These results suggest that ML can provide an accurate prediction of relapse in RA patients, and the use of predictive algorithms may facilitate personalized treatment options

A New Deep State-Space Analysis Framework for Patient Latent State Estimation and Classification from EHR Time Series Data

Many diseases, including cancer and chronic conditions, require extended treatment periods and long-term strategies. Machine learning and AI research focusing on electronic health records (EHRs) have emerged to address this need. Effective treatment strategies involve more than capturing sequential changes in patient test values. It requires an explainable and clinically interpretable model by capturing the patient's internal state over time. In this study, we propose the "deep state-space analysis framework," using time-series unsupervised learning of EHRs with a deep state-space model. This framework enables learning, visualizing, and clustering of temporal changes in patient latent states related to disease progression. We evaluated our framework using time-series laboratory data from 12,695 cancer patients. By estimating latent states, we successfully discover latent states related to prognosis. By visualization and cluster analysis, the temporal transition of patient status and test items during state transitions characteristic of each anticancer drug were identified. Our framework surpasses existing methods in capturing interpretable latent space. It can be expected to enhance our comprehension of disease progression from EHRs, aiding treatment adjustments and prognostic determinations.Comment: 21 pages, 6 figure

Distinct but interchangeable subpopulations of colorectal cancer cells with different growth fates and drug sensitivity

大腸がん細胞の増殖運命の違いと薬剤感受性 --その柔軟性を決めるメカニズム--. 京都大学プレスリリース. 2023-01-20.Dynamic changes in cell properties lead to intratumor heterogeneity; however, the mechanisms of nongenetic cellular plasticity remain elusive. When the fate of each cell from colorectal cancer organoids was tracked through a clonogenic growth assay, the cells showed a wide range of growth ability even within the clonal organoids, consisting of distinct subpopulations; the cells generating large spheroids and the cells generating small spheroids. The cells from the small spheroids generated only small spheroids (S-pattern), while the cells from the large spheroids generated both small and large spheroids (D-pattern), both of which were tumorigenic. Transition from the S-pattern to the D-pattern occurred by various extrinsic triggers, in which Notch signaling and Musashi-1 played a key role. The S-pattern spheroids were resistant to chemotherapy and transited to the D-pattern upon drug treatment through Notch signaling. As the transition is linked to the drug resistance, it can be a therapeutic target

Murine liver allograft transplantation: Tolerance and donor cell chimerism

Nonarterialized orthotopic liver transplantation with no immunosuppression was performed in 13 mouse‐strain combinations. Two strain combinations with major histocompatibility complex class I and class II and minor histocompatibility complex disparity had 20% and 33% survival of more than 100 days, but the other 11 combinations, including four that were fully allogeneic and all with only class I, class II or minor disparities, yielded 45% to 100% survival of more than 100 days. Long‐living recipients permanently accepted donor‐strain heterotopic hearts transplanted on the same day or donor‐strain skin 3 mo after liver transplantation, in spite of detectable antidonor in vitro activity with mixed lymphocyte reaction and cellmediated lymphocytotoxicity testing (split tolerance). In further donor‐specific experiments, liver grafts were not rejected by presensitized major histocompatibility complex class I‐disparate recipients and they protected donor‐strain skin grafts from second set (or any) rejection. Less frequently, liver transplantation rescued rejecting skin grafts placed 1 wk earlier in major histocompatibility complex class I, class II and minor histocompatibility complex, class II or minor histocompatibility complex‐disparate strain combinations. Donor‐derived leukocyte migration to the central lymphoid organs occurred within 1 to 2 hr after liver transplantation in all animals examined, persisted in the surviving animals until they were killed (>375 days), and was demonstrated with double‐immunolabeling to be multilineage. The relation of these findings to so‐called hepatic tolerogenicity and to tolerance in general is discussed. (HEPATOLOGY 1994;19:916–924.) Copyright © 1994 American Association for the Study of Liver Disease

Combination treatment with highly bioavailable curcumin and NQO1 inhibitor exhibits potent antitumor effects on esophageal squamous cell carcinoma

Background: Esophageal squamous cell carcinoma (ESCC) is one of the most intractable cancers, so the development of novel therapeutics has been required to improve patient outcomes. Curcumin, a polyphenol from Curcuma longa, exhibits various health benefits including antitumor effects, but its clinical utility is limited because of low bioavailability. Theracurmin® (THC) is a highly bioavailable curcumin dispersed with colloidal submicron particles. Methods: We examined antitumor effects of THC on ESCC cells by cell viability assay, colony and spheroid formation assay, and xenograft models. To reveal its mechanisms, we investigated the levels of reactive oxygen species (ROS) and performed microarray gene expression analysis. According to those analyses, we focused on NQO1, which involved in the removal of ROS, and examined the effects of NQO1-knockdown or overexpression on THC treatment. Moreover, the therapeutic effect of THC and NQO1 inhibitor on ESCC patient-derived xenografts (PDX) was investigated. Results: THC caused cytotoxicity in ESCC cells, and suppressed the growth of xenografted tumors more efficiently than curcumin. THC increased ROS levels and activated the NRF2–NMRAL2P–NQO1 expressions. Inhibition of NQO1 in ESCC cells by shRNA or NQO1 inhibitor resulted in an increased sensitivity of cells to THC, whereas overexpression of NQO1 antagonized it. Notably, NQO1 inhibitor significantly enhanced the antitumor effects of THC in ESCC PDX tumors. Conclusions: These findings suggest the potential usefulness of THC and its combination with NQO1 inhibitor as a therapeutic option for ESCC