The integration of multidisciplinary approaches revealed PTGES3 as a novel drug target for breast cancer treatment.

BACKGROUND: The main challenge in personalized treatment of breast cancer (BC) is how to integrate massive amounts of computing resources and data. This study aimed to identify a novel molecular target that might be effective for BC prognosis and for targeted therapy by using network-based multidisciplinary approaches. METHODS: Differentially expressed genes (DEGs) were first identified based on ESTIMATE analysis. A risk model in the TCGA-BRCA cohort was constructed using the risk score of six DEGs and validated in external and clinical in-house cohorts. Subsequently, independent prognostic factors in the internal and external cohorts were evaluated. Cell viability CCK-8 and wound healing assays were performed after PTGES3 siRNA was transiently transfected into the BC cell lines. Drug prediction and molecular docking between PTGES3 and drugs were further analyzed. Cell viability and PTGES3 expression in two BC cell lines after drug treatment were also investigated. RESULTS: A novel six-gene signature (including APOOL, BNIP3, F2RL2, HINT3, PTGES3 and RTN3) was used to establish a prognostic risk stratification model. The risk score was an independent prognostic factor that was more accurate than clinicopathological risk factors alone in predicting overall survival (OS) in BC patients. A high risk score favored tumor stage/grade but not OS. PTGES3 had the highest hazard ratio among the six genes in the signature, and its mRNA and protein levels significantly increased in BC cell lines. PTGES3 knockdown significantly inhibited BC cell proliferation and migration. Three drugs (gedunin, genistein and diethylstilbestrol) were confirmed to target PTGES3, and genistein and diethylstilbestrol demonstrated stronger binding affinities than did gedunin. Genistein and diethylstilbestrol significantly inhibited BC cell proliferation and reduced the protein and mRNA levels of PTGES3. CONCLUSIONS: PTGES3 was found to be a novel drug target in a robust six-gene prognostic signature that may serve as a potential therapeutic strategy for BC.

Synthesis and In Vitro Antitumor Activity Evaluation of Gefitinib-1,2,3-Triazole Derivatives.

In this study, 14 structurally novel gefitinib-1,2,3-triazole derivatives were synthesized using a click chemistry approach and characterized by 1H NMR, 13C NMR and high-resolution mass spectrometry (HRMS). Preliminary cell counting kit-8 results showed that most of the compounds exhibit excellent antitumor activity against epidermal growth factor receptor wild-type lung cancer cells NCI-H1299, A549 and NCI-H1437. Among them, 4b and 4c showed the most prominent inhibitory effects. The half maximal inhibitory concentration (IC50) values of 4b were 4.42 ± 0.24 µM (NCI-H1299), 3.94 ± 0.01 µM (A549) and 1.56 ± 0.06 µM (NCI-1437). The IC50 values of 4c were 4.60 ± 0.18 µM (NCI-H1299), 4.00 ± 0.08 µM (A549) and 3.51 ± 0.05 µM (NCI-H1437). Furthermore, our results showed that 4b and 4c could effectively inhibit proliferation, colony formation and cell migration in a concentration-dependent manner, as well as induce apoptosis in H1299 cells. In addition, 4b and 4c exerted its anti-tumor effects by inducing cell apoptosis, upregulating the expression of cleaved-caspase 3 and cleaved-PARP and downregulating the protein levels of Bcl-2. Based on these results, it is suggested that 4b and 4c be developed as potential new drugs for lung cancer treatment.

Discovery of Dolutegravir Derivative against Liver Cancer via Inducing Autophagy and DNA Damage.

We introduced a terminal alkyne into the core structure of dolutegravir, resulting in the synthesis of 34 novel dolutegravir-1,2,3-triazole compounds through click chemistry. These compounds exhibited remarkable inhibitory activities against two hepatocellular carcinoma cell lines, Huh7 and HepG2. Notably, compounds 5e and 5p demonstrated exceptional efficacy, particularly against Huh7 cells, with IC50 values of 2.64 and 5.42 µM. Additionally, both compounds induced apoptosis in Huh7 cells, suppressed tumor cell clone formation, and elevated reactive oxygen species (ROS) levels, further promoting tumor cell apoptosis. Furthermore, compounds 5e and 5p activated the LC3 signaling pathway, inducing autophagy, and triggered the γ-H2AX signaling pathway, resulting in DNA damage in tumor cells. Compound 5e exhibited low toxicity, highlighting its potential as a promising anti-tumor drug.

Development of Glycosylation-Modified DPPA-1 Compounds as Innovative PD-1/PD-L1 Blockers: Design, Synthesis, and Biological Evaluation.

In the context of peptide drug development, glycosylation plays a pivotal role. Accordingly, L-type peptides were synthesized predicated upon the PD-1/PD-L1 blocker DPPA-1. Subsequent glycosylation resulted in the production of two distinct glycopeptides, D-glu-LPPA-1 and D-gal-LPPA-1, by using D-glucose (D-glu) and D-galactose (D-gal), respectively, during glycosylation. Both glycopeptides significantly inhibited the interaction between PD-1 and PD-L1, and the measured half maximal inhibitory concentrations (IC50s) were 75.5 µM and 101.9 µM for D-glu-LPPA-1 and D-gal-LPPA-1, respectively. Furthermore, D-gal-LPPA-1 displayed a pronounced ability to restore T-cell functionality. In an MC38 tumor-bearing mouse model, D-gal-LPPA-1 demonstrated a significant inhibitory effect. Notably, D-gal-LPPA-1 substantially augmented the abundance and functionality of CD8+ T cells in the tumor microenvironment. Additionally, in the lymph nodes and spleens, D-gal-LPPA-1 significantly increased the proportion of CD8+ T cells secreting interferon-gamma (IFN-γ). These strong findings position D-gal-LPPA-1 as a potent enhancer of the antitumor immune response in MC38 tumor-bearing mice, underscoring its potential as a formidable PD-1/PD-L1 blocking agent.

Discovery of dolutegravir-1,2,3-triazole derivatives against prostate cancer via inducing DNA damage.

Prostate cancer (PCa) is the second most frequently diagnosed cancer among men, causing a huge number of deaths each year. Traditional chemotherapy for PCa mostly focused on targeting androgen receptors. However, some of the patients would develop resistance to hormonal therapy. In these cases, it is suggested for these patients to administer treatments in combination with other chemotherapeutics. Current chemotherapeutics for metastatic castration-resistant PCa could hardly reach satisfying effects, therefore it is crucial to explore novel agents with low cytotoxicity. Herein, a common drug against the human immunodeficiency virus (HIV), the dolutegravir (DTG) was modified to become a series of dolutegravir-1,2,3-triazole derivatives. Among these compounds, the 4d and 4q derivatives were verified with high anti-tumor efficiency, suppressing the proliferation of the prostate cancer cells PC3 and DU145. These compounds function by binding to the poly (adenosine diphosphate-ribose) polymerase (PARP), inactivating the PARP and inducing DNA damage in cancer cells. It is noteworthy that the 4d and 4q derivatives showed almost no impact on normal cells and mice. Thereby, the results reveal that these dolutegravir-1,2,3-triazole compounds are potential chemotherapeutics for PCa treatment.

Novel Compounds with Promising IDO1 Inhibitory Activity As New Cancer Drug Candidates: Derivatives Of N, N'-diphenylurea Linked With 1,2,3-triazole.

To explore potential indoleamine 2,3-dioxygenase 1 (IDO1) inhibitors, we designed a series of compounds incorporating urea and 1,2,3-triazole structures. IDO1 enzymatic activity experiments with the synthesized compounds were used to verify their molecular-level activity; for instance, the half maximal inhibitory concentration value of compound 3c was 0.07 µM. Our research has yielded a series of novel IDO1 inhibitors which may be beneficial in the development of drugs targeting IDO1 for cancer treatment.

FluReassort: a database for the study of genomic reassortments among influenza viruses.

Genomic reassortment is an important genetic event in the generation of emerging influenza viruses, which can cause numerous serious flu endemics and epidemics within hosts or even across different hosts. However, there is no dedicated and comprehensive repository for reassortment events among influenza viruses. Here, we present FluReassort, a database for understanding the genomic reassortment events in influenza viruses. Through manual curation of thousands of literature references, the database compiles 204 reassortment events among 56 subtypes of influenza A viruses isolated in 37 different countries. FluReassort provides an interface for the visualization and evolutionary analysis of reassortment events, allowing users to view the events through the phylogenetic analysis with varying parameters. The reassortment networks in FluReassort graphically summarize the correlation and causality between different subtypes of the influenza virus and facilitate the description and interpretation of the reassortment preference among subtypes. We believe FluReassort is a convenient and powerful platform for understanding the evolution of emerging influenza viruses. FluReassort is freely available at https://www.jianglab.tech/FluReassort.

Systematic identification and characterization of repeat sequences in African swine fever virus genomes.

African swine fever virus (ASFV) is a large DNA virus that infects domestic pigs with high morbidity and mortality rates. Repeat sequences, which are DNA sequence elements that are repeated more than twice in the genome, play an important role in the ASFV genome. The majority of repeat sequences, however, have not been identified and characterized in a systematic manner. In this study, three types of repeat sequences, including microsatellites, minisatellites and short interspersed nuclear elements (SINEs), were identified in the ASFV genome, and their distribution, structure, function, and evolutionary history were investigated. Most repeat sequences were observed in noncoding regions and at the 5' end of the genome. Noncoding repeat sequences tended to form enhancers, whereas coding repeat sequences had a lower ratio of alpha-helix and beta-sheet and a higher ratio of loop structure and surface amino acids than nonrepeat sequences. In addition, the repeat sequences tended to encode penetrating and antimicrobial peptides. Further analysis of the evolution of repeat sequences revealed that the pan-repeat sequences presented an open state, showing the diversity of repeat sequences. Finally, CpG islands were observed to be negatively correlated with repeat sequence occurrences, suggesting that they may affect the generation of repeat sequences. Overall, this study emphasizes the importance of repeat sequences in ASFVs, and these results can aid in understanding the virus's function and evolution.

Discovery of novel IDO1 inhibitors via structure-based virtual screening and biological assays.

Indoleamine 2,3-dioxygenase 1 (IDO1) is a heme-containing enzyme that catalyzes the first and rate-limiting step in catabolism of tryptophan via the kynurenine pathway, which plays a pivotal role in the proliferation and differentiation of T cells. IDO1 has been proven to be an attractive target for many diseases, such as breast cancer, lung cancer, colon cancer, prostate cancer, etc. In this study, docking-based virtual screening and bioassays were conducted to identify novel inhibitors of IDO1. The cellular assay demonstrated that 24 compounds exhibited potent inhibitory activity against IDO1 at micromolar level, including 8 compounds with IC50 values below 10 µM and the most potent one (compound 1) with IC50 of 1.18 ± 0.04 µM. Further lead optimization based on similarity searching strategy led to the discovery of compound 28 as an excellent inhibitor with IC50 of 0.27 ± 0.02 µM. Then, the structure-activity relationship of compounds 1, 2, 8 and 14 analogues is discussed. The interaction modes of two compounds against IDO1 were further explored through a Python Based Metal Center Parameter Builder (MCPB.py) molecular dynamics simulation, binding free energy calculation and electrostatic potential analysis. The novel IDO1 inhibitors of compound 1 and its analogues could be considered as promising scaffold for further development of IDO1 inhibitors.

Dolutegravir derivative inhibits proliferation and induces apoptosis of non-small cell lung cancer cells via calcium signaling pathway.

Non-small cell lung cancer (NSCLC) is the most prevalent type of lung cancer. However, there has been little improvement in its cure rate in the last 30 years, due to its intricate heterogeneity and drug resistance. Accumulating evidences have demonstrated that dysregulation of calcium (Ca2+) homeostasis contributes to oncogenesis and promotes tumor development. Inhibitors of Ca2+ channels/transporters to restore intracellular Ca2+ level were found to arrest tumor cell division, induce apoptosis, and suppress tumor growth both in vitro and in vivo. Dolutegravir (DTG), which is a first-line drug for Acquired Immune Deficiency Syndrome (AIDs) treatment, has been shown to increase intracellular Ca2+ levels and Reactive oxygen species (ROS) levels in human erythrocytes, leading to suicidal erythrocyte death or eryptosis. To explore the potential of DTG as an antitumor agent, we have designed and synthesized a panel of compounds based on the principle of biologically active substructure splicing of DTG. Our data demonstrated that 7-methoxy-4-methyl-6,8-dioxo-N-(3-(1-(2-(trifluoromethyl)phenyl)-1H-1,2,3-triazol-4-yl)phenyl)-3,4,6,8,12,12a-hexahydro-2H-pyrido[1',2':4,5]pyrazino[2,1-b][1,3]oxazine-9-carboxamide (DTHP), a novel derivative of DTG, strongly inhibited the colony-forming ability and proliferation of NSCLC cells, but displayed no cytotoxicity to normal lung cells. DTHP treatment also induced apoptosis and upregulate intracellular Ca2+ level in NSCLC cells significantly. Inhibiting Ca2+ signaling alleviated DTHP-induced apoptosis, suggesting the perturbation of intracellular Ca2+ is responsible for DTHP-induced apoptosis. We further discovered that DTHP activates AMPK signaling pathway through binding to SERCA, a Ca2+-ATPase. On the other hand, DTHP treatment promoted mitochondrial ROS production, causing mitochondrial dysfunction and cell death. Finally, DTHP effectively inhibited tumor growth in the mouse xenograft model of lung cancer with low toxicity to normal organs. Taken together, our work identified DTHP as a superior antitumor agent, which will provide a novel strategy for the treatment of NSCLC with potential clinical application.

LATTE: A knowledge-based method to normalize various expressions of laboratory test results in free text of Chinese electronic health records.

BACKGROUND: A wealth of clinical information is buried in free text of electronic health records (EHR), and converting clinical information to machine-understandable form is crucial for the secondary use of EHRs. Laboratory test results, as one of the most important types of clinical information, are written in various styles in free text of EHRs. This has brought great difficulties for data integration and utilization of EHRs. Therefore, developing technology to normalize different expressions of laboratory test results in free text is indispensable for the secondary use of EHRs. METHODS: In this study, we developed a knowledge-based method named LATTE (transforming lab test results), which could transform various expressions of laboratory test results into a normalized and machine-understandable format. We first identified the analyte of a laboratory test result with a dictionary-based method and then designed a series of rules to detect information associated with the analyte, including its specimen, measured value, unit of measure, conclusive phrase and sampling factor. We determined whether a test result is normal or abnormal by understanding the meaning of conclusive phrases or by comparing its measured value with an appropriate normal range. Finally, we converted various expressions of laboratory test results, either in numeric or textual form, into a normalized form as "specimen-analyte-abnormality". With this method, a laboratory test with the same type of abnormality would have the same representation, regardless of the way that it is mentioned in free text. RESULTS: LATTE was developed and optimized on a training set including 8894 laboratory test results from 756 EHRs, and evaluated on a test set including 3740 laboratory test results from 210 EHRs. Compared to experts' annotations, LATTE achieved a precision of 0.936, a recall of 0.897 and an F1 score of 0.916 on the training set, and a precision of 0.892, a recall of 0.843 and an F1 score of 0.867 on the test set. For 223 laboratory tests with at least two different expression forms in the test set, LATTE transformed 85.7% (2870/3350) of laboratory test results into a normalized form. Besides, LATTE achieved F1 scores above 0.8 for EHRs from 18 of 21 different hospital departments, indicating its generalization capabilities in normalizing laboratory test results. CONCLUSION: In conclusion, LATTE is an effective method for normalizing various expressions of laboratory test results in free text of EHRs. LATTE will facilitate EHR-based applications such as cohort querying, patient clustering and machine learning. AVAILABILITY: LATTE is freely available for download on GitHub (https://github.com/denglizong/LATTE).

Design, synthesis and antitumor activity of icotinib derivatives.

EGFR-TK pathway is of high importance for the treatment of non-small-cell lung cancers (NSCLC), and it will be challenging to develop anti-tumor drugs that could inhibit both EGFR wild-type and mutant tumor cells. Here, a series of icotinib derivatives containing 1,2,3-triazole moiety were designed and synthesized through copper(I)-catalyzed azide-alkyne cycloaddition (CuAAC) reactions. Preliminary CCK-8 assay showed that the prepared icotinib-1,2,3-triazole compounds such as a7 or a12 demonstrated potent in vitro antitumor activity against the NSCLC cells expressing both wild type EGFR and mutational EGFR. Further, the mechanism of action for compounds a7 and a12 induced NSCLC cells death was also detailed, and the results suggested a possible induced NSCLC cells death via inducing mitochondrial apoptosis and arresting cell cycle. Remarkably, the inhibition of EGFR by these icotinib derivatives was also studied. The results showed that compound a12 was a potent inhibitor for EGFR with IC50 value of 1.49 µM. Combining these results, an EGFR inhibitor a12 represents a promising new anti-NSCLC candidate that could induce apoptosis and arrest cell cycle.

ORCA: a COBRA toolbox extension for model-driven discovery and analysis.

SUMMARY: Over past decades, constraint-based modelling has emerged as an important approach to obtain referential information about mechanisms behind biological phenotypes and identify physiological and perturbed metabolic states at genome-scale. However, application of this novel approach to systems biology in biotechnology is still hindered by the functionalities of the existing modelling software. To augment the usability of the constraint-based approach for various use scenarios, we present ORCA, a Matlab package, which extends the scope of established Constraint-Based Reconstruction and Analysis metabolic modelling and includes three unique functionalities: (i) a framework method integrating three analyses of multi-objective optimization, robustness analysis and fractional benefit analysis, (ii) metabolic pathways identification with futile loop elimination and (iii) a dynamic flux balance analysis framework incorporating kinetic constraints. AVAILABILITY AND IMPLEMENTATION: ORCA is freely available to academic users and is downloadable from https://sourceforge.net/projects/exorca/; a mini-tutorial is supplied in the package for training purposes as well as a software manual.

Synthesis of compounds based on the active domain of cabotegravir and their application in inhibiting tumor cells activity.

Structural modification based on existing drugs, which ensures the safety of marketed drugs, is an essential approach in developing new drugs. In this study, we modified the structure of cabotegravir by introducing the front alkyne on the core structure through chemical reaction, resulting in the synthesis of 9 compounds resembling 1,2,3-triazoles. The potential of these new cabotegravir derivatives as tumor suppressors in gastrointestinal tumors was investigated. Based on the MTT experiment, most compounds showed a reduction in the viability of KYSE30 and HCT116 cells. Notably, derivatives 5b and 5h exhibited the most significant inhibitory effects. To further explore the effects of derivatives 5b and 5h on gastrointestinal tumors, KYSE30 cells were chosen as a representative cell line. Both derivatives can effectively curtail the migration and invasion capabilities of KYSE30 cells and induce apoptosis in a dose-dependent manner. We further demonstrated these derivatives induce cell apoptosis in KYSE30 cells by inhibiting the expression of Stat3 protein and Smad2/3 protein. Based on the above results, we suggest they show promise in developing drugs for esophageal squamous cell carcinoma.

Curation, nomenclature, and topological classification of receptor-like kinases from 528 plant species for novel domain discovery and functional inference.

Receptor-like kinases (RLKs) are the most numerous signal transduction components in plants and play important roles in determining how different plants adapt to their ecological environments. Research on RLKs has focused mainly on a small number of typical RLK members in a few model plants. There is an urgent need to study the composition, distribution, and evolution of RLKs at the holistic level to increase our understanding of how RLKs assist in the ecological adaptations of different plant species. In this study, we collected the genome assemblies of 528 plant species and constructed an RLK dataset. Using this dataset, we identified and characterized 524 948 RLK family members. Each member underwent systematic topological classification and was assigned a gene ID based on a unified nomenclature system. Furthermore, we identified two novel extracellular domains in some RLKs, designated Xiao and Xiang. Evolutionary analysis of the RLK family revealed that the RLCK-XVII and RLCK-XII-2 classes were present exclusively in dicots, suggesting that diversification of RLKs between monocots and dicots may have led to differences in downstream cytoplasmic responses. We also used an interaction proteome to help empower data mining for inference of new RLK functions from a global perspective, with the ultimate goal of understanding how RLKs shape the adaptation of different plants to the environments/ecosystems. The assembled RLK dataset, together with annotations and analytical tools, forms an integrated foundation of multiomics data that is publicly accessible via the metaRLK web portal (http://metaRLK.biocloud.top).

Discovery of gefitinib-1,2,3-triazole derivatives against lung cancer via inducing apoptosis and inhibiting the colony formation.

A series of 20 novel gefitinib derivatives incorporating the 1,2,3-triazole moiety were designed and synthesized. The synthesized compounds were evaluated for their potential anticancer activity against EGFR wild-type human non-small cell lung cancer cells (NCI-H1299, A549) and human lung adenocarcinoma cells (NCI-H1437) as non-small cell lung cancer. In comparison to gefitinib, Initial biological assessments revealed that several compounds exhibited potent anti-proliferative activity against these cancer cell lines. Notably, compounds 7a and 7j demonstrated the most pronounced effects, with an IC50 value of 3.94 ± 0.17 µmol L-1 (NCI-H1299), 3.16 ± 0.11 µmol L-1 (A549), and 1.83 ± 0.13 µmol L-1 (NCI-H1437) for 7a, and an IC50 value of 3.84 ± 0.22 µmol L-1 (NCI-H1299), 3.86 ± 0.38 µmol L-1 (A549), and 1.69 ± 0.25 µmol L-1 (NCI-H1437) for 7j. These two compounds could inhibit the colony formation and migration ability of H1299 cells, and induce apoptosis in H1299 cells. Acute toxicity experiments on mice demonstrated that compound 7a exhibited low toxicity in mice. Based on these results, it is proposed that 7a and 7j could potentially be developed as novel drugs for the treatment of lung cancer.

PinMyMetal: A hybrid learning system to accurately model metal binding sites in macromolecules.

Metal ions are vital components in many proteins for the inference and engineering of protein function, with coordination complexity linked to structural (4-residue predominate), catalytic (3-residue predominate), or regulatory (2-residue predominate) roles. Computational tools for modeling metal ions in protein structures, especially for transient, reversible, and concentration-dependent regulatory sites, remain immature. We present PinMyMetal (PMM), a sophisticated hybrid machine learning system for predicting zinc ion localization and environment in macromolecular structures. Compared to other predictors, PMM excels in predicting regulatory sites (median deviation of 0.34 Å), demonstrating superior accuracy in locating catalytic sites (median deviation of 0.27 Å) and structural sites (median deviation of 0.14 Å). PMM assigns a certainty score to each predicted site based on local structural and physicochemical features independent of homolog presence. Interactive validation through our server, CheckMyMetal, expands PMM's scope, enabling it to pinpoint and validates diverse functional zinc sites from different structure sources (predicted structures, cryo-EM and crystallography). This facilitates residue-wise assessment and robust metal binding site design. The lightweight PMM system demands minimal computing resources and is available at https://PMM.biocloud.top. While currently trained on zinc, the PMM workflow can easily adapt to other metals through expanded training data.

Synthesis and antitumor activity of dolutegravir derivatives bearing 1,2,3-triazole moieties.

Modification of marketed drugs is an important way to develop drugs because its safety and clinical applicability. Oxygen-nitrogen heterocycles are a class of important active substances discovered in the process of new drug development. Dolutegravir, an HIV drug with a nitrogen-oxygen heterocycle structure, has the potential ability to inhibit cell survival. In order to find and explore novel anti-tumor drugs, new dolutegravir derivatives bearing different 1,2,3-triazole moieties were prepared via click reactions. In vitro biological experiments performed in several lung cancer cell lines suggested that these novel compounds displayed potent anti-tumor ability. Especially, the compound 9e with a substituent of 2-methyl-3-nitrophenyl and the compound 9p with a substituent of 3-trifluoromethylphenyl were effective against PC-9 cell line with IC50 values of 3.83 and 3.17 µM, respectively. Moreover, compounds 9e and 9p were effective against H460 and A549 cells. Further studies suggested that compounds 9e and 9p could induce cancer cell apoptosis in PC-9 and H460, inhibit cancer cell proliferation, change the cell cycle, and increase the level of reactive oxygen species (ROS) which further induce tumor cell apoptosis. In addition, compounds 9e and 9p increased LC3 protein expression which was the key regulator in autophagy signaling pathway in PC-9 cells. Compound 9e also showed low toxicity against normal cells, and could be regarded as an interesting lead compound for further structure optimization.

Genome-scale stoichiometry analysis to elucidate the innate capability of the cyanobacterium Synechocystis for electricity generation.

Synechocystis sp. PCC 6803 has been considered as a promising biocatalyst for electricity generation in recent microbial fuel cell research. However, the innate maximum current production potential and underlying metabolic pathways supporting the high current output are still unknown. This is mainly due to the fact that the high-current production cell phenotype results from the interaction among hundreds of reactions in the metabolism and it is impossible for reductionist methods to characterize the pathway selection in such a metabolic state. In this study, we employed computational metabolic techniques, flux balance analysis, and flux variability analysis, to exploit the maximum current outputs of Synechocystis sp. PCC 6803, in five electron transfer cases, namely, ferredoxin- and plastoquinol-dependent electron transfers under photoautotrophic cultivation, and NADH-dependent mediated electron transfer under photoautotrophic, heterotrophic, and mixotrophic conditions. In these five modes, the maximum current outputs were computed as 0.198, 0.7918, 0.198, 0.4652, and 0.4424 A gDW⁻¹, respectively. Comparison of the five operational modes suggests that plastoquinol-/c-type cytochrome-targeted electricity generation had an advantage of liberating the highest current output achievable for Synechocystis sp. PCC 6803. On the other hand, the analysis indicates that the currency metabolite, NADH-, dependent electricity generation can rely on a number of reactions from different pathways, and is thus more robust against environmental perturbations.

MantaID: a machine learning-based tool to automate the identification of biological database IDs.

The number of biological databases is growing rapidly, but different databases use different identifiers (IDs) to refer to the same biological entity. The inconsistency in IDs impedes the integration of various types of biological data. To resolve the problem, we developed MantaID, a data-driven, machine learning-based approach that automates identifying IDs on a large scale. The MantaID model's prediction accuracy was proven to be 99%, and it correctly and effectively predicted 100,000 ID entries within 2 min. MantaID supports the discovery and exploitation of ID from large quantities of databases (e.g. up to 542 biological databases). An easy-to-use freely available open-source software R package, a user-friendly web application and application programming interfaces were also developed for MantaID to improve applicability. To our knowledge, MantaID is the first tool that enables an automatic, quick, accurate and comprehensive identification of large quantities of IDs and can therefore be used as a starting point to facilitate the complex assimilation and aggregation of biological data across diverse databases.