Retrospective studies have demonstrated that nearly 50% of patients with ovarian cancer with normal cancer antigen 125 (CA125) levels have persistent disease; however, prospectively distinguishing between patients is currently impossible. Here, we demonstrate that for one patient, with the first reported fibroblast growth factor receptor 2 (FGFR2) fusion transcript in ovarian cancer, circulating tumor DNA (ctDNA) is a more sensitive and specific biomarker than CA125, and it can also inform on a candidate therapeutic. For a 4-year period, during which the patient underwent primary debulking surgery and chemotherapy, tumor recurrences, and multiple chemotherapeutic regimens, blood samples were longitudinally collected and stored. Whereas postsurgical CA125 levels were elevated only three times for 28 measurements, the FGFR2 fusion ctDNA biomarker was readily detectable by quantitative real-time reverse transcription-polymerase chain reaction (PCR) in all of these same blood samples and in the tumor recurrences. Given the persistence of the FGFR2 fusion, we treated tumor cells derived from this patient and others with the FGFR2 inhibitor BGJ398. Only tumor cells derived from this patient were sensitive to FGFR2 inhibitor treatment. Using the same methodologic approach, we demonstrate in a second patient with a different fusion that PCR and agarose gel electrophoresis can also be used to identify tumor-specific DNA in the circulation. Taken together, we demonstrate that a relatively inexpensive, PCR-based ctDNA surveillance assay can outperform CA125 in identifying occult disease.

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The contribution of newly designed salt bridges to protein stabilization remains controversial even today. In order to solve this problem, we investigated salt bridges from two aspects: spatial distribution and evolutionary characteristics of salt bridges. Firstly, we analyzed spatial distribution of salt bridges in proteins, elucidating the basic requirements of forming salt bridges. Then, from an evolutionary point of view, the evolutionary characteristics of salt bridges as well as their neighboring residues were investigated in our study. The results demonstrate that charged residues appear more frequently than other neutral residues at certain positions of sequence even under evolutionary pressure, which are able to form electrostatic interactions that could increase the evolutionary stability of corresponding amino acid regions, enhancing their importance to stability of proteins. As a corollary, we conjectured that the newly designed salt bridges with more contribution to proteins, not only, are qualified spatial distribution of salt bridges, but also, are needed to further increase the evolutionary stability of corresponding amino acid regions. Based on analysis, the 8 mutations were accordingly constructed in the 1,4-α-glucan branching enzyme (EC 2.4.1.18, GBE) from Geobacillus thermoglucosidans STB02, of which 7 mutations improved thermostability of GBE. The enhanced thermostability of 7 mutations might be a result of additional salt bridges on residue positions that at least one of amino acids positions is conservative, improving their contribution of stabilization to proteins.

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BackgroundFlow cytometry, with its high throughput nature, combined with the ability to measure an increasing number of cell parameters at once can surpass the throughput of prevalent genomic and metagenomic approaches in the study of microbiomes. Novel computational approaches to analyze flow cytometry data will result in greater insights and actionability as compared to traditional tools used in the analysis of microbiomes. This paper is a demonstration of the fruitfulness of machine learning in analyzing microbial flow cytometry data generated in anaerobic microbiome perturbation experiments.ResultsAutoencoders were found to be powerful in detecting anomalies in flow cytometry data from nanoparticles and carbon sources perturbed anaerobic microbiomes but was marginal in predicting perturbations due to antibiotics. A comparison between different algorithms based on predictive capabilities suggested that gradient boosting (GB) and deep learning, i.e. feed forward artificial neural network with three hidden layers (DL) were marginally better under tested conditions at predicting overall community structure while distributed random forests (DRF) worked better for predicting the most important putative microbial group(s) in the anaerobic digesters viz. methanogens, and it can be optimized with better parameter tuning. Predictive classification patterns with DL (feed forward artificial neural network with three hidden layers) were found to be comparable to previously demonstrated multivariate analysis. The potential applications of this approach have been demonstrated for monitoring the syntrophic resilience of the anaerobic microbiomes perturbed by synthetic nanoparticles as well as antibiotics.ConclusionMachine learning can benefit the microbial flow cytometry research community by providing rapid screening and characterization tools to discover patterns in the dynamic response of microbiomes to several stimuli.Electronic supplementary materialThe online version of this article (10.1186/s13036-018-0112-9) contains supplementary material, which is available to authorized users.