SAGAS: Simulated annealing and greedy algorithm scheduler for laboratory automation.

During laboratory automation of life science experiments, coordinating specialized instruments and human experimenters for various experimental procedures is important to minimize the execution time. In particular, the scheduling of life science experiments requires the consideration of time constraints by mutual boundaries (TCMB) and can be formulated as the "scheduling for laboratory automation in biology" (S-LAB) problem. However, existing scheduling methods for the S-LAB problems have difficulties in obtaining a feasible solution for large-size scheduling problems at a time sufficient for real-time use. In this study, we proposed a fast schedule-finding method for S-LAB problems, SAGAS (Simulated annealing and greedy algorithm scheduler). SAGAS combines simulated annealing and the greedy algorithm to find a scheduling solution with the shortest possible execution time. We have performed scheduling on real experimental protocols and shown that SAGAS can search for feasible or optimal solutions in practicable computation time for various S-LAB problems. Furthermore, the reduced computation time by SAGAS enables us to systematically search for laboratory automation with minimum execution time by simulating scheduling for various laboratory configurations. This study provides a convenient scheduling method for life science automation laboratories and presents a new possibility for designing laboratory configurations.

Bioinformatics in bioscience and bioengineering: Recent advances, applications, and perspectives.

Recent advances have led to the emergence of highly comprehensive and analytical approaches, such as omics analysis and high-resolution, time-resolved bioimaging analysis. These technologies have made it possible to obtain vast data from a single measurement. Subsequently, large datasets have pioneered the data-driven approach, an alternative to the traditional hypothesis-testing system, for researchers. However, processing, interpreting, and elucidating enormous datasets is no longer possible without computation. Bioinformatics is a field that has developed over long periods, intending to understand biological phenomena using methods collected from information science and statistics, thus solving this proposed research challenge. This review presents the latest methodologies and applications in sequencing, imaging, and mass spectrometry that were developed using bioinformatics. We presented the features of individual techniques and outlines in each part, avoiding the use of complex algorithms and formulas to allow beginning researchers to understand an overview. In the section on sequencing, we focused on comparative genomic, transcriptomic, and bacterial microbiome analyses, which are frequently used as applications of next-generation sequencing. Bioinformatic methods for handling sequence data and case studies were described. In the section on imaging, we introduced the analytical methods and microscopy imaging informatics techniques used in animal cell biology and plant physiology. We introduce informatics technologies for maximizing the value of measured data, including predicting the structure of unknown molecules and untargeted analysis in the section on mass spectrometry. Finally, we discuss the future outlook of this field. We anticipate that this review will assist biologists in using bioinformatics more effectively.

Automated evaluation of retinal pigment epithelium disease area in eyes with age-related macular degeneration.

The retinal pigment epithelium (RPE) is essential for the survival and function of retinal photoreceptor cells. RPE dysfunction causes various retinal diseases including age-related macular degeneration (AMD). Clinical studies on ES/iPS cell-derived RPE transplantation for RPE dysfunction-triggered diseases are currently underway. Quantification of the diseased RPE area is important to evaluate disease progression or the therapeutic effect of RPE transplantation. However, there are no standard protocols. To address this issue, we developed a 2-step software that enables objective and efficient quantification of RPE-disease area changes by analyzing the early-phase hyperfluorescent area in fluorescein angiography (FA) images. We extracted the Abnormal region. This extraction was based on deep learning-based discrimination. We scored the binarized extracted area using an automated program. Our program's performance for the same eye from the serial image captures was within 3.1 ± 7.8% error. In progressive AMD, the trend was consistent with human assessment, even when FA images from two different visits were compared. This method was applicable to quantifying RPE-disease area changes over time, evaluating iPSC-RPE transplantation images, and a disease other than AMD. Our program may contribute to the assessment of the clinical course of RPE-disease areas in routine clinics and reduce the workload of researchers.

Acceleration of target production in co-culture by enhancing intermediate consumption through adaptive laboratory evolution.

Co-culture is a promising way to alleviate metabolic burden by dividing the metabolic pathways into several modules and sharing the conversion processes with multiple strains. Since an intermediate is passed from the donor to the recipient via the extracellular environment, it is inevitably diluted. Therefore, enhancing the intermediate consumption rate is important for increasing target productivity. In the present study, we demonstrated the enhancement of mevalonate consumption in Escherichia coli by adaptive laboratory evolution and applied the evolved strain to isoprenol production in an E. coli (upstream: glucose to mevalonate)-E. coli (downstream: mevalonate to isoprenol) co-culture. An engineered mevalonate auxotroph strain was repeatedly sub-cultured in a synthetic medium supplemented with mevalonate, where the mevalonate concentration was decreased stepwise from 100 to 20 µM. In five parallel evolution experiments, all growth rates gradually increased, resulting in five evolved strains. Whole-genome re-sequencing and reverse engineering identified three mutations involved in enhancing mevalonate consumption. After introducing nudF gene for producing isoprenol, the isoprenol-producing parental and evolved strains were respectively co-cultured with a mevalonate-producing strain. At an inoculation ratio of 1:3 (upstream:downstream), isoprenol production using the evolved strain was 3.3 times higher than that using the parental strain.

Optimal Scheduling for Laboratory Automation of Life Science Experiments with Time Constraints.

In automated laboratories consisting of multiple different types of instruments, scheduling algorithms are useful for determining the optimal allocations of instruments to minimize the time required to complete experimental procedures. However, previous studies on scheduling algorithms for laboratory automation have not emphasized the time constraints by mutual boundaries (TCMBs) among operations, which is important in procedures involving live cells or unstable biomolecules. Here, we define the "scheduling for laboratory automation in biology" (S-LAB) problem as a scheduling problem for automated laboratories in which operations with TCMBs are performed by multiple different instruments. We formulate an S-LAB problem as a mixed-integer programming (MIP) problem and propose a scheduling method using the branch-and-bound algorithm. Simulations show that our method can find the optimal schedules of S-LAB problems that minimize overall execution time while satisfying the TCMBs. Furthermore, we propose the use of our scheduling method for the simulation-based design of job definitions and laboratory configurations.

A Variable Scheduling Maintenance Culture Platform for Mammalian Cells.

Cell culturing is a basic experimental technique in cell biology and medical science. However, culturing high-quality cells with a high degree of reproducibility relies heavily on expert skills and tacit knowledge, and it is not straightforward to scale the production process due to the education bottleneck. Although many automated culture systems have been developed and a few have succeeded in mass production environments, very few robots are permissive of frequent protocol changes, which are often required in basic research environments. LabDroid is a general-purpose humanoid robot with two arms that performs experiments using the same tools as humans. Combining our newly developed AI software with LabDroid, we developed a variable scheduling system that continuously produces subcultures of cell lines without human intervention. The system periodically observes the cells on plates with a microscope, predicts the cell growth curve by processing cell images, and decides the best times for passage. We have succeeded in developing a system that maintains the cultures of two HEK293A cell plates with no human intervention for 192 h.

High-throughput laboratory evolution reveals evolutionary constraints in Escherichia coli.

Understanding the constraints that shape the evolution of antibiotic resistance is critical for predicting and controlling drug resistance. Despite its importance, however, a systematic investigation of evolutionary constraints is lacking. Here, we perform a high-throughput laboratory evolution of Escherichia coli under the addition of 95 antibacterial chemicals and quantified the transcriptome, resistance, and genomic profiles for the evolved strains. Utilizing machine learning techniques, we analyze the phenotype-genotype data and identified low dimensional phenotypic states among the evolved strains. Further analysis reveals the underlying biological processes responsible for these distinct states, leading to the identification of trade-off relationships associated with drug resistance. We also report a decelerated evolution of ß-lactam resistance, a phenomenon experienced by certain strains under various stresses resulting in higher acquired resistance to ß-lactams compared to strains directly selected by ß-lactams. These findings bridge the genotypic, gene expression, and drug resistance gap, while contributing to a better understanding of evolutionary constraints for antibiotic resistance.

Understanding metabolic adaptation by using bacterial laboratory evolution and trans-omics analysis.

Many diseases such as metabolic syndrome, cancer, inflammatory diseases, and pathological phenomena can be understood as an adaptive reconstitution of the metabolic state (metabolic adaptation). One of the effective approaches to reveal the property of metabolic networks is using model organisms such as microorganisms that are easier to experiment with than higher organisms. Using the laboratory evolution approach, we can elucidate the evolutionary dynamics in various stress environments, which provide us an understanding of the metabolic adaptation. In addition, the integration of omics data and phenotypic data enables us to clarify the genetic and phenotypic alterations during adaptation. In this review, we describe our recent studies on bacterial laboratory evolution and the omics approach to clarify the stress adaptation process. We have also obtained high-dimensional phenotypic data using our automated culture system. By combining these genomic and transcriptomic data within high-throughput phenotypic data, we can discuss the complex trans-omics network of metabolic adaptation.

Suppression of antibiotic resistance evolution by single-gene deletion.

Antibiotic treatment generally results in the selection of resistant bacterial strains, and the dynamics of resistance evolution is dependent on complex interactions between cellular components. To better characterize the mechanisms of antibiotic resistance and evaluate its dependence on gene regulatory networks, we performed systematic laboratory evolution of Escherichia coli strains with single-gene deletions of 173 transcription factors under three different antibiotics. This resulted in the identification of several genes whose deletion significantly suppressed resistance evolution, including arcA and gutM. Analysis of double-gene deletion strains suggested that the suppression of resistance evolution caused by arcA and gutM deletion was not caused by epistatic interactions with mutations known to confer drug resistance. These results provide a methodological basis for combinatorial drug treatments that may help to suppress the emergence of resistant pathogens by inhibiting resistance evolution.

High-throughput identification of the sensitivities of an Escherichia coli ΔrecA mutant strain to various chemical compounds.

Antibiotic resistance is considered a global threat to public health. Adaptive resistance mutations and the acquisition of resistance genes by horizontal gene transfer are known to be facilitated by the RecA-dependent SOS response during antibiotic treatment, making RecA inhibitors promising agents for the prevention of antibiotic resistance. However, the impact of RecA inactivation on antibiotic sensitivities remains unclear. Therefore, in this study, we performed high-throughput screening to determine the minimum inhibitory concentrations (MICs) of 217 chemicals, including both antibiotics and toxic chemicals of unknown drug action, in the wild-type MDS42 and the ΔrecA mutant strains of Escherichia coli. The ΔrecA mutant showed increased sensitivity to DNA-damaging agents, DNA replication inhibitors, and chromate stress, as well as to other chemicals, such as S-(2-aminoethyl)-L-cysteine, L-histidine, ruthenium red, D-penicillamine, carbonyl cyanide 3-chlorophenylhydrazone (CCCP), cerulenin, and L-cysteine. Microarray analysis showed further that the ΔrecA mutant had lower expressions of glnK, nac, and glnLG, which encode nitrogen assimilation regulators, as well as amtB, which encodes an ammonium transporter, compared with the wild type. These findings suggest that the ΔrecA mutation affects not only the SOS response but also amino acid metabolism.

Understanding and engineering alcohol-tolerant bacteria using OMICS technology.

Microbes are capable of producing alcohols, making them an important source of alternative energy that can replace fossil fuels. However, these alcohols can be toxic to the microbes themselves, retaring or inhibiting cell growth and decreasing the production yield. One solution is improving the alcohol tolerance of such alcohol-producing organisms. Advances in omics technologies, including transcriptomic, proteomic, metabolomic, and genomic technologies, have helped us understand the complex mechanisms underlying alcohol toxicity, and such advances could assist in devising strategies for engineering alcohol-tolerant strains. This review highlights these advances and discusses strategies for improving alcohol tolerance using omics analyses.

Raman spectral signature reflects transcriptomic features of antibiotic resistance in Escherichia coli.

To be able to predict antibiotic resistance in bacteria from fast label-free microscopic observations would benefit a broad range of applications in the biological and biomedical fields. Here, we demonstrate the utility of label-free Raman spectroscopy in monitoring the type of resistance and the mode of action of acquired resistance in a bacterial population of Escherichia coli, in the absence of antibiotics. Our findings are reproducible. Moreover, we identified spectral regions that best predicted the modes of action and explored whether the Raman signatures could be linked to the genetic basis of acquired resistance. Spectral peak intensities significantly correlated (False Discovery Rate, p < 0.05) with the gene expression of some genes contributing to antibiotic resistance genes. These results suggest that the acquisition of antibiotic resistance leads to broad metabolic effects reflected through Raman spectral signatures and gene expression changes, hinting at a possible relation between these two layers of complementary information.

Enhancement of K-strategy evolution in histidine utilization using a container with compartments.

Evolutionary strategies in growth improvement can be classified into r- or K-strategies. The former strategy corresponds to an evolutionary increase in growth rate, whereas the latter corresponds to an increase in the maximum amount of organisms or carrying capacity. What determines the strategies to be adopted during evolution? Spatial structures that compartmentalize the population into small patches are key to inducing the K-strategy. Interestingly, previous evolution experiments using Escherichia coli in a glucose-limited batch culture showed that carrying capacity could improve evolutionally even in the absence of spatial structures. However, it is unclear if the lack of spatial structures can direct evolution toward high carrying capacity for utilization of other resources. To address this question, we established a simplified evolution experiment using histidine-requiring E. coli grown under histidine limitation in a container with compartments. We confirmed the importance of spatial structures in K-strategy evolution in histidine utilization. Whole genome sequencing of the K-adapted strains showed functional variety of the mutated genes during the fitness-increasing period. These results validate the importance of spatial structures and imply that restriction of K-strategy evolution on a sort of nutrients is attributable to a paucity of appropriate selection rather than a paucity of causal mutation.

Toward prediction and control of antibiotic-resistance evolution.

The emergence of antibiotic-resistant bacteria is a serious public concern. To deal with this problem, recent advances in technology and the use of laboratory evolution experiments have provided valuable information on the phenotypic and genotypic changes that occur during the evolution of resistance. These studies have demonstrated the existence of evolutionary constraints on the development of drug-resistance, which suggests predictability in its evolution. In this review, we focus on the possibility to predict and control the evolution of antibiotic resistance, based on quantitative analysis of phenotypic and genotypic changes observed in bacterial laboratory evolution. We emphasize the key challenges in evolutionary biology that will contribute to the development of appropriate treatment strategies for preventing resistance evolution.

Application of adaptive laboratory evolution to overcome a flux limitation in an Escherichia coli production strain.

Gene deletion strategies using flux balance analysis (FBA) have improved the growth-coupled production of various compounds. However, the productivities were often below the expectation because the cells failed to adapt to these genetic perturbations. Here, we demonstrate the productivity of the succinate of the designed gene deletion strain was improved by adaptive laboratory evolution (ALE). Although FBA predicted deletions of adhE-pykAF-gldA-pflB lead to produce succinate from glycerol with a yield of 0.45 C-mol/C-mol, the knockout mutant did not produce only 0.08 C-mol/Cmol, experimentally. After the ALE experiments, the highest succinate yield of an evolved strain reached to the expected value. Genome sequencing analysis revealed all evolved strains possessed novel mutations in ppc of I829S or R849S. In vitro enzymatic assay and metabolic profiling analysis revealed that these mutations desensitizing an allosteric inhibition by L-aspartate and improved the flux through Ppc, while the activity of Ppc in the unevolved strain was tightly regulated by L-aspartate. These result demonstrated that the evolved strains achieved the improvement of succinate production by expanding the flux space of Ppc, realizing the predicted metabolic state by FBA.

Prediction of Cross-resistance and Collateral Sensitivity by Gene Expression profiles and Genomic Mutations.

In adaptive evolution, an increase in fitness to an environment is frequently accompanied by changes in fitness to other environmental conditions, called cross-resistance and sensitivity. Although the networks between fitness changes affect the course of evolution substantially, the mechanisms underlying such fitness changes are yet to be fully elucidated. Herein, we performed high-throughput laboratory evolution of Escherichia coli under various stress conditions using an automated culture system, and quantified how the acquisition of resistance to one stressor alters the resistance to other stressors. We demonstrated that resistance changes could be quantitatively predicted based on changes in the transcriptome of the resistant strains. We also identified several genes and gene functions, for which mutations were commonly fixed in the strains resistant to the same stress, which could partially explain the observed cross-resistance and collateral sensitivity. The integration of transcriptome and genome data enabled us to clarify the bacterial stress resistance mechanisms.

Time-programmable drug dosing allows the manipulation, suppression and reversal of antibiotic drug resistance in vitro.

Multi-drug strategies have been attempted to prolong the efficacy of existing antibiotics, but with limited success. Here we show that the evolution of multi-drug-resistant Escherichia coli can be manipulated in vitro by administering pairs of antibiotics and switching between them in ON/OFF manner. Using a multiplexed cell culture system, we find that switching between certain combinations of antibiotics completely suppresses the development of resistance to one of the antibiotics. Using this data, we develop a simple deterministic model, which allows us to predict the fate of multi-drug evolution in this system. Furthermore, we are able to reverse established drug resistance based on the model prediction by modulating antibiotic selection stresses. Our results support the idea that the development of antibiotic resistance may be potentially controlled via continuous switching of drugs.

Improvement of isopropanol tolerance of Escherichia coli using adaptive laboratory evolution and omics technologies.

Isopropanol (IPA) is the secondary alcohol that can be dehydrated to yield propylene. To produce IPA using microorganisms, a significant issue is that the toxicity of IPA causes retardation or inhibition of cell growth, decreasing the yield. One possible strategy to overcome this problem is to improve IPA tolerance of production organisms. For the understanding of tolerance to IPA, we performed parallel adaptive laboratory evolution (ALE) of Escherichia coli under IPA stress. To identify the genotypic change during ALE, we performed genome re-sequencing analyses of obtained tolerant strains. To verify which mutations were contributed to IPA tolerance, we constructed the mutant strains and quantify the IPA tolerance of the constructed mutants. From these analyses, we found that five mutations (relA, marC, proQ, yfgO, and rraA) provided the increase of IPA tolerance. To understand the phenotypic change during ALE, we performed transcriptome analysis of tolerant strains. From transcriptome analysis, we found that expression levels of genes related to biosynthetic pathways of amino acids, iron ion homeostasis, and energy metabolisms were changed in the tolerant strains. Results from these experiments provide fundamental bases for designing IPA tolerant strains for industrial purposes.

Acceleration and suppression of resistance development by antibiotic combinations.

BACKGROUND: The emergence and spread of antibiotic resistance in bacteria is becoming a global public health problem. Combination therapy, i.e., the simultaneous use of multiple antibiotics, is used for long-term treatment to suppress the emergence of resistant strains. However, the effect of the combinatorial use of multiple drugs on the development of resistance remains elusive, especially in a quantitative assessment. RESULTS: To understand the evolutionary dynamics under combination therapy, we performed laboratory evolution of Escherichia coli under simultaneous addition of two-drug combinations. We demonstrated that simultaneous addition of a certain combinations of two drugs with collateral sensitivity to each other could suppress the acquisition of resistance to both drugs. Furthermore, we found that the combinatorial use of enoxacin, a DNA replication inhibitor, with Chloramphenicol can accelerate acquisition of resistance to Chloramphenicol. Genome resequencing analyses of the evolved strains suggested that the acceleration of resistance acquisition was caused by an increase of mutation frequency when enoxacin was added. CONCLUSIONS: Integration of laboratory evolution and whole-genome sequencing enabled us to characterize the development of resistance in bacteria under combination therapy. These results provide a basis for rational selection of antibiotic combinations that suppress resistance development effectively.

Expression Profiling of Antibiotic-Resistant Bacteria Obtained by Laboratory Evolution.

To elucidate the mechanisms of antibiotic resistance, integrating phenotypic and genotypic features in resistant strains is important. Here, we describe the expression profiling of antibiotic-resistant Escherichia coli strains obtained by laboratory evolution, and a method for extracting a small number of genes whose expression changes can contribute to the acquisition of resistance.