Function of Proline Residues of MotA in Torque Generation by the Flagellar Motor of Escherichia coli

Bacterial flagellar motors obtain energy for rotation from the membrane gradient of protons or, in some species, sodium ions. The molecular mechanism of flagellar rotation is not understood. MotA and MotB are integral membrane proteins that function in proton conduction and are believed to form the stator of the motor. Previous mutational studies identified two conserved proline residues in MotA (Pro 173 and Pro 222 in the protein from Escherichia coli) and a conserved aspartic acid residue in MotB (Asp 32) that are important for function. Asp 32 of MotB probably forms part of the proton path through the motor. To learn more about the roles of the conserved proline residues of MotA, we examined motor function in Pro 173 and Pro 222 mutants, making measurements of torque at high load, speed at low and intermediate loads, and solvent-isotope effects (D2O versus H2O). Proton conduction by wild-type and mutant MotA-MotB channels was also assayed, by a growth defect that occurs upon overexpression. Several different mutations of Pro 173 reduced the torque of the motor under high load, and a few prevented motor rotation but still allowed proton flow through the MotA-MotB channels. These and other properties of the mutants suggest that Pro 173 has a pivotal role in coupling proton flow to motor rotation and is positioned in the channel near Asp 32 of MotB. Replacements of Pro 222 abolished function in all assays and were strongly dominant. Certain Pro 222 mutant proteins prevented swimming almost completely when expressed at moderate levels in wild-type cells. This dominance might be caused by rotor-stator jamming, because it was weaker when FliG carried a mutation believed to increase rotor-stator clearance. We propose a mechanism for torque generation, in which specific functions are suggested for the proline residues of MotA and Asp32 of MotB.     

A field guide to cultivating computational biology

Evolving in sync with the computation revolution over the past 30 years, computational biology has emerged as a mature scientific field. While the field has made major contributions toward improving scientific knowledge and human health, individual computational biology practitioners at various institutions often languish in career development. As optimistic biologists passionate about the future of our field, we propose solutions for both eager and reluctant individual scientists, institutions, publishers, funding agencies, and educators to fully embrace computational biology. We believe that in order to pave the way for the next generation of discoveries, we need to improve recognition for computational biologists and better align pathways of career success with pathways of scientific progress. With 10 outlined steps, we call on all adjacent fields to move away from the traditional individual, single-discipline investigator research model and embrace multidisciplinary, data-driven, team science.

Do you want to attract computational biologists to your project or to your department? Despite the major contributions of computational biology, those attempting to bridge the interdisciplinary gap often languish in career advancement, publication, and grant review. Here, sixteen computational biologists around the globe present "A field guide to cultivating computational biology," focusing on solutions.

Biology in the digital era requires computation and collaboration. A modern research project may include multiple model systems, use multiple assay technologies, collect varying data types, and require complex computational strategies, which together make effective design and execution difficult or impossible for any individual scientist. While some labs, institutions, funding bodies, publishers, and other educators have already embraced a team science model in computational biology and thrived [1–7], others who have not yet fully adopted it risk severely lagging behind the cutting edge. We propose a general solution: “deep integration” between biology and the computational sciences. Many different collaborative models can yield deep integration, and different problems require different approaches (Fig 1).Open in a separate windowFig 1Supporting interdisciplinary team science will accelerate biological discoveries.Scientists who have little exposure to different fields build silos, in which they perform science without external input. To solve hard problems and to extend your impact, collaborate with diverse scientists, communicate effectively, recognize the importance of core facilities, and embrace research parasitism. In biologically focused parasitism, wet lab biologists use existing computational tools to solve problems; in computationally focused parasitism, primarily dry lab biologists analyze publicly available data. Both strategies maximize the use and societal benefit of scientific data.In this article, we define computational science extremely broadly to include all quantitative approaches such as computer science, statistics, machine learning, and mathematics. We also define biology broadly, including any scientific inquiry pertaining to life and its many complications. A harmonious deep integration between biology and computer science requires action—we outline 10 immediate calls to action in this article and aim our speech directly at individual scientists, institutions, funding agencies, and publishers in an attempt to shift perspectives and enable action toward accepting and embracing computational biology as a mature, necessary, and inevitable discipline (Box 1).Box 1. Ten calls to action for individual scientists, funding bodies, publishers, and institutions to cultivate computational biology. Many actions require increased funding support, while others require a perspective shift. For those actions that require funding, we believe convincing the community of need is the first step toward agencies and systems allocating sufficient support

1. Respect collaborators’ specific research interests and motivationsProblem: Researchers face conflicts when their goals do not align with collaborators. For example, projects with routine analyses provide little benefit for computational biologists.Solution: Explicit discussion about interests/expertise/goals at project onset.Opportunity: Clearly defined expectations identify gaps, provide commitment to mutual benefit.
2. Seek necessary input during project design and throughout the project life cycleProblem: Modern research projects require multiple experts spanning the project’s complexity.Solution: Engage complementary scientists with necessary expertise throughout the entire project life cycle.Opportunity: Better designed and controlled studies with higher likelihood for success.
3. Provide and preserve budgets for computational biologists’ workProblem: The perception that analysis is “free” leads to collaborator budget cuts.Solution: When budget cuts are necessary, ensure that they are spread evenly.Opportunity: More accurate, reproducible, and trustworthy computational analyses.
4. Downplay publication author order as an evaluation metric for computational biologistsProblem: Computational biologist roles on publications are poorly understood and undervalued.Solution: Journals provide more equitable opportunities, funding bodies and institutions improve understanding of the importance of team science, scientists educate each other.Opportunity: Engage more computational biologist collaborators, provide opportunities for more high-impact work.
5. Value software as an academic productProblem: Software is relatively undervalued and can end up poorly maintained and supported, wasting the time put into its creation.Solution: Scientists cite software, and funding bodies provide more software funding opportunities.Opportunity: More high-quality maintainable biology software will save time, reduce reimplementation, and increase analysis reproducibility.
6. Establish academic structures and review panels that specifically reward team scienceProblem: Current mechanisms do not consistently reward multidisciplinary work.Solution: Separate evaluation structures to better align peer review to reward indicators of team science.Opportunity: More collaboration to attack complex multidisciplinary problems.
7. Develop and reward cross-disciplinary training and mentoringProblem: Academic labs and institutions are often insufficiently equipped to provide training to tackle the next generation of biological problems, which require computational skills.Solution: Create better training programs aligned to necessary on-the-job skills with an emphasis on communication, encourage wet/dry co-mentorship, and engage younger students to pursue computational biology.Opportunity: Interdisciplinary students uncover important insights in their own data.
8. Support computing and experimental infrastructure to empower computational biologistsProblem: Individual computational labs often fund suboptimal cluster computing systems and lack access to data generation facilities.Solution: Institutions can support centralized compute and engage core facilities to provide data services.Opportunity: Time and cost savings for often overlooked administrative tasks.
9. Provide incentives and mechanisms to share open data to empower discovery through reanalysisProblem: Data are often siloed and have untapped potential.Solution: Provide institutional data storage with standardized identifiers and provide separate funding mechanisms and publishing venues for data reuse.Opportunity: Foster new breed of researchers, “research parasites,” who will integrate multimodal data and enhance mechanistic insights.
10. Consider infrastructural, ethical, and cultural barriers to clinical data accessProblem: Identifiable health data, which include sensitive information that must be kept hidden, are distributed and disorganized, and thus underutilized.Solution: Leadership must enforce policies to share deidentifiable data with interoperable metadata identifiers.Opportunity: Derive new insights from multimodal data integration and build datasets with increased power to make biological discoveries.

     

Species traits and channel architecture mediate flow disturbance impacts on invertebrate drift

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An Assessment Framework for Optimal FMS Effectiveness

Equipment failures in an FMS are significant to performance and can lead to costly, incorrect decisions. Fortunately, effectiveness measurement techniques can be mapped to clever modeling frameworks to help predict, track, and then improve upon the FMS performability or mission effectiveness, and improve maintenance. This article provides sources and guidelines for efficient and effective FMS modeling, a framework for applying the modeling to predict the impact on customers from their point of view, and a method for tying it all together for improving the FMS effectiveness. It is not enough to simply examine the working and failed states of an FMS or even to calculate common reliability metrics. It is necessary to consider the FMS as a whole, and that system includes the needs of the customer and the business. It is also necessary to be purposeful about the measures of performance selected and to support the measures of effectiveness. In this article, we present: a framework for considering customer needs in the measures of effectiveness for FMS; modeling approaches for solving for effectiveness measures; and an example to show how to apply it to an FMS, to improve it or plan for meeting specific customer needs.     

An E2–F12 complex is required for intracellular enveloped virus morphogenesis during vaccinia infection

The vaccinia virus protein, F12, has been suggested to play an important role in microtubule-based transport of intracellular enveloped virus (IEV). We found that GFP-F12 is recruited to IEV moving on microtubules but is released from virus particles when they switch to actin-based motility. In the absence of F12, although the majority of IEV remain close to their peri-nuclear site of assembly, a small number of IEV still move with linear trajectories at speeds of 0.85 μm s⁻¹, consistent with microtubule transport. Using a recombinant virus expressing GST-F12, we found that the viral protein E2 interacts directly with F12. In infected cells, GFP-E2 is observed on moving IEV as well as in the Golgi region, but is not associated with actin tails. In the absence of E2L, IEV accumulate in the peri-nuclear region and F12 is not recruited. Conversely, GFP-E2 is not observed on IEV in the absence of F12. Ultra-structural analysis of ΔE2L- and ΔF12L-infected cells reveals that loss of either protein results in defects in membrane wrapping during IEV formation. We suggest that E2 and F12 function as a complex that is necessary for IEV morphogenesis prior to their microtubule-based transport towards the plasma membrane.     

F11-Mediated Inhibition of RhoA Signalling Enhances the Spread of Vaccinia Virus In Vitro and In Vivo in an Intranasal Mouse Model of Infection

The cortical actin cytoskeleton beneath the plasma membrane represents a physical barrier that vaccinia virus has to overcome during its exit from an infected cell. Previous observations using overexpression and pharmacological approaches suggest that vaccinia enhances its release by modulating the cortical actin cytoskeleton by inhibiting RhoA signalling using the viral protein F11. We have now examined the role of F11 and its ability to interact with RhoA to inhibit its downstream signalling in the spread of vaccinia infection both in vitro and in vivo. Live cell imaging over 48 hours reveals that loss of F11 or its ability to bind RhoA dramatically reduces the rate of cell-to-cell spread of the virus in a cell monolayer. Cells infected with the ΔF11L virus also maintained their cell-to-cell contacts, and did not undergo virus-induced motility as observed during wild-type infections. The ΔF11L virus is also attenuated in intranasal mouse models of infection, as it is impaired in its ability to spread from the initial sites of infection to the lungs and spleen. Loss of the ability of F11 to bind RhoA also reduces viral spread in vivo. Our results clearly establish that viral-mediated inibition of RhoA signalling can enhance the spread of infection not only in cell monolayers, but also in vivo.     

The induction of preterm labor in rhesus macaques is determined by the strength of immune response to intrauterine infection

Intrauterine infection/inflammation (IUI) is a major contributor to preterm labor (PTL). However, IUI does not invariably cause PTL. We hypothesized that quantitative and qualitative differences in immune response exist in subjects with or without PTL. To define the triggers for PTL, we developed rhesus macaque models of IUI driven by lipopolysaccharide (LPS) or live Escherichia coli. PTL did not occur in LPS challenged rhesus macaques, while E. coli–infected animals frequently delivered preterm. Although LPS and live E. coli both caused immune cell infiltration, E. coli–infected animals showed higher levels of inflammatory mediators, particularly interleukin 6 (IL-6) and prostaglandins, in the chorioamnion-decidua and amniotic fluid (AF). Neutrophil infiltration in the chorio-decidua was a common feature to both LPS and E. coli. However, neutrophilic infiltration and IL6 and PTGS2 expression in the amnion was specifically induced by live E. coli. RNA sequencing (RNA-seq) analysis of fetal membranes revealed that specific pathways involved in augmentation of inflammation including type I interferon (IFN) response, chemotaxis, sumoylation, and iron homeostasis were up-regulated in the E. coli group compared to the LPS group. Our data suggest that the intensity of the host immune response to IUI may determine susceptibility to PTL.     

Enhanced isoprene-related tolerance of heat- and light-stressed photosynthesis at low, but not high, CO2 concentrations

The principal function of isoprene biosynthesis in plants remains unclear, but emission rates are positively correlated with temperature and light, supporting a role for isoprene in maintaining photosynthesis under transient heat and light stress from sunflecks. Isoprene production is also inversely correlated with CO(2) concentrations, implying that rising CO(2) may reduce the functional importance of isoprene. To understand the importance of isoprene in maintaining photosynthesis during sunflecks, we used RNAi technology to suppress isoprene production in poplar seedlings and compared the responses of these transgenic plants to wild-type and empty-vector control plants. We grew isoprene-emitting and non-emitting trees at low (190 ppm) and high (590 ppm) CO(2) concentrations and compared their photosynthetic responses to short, transient periods of high light and temperature, as well as their photosynthetic thermal response at constant light. While there was little difference between emitting and non-emitting plants in their photosynthetic responses to simulated sunflecks at high CO(2), isoprene-emitting trees grown at low CO(2) had significantly greater photosynthetic sunfleck tolerance than non-emitting plants. Net photosynthesis at 42°C was 50% lower in non-emitters than in isoprene-emitting trees at low CO(2), but only 22% lower at high CO(2). Dark respiration rates were significantly higher in non-emitting poplar from low CO(2), but there was no difference between isoprene-emitting and non-emitting lines at high CO(2). We propose that isoprene biosynthesis may have evolved at low CO(2) concentrations, where its physiological effect is greatest, and that rising CO(2) will reduce the functional benefit of isoprene in the near future.