Non-genetic adaptation by collective migration.

Collective behaviors require coordination of individuals. Thus, a population must adjust its phenotypic distribution to adapt to changing environments. How can a population regulate its phenotypic distribution? One strategy is to utilize specialized networks for gene regulation and maintaining distinct phenotypic subsets. Another involves genetic mutations, which can be augmented by stress-response pathways. Here, we studied how a migrating bacterial population regulates its phenotypic distribution to traverse across diverse environments. We generated isogenic Escherichia coli populations with varying distributions of swimming behaviors and observed their phenotype distributions during migration in liquid and porous environments. Surprisingly, we found that during collective migration, the distributions of swimming phenotypes adapt to the environment without mutations or gene regulation. Instead, adaptation is caused by the dynamic and reversible enrichment of high-performing swimming phenotypes within each environment. This adaptation mechanism is supported by a recent theoretical study, which proposed that the phenotypic composition of a migrating population results from a balance between cell growth generating diversity and collective migration eliminating the phenotypes that are unable to keep up with the migrating group. Furthermore, by examining chemoreceptor abundance distributions during migration towards different attractants, we found that this mechanism acts on multiple chemotaxis-related traits simultaneously. Our findings reveal that collective migration itself can enable cell populations with continuous, multi-dimensional phenotypes to flexibly and rapidly adapt their phenotypic composition to diverse environmental conditions. Significance statement: Conventional cell adaptation mechanisms, like gene regulation and random phenotypic switching, act swiftly but are limited to a few traits, while mutation-driven adaptations unfold slowly. By quantifying phenotypic diversity during bacterial collective migration, we discovered an adaptation mechanism that rapidly and reversibly adjusts multiple traits simultaneously. By dynamically balancing the elimination of phenotypes unable to keep pace with generation of diversity through growth, this process enables populations to tune their phenotypic composition based on the environment, without the need for gene regulation or mutations. Given the prevalence of collective migration in microbes, cancers, and embryonic development, non-genetic adaptation through collective migration may be a universal mechanism for populations to navigate diverse environments, offering insights into broader applications across various fields.

Direct measurement of dynamic attractant gradients reveals breakdown of the Patlak-Keller-Segel chemotaxis model.

Chemotactic bacteria not only navigate chemical gradients, but also shape their environments by consuming and secreting attractants. Investigating how these processes influence the dynamics of bacterial populations has been challenging because of a lack of experimental methods for measuring spatial profiles of chemoattractants in real time. Here, we use a fluorescent sensor for aspartate to directly measure bacterially generated chemoattractant gradients during collective migration. Our measurements show that the standard Patlak-Keller-Segel model for collective chemotactic bacterial migration breaks down at high cell densities. To address this, we propose modifications to the model that consider the impact of cell density on bacterial chemotaxis and attractant consumption. With these changes, the model explains our experimental data across all cell densities, offering insight into chemotactic dynamics. Our findings highlight the significance of considering cell density effects on bacterial behavior, and the potential for fluorescent metabolite sensors to shed light on the complex emergent dynamics of bacterial communities.

Life-history traits of a tropical bagrid catfish, Mystus mysticetus Roberts, 1992, caught from the Mekong Delta, Vietnam.

Population's biological parameters, including length at first capture, mortalities, exploitation rates, growth coefficient, longevity, and recruitment times, are essential in assessing fishing status, but there is no data on Mystus mysticetus. Therefore, the study was conducted to provide these parameters to assess the fishing status of this species at Cai Rang, Can Tho (CRCT) and Long Phu, Soc Trang (LPST). A collection of 741 individual fish was used for analysis and showed that most fish size groups ranged from 9.0 cm to 12.0 cm, and the asymptotic length was 16.8 cm for both CRCT and LPST populations. The fish population von Bertalanffy curve was L t = 16.80(1 - e-0.51(t + 0.38)) at CRCT and L t = 16.80(1 - e-0.48(t + 0.40)) at LPST. The fish growth coefficient at CRCT (2.16) was higher than at LPST (2.13), whereas the reverse case was true for longevity ranging from 5.88 years (at CRCT) to 6.25 years (at LPST). At CRCT, fishing mortality, natural mortality, total mortality, and exploitation rate were 0.69/year, 1.40/year, 2.09/year, and 0.33, respectively; at LPST, these values were 0.75/year, 1.33/year, 2.08/year, and 0.36, respectively. Although the population parameter of this fish species exhibited a spatial variation, both CRCT and LPST fish resources have not been subjected to overexploit because E (0.33 at CRCT and 0.36 at LPST) is lower than E 0.1 (0.707 at CRCT and 0.616 at LPST).

Direct measurement of dynamic attractant gradients reveals breakdown of the Patlak-Keller-Segel chemotaxis model.

Chemotactic bacteria not only navigate chemical gradients, but also shape their environments by consuming and secreting attractants. Investigating how these processes influence the dynamics of bacterial populations has been challenging because of a lack of experimental methods for measuring spatial profiles of chemoattractants in real time. Here, we use a fluorescent sensor for aspartate to directly measure bacterially generated chemoattractant gradients during collective migration. Our measurements show that the standard Patlak-Keller-Segel model for collective chemotactic bacterial migration breaks down at high cell densities. To address this, we propose modifications to the model that consider the impact of cell density on bacterial chemotaxis and attractant consumption. With these changes, the model explains our experimental data across all cell densities, offering new insight into chemotactic dynamics. Our findings highlight the significance of considering cell density effects on bacterial behavior, and the potential for fluorescent metabolite sensors to shed light on the complex emergent dynamics of bacterial communities.

FliL and its paralog MotF have distinct roles in the stator activity of the Sinorhizobium meliloti flagellar motor.

The bacterial flagellum is a complex macromolecular machine that drives bacteria through diverse fluid environments. Although many components of the flagellar motor are conserved across species, the roles of FliL are numerous and species-specific. Here, we have characterized an additional player required for flagellar motor function in Sinorhizobium meliloti, MotF, which we have identified as a FliL paralog. We performed a comparative analysis of MotF and FliL, identified interaction partners through bacterial two-hybrid and pull-down assays, and investigated their roles in motility and motor rotation. Both proteins form homooligomers, and interact with each other, and with the stator proteins MotA and MotB. The ∆motF mutant exhibits normal flagellation but its swimming behavior and flagellar motor activity are severely impaired and erratic. In contrast, the ∆fliL mutant is mostly aflagellate and nonmotile. Amino acid substitutions in cytoplasmic regions of MotA or disruption of the proton channel plug of MotB partially restored motor activity to the ∆motF but not the ∆fliL mutant. Altogether, our findings indicate that both, MotF and FliL, are essential for flagellar motor torque generation in S. meliloti. FliL may serve as a scaffold for stator integration into the motor, and MotF is required for proton channel modulation.

ECNet is an evolutionary context-integrated deep learning framework for protein engineering.

Machine learning has been increasingly used for protein engineering. However, because the general sequence contexts they capture are not specific to the protein being engineered, the accuracy of existing machine learning algorithms is rather limited. Here, we report ECNet (evolutionary context-integrated neural network), a deep-learning algorithm that exploits evolutionary contexts to predict functional fitness for protein engineering. This algorithm integrates local evolutionary context from homologous sequences that explicitly model residue-residue epistasis for the protein of interest with the global evolutionary context that encodes rich semantic and structural features from the enormous protein sequence universe. As such, it enables accurate mapping from sequence to function and provides generalization from low-order mutants to higher-order mutants. We show that ECNet predicts the sequence-function relationship more accurately as compared to existing machine learning algorithms by using ~50 deep mutational scanning and random mutagenesis datasets. Moreover, we used ECNet to guide the engineering of TEM-1 ß-lactamase and identified variants with improved ampicillin resistance with high success rates.

Multiple CheY Proteins Control Surface-Associated Lifestyles of Azospirillum brasilense.

Bacterial chemotaxis is the directed movement of motile bacteria in gradients of chemoeffectors. This behavior is mediated by dedicated signal transduction pathways that couple environment sensing with changes in the direction of rotation of flagellar motors to ultimately affect the motility pattern. Azospirillum brasilense uses two distinct chemotaxis pathways, named Che1 and Che4, and four different response regulators (CheY1, CheY4, CheY6, and CheY7) to control the swimming pattern during chemotaxis. Each of the CheY homologs was shown to differentially affect the rotational bias of the polar flagellum and chemotaxis. The role, if any, of these CheY homologs in swarming, which depends on a distinct lateral flagella system or in attachment is not known. Here, we characterize CheY homologs' roles in swimming, swarming, and attachment to abiotic and biotic (wheat roots) surfaces and biofilm formation. We show that while strains lacking CheY1 and CheY6 are still able to navigate air gradients, strains lacking CheY4 and CheY7 are chemotaxis null. Expansion of swarming colonies in the presence of gradients requires chemotaxis. The induction of swarming depends on CheY4 and CheY7, but the cells' organization as dense clusters in productive swarms appear to depend on functional CheYs but not chemotaxis per se. Similarly, functional CheY homologs but not chemotaxis, contribute to attachment to both abiotic and root surfaces as well as to biofilm formation, although these effects are likely dependent on additional cell surface properties such as adhesiveness. Collectively, our data highlight distinct roles for multiple CheY homologs and for chemotaxis on swarming and attachment to surfaces.

The Azospirillum brasilense Core Chemotaxis Proteins CheA1 and CheA4 Link Chemotaxis Signaling with Nitrogen Metabolism.

Bacterial chemotaxis affords motile bacteria the ability to navigate the environment to locate niches for growth and survival. At the molecular level, chemotaxis depends on chemoreceptor signaling arrays that interact with cytoplasmic proteins to control the direction of movement. In Azospirillum brasilense, chemotaxis is mediated by two distinct chemotaxis pathways: Che1 and Che4. Both Che1 and Che4 are critical in the A. brasilense free-living and plant-associated lifestyles. Here, we use whole-cell proteomics and metabolomics to characterize the role of chemotaxis in A. brasilense physiology. We found that mutants lacking CheA1 or CheA4 or both are affected in nonchemotaxis functions, including major changes in transcription, signaling transport, and cell metabolism. We identify specific effects of CheA1 and CheA4 on nitrogen metabolism, including nitrate assimilation and nitrogen fixation, that may depend, at least, on the transcriptional control of rpoN, which encodes RpoN, a global regulator of metabolism, including nitrogen. Consistent with proteomics, the abundance of several nitrogenous compounds (purines, pyrimidines, and amino acids) changed in the metabolomes of the chemotaxis mutants relative to the parental strain. Further, we uncover novel, and yet uncharacterized, layers of transcriptional and posttranscriptional control of nitrogen metabolism regulators. Together, our data reveal roles for CheA1 and CheA4 in linking chemotaxis and nitrogen metabolism, likely through control of global regulatory networks.IMPORTANCE Bacterial chemotaxis is widespread in bacteria, increasing competitiveness in diverse environments and mediating associations with eukaryotic hosts ranging from commensal to beneficial and pathogenic. In most bacteria, chemotaxis signaling is tightly linked to energy metabolism, with this coupling occurring through the sensory input of several energy-sensing chemoreceptors. Here, we show that in A. brasilense the chemotaxis proteins have key roles in modulating nitrogen metabolism, including nitrate assimilation and nitrogen fixation, through novel and yet unknown regulations. These results are significant given that A. brasilense is a model bacterium for plant growth promotion and free-living nitrogen fixation and is used as a bio-inoculant for cereal crops. Chemotaxis signaling in A. brasilense thus links locomotor behaviors to nitrogen metabolism, allowing cells to continuously and reciprocally adjust metabolism and chemotaxis signaling as they navigate gradients.

Biosystems Design by Machine Learning.

Biosystems such as enzymes, pathways, and whole cells have been increasingly explored for biotechnological applications. However, the intricate connectivity and resulting complexity of biosystems poses a major hurdle in designing biosystems with desirable features. As -omics and other high throughput technologies have been rapidly developed, the promise of applying machine learning (ML) techniques in biosystems design has started to become a reality. ML models enable the identification of patterns within complicated biological data across multiple scales of analysis and can augment biosystems design applications by predicting new candidates for optimized performance. ML is being used at every stage of biosystems design to help find nonobvious engineering solutions with fewer design iterations. In this review, we first describe commonly used models and modeling paradigms within ML. We then discuss some applications of these models that have already shown success in biotechnological applications. Moreover, we discuss successful applications at all scales of biosystems design, including nucleic acids, genetic circuits, proteins, pathways, genomes, and bioprocesses. Finally, we discuss some limitations of these methods and potential solutions as well as prospects of the combination of ML and biosystems design.

Specific Root Exudate Compounds Sensed by Dedicated Chemoreceptors Shape Azospirillum brasilense Chemotaxis in the Rhizosphere.

Plant roots shape the rhizosphere community by secreting compounds that recruit diverse bacteria. Colonization of various plant roots by the motile alphaproteobacterium Azospirillum brasilense causes increased plant growth, root volume, and crop yield. Bacterial chemotaxis in this and other motile soil bacteria is critical for competitive colonization of the root surfaces. The role of chemotaxis in root surface colonization has previously been established by endpoint analyses of bacterial colonization levels detected a few hours to days after inoculation. More recently, microfluidic devices have been used to study plant-microbe interactions, but these devices are size limited. Here, we use a novel slide-in chamber that allows real-time monitoring of plant-microbe interactions using agriculturally relevant seedlings to characterize how bacterial chemotaxis mediates plant root surface colonization during the association of A. brasilense with Triticum aestivum (wheat) and Medicago sativa (alfalfa) seedlings. We track A. brasilense accumulation in the rhizosphere and on the root surfaces of wheat and alfalfa. A. brasilense motile cells display distinct chemotaxis behaviors in different regions of the roots, including attractant and repellent responses that ultimately drive surface colonization patterns. We also combine these observations with real-time analyses of behaviors of wild-type and mutant strains to link chemotaxis responses to distinct chemicals identified in root exudates to specific chemoreceptors that together explain the chemotactic response of motile cells in different regions of the roots. Furthermore, the bacterial second messenger c-di-GMP modulates these chemotaxis responses. Together, these findings illustrate dynamic bacterial chemotaxis responses to rhizosphere gradients that guide root surface colonization.IMPORTANCE Plant root exudates play critical roles in shaping rhizosphere microbial communities, and the ability of motile bacteria to respond to these gradients mediates competitive colonization of root surfaces. Root exudates are complex chemical mixtures that are spatially and temporally dynamic. Identifying the exact chemical(s) that mediates the recruitment of soil bacteria to specific regions of the roots is thus challenging. Here, we connect patterns of bacterial chemotaxis responses and sensing by chemoreceptors to chemicals found in root exudate gradients and identify key chemical signals that shape root surface colonization in different plants and regions of the roots.

Biosurfactant-Mediated Membrane Depolarization Maintains Viability during Oxygen Depletion in Bacillus subtilis.

The presence or absence of oxygen in the environment is a strong effector of cellular metabolism and physiology. Like many eukaryotes and some bacteria, Bacillus subtilis primarily utilizes oxygen during respiration to generate ATP. Despite the importance of oxygen for B. subtilis survival, we know little about how populations adapt to shifts in oxygen availability. Here, we find that when oxygen was depleted from stationary phase B. subtilis cultures, ∼90% of cells died while the remaining cells maintained colony-forming ability. We discover that production of the antimicrobial surfactin confers two oxygen-related fitness benefits: it increases aerobic growth yield by increasing oxygen diffusion, and it maintains viability during oxygen depletion by depolarizing the membrane. Strains unable to produce surfactin exhibited an ∼50-fold reduction in viability after oxygen depletion. Surfactin treatment of these cells led to membrane depolarization and reduced ATP production. Chemical and genetic perturbations that alter oxygen consumption or redox state support a model in which surfactin-mediated membrane depolarization maintains viability through slower oxygen consumption and/or a shift to a more reduced metabolic profile. These findings highlight the importance of membrane potential in regulating cell physiology and growth, and demonstrate that antimicrobials that depolarize cell membranes can benefit cells when the terminal electron acceptor in respiration is limiting. This foundational knowledge has deep implications for environmental microbiology, clinical anti-bacterial therapy, and industrial biotechnology.

Multiple CheY Homologs Control Swimming Reversals and Transient Pauses in Azospirillum brasilense.

Chemotaxis, together with motility, helps bacteria foraging in their habitat. Motile bacteria exhibit a variety of motility patterns, often controlled by chemotaxis, to promote dispersal. Motility in many bacteria is powered by a bidirectional flagellar motor. The flagellar motor has been known to briefly pause during rotation because of incomplete reversals or stator detachment. Transient pauses were previously observed in bacterial strains lacking CheY, and these events could not be explained by incomplete motor reversals or stator detachment. Here, we systematically analyzed swimming trajectories of various chemotaxis mutants of the monotrichous soil bacterium, Azospirillum brasilense. Like other polar flagellated bacterium, the main swimming pattern in A. brasilense is run and reverse. A. brasilense also uses run-pauses and putative run-reverse-flick-like swimming patterns, although these are rare events. A. brasilense mutant derivatives lacking the chemotaxis master histidine kinase, CheA4, or the central response regulator, CheY7, also showed transient pauses. Strikingly, the frequency of transient pauses increased dramatically in the absence of CheY4. Our findings collectively suggest that reversals and pauses are controlled through signaling by distinct CheY homologs, and thus are likely to be functionally important in the lifestyle of this soil organism.