Two transcriptional cascades orchestrate cockroach leg regeneration.

The mystery of appendage regeneration has fascinated humans for centuries, while the underlying regulatory mechanisms remain unclear. In this study, we establish a transcriptional landscape of regenerating leg in the American cockroach, Periplaneta americana, an ideal model in appendage regeneration studies showing remarkable regeneration capacity. Through a large-scale in vivo screening, we identify multiple signaling pathways and transcription factors controlling leg regeneration. Specifically, zfh-2 and bowl contribute to blastema cell proliferation and morphogenesis in two transcriptional cascades: bone morphogenetic protein (BMP)/JAK-STAT-zfh-2-B-H2 and Notch-drm/bowl-bab1. Notably, we find zfh-2 is working as a direct target of BMP signaling to promote cell proliferation in the blastema. These mechanisms might be conserved in the appendage regeneration of vertebrates from an evolutionary perspective. Overall, our findings reveal that two crucial transcriptional cascades orchestrate distinct cockroach leg regeneration processes, significantly advancing the comprehension of molecular mechanism in appendage regeneration.

Blind assessment of monomeric AlphaFold2 protein structure models with experimental NMR data.

Recent advances in molecular modeling of protein structures are changing the field of structural biology. AlphaFold-2 (AF2), an AI system developed by DeepMind, Inc., utilizes attention-based deep learning to predict models of protein structures with high accuracy relative to structures determined by X-ray crystallography and cryo-electron microscopy (cryoEM). Comparing AF2 models to structures determined using solution NMR data, both high similarities and distinct differences have been observed. Since AF2 was trained on X-ray crystal and cryoEM structures, we assessed how accurately AF2 can model small, monomeric, solution protein NMR structures which (i) were not used in the AF2 training data set, and (ii) did not have homologous structures in the Protein Data Bank at the time of AF2 training. We identified nine open-source protein NMR data sets for such "blind" targets, including chemical shift, raw NMR FID data, NOESY peak lists, and (for 1 case) 15N-1H residual dipolar coupling data. For these nine small (70-108 residues) monomeric proteins, we generated AF2 prediction models and assessed how well these models fit to these experimental NMR data, using several well-established NMR structure validation tools. In most of these cases, the AF2 models fit the NMR data nearly as well, or sometimes better than, the corresponding NMR structure models previously deposited in the Protein Data Bank. These results provide benchmark NMR data for assessing new NMR data analysis and protein structure prediction methods. They also document the potential for using AF2 as a guiding tool in protein NMR data analysis, and more generally for hypothesis generation in structural biology research.

Blind Assessment of Monomeric AlphaFold2 Protein Structure Models with Experimental NMR Data.

Recent advances in molecular modeling of protein structures are changing the field of structural biology. AlphaFold-2 (AF2), an AI system developed by DeepMind, Inc., utilizes attention-based deep learning to predict models of protein structures with high accuracy relative to structures determined by X-ray crystallography and cryo-electron microscopy (cryoEM). Comparing AF2 models to structures determined using solution NMR data, both high similarities and distinct differences have been observed. Since AF2 was trained on X-ray crystal and cryoEM structures, we assessed how accurately AF2 can model small, monomeric, solution protein NMR structures which (i) were not used in the AF2 training data set, and (ii) did not have homologous structures in the Protein Data Bank at the time of AF2 training. We identified nine open source protein NMR data sets for such "blind" targets, including chemical shift, raw NMR FID data, NOESY peak lists, and (for 1 case) 15 N- 1 H residual dipolar coupling data. For these nine small (70 - 108 residues) monomeric proteins, we generated AF2 prediction models and assessed how well these models fit to these experimental NMR data, using several well-established NMR structure validation tools. In most of these cases, the AF2 models fit the NMR data nearly as well, or sometimes better than, the corresponding NMR structure models previously deposited in the Protein Data Bank. These results provide benchmark NMR data for assessing new NMR data analysis and protein structure prediction methods. They also document the potential for using AF2 as a guiding tool in protein NMR data analysis, and more generally for hypothesis generation in structural biology research. Highlights: AF2 models assessed against NMR data for 9 monomeric proteins not used in training.AF2 models fit NMR data almost as well as the experimentally-determined structures. RPF-DP, PSVS , and PDBStat software provide structure quality and RDC assessment. RPF-DP analysis using AF2 models suggests multiple conformational states.

Development of a Bayesian inference model for assessing ventilation condition based on CO2 meters in primary schools.

Outdoor fresh air ventilation plays a significant role in reducing airborne transmission of diseases in indoor spaces. School classrooms are considerably challenged during the COVID-19 pandemic because of the increasing need for in-person education, untimely and incompleted vaccinations, high occupancy density, and uncertain ventilation conditions. Many schools started to use CO2 meters to indicate air quality, but how to interpret the data remains unclear. Many uncertainties are also involved, including manual readings, student numbers and schedules, uncertain CO2 generation rates, and variable indoor and ambient conditions. This study proposed a Bayesian inference approach with sensitivity analysis to understand CO2 readings in four primary schools by identifying uncertainties and calibrating key parameters. The outdoor ventilation rate, CO2 generation rate, and occupancy level were identified as the top sensitive parameters for indoor CO2 levels. The occupancy schedule becomes critical when the CO2 data are limited, whereas a 15-min measurement interval could capture dynamic CO2 profiles well even without the occupancy information. Hourly CO2 recording should be avoided because it failed to capture peak values and overestimated the ventilation rates. For the four primary school rooms, the calibrated ventilation rate with a 95% confidence level for fall condition is 1.96±0.31 ACH for Room #1 (165 m3 and 20 occupancies) with mechanical ventilation, and for the rest of the naturally ventilated rooms, it is 0.40±0.08 ACH for Room #2 (236 m3 and 21 occupancies), 0.30±0.04 or 0.79±0.06 ACH depending on occupancy schedules for Room #3 (236 m3 and 19 occupancies), 0.40±0.32,0.48±0.37,0.72±0.39 ACH for Room #4 (231 m3 and 8-9 occupancies) for three consecutive days.

DIY liquid handling robots for integrated STEM education and life science research.

Automation has played a key role in improving the safety, accuracy, and efficiency of manufacturing and industrial processes and has the potential to greatly increase throughput in the life sciences. However, the lack of accessible entry-point automation hardware in life science research and STEM education hinders its widespread adoption and development for life science applications. Here we investigate the design of a low-cost (~$150) open-source DIY Arduino-controlled liquid handling robot (LHR) featuring plastic laser-cut parts. The robot moves in three axes with 0.5 mm accuracy and reliably dispenses liquid down to 20 µL. The open source, modular design allows for flexibility and easy modification. A block-based programming interface (Snap4Arduino) further extends the accessibility of this robot, encouraging adaptation and use by educators, hobbyists and beginner programmers. This robot was co-designed with teachers, and we detail the teachers' feedback in the context of a qualitative study. We conclude that affordable and accessible LHRs similar to this one could provide a useful educational tool to be deployed in classrooms, and LHR-based curricula may encourage interest in STEM and effectively introduce automation technology to life science enthusiasts.

SpecDB: A relational database for archiving biomolecular NMR spectral data.

NMR is a valuable experimental tool in the structural biologist's toolkit to elucidate the structures, functions, and motions of biomolecules. The progress of machine learning, particularly in structural biology, reveals the critical importance of large, diverse, and reliable datasets in developing new methods and understanding in structural biology and science more broadly. Biomolecular NMR research groups produce large amounts of data, and there is renewed interest in organizing these data to train new, sophisticated machine learning architectures and to improve biomolecular NMR analysis pipelines. The foundational data type in NMR is the free-induction decay (FID). There are opportunities to build sophisticated machine learning methods to tackle long-standing problems in NMR data processing, resonance assignment, dynamics analysis, and structure determination using NMR FIDs. Our goal in this study is to provide a lightweight, broadly available tool for archiving FID data as it is generated at the spectrometer, and grow a new resource of FID data and associated metadata. This study presents a relational schema for storing and organizing the metadata items that describe an NMR sample and FID data, which we call Spectral Database (SpecDB). SpecDB is implemented in SQLite and includes a Python software library providing a command-line application to create, organize, query, backup, share, and maintain the database. This set of software tools and database schema allow users to store, organize, share, and learn from NMR time domain data. SpecDB is freely available under an open source license at https://github.rpi.edu/RPIBioinformatics/SpecDB.

Scale-free vertical tracking microscopy.

The behavior and microscale processes associated with freely suspended organisms, along with sinking particles underlie key ecological processes in the ocean. Mechanistically studying such multiscale processes in the laboratory presents a considerable challenge for microscopy: how to measure single cells at microscale resolution, while allowing them to freely move hundreds of meters in the vertical direction? Here we present a solution in the form of a scale-free, vertical tracking microscope, based on a 'hydrodynamic treadmill' with no bounds for motion along the axis of gravity. Using this method to bridge spatial scales, we assembled a multiscale behavioral dataset of nonadherent planktonic cells and organisms. Furthermore, we demonstrate a 'virtual-reality system for single cells', wherein cell behavior directly controls its ambient environmental parameters, enabling quantitative behavioral assays. Our method and results exemplify a new paradigm of multiscale measurement, wherein one can observe and probe macroscale and ecologically relevant phenomena at microscale resolution. Beyond the marine context, we foresee that our method will allow biological measurements of cells and organisms in a suspended state by freeing them from the confines of the coverslip.

Bacterial Evolution in High-Osmolarity Environments.

Bacteria must maintain a cytosolic osmolarity higher than that of their environment in order to take up water. High-osmolarity environments therefore present formidable stress to bacteria. To explore the evolutionary mechanisms by which bacteria adapt to high-osmolarity environments, we selected Escherichia coli in media with a variety of osmolytes and concentrations for 250 generations. Adaptation was osmolyte dependent, with sorbitol stress generally resulting in increased fitness under conditions with higher osmolarity, while selection in high concentrations of proline resulted in increased fitness specifically on proline. Consistent with these phenotypes, sequencing of the evolved populations showed that passaging in proline resulted in specific mutations in an associated metabolic pathway that increased the ability to utilize proline for growth, while evolution in sorbitol resulted in mutations in many different genes that generally resulted in improved growth under high-osmolarity conditions at the expense of growth at low osmolarity. High osmolarity decreased the growth rate but increased the mean cell volume compared with growth on proline as the sole carbon source, demonstrating that osmolarity-induced changes in growth rate and cell size follow an orthogonal relationship from the classical Growth Law relating cell size and nutrient quality. Isolates from a sorbitol-evolved population that captured the likely temporal sequence of mutations revealed by metagenomic sequencing demonstrated a trade-off between growth at high osmolarity and growth at low osmolarity. Our report highlights the utility of experimental evolution for dissecting complex cellular networks and environmental interactions, particularly in the case of behaviors that can involve both specific and general metabolic stressors.IMPORTANCE For bacteria, maintaining higher internal solute concentrations than those present in the environment allows cells to take up water. As a result, survival is challenging in high-osmolarity environments. To investigate how bacteria adapt to high-osmolarity environments, we maintained Escherichia coli in a variety of high-osmolarity solutions for hundreds of generations. We found that the evolved populations adopted different strategies to improve their growth rates depending on the osmotic passaging condition, either generally adapting to high-osmolarity conditions or better metabolizing the osmolyte as a carbon source. Single-cell imaging demonstrated that enhanced fitness was coupled to faster growth, and metagenomic sequencing revealed mutations that reflected growth trade-offs across osmolarities. Our study demonstrated the utility of long-term evolution experiments for probing adaptation occurring during environmental stress.

Evidence from high-altitude acclimatization for an integrated cerebrovascular and ventilatory hypercapnic response but different responses to hypoxia.

Ventilation and cerebral blood flow (CBF) are both sensitive to hypoxia and hypercapnia. To compare chemosensitivity in these two systems, we made simultaneous measurements of ventilatory and cerebrovascular responses to hypoxia and hypercapnia in 35 normal human subjects before and after acclimatization to hypoxia. Ventilation and CBF were measured during stepwise changes in isocapnic hypoxia and iso-oxic hypercapnia. We used MRI to quantify actual cerebral perfusion. Measurements were repeated after 2 days of acclimatization to hypoxia at 3,800 m altitude (partial pressure of inspired O2 = 90 Torr) to compare plasticity in the chemosensitivity of these two systems. Potential effects of hypoxic and hypercapnic responses on acute mountain sickness (AMS) were assessed also. The pattern of CBF and ventilatory responses to hypercapnia were almost identical. CO2 responses were augmented to a similar degree in both systems by concomitant acute hypoxia or acclimatization to sustained hypoxia. Conversely, the pattern of CBF and ventilatory responses to hypoxia were markedly different. Ventilation showed the well-known increase with acute hypoxia and a progressive decline in absolute value over 25 min of sustained hypoxia. With acclimatization to hypoxia for 2 days, the absolute values of ventilation and O2 sensitivity increased. By contrast, O2 sensitivity of CBF or its absolute value did not change during sustained hypoxia for up to 2 days. The results suggest a common or integrated control mechanism for CBF and ventilation by CO2 but different mechanisms of O2 sensitivity and plasticity between the systems. Ventilatory and cerebrovascular responses were the same for all subjects irrespective of AMS symptoms. NEW & NOTEWORTHY Ventilatory and cerebrovascular hypercapnic response patterns show similar plasticity in CO2 sensitivity following hypoxic acclimatization, suggesting an integrated control mechanism. Conversely, ventilatory and cerebrovascular hypoxic responses differ. Ventilation initially increases but adapts with prolonged hypoxia (hypoxic ventilatory decline), and ventilatory sensitivity increases following acclimatization. In contrast, cerebral blood flow hypoxic sensitivity remains constant over a range of hypoxic stimuli, with no cerebrovascular acclimatization to sustained hypoxia, suggesting different mechanisms for O2 sensitivity in the two systems.