Biology subjects

Russ, J.

Publications and source records attributed to Russ, J..

7 recordsLinked to original sources

bioRxiv2026

Experimental and computational analyses of KEA3-dependent photosynthetic responses uncover species-specific molecular variation

- NPQ relaxation is thought to limit crop biomass and yield under natural, fluctuating light. The thylakoid K/H exchanger KEA3 accelerates NPQ relaxation by lowering the lumen proton concentration and thus should affect biomass and yield under frequent light fluctuations. - To investigate the impact of KEA3-dependent NPQ relaxation on Solanum lycopersicum (tomato), we generated knock-out and over-expression lines and measured NPQ, gas exchange, biomass and yield. We constructed a mathematical model to compare species-specific NPQ relaxation dynamics in tomato and the model plant Arabidopsis thaliana. - In polytunnel and controlled fluctuating-light conditions, KEA3-mediated NPQ relaxation did not translate into consistent differences in tomato biomass or yield. Although KEA3 levels strongly affected NPQ relaxation, more so than in A.thaliana, the resulting effects on CO2 assimilation were minor. Modeling results indicated that, besides minor changes in KEA3 regulation, the activity of PsbS, the primary NPQ trigger, largely determines the species-specific differences in NPQ relaxation. Structural models from both species predict sequence variation with impact on PsbS activation. - Manipulating KEA3 levels in tomato did not link NPQ relaxation with productivity. However, the results support robust cross-species mathematical model that accurately predicts the molecular interactions governing NPQ dynamics.

plant biology↗

bioRxiv2026

Systemic hypoxia drives glycogen-fueled progression of lung adenocarcinoma

In advanced stages, lung adenocarcinoma obstructs airways and disrupts ventilation-perfusion relationships in the lung, causing systemic hypoxemia and enabling a feed-forward loop that accelerates malignancy. Systemic hypoxemia is also experienced due to common respiratory comorbidities such as chronic obstructive pulmonary disease (COPD) and obstructive sleep apnea (OSA), potentially accelerating malignancy. In a statewide electronic health record network, pre-existing COPD (598 matched pairs) or sleep apnea (235 matched pairs) independently predicted worse survival following incident lung cancer diagnosis. Since the mechanistic basis of the link between malignancy and hypoxia is not well understood, we created systemic hypoxia in KrasLSL-G12D/+;Trp53fl/fl (KP) mice by delivering low inspired oxygen concentrations (8% inspired oxygen; 8 h daily). Hypoxia nearly doubled tumor multiplicity and selectively remodeled cancer central carbon metabolism. Spatially resolved metabolomics revealed marked tumor-compartment glycogen accumulation, elevated tricarboxylic-acid cycle intermediates, and depleted glycolytic pools. Quantitative proteomics across cellular models and autochthonous tumors demonstrated that systemic hypoxia drives glycogen mobilization selectively through the lysosomal enzyme acid -glucosidase (GAA). Tumor-cell-autonomous deletion of GAA eliminated the hypoxia-driven growth advantage and disrupted downstream anabolic biosynthetic pathways. Thus, systemic hypoxia drives lung adenocarcinoma expansion by mobilizing lysosomal glycogen reserves through GAA to sustain proliferative growth.

cancer biology↗

bioRxiv2025

Continuous dynamics of cooperation and competition in social foraging

Real-life social interactions often unfold continuously and involve dynamic cooperation and competition, yet most studies rely on discrete games that do not capture the adaptive and graded nature of continuous sensorimotor decisions. To address this gap, we developed the Cooperation-Competition Foraging game -- an ecologically grounded paradigm in which pairs of participants (dyads) navigate a continuous shared space under face-to-face visibility, deciding in real-time to collect rewarded targets either individually or jointly. Dyads (n=58, 116 participants) spontaneously converged on distinct stable strategies along the cooperation-competition spectrum, forming three groups: cooperative, intermediate, and competitive. Despite the behavioral complexity, our computational model, which incorporated travel path minimization, sensorimotor communication, and recent choice history, predicted dyadic decisions with 87% accuracy, and linked prediction certainty with ensuing dynamics of spatiotemporal coordination. Further modeling revealed how sensorimotor factors, such as movement speed and skill, shape distinct strategies and payoffs. Crucially, we quantify the cost of cooperation, demonstrating that in many dyads prosocial tendencies outweigh the individual benefits of exploiting skill advantages. Our versatile framework provides a predictive, mechanistic account of how social and embodied drivers promote the emergence of dynamic cooperation and competition, and offers rigorous metrics for investigating the neural basis of naturalistic social interactions, and for linking personality traits to distinct strategies.

neuroscience↗

bioRxiv2024

Adapting protein language models for structure-conditioned design

Generative models for protein design trained on experimentally determined structures have proven useful for a variety of design tasks. However, such methods are limited by the quantity and diversity of structures used for training, which represent a small, biased fraction of protein space. Here, we describe proseLM, a method for protein sequence design based on adaptation of protein language models to incorporate structural and functional context. We show that proseLM benefits from the scaling trends of underlying language models, and that the addition of non-protein context - nucleic acids, ligands, and ions - improves recovery of native residues during design by 4-5% across model scales. These improvements are most pronounced for residues that directly interface with non-protein context, which are faithfully recovered at rates >70% by the most capable proseLM models. We experimentally validated proseLM by optimizing the editing efficiency of genome editors in human cells, achieving a 50% increase in base editing activity, and by redesigning therapeutic antibodies, resulting in a PD-1 binder with 2.2 nM affinity.

bioengineering↗

bioRxiv2024

Design of highly functional genome editors by modeling the universe of CRISPR-Cas sequences

Gene editing has the potential to solve fundamental challenges in agriculture, biotechnology, and human health. CRISPR-based gene editors derived from microbes, while powerful, often show significant functional tradeoffs when ported into non-native environments, such as human cells. Artificial intelligence (AI) enabled design provides a powerful alternative with potential to bypass evolutionary constraints and generate editors with optimal properties. Here, using large language models (LLMs) trained on biological diversity at scale, we demonstrate the first successful precision editing of the human genome with a programmable gene editor designed with AI. To achieve this goal, we curated a dataset of over one million CRISPR operons through systematic mining of 26 terabases of assembled genomes and meta-genomes. We demonstrate the capacity of our models by generating 4.8x the number of protein clusters across CRISPR-Cas families found in nature and tailoring single-guide RNA sequences for Cas9-like effector proteins. Several of the generated gene editors show comparable or improved activity and specificity relative to SpCas9, the prototypical gene editing effector, while being 400 mutations away in sequence. Finally, we demonstrate an AI-generated gene editor, denoted as OpenCRISPR-1, exhibits compatibility with base editing. We release OpenCRISPR-1 publicly to facilitate broad, ethical usage across research and commercial applications.

synthetic biology↗

bioRxiv2022

Deep Phenotyping and Lifetime Trajectories Reveal Limited Effects of Longevity Regulators on the Aging Process in C57BL/6J Mice

Current concepts regarding the biology of aging are based on studies aimed at identifying factors regulating natural lifespan. However, lifespan as a sole proxy measure for aging can be of limited value because it may be restricted by specific sets of pathologies, rather than by general physiological decline. Here, we employed large-scale phenotyping to analyze hundreds of phenotypes and thousands of molecular markers across tissues and organ systems in a single study of aging male C57BL/6J mice. For each phenotype, we established lifetime profiles to determine when age-dependent phenotypic change is first detectable relative to the young adult baseline. We examined central genetic and environmental lifespan regulators (putative anti-aging interventions, PAAIs; the following PAAIs were examined: mTOR loss-of-function, loss-of-function in growth hormone signaling, dietary restriction) for a possible countering of the signs and symptoms of aging. Importantly, in our study design, we included young treated groups of animals, subjected to PAAIs prior to the onset of detectable age-dependent phenotypic change. In parallel to our studies in mice, we assessed genetic variants for their effects on age-sensitive phenotypes in humans. We observed that, surprisingly, many PAAI effects influenced phenotypes long before the onset of detectable age-dependent changes, rather than altering the rate at which these phenotypes developed with age. Accordingly, this subset of PAAI effects does not reflect a targeting of age-dependent phenotypic change. Overall, our findings suggest that comprehensive phenotyping, including the controls built in our study, is critical for the investigation of PAAIs as it facilitates the proper interpretation of the mechanistic mode by which PAAIs influence biological aging. HighlightsO_LIPhenotyping at scale defines lifetime trajectories of age-dependent changes in C57BL/6J mice C_LIO_LICentral genetic and environmental lifespan regulators (putative anti-aging interventions; PAAIs) influence age-sensitive phenotypes (ASPs) often long before the appearance of age-dependent changes in these ASPs C_LIO_LICorresponding genetic variants in humans also have age-independent effects C_LIO_LIMany PAAI effects shift the baseline of ASPs rather than slowing their rate of change C_LI

physiology↗

bioRxiv2017

Bat Detective - Deep Learning Tools for Bat Acoustic Signal Detection

O_LIPassive acoustic sensing has emerged as a powerful tool for quantifying anthropogenic impacts on biodiversity, especially for echolocating bat species. To better assess bat population trends there is a critical need for accurate, reliable, and open source tools that allow the detection and classification of bat calls in large collections of audio recordings. The majority of existing tools are commercial or have focused on the species classification task, neglecting the important problem of first localizing echolocation calls in audio which is particularly problematic in noisy recordings.\nC_LIO_LIWe developed a convolutional neural network (CNN) based open-source pipeline for detecting ultrasonic, full-spectrum, search-phase calls produced by echolocating bats (BatDetect). Our deep learning algorithms (CNN FULL and CNN FAST) were trained on full-spectrum ultrasonic audio collected along road-transects across Romania and Bulgaria by citizen scientists as part of the iBats programme and labelled by users of www.batdetective.org. We compared the performance of our system to other algorithms and commercial systems on expert verified test datasets recorded from different sensors and countries. As an example application, we ran our detection pipeline on iBats monitoring data collected over five years from Jersey (UK), and compared results to a widely-used commercial system.\nC_LIO_LIHere, we show that both CNNFULL and CNNFAST deep learning algorithms have a higher detection performance (average precision, and recall) of search-phase echolocation calls with our test sets, when compared to other existing algorithms and commercial systems tested. Precision scores for commercial systems were reasonably good across all test datasets (>0.7), but this was at the expense of recall rates. In particular, our deep learning approaches were better at detecting calls in road-transect data, which contained more noisy recordings. Our comparison of CNNFULL and CNNFAST algorithms was favourable, although CNNFAST had a slightly poorer performance, displaying a trade-off between speed and accuracy. Our example monitoring application demonstrated that our open-source, fully automatic, BatDetect CNNFAST pipeline does as well or better compared to a commercial system with manual verification previously used to analyse monitoring data.\nC_LIO_LIWe show that it is possible to both accurately and automatically detect bat search-phase echolocation calls, particularly from noisy audio recordings. Our detection pipeline enables the automatic detection and monitoring of bat populations, and further facilitates their use as indicator species on a large scale, particularly when combined with automatic species identification. We release our system and datasets to encourage future progress and transparency.\nC_LI

ecology↗