Biology subjects

Brooks, L.

Publications and source records attributed to Brooks, L..

7 recordsLinked to original sources

bioRxiv2026

Inertial effects on work production in sub-maximally activated skeletal muscle

Mass is a fundamental aspect of muscle contractile function, yet the inertial effects of inactive muscle mass is generally neglected in modeling and not quantified in studies on small muscles or isolated fibers. However, during submaximal contractions, inactive muscle tissue may take longer to be accelerated by active fibers, and may be subject to prolonged deceleration, both of which may potentially reduce force development and work output. We sought to test if inactive tissue mass imposes an inertial penalty on muscle performance, using in situ sinusoidal work-loop experiments on rat plantaris muscles. Regional fascicle dynamics, measured across supramaximal and submaximal levels of activation, showed that decreasing activation significantly reduced fascicle strain and increased both shortening and lengthening latency. Contrary to our predictions, however, reductions in work, beyond those explained by decreased fascicle strain, were negligible. Normalized work did not decline disproportionately relative to force, suggesting no clear inertial penalty on work at this muscle size. Our findings suggest that while inactive muscle mass influences the dynamics of submaximal contractions, its impact on work during submaximal contractions at small muscle sizes is limited.

physiology↗

bioRxiv2026

First Report of Soft Rot Caused by Pectobacterium brasiliense on Summer Squash in Mississippi, USA

Summer squash (Cucurbita pepo) is a popular vegetable in Mississippi. These are harvested during the tender and immature stages. This vegetable is known to be a good source of vitamins A and C, as well as potassium, and iron. Small farms typically sell summer squash directly to consumers through local farmers markets. In this study, we isolated and identified a soft rot causing bacteria, Pectobacterium brasiliense strain 25ASUB12 (GenBank: PX884501), from symptomatic fruit of field-grown summer squash in Mississippi. We deployed both phenotypic and molecular techniques to identify this important pathogen which has a wide host range, including cucumbers, potatoes, and tomatoes.

pathology↗

bioRxiv2025

Isolation and Characterization of Potential Pathogenic Fungi Associated with Watermelons in Southwest Mississippi

Watermelon is an important specialty crop in the USA. Fungal diseases primarily challenge the production and productivity of watermelon. The understanding of prevailing diseases, gained through genotypic and phenotypic techniques, eventually helps researchers develop disease management strategies. This study involved scouting and sampling of a watermelon research field in Southwest Mississippi for diseases. Nine distinct potential pathogenic fungi were identified from symptomatic leaves and fruits through development of pathogen pure cultures followed by PCR amplification of internal transcribed spacer regions and sequencing. The identified fungi were Didymella americana, Diaporthe melonis, Fusarium incarnatum, Alternaria longissima, Curvularia pseudobrachyspora, Xylaria multiplex, Phoma sp., Nigrospora sphaerica, and Fusarium chlamydosporum. The fungal isolates were ascomycetes at the phylum-level and belonged to the orders of fungi: Pleosporales, Hypocreales, Diaporthales, Xylariales, and Trichosphaeriales. Future research studies include pathogenicity assays with a broader host range, whole genome sequencing to develop deeper understanding of the pathogenic fungi, and formulation of disease management strategies for high-yielding, superior quality, and profitable watermelon production.

pathology↗

bioRxiv2024

Computational modeling of drug response identifies mutant-specific constraints for dosing panRAF and MEK inhibitors in melanoma

Purpose: This study explores the potential of preclinical in vitro cell line response data and computational modeling in identifying optimal dosage requirements of pan-RAF (Belvarafenib) and MEK (Cobimetinib) inhibitors in melanoma treatment. Our research is motivated by the critical role of drug combinations in enhancing anti-cancer responses and the need to close the knowledge gap around selecting effective dosing strategies to maximize their potential. Results: In a drug combination screen of 43 melanoma cell lines, we identified unique dosage landscapes of panRAF and MEK inhibitors for NRAS vs BRAF mutant melanomas. Both experienced benefits, but with a notably more synergistic and narrow dosage range for NRAS mutant melanoma. Computational modeling and molecular experiments attributed the difference to a mechanism of adaptive resistance by negative feedback. We validated in vivo translatability of in vitro dose-response maps by accurately predicting tumor growth in xenografts. Then, we analyzed pharmacokinetic and tumor growth data from Phase 1 clinical trials of Belvarafenib with Cobimetinib to show that the synergy requirement imposes stricter precision dose constraints in NRAS mutant melanoma patients. Conclusion: Leveraging pre-clinical data and computational modeling, our approach proposes dosage strategies that can optimize synergy in drug combinations, while also bringing forth the real-world challenges of staying within a precise dose range. Simple SummaryCombining drugs is crucial for enhancing anti-cancer responses. However, the potential of pre-clinical data in identifying suitable combinations and dosage is often underutilized. In this study, we leverage preclinical in vitro cell line drug response data and computational modeling of signal transduction and of pharmacokinetics to elucidate distinct dose requirements for the combination of pan-RAF and MEK inhibitors in melanoma. Our findings reveal a more synergistic, but narrower dosing landscape in NRAS vs BRAF mutant melanoma, which we linked to a mechanism of adaptive resistance through negative feedback. Further, our analysis suggests the importance of drug dosing strategies to optimize synergy based on mutational context, yet highlights the real-world challenges of maintaining a narrow dose range. This approach establishes a framework for translational investigation of drug responses in the refinement of combination therapy, balancing the potential for synergy and practical feasibility in cancer treatment planning.

systems biology↗

bioRxiv2021

Antibiotic Resistance in Bacillus-based Biopesticide Products

The crisis of antibiotic resistant bacterial infections is one of the most pressing public health issues. Common agricultural practices have been implicated in the generation of antibiotic resistant bacteria. Biopesticides, live bacteria used for pest control, are non-pathogenic and considered safe for consumption. Application of bacteria-based pesticides to crops in high concentrations raises the possibility of unintentional contributions to the movement and generation of antibiotic resistance genes in the environment. However, the presence of clinically relevant antibiotic resistance genes and their resistance phenotypes are currently unknown. Here we use a combination of multiple bioinformatic and microbiological techniques to define resistomes of widely used biopesticides and determine how the presence of suspected antibiotic resistance genes translates to observable resistance phenotypes in several biopesticide products. Our results demonstrate that biopesticide products are reservoirs of clinically relevant antibiotic resistance genes and bear resistance to multiple drug classes. ImportanceThis is the first study to specifically address antibiotic resistance in widely distributed bacterial strains used as commercial biopesticides. Safety assessments of commercial live bacterial biopesticide products do not include antibiotic resistance phenotype identification. We identify antibiotic resistance genes in all live bacterial strains examined, and resistant phenotypes in all strains tested for antibiotic susceptibility. This work demonstrates that biopesticides potentially play a critical role as reservoirs and vectors of antibiotic resistance in the broader environmental resistome that is to date, unstudied.

microbiology↗

bioRxiv2020

The evolution of non-motif selectivity determinants in Monosiga brevicollis PDZ domains

The evolution of signaling pathways is complex and well-studied. In particular, the emergence of animal multicellularity had a major impact on protein-protein interactions and signaling systems in eukaryotic cells. However, choanoflagellates, our closest non-metazoan ancestor, contain a number of closely related signaling and trafficking proteins and domains. In addition, because choanoflagellates can adopt a rosette-/multicellular-like state, a lot can be gained by comparing proteins involved in choanoflagellate and human signaling pathways. Here, we look at how selectivity determinants evolved in the PDZ domain. There are over 250 PDZ domains in the human proteome, which are involved in critical protein-protein interactions that result in large multimolecular complexes, e.g., in the postsynaptic density of neuronal synapses. Binding of C-terminal sequences by PDZ domains is often transient and recognition typically involves 6 residues or less, with only 2 residues specifying the binding motif. We solved high resolution crystal structures of Monosiga brevicollis PDZ domains homologous to human Dlg1 PDZ2, Dlg1 PDZ3, GIPC, and SHANK1 PDZ domains to investigate if the non-motif preferences are conserved, despite hundreds of millions of years of evolution. We also calculated binding affinities for GIPC, SHANK1, and SNX27 PDZ domains from M. brevicollis. Overall, we found that peptide selectivity is conserved between these two disparate organisms, with one exception, mbDLG-3. In addition, we identify 178 PDZ domains in the M. brevicollis proteome, including 11 new sequences, which we verified using Rosetta and homology modeling. Overall, our results provide novel insight into signaling pathways in the choanoflagellate organism.

biochemistry↗

bioRxiv2018

Forecasting seasonal influenza in the U.S.: A collaborative multi-year, multi-model assessment of forecast performance

Influenza infects an estimated 9 to 35 million individuals each year in the United States and is a contributing cause for between 12,000 and 56,000 deaths annually. Seasonal outbreaks of influenza are common in temperate regions of the world, with highest incidence typically occurring in colder and drier months of the year. Real-time forecasts of influenza transmission can inform public health response to outbreaks. We present the results of a multi-institution collaborative effort to standardize the collection and evaluation of forecasting models for influenza in the US for the 2010/2011 through 2016/2017 influenza seasons. For these seven seasons, we assembled weekly real-time forecasts of 7 targets of public health interest from 22 different models. We compared forecast accuracy of each model relative to a historical baseline seasonal average. Across all regions of the US, over half of the models showed consistently better performance than the historical baseline when forecasting incidence of influenza-like illness 1, 2 and 3 weeks ahead of available data and when forecasting the timing and magnitude of the seasonal peak. In some regions, delays in data reporting were strongly and negatively associated with forecast accuracy. More timely reporting and an improved overall accessibility to novel and traditional data sources are needed to improve forecasting accuracy and its integration with real-time public health decision-making.

epidemiology↗