Biology subjects

Ramirez, D. A.

Publications and source records attributed to Ramirez, D. A..

8 recordsLinked to original sources

bioRxiv2026

Prediction of late blight severity in a large panel of potato genotypes using low-altitude aerial images and machine learning methods

Potato (Solanum tuberosum L.) is a staple crop crucial to global food security, yet its production is severely threatened by late blight (LB), caused by Phytophthora infestans, one of the most destructive plant diseases worldwide. Breeding programs for LB resistance have traditionally relied on labor-intensive and subjective visual assessments, which limit scalability and consistency, particularly in early-generation trials. Unmanned aerial vehicle (UAV)-based remote sensing combined with machine learning (ML) offers a promising alternative for objective, high-throughput disease phenotyping. This study evaluated the potential of UAV-derived multispectral imagery and ML techniques to estimate LB severity across large and genetically diverse potato breeding populations, comprising 2,745 clones in one trial and 492 accessions in another, conducted in Oxapampa, Pasco, Peru. We compared vegetation index-based approaches with a machine learning framework that integrates K-means clustering and Kernel Ridge Regression (KRR) and assessed their ability to capture genotypic variation and support selection decisions. NDVI consistently showed a strong correlation with visually assessed LB severity, particularly at advanced stages of disease development, enabling objective discrimination between healthy and diseased canopy tissues. However, the KRR-based approach outperformed linear NDVI-based models by capturing nonlinear relationships between spectral responses and disease progression. Estimates of LB severity derived from NDVI and KRR models, expressed as best linear unbiased estimates (BLUEs), showed strong and biologically consistent relationships with the area under the disease progress curve (AUDPC), particularly during later UAV acquisitions. Selection coincidence between UAV-derived estimates and AUDPC-based rankings was substantially higher at intermediate to advanced stages of disease progression, suggesting that UAV assessments at these stages may capture sufficient phenotypic variation to distinguish genotypes. These findings indicate that UAV-based multispectral phenotyping, especially when integrated with ML, provides a practical and scalable approach for assessing LB severity in potato breeding programs while reducing the need for time-consuming field evaluations.

plant biology↗

bioRxiv2026

AncientMetagenomeDir dating metadataset highlights need for standardised radiocarbon reporting in ancient DNA

Ancient DNA is a valuable data source for the understanding of our past. However, to effectively interpret this data, it is essential to know the age of the samples from which the DNA is obtained. Although the field of palaeogenomics has been recognised for its robust open data sharing practices, dating information associated with analysed samples is not reported consistently across palaeogenomic studies, nor is it included as metadata in most genetic data repositories. Here, we describe the addition of standardised precise dating information for ancient microbial genomes into the AncientMetagenomeDir metadata repository of published ancient metagenomic samples. This extension currently includes dating information for over 700 ancient microbial genomic datasets, of which 333 are dated using historical, contextual, or stratigraphic methods, and 405 are radiocarbon dated. We quantitatively assess the quality of radiocarbon date reporting and find that, despite established reporting conventions, radiocarbon dating information is often reported inconsistently across ancient metagenomic studies. This new resource provides ancient microbial researchers with standardised dating information that facilitates more accurate and consistent analysis of metagenomic sequencing data. The dataset also highlights the need for greater standardisation of radiocarbon date reporting in original publications in order to allow effective reuse of this and future ancient microbial data.

bioinformatics↗

bioRxiv2025

Can a history of crop rotations improve the prediction of soil organic carbon in the Andes? integrating machine learning multi-annual crop classification as a proxy of soil management

Soil organic carbon (SOC) is a crucial component related to various processes that ensure soil health and function. Its modeling is vital for assessing and monitoring soil degradation caused by the potential impact of agricultural activities. This study aimed to model SOC in the Northern highlands of Peru, characterized by a high amount of SOC, which is being affected by crop expansion. Crop rotation (CR) was incorporated into a modeling exercise using remote sensing data, fieldwork, and farmer surveys. A multi-year classification model with seven cropland classes was developed using data collected from 534 fields across 2022-2024, including 189 soil samples. Each cropland field was represented as a polygon delineating its boundaries and indicating its dominant crop cover. Time series of multispectral Sentinel-2 Level-2A Top of Canopy imagery were used to derive phenological features--such as the timing of maximum canopy cover and the length of the growing period--based on Normalized Difference Vegetation Index (NDVI) time series. A Random Forest classifier was used as the baseline model. The cropland classification model demonstrated strong overall performance, with F1 scores ranging from 0.81 to 0.98 across the different classes. The model performed well for lupin and pasture but scored lower for beans and potatoes. Predictions of cropland classes from 2019 to 2022 were created, resulting in frequency layers that represent crop rotations. Four feature configurations were evaluated: (i) including all features as a benchmark, (ii) excluding climatology, (iii) excluding crop rotation history, and (iv) excluding soil properties. Configurations including all features and excluding crop rotation history showed the highest performance (R2 = 0.63), while those excluding climatology or soil properties performed worse (R2 {approx} 0.52-0.53). Although soil features were the most important, fallow frequency emerged as the most critical predictor of SOC in crop rotations. When soil data were excluded, fallow frequency, combined with climatic features, explained over half of the SOC variability. The findings emphasize the importance of incorporating CR into SOC mapping efforts.

biophysics↗

bioRxiv2025

Activating RET Mutations Promotes Osteoblastic Bone Metastases in Medullary Thyroid Cancer

Development of bone metastases increases mortality in patients with medullary thyroid cancer (MTC), with [~]50% survival at 5 years after diagnosis, but the underlying mechanisms are unknown. We show that patient-derived MTC cells (RETC634W mutant TT cells and RETM918T mutant MZCRC1 cells) promote an osteoblastic phenotype due to reduced bone resorption. Mechanistically, activated RET increases the expression of osteoprotegerin (OPG), an inhibitor of bone resorption, leading to decreased osteoclast differentiation. Furthermore, RET knockdown or pharmacological RET inhibition attenuates tumor burden and osteoblastic lesions in MTC-bearing femurs. Circulating levels of OPG were increased in the plasma of MTC patients who developed bone metastases and this was associated with poor overall survival. Patients who were treated with multi-kinase inhibitors have lower circulating levels of OPG. These novel findings identify a link between the RET signaling pathway and abnormal osteoblastic bone formation and suggest OPG as a potential biomarker of MTC bone metastases. HighlightsO_LIPatient-derived MTC cells promote osteoblastic lesions in a mouse model. C_LIO_LIActivating the RET mutation promotes osteoprotegerin. C_LIO_LIBlocking RET kinase activity inhibits tumor growth and the osteoblastic lesion phenotype. C_LIO_LIHigh levels of circulating osteoprotegerin are associated with poor overall survival. C_LI

cancer biology↗

bioRxiv2025

Nonspecific interactions can lead to liquid-liquid phase separation in coiled-coil proteins models

Liquid-liquid phase separation (LLPS) is one mechanism that cells can use to organize biomolecules spatially and functionally. Some coiled-coil (CC) proteins, such as the centrosomal proteins pericentrin and spd-5, are thought to LLPS, but it is currently unknown what parts of these proteins facilitate the process. It is thought, however, that the numerous CC domains in these proteins might be contributing to their LLPS. We recently showed, using computational studies and designed proteins, that CC domains can facilitate LLPS through specific interactions between the CC domains themselves, meaning that each CC was designed to interact only with a subset of other CCs in the system. This is in contrast to nonspecific interactions, where all CCs would be able to interact with all other CCs in the system, which is akin to some interactions (e.g.{pi} -{pi}) seen in phase-separating intrinsically disordered proteins. Because the specificity of interactions between natural CC domains is tunable in a sequence-dependent fashion, CC domains present a unique system that allows us to investigate the contributions of specific versus nonspecific interactions on LLPS. We show, in our computational system, that CC proteins with nonspecific interactions can LLPS but with less propensity compared to specific interactions. The LLPS propensity of CC proteins with nonspecific interactions can be improved by altering the structure and dynamics of linker segments, without directly changing the specificity of interactions. We also demonstrate that the number of intra-chain CC contacts plays a direct role in determining LLPS for nonspecifically interacting proteins. These results have broad implications for the role of linker segments--protein features beyond the interaction domains e.g. stickers--in protein LLPS and the formation of biomolecular condensates. STATEMENT OF SIGNIFICANCEModel coiled-coil proteins, which use coiled-coil domains as stickers, are capable of phase separation in a regime where intra-protein contacts interfere with the interactions which support phase separation. We explore ways to increase phase separation propensity without changing interaction specificity and find that the structure and size of spacers impacts LLPS propensity by affecting the formation of intra-chain interactions. This work demonstrates that protein LLPS might be controllable without directly affecting the cohesive parts of a protein i.e. stickers. This work also suggests that LLPS propensity might be a broadly accessible phenomenon for coiled-coil proteins.

biophysics↗

bioRxiv2024

Global genomics of the man-o'-war (Physalia) reveal biodiversity at the ocean surface

The open ocean is a vast, highly connected environment, and the organisms found there have been hypothesized to represent massive, well-mixed populations. Of these, the Portuguese man-o-war (Physalia) is uniquely suited to dispersal, sailing the ocean surface with a muscular crest. We tested the hypothesis of a single, panmictic Physalia population by sequencing 133 genomes, and found five distinct lineages, with multiple lines of evidence showing strong reproductive isolation despite range overlap. We then scored thousands of citizen-science photos and identified four recognizable morphologies linked to these lineages. Within lineages, we detected regionally endemic subpopulations, connected by winds and currents, and identified individual long-distance dispersal events. We find that, even in these sailing species, genetic variation is highly partitioned geographically across the open ocean. SummaryThe open ocean is a vast and highly connected environment. The organisms that live there have a significant capacity for dispersal and few geographic boundaries to separate populations. Of these, the Portuguese man-o-war or bluebottle (genus Physalia) is uniquely suited to long-distance travel, using its gas-filled float and muscular crest to catch the wind and sail the sea surface. Physalia are distributed across the globe, and like many pelagic organisms, have been hypothesized to represent a massive, well-mixed population that extends across ocean basins. We tested this hypothesis by sequencing whole genomes of 133 samples collected from waters of over a dozen countries around the globe. Our results revealed five distinct lineages, with multiple lines of evidence indicating strong reproductive isolation, despite regions of range overlap. We combined these data with an independent dataset of thousands of images of Physalia uploaded to the citizen-science website inaturalist.org, which we scored for morphological characters including sail size, tentacle arrangement, and color. From these images, we identified four recognizable morphologies, described their geographical distribution, and linked them to four of the lineages identified with genomic data. We conclude there are at least four species, three of which correspond to species proposed by scientists in the 18th and 19th centuries: P. physalis, P utriculus, and P. megalista, along with one as yet unnamed species Physalia sp. from the Tasman Sea. Within each species, we observe significant population structure, with evidence of persistent subpopulations at a regional scale, as well as evidence for individual long-distance dispersal events. Our findings indicate that, instead of one well-mixed, cosmopolitan species, there are in fact multiple Physalia species with distinct but overlapping ranges, each made up of regionally endemic subpopulations that are connected by major ocean currents and wind patterns.

zoology↗

bioRxiv2023

Coiled-coil domains are sufficient to drive liquid-liquid phase separation of proteins in molecular models

Liquid-liquid phase separation (LLPS) is thought to be a main driving force in the formation of membraneless organelles. Examples of such organelles include the centrosome, central spindle, and stress granules. Recently, it has been shown that coiled-coil (CC) proteins, such as the centrosomal proteins pericentrin, spd-5, and centrosomin, might be capable of LLPS. CC domains have physical features that could make them the drivers of LLPS, but it is unknown if they play a direct role in the process. We developed a coarse-grained simulation framework for investigating the LLPS propensity of CC proteins, in which interactions which support LLPS arise solely from CC domains. We show, using this framework, that the physical features of CC domains are sufficient to drive LLPS of proteins. The framework is specifically designed to investigate how the number of CC domains, as well as multimerization state of CC domains, can affect LLPS. We show that small model proteins with as few as two CC domains can phase separate. Increasing the number of CC domains up to four per protein can somewhat increase LLPS propensity. We demonstrate that trimer-forming and tetramer-forming CC domains have a dramatically higher LLPS propensity than dimer-forming coils, which shows that multimerization state has a greater effect on LLPS than the number of CC domains per protein. These data support the hypothesis of CC domains as drivers of protein LLPS, and has implications in future studies to identify the LLPS-driving regions of centrosomal and central spindle proteins. STATEMENT OF SIGNIFICANCEThe liquid-liquid phase separation of coiled-coil proteins has been implicated in the formation of membraneless organelles such as the centrosome and central spindle. Little is known about the features of these proteins that might drive their phase separation. We developed a modeling framework to investigate the potential role of coiled-coil domains in phase separation, and show that these domains are sufficient to drive the phenomenon in simulation. We additionally show the importance of multimerization state on the ability for such proteins to phase separate. This work suggests that coiled-coil domains should be considered for their contribution to protein phase separation.

biophysics↗

bioRxiv2018

Cortical Actomyosin Flows are Generated by Actomyosin Cluster Vibrations in Vitro

Cortical actomyosin flows play pivotal roles in cell motility, cell division and animal morphogenesis. According to many model systems, myosin motor induced local contractions are key for generating cortical flows. However, the original mechanism how large-scale directed flows emerge from local motor activity in an apparently isotropic cortex is unknown. We reconstituted and confined minimal actomyosin cortices to the interfaces of emulsion droplets. The presence of ATP leads to myosin-induced cortical contractions that self-organize into directed flow-like actomyosin motions. By combining our experiments with theory, we found that the large-scale directional motion of actomyosin clusters emerges from individual asymmetric cluster vibrations, caused by intrinsic non-isotropic ATP consumption, in conjunction with spherical confinement. By tracking individual actomyosin clusters, we identified fingerprints of vibrational states as the basis of directed motions. These vibrations may represent a generic key driver of directed actomyosin flows under spatial confinement in vitro and in living systems.

synthetic biology↗