Biology subjects

Sandler, M.

Publications and source records attributed to Sandler, M..

4 recordsLinked to original sources

bioRxiv2026

Shared genomic signatures of Arctic adaptation among distantly related plant lineages

Extreme environments impose strong selection pressures that can drive convergent adaptation among distantly related species. Arctic plants survive low temperatures, 24-hour light in summer and darkness in winter, short growing seasons, limited precipitation, nutrient-poor soils, and extreme seasonal variation. To test if these conditions are associated with shared genomic signatures, we generated a chromosome-level genome assembly for Oxyria digyna and compared it with existing reference genomes for three distantly related Arctic species (Cochlearia groenlandica, Draba nivalis, Dryas octopetala) and for 16 non-Arctic species from the same families. We identified 214 orthogroups with signatures of positive selection in all Arctic species, 1.6 times the number shared among the non-Arctic species. Although no gene ontology (GO) categories remained significantly enriched after multiple-testing correction, descriptive annotations included genes involved in general functions including protein folding, glycolysis, transcription, and oxidative-stress responses. Orthogroups showing copy number variation were associated with biotic interactions, sexual reproduction, and nutrient acquisition, suggesting that species may respond to biotic and nutrient stresses by shifts in gene copy number. Arctic and alpine genomes contained lower proportions of some repetitive elements, including terminal inverted repeats, and smaller regions of centromeric repeats than non-Arctic genomes. Overall, we see shared genes under positive selection, similar functions of genes with high copy number variation, and lower counts of some repetitive elements in the Arctic plant genomes. Our findings reveal a shared genomic signature of Arctic adaptation across deeply diverged plant lineages, suggesting that evolution in extreme environments repeatedly targets similar genomic features.

genomics↗

bioRxiv2023

Tools and methods for high-throughput single-cell imaging with the mother machine

Despite much progress, image processing remains a significant bottleneck for high-throughput analysis of microscopy data. One popular platform for single-cell time-lapse imaging is the mother machine, which enables long-term tracking of microbial cells under precisely controlled growth conditions. While several mother machine image analysis pipelines have been developed in the past several years, adoption by a non-expert audience remains a challenge. To fill this gap, we implemented our own software, MM3, as a plugin for the multidimensional image viewer napari. napari-MM3 is a complete and modular image analysis pipeline for mother machine data, which takes advantage of the high-level interactivity of napari. Here, we give an overview of napari-MM3 and test it against several well-designed and widely-used image analysis pipelines, including BACMMAN and DeLTA. Researchers often analyze mother machine data with custom scripts using varied image analysis methods, but a quantitative comparison of the output of different pipelines has been lacking. To this end, we show that key single-cell physiological parameter correlations and distributions are robust to the choice of analysis method. However, we also find that small changes in thresholding parameters can systematically alter parameters extracted from single-cell imaging experiments. Moreover, we explicitly show that in deep learning based segmentation, "what you put is what you get" (WYPIWYG) - i.e., pixel-level variation in training data for cell segmentation can propagate to the model output and bias spatial and temporal measurements. Finally, while the primary purpose of this work is to introduce the image analysis software that we have developed over the last decade in our lab, we also provide information for those who want to implement mother-machine-based high-throughput imaging and analysis methods in their research.

systems biology↗

bioRxiv2023

Differences in spore size and atmospheric survival shape stark contrasts in the dispersal dynamics of two closely related fungal pathogens

A frequently ignored but critical aspect of microbial dispersal is survival in the atmosphere. We exposed spores of two closely related, morphologically dissimilar, and economically important fungal pathogens to typical atmospheric environments and modeled their movement in the troposphere. We first measured the mortality of Alternaria solani and A. alternata conidia exposed to ranges of solar radiation, relative humidity, and temperature. We then measured survival in an advantageous environment over 12 days. A. solani conidia are nearly 10 times larger than A. alternata conidia and most die after 24 hours. By contrast, over half of A. alternata conidia remained viable at 12 days. The greater viability of the smaller spores is counterintuitive as larger spores are assumed to be more durable. To elucidate the consequences of survival rates for dispersal, we deployed models of atmospheric spore movement across North American. We predict 99% of the larger A. solani conidia settle within 24 hours, with a maximum dispersal distance of 100 km. By contrast, most A. alternata conidia remain airborne for more than 12 days and long-distance dispersal is possible, e.g., from Wisconsin to the Atlantic Ocean. We observe that the larger conidia of A. solani survive poorly but also land sooner and move over shorter distances as compared to the smaller conidia of A. alternata. Our data relating larger spore size to poorer survival in the atmosphere and shorter distances travelled likely translate to other fungal species and highlight the potential for starkly different dispersal dynamics among even closely related fungi.

microbiology↗

bioRxiv2019

Pharmacological and fasting-induced activation of SIRT1/LXRα signaling alleviates diabetes-induced retinopathy.

In diabetes, the retina, a tissue with unique metabolic needs, demonstrates dysregulation of the intricate balance between nutrient availability and utilization. This results in cholesterol accumulation, pro-inflammatory and pro-apoptotic changes, and consequently neurovascular damage. Sirtuin 1 (SIRT1), a nutrient sensing deacetylase, is downregulated in the diabetic retina. In this study, the effect of SIRT1 stimulation by fasting or by pharmacological activation using SRT1720, was evaluated on retinal cholesterol metabolism, inflammation and neurovascular damage. SIRT1 activation, in retinal endothelial cells (REC) and neuronal retinal progenitor cells (R28), led to Liver X Receptor alpha (LXR) deacetylation and subsequent increased activity, as measured by increased ATP-binding cassette transporter (ABC) A1 and G1 mRNA expression. In turn, increased cholesterol export resulted in decreased REC cholesterol levels. SIRT1 activation also led to decreased inflammation. SIRT1 activation, in vivo, prevented diabetes-induced inflammation and vascular and neural degeneration. Diabetes-induced visual function impairment, as measured by electroretinogram and optokinetic response, was significantly improved as a result of SIRT1 activation. Taken together, activation of SIRT1 signaling is an effective therapeutic strategy that provides a mechanistic link between the advantageous effects associated with fasting regimes and prevention of diabetic retinopathy (DR).

cell biology↗