Biology subjects

Borowsky, R.

Publications and source records attributed to Borowsky, R..

10 recordsLinked to original sources

bioRxiv2023

Gene loss and relaxed selection of plaat1 in vertebrates adapted to low-light environments

Gene loss is an important mechanism for evolution in low-light or cave environments where visual adaptations often involve a reduction or loss of eyesight. The plaat gene family are phospholipases essential for the degradation of organelles in the lens of the eye. They translocate to damaged organelle membranes, inducing them to rupture. This rupture is required for lens transparency and is essential for developing a functioning eye. Plaat3 is thought to be responsible for this role in mammals, while plaat1 is thought to be responsible in other vertebrates. We used a macroevolutionary approach and comparative genomics to examine the origin, loss, synteny, and selection of plaat1 across bony fishes and tetrapods. We show that plaat1 (likely ancestral to all bony fish + tetrapods) has been lost in squamates and is significantly degraded in lineages of low-visual acuity and blind mammals and fish. Our findings suggest that plaat1 is important for visual acuity across bony vertebrates, and that its loss through relaxed selection and pseudogenization may have played a role in the repeated evolution of visual systems in low-light-environments. Our study sheds light on the importance of gene-loss in trait evolution and provides insights into the mechanisms underlying visual acuity in low-light environments.

evolutionary biology↗

bioRxiv2023

Activity in the reticulospinal tract scales with handgrip force in humans

IntroductionThe neural pathways that contribute to force production in humans are currently poorly understood, as the relative roles of the corticospinal tract and brainstem pathways, such as the reticulospinal tract (RST), vary substantially across species. Using functional magnetic resonance imaging (fMRI) we aimed to measure activation in the pontine reticular nuclei during different submaximal handgrip contractions to determine the potential role of the RST in force modulation. MethodsThirteen neurologically intact participants (age: 28 {+/-} 6 years) performed unilateral handgrip contractions at 25%, 50%, 75% of maximum voluntary contraction during brain scans. We quantified the magnitude of RST activation from the contralateral and ipsilateral sides in addition to the contralateral primary motor cortex during each of the three contraction intensities. ResultsA repeated measures ANOVA demonstrated a significant main effect of force (p = 0.012, {eta} p 2 = 0.307) for RST activation, independent of side (i.e., activation increased with force for both contralateral and ipsilateral nuclei). Further analyses of these data involved calculating the linear slope between the magnitude of activation and handgrip force for each ROI at the individual-level. One-sample t-tests on the slopes revealed significant group-level scaling for the RST bilaterally, but only the ipsilateral RST remained significant after correcting for multiple comparisons. ConclusionsHere, we show evidence of task dependent activation in the RST pontine nuclei that was positively related to handgrip force. These data build on a growing body of literature that highlights the RST as a functionally relevant motor pathway for force modulation in humans. New & NoteworthyIn this short report, we used a task-based fMRI paradigm to show that activity in the reticulospinal tract, but not the contralateral motor cortex, scales linearly with increasing force during a handgrip task. These findings directly support recently proposed hypotheses that the reticulospinal tract may play an important role in modulating force production in humans.

neuroscience↗

bioRxiv2022

Comparing models of information transfer in the structural brain network and their relationship to functional connectivity: Diffusion versus shortest path routing

The relationship between structural and functional connectivity in the human brain is a core question in network neuroscience, and a topic of paramount importance to our ability to meaningfully describe and predict functional outcomes. Graph theory has been used to produce measures based on the structural connectivity network that are related to functional connectivity. These measures are commonly based on either the shortest path routing model or the diffusion model, which carry distinct assumptions about how information is transferred through the network. Unlike shortest path routing, which assumes the most efficient path is always known, the diffusion model makes no such assumption, and lets information diffuse in parallel based on the number of connections to other regions. Past research has also developed hybrid measures that use concepts from both models, which have better predicted the functional connectivity from structural connectivity than shortest path length alone. We examined the extent to which each of these models can account for the structure-function relationship of interest using graph theory measures that are exclusively based on each model. This analysis was performed on multiple parcellations of the Human Connectome Project using multiple approaches, which all converged on the same finding. We found that the diffusion model accounts for much more variance in functional connectivity than the shortest path routing model, suggesting that the diffusion model is better suited to describing the structure-function relationship in the human brain at the macroscale.

neuroscience↗

bioRxiv2021

Considering unique, shared, and dominant brain activation in the Visual Word Form Area and Lateral Occipital Cortex: A comparison of separate and combined word reading and picture naming

Identifying printed words and pictures concurrently is ubiquitous in daily tasks, and so it is important to consider the extent to which reading words and naming pictures may share a cognitive-neurophysiological functional architecture. Two functional magnetic resonance imaging (fMRI) experiments examined whether reading along the left ventral occipitotemporal region (vOT; often referred to as a visual word form area, VWFA) has activation that is overlapping with referent pictures (i.e., both conditions significant and shared, or with one significantly more dominant) or unique (i.e., one condition significant, the other not), and whether picture naming along the right lateral occipital complex (LOC) has overlapping or unique activation relative to referent words. Experiment 1 used familiar regular and exception words (to force lexical reading) and their corresponding pictures in separate naming blocks, and showed dominant activation for pictures in the LOC, and shared activation in the VWFA for exception words and their corresponding pictures (regular words did not elicit significant VWFA activation). Experiment 2 controlled for visual complexity by superimposing the words and pictures and instructing participants to either name the word or the picture, and showed primarily shared activation in the VWFA and LOC regions for both word reading and picture naming, with some dominant activation for pictures in the LOC. Overall, these results highlight the importance of including exception words to force lexical reading when comparing to picture naming, and the significant shared activation in VWFA and LOC serves to challenge specialized models of reading or picture naming.

neuroscience↗

bioRxiv2021

Ipsilesional motor cortex activation with high-force unimanual handgrip contractions of the less-affected limb in participants with stroke

Stroke is a leading cause of severe disability that often presents with unilateral motor impairment. Conventional rehabilitation approaches focus on motor practice of the affected limb and aim to suppress brain activity in the contralesional hemisphere to facilitate ipsilesional hemispheric neuroplasticity subserving motor recovery. Previous research has also demonstrated that exercise of the less-affected limb can promote motor recovery of the affected limb through the interlimb transfer of the trained motor task, termed cross-education. One of the leading theories for cross-education proposes that the interlimb transfer manifests from ipsilateral cortical activity during unimanual motor tasks, and that this ipsilateral cortical activity results in motor related neuroplasticity giving rise to contralateral improvements in motor performance. Conversely, exercise of the less-affected limb promotes contralesional brain activity which is typically viewed as contraindicated in stroke recovery due to the interhemispheric inhibitory influence onto the ipsilesional hemisphere. High-force unimanual handgrip contractions are known to increase ipsilateral brain activation in control participants, but it remains to be determined if this would be observed in participants with stroke. Therefore, this study aimed to determine how parametric increases in handgrip force during repeated contractions with the less-affected limb impacts brain activity bilaterally in participants with stroke and in a cohort of neurologically intact controls. In this study, higher force contractions were found to increase brain activation in the ipsilesional/ipsilateral hemisphere in both groups (p = .002), but no between group differences were observed. These data suggest that high-force exercise with the less-affected limb may promote ipsilesional cortical plasticity to promote motor recovery of the affected-limb in participants with stroke.

neuroscience↗

bioRxiv2021

Structure Can Predict Function in the Human Brain: A Graph Neural Network Deep Learning Model of Functional Connectivity and Centrality based on Structural Connectivity

Although functional connectivity and associated graph theory measures (e.g., centrality; how centrally important to the network a region is) are widely used in brain research, the full extent to which these functional measures are related to the underlying structural connectivity is not yet fully understood. Graph neural network deep learning methods have not yet been applied for this purpose, and offer an ideal model architecture for working with connectivity data given their ability to capture and maintain inherent network structure. This model applied here to predict functional connectivity and centrality from structural connectivity accounted for 89% of the variance in mean functional connectivity, 56% of the variance in individual-level functional connectivity, 99% of the variance in mean functional centrality, and 81% of the variance in individual-level functional centrality. This model provides a new benchmark for performance and represents a novel finding that functional centrality can be robustly predicted from structural connectivity. Regions of particular importance to the models performance as determined through lesioning are discussed, whereby regions with higher centrality have a higher impact on model performance. Future research on models of patient, demographic, or behavioural data can also benefit from this graph neural network method as it is ideally-suited for capturing connectivity and centrality in brain networks. These results have set a new benchmark for prediction of functional connectivity from structural connectivity, and models like this may ultimately lead to a way to predict functional connectivity in individuals who are unable to do fMRI tasks (e.g., non-responsive patients).

neuroscience↗

bioRxiv2020

A chromosome level genome of Astyanax mexicanus surface fish for comparing population-specific genetic differences contributing to trait evolution.

Identifying the genetic factors that underlie complex traits is central to understanding the mechanistic underpinnings of evolution. In nature, adaptation to severe environmental change, such as encountered following colonization of caves, has dramatically altered genomes of species over varied time spans. Genomic sequencing approaches have identified mutations associated with troglomorphic trait evolution, but the functional impacts of these mutations remain poorly understood. The Mexican Tetra, Astyanax mexicanus, is abundant in the surface waters of northeastern Mexico, and also inhabits at least 30 different caves in the region. Cave-dwelling A. mexicanus morphs are well adapted to subterranean life and many populations appear to have evolved troglomorphic traits independently, while the surface-dwelling populations can be used as a proxy for the ancestral form. Here we present a high-resolution, chromosome-level surface fish genome, enabling the first genome-wide comparison between surface fish and cavefish populations. Using this resource, we performed quantitative trait locus (QTL) mapping analyses for pigmentation and eye size and found new candidate genes for eye loss such as dusp26. We used CRISPR gene editing in A. mexicanus to confirm the essential role of a gene within an eye size QTL, rx3, in eye formation. We also generated the first genome-wide evaluation of deletion variability that includes an analysis of the impact on protein-coding genes across cavefish populations to gain insight into this potential source of cave adaptation. The new surface fish genome reference now provides a more complete resource for comparative, functional, developmental and genetic studies of drastic trait differences within a species.Competing Interest StatementThe authors have declared no competing interest.View Full Text

genomics↗

bioRxiv2019

Brain structural connectivity predicts brain functional complexity: DTI derived centrality accounts for variance in fractal properties of fMRI signal

The complexity of brain activity has recently been investigated using the Hurst (H) exponent, which describes the extent to which functional magnetic resonance imaging (fMRI) blood oxygen-level dependent (BOLD) activity is self-similar vs. complex. For example, research has demonstrated that fMRI activity is more complex before than after consumption of alcohol and during task than resting state. The measurement of H in fMRI is a novel method that requires the investigation of additional factors contributing to complexity. Graph theory metrics of centrality can assess how centrally important to the brain network each region is, based on diffusion tensor imaging (DTI) counts of probabilistic white matter (WM) tracts. DTI derived centrality was hypothesized to account for the complexity of functional activity, based on the supposition that more sources of information to integrate should result in more complex activity. FMRI BOLD complexity as measured by H was associated with five brain region centrality measures: degree, eigenvector, PageRank, current flow betweenness, and current flow closeness centrality. Multiple regression analyses demonstrated that degree centrality was the most robust predictor of complexity, whereby greater centrality was associated with increased complexity (lower H). Regions known to be highly connected, including the thalamus and hippocampus, notably were among the highest in centrality and complexity. This research has led to a greater understanding of how brain region characteristics such as DTI centrality relate to the novel Hurst exponent approach for assessing brain activity complexity, and implications for future research that employ these measures are discussed.

neuroscience↗

bioRxiv2018

The role of gene flow in rapid and repeated evolution of cave related traits in Mexican tetra, Astyanax mexicanus

Understanding the molecular basis of repeated evolved phenotypes can yield key insights into the evolutionary process. Quantifying the amount of gene flow between populations is especially important in interpreting mechanisms of repeated phenotypic evolution, and genomic analyses have revealed that admixture is more common between diverging lineages than previously thought. In this study, we resequenced and analyzed nearly 50 whole genomes of the Mexican tetra from three blind cave populations, two surface populations, and outgroup samples. We confirmed that cave populations are polyphyletic and two Astyanax mexicanus lineages are present in our dataset. The two lineages likely diverged [~]257k generations ago, which, assuming 1 generation per year, is substantially younger than previous mitochondrial estimates of 5-7mya. Divergence of cave populations from their phylogenetically closest surface population likely occurred between [~]161k - 191k generations ago. The favored demographic model for most population pairs accounts for divergence with secondary contact and heterogeneous gene flow across the genome, and we rigorously identified abundant gene flow between cave and surface fish, between caves, and between separate lineages of cave and surface fish. Therefore, the evolution of cave-related traits occurred more rapidly than previously thought, and trogolomorphic traits are maintained despite substantial gene flow with surface populations. After incorporating these new demographic estimates, our models support that selection may drive the evolution of cave-derived traits, as opposed to the classic hypothesis of disuse and drift. Finally, we show that a key QTL is enriched for genomic regions with very low divergence between caves, suggesting that regions important for cave phenotypes may be transferred between caves via gene flow. In sum, our study shows that shared evolutionary history via gene flow must be considered in studies of independent, repeated trait evolution.

evolutionary biology↗

bioRxiv2017

Insulin resistance in cavefish as an adaptation to a nutrient-limited environment

Periodic food shortage is one of the biggest challenges organisms face in natural habitats. How animals cope with nutrient limited conditions is an active area of study, of particular relevance in the context of the current increasing destabilization of global climate. Caves represent an extreme setting where animals have adapted to nutrient-limited conditions, as most cave environments lack a primary energy source. Here we show that cave-adapted populations of the Mexican Tetra, Astyanax mexicanus, have dysregulated blood glucose homeostasis and are insulin resistant compared to the river-adapted population. We found that multiple cave populations carry a mutation in the insulin receptor that leads to decreased insulin binding in vitro. Surface/cave hybrid fish carrying the allele weigh more than non-carriers, and zebrafish genetically engineered to carry the mutation similarly have increased body weight and insulin resistance. Higher bodyweight may be advantageous in the cave as a strategy to cope with infrequent food. In humans, the identical mutation in the insulin receptor leads to a severe form of insulin resistance and dramatically reduced life-span. However, cavefish have a similar lifespan to surface fish (of greater than fourteen years) and do not accumulate advanced glycated end products (AGEs) in the blood that are typically associated with progression of diabetes-associated pathologies. Our findings raise the intriguing hypothesis that cavefish have acquired compensatory mechanisms that allow them to circumvent the typical negative effects associated with failure to regulate blood glucose.

evolutionary biology↗