Biology subjects

Lind, J.

Publications and source records attributed to Lind, J..

5 recordsLinked to original sources

bioRxiv2024

Species wide inventory of Arabidopsis thaliana organellar variation reveals ample phenotypic variation for photosynthetic performance

Efforts to improve photosynthetic performance are increasingly employing natural genetic variation. However, genetic variation in the organellar genomes (plasmotypes) is often disregarded due to the difficulty of studying the plasmotypes and the lack of evidence that this is a worthwhile investment. Here, we systematically phenotyped plasmotype diversity using Arabidopsis thaliana as a model species. A reanalysis of whole genome resequencing data of 1,531 representative accessions shows that the genetic diversity amongst the mitochondrial genomes is eight times lower than amongst the chloroplast genomes. Plasmotype diversity of the accessions divides the species into two major phylogenetic clusters, within which highly divergent subclusters are distinguished. We combined plasmotypes from 60 A. thaliana accessions with the nuclear genomes (nucleotypes) of four A. thaliana accessions to create a panel of 232 novel cytonuclear genotypes (cybrids). The cybrid plants were grown in a range of different light and temperature conditions and phenotyped using high-throughput phenotyping platforms. Analysis of the phenotypes showed that several plasmotypes alone or in interaction with the nucleotypes have significant effects on photosynthesis, and that the effects are highly dependent on the environment. Moreover, we introduce Plasmotype Association Studies (PAS) as a novel method to reveal plasmotypic effects. Within A. thaliana, several organellar variants can influence photosynthetic phenotypes, which emphasizes the valuable role this variation has on improving photosynthetic performance. The increasing feasibility of producing cybrids in various species calls for further research into how these phenotypes may support breeding goals in crop species. Significance statementPhotosynthesis is one of the few crop traits that has been largely unaddressed which can contribute to increasing crop yield potential. Exploiting genetic variation within organellar genomes presents a promising, yet untapped resource to improve photosynthesis. However, the extent of organellar variation and its impact on photosynthesis within a species remains largely unknown. Using Arabidopsis thaliana as a model species, we revealed highly divergent clusters of organellar variation. We constructed 232 novel combinations of species representative organellar and nuclear genomes, referred to as cybrids. High-throughput phenotyping of these cybrids revealed that organellar variants can substantially impact photosynthesis in different environments. These findings indicate that organellar genomes may be a valuable resource for improving photosynthesis in crops.

plant biology↗

bioRxiv2021

Can associative learning be the general process for intelligent behavior in non-human animals?

The general process- and adaptive specialization hypotheses represent two contrasting explanations for understanding intelligence in non-human animals. The general process hypothesis proposes that associative learning underlies all learning, whereas the adaptive specialization hypothesis suggests additional distinct learning processes required for intelligent behavior. Here, we use a selection of experimental paradigms commonly used in comparative cognition to explore these hypotheses. We tested if a novel computational model of associative learning -- A-learning -- could solve the problems presented in these tests. Results show that this formulation of associative learning suffices as a mechanism for general animal intelligence, without the need for adaptive specialization, as long as adequate motor- and perceptual systems are there to support learning. In one of the tests, however, the addition of a short-term trace memory was required for A-learning to solve that particular task. We further provide a case study showcasing the flexibility, and lack thereof, of associative learning, when looking into potential learning of self-control and the development of behavior sequences. From these simulations we conclude that the challenges do not so much involve the complexity of a learning mechanism, but instead lie in the development of motor- and perceptual systems, and internal factors that motivate agents to explore environments with some precision, characteristics of animals that have been fine-tuned by evolution for million of years. Author summaryWhat causes animal intelligence? One hypothesis is that, among vertebrates, intelligence relies upon the same general processes for both memory and learning. A contrasting hypothesis states that important aspects of animal intelligence come from species- and problem specific cognitive adaptations. Here, we use a recently formulated model of associative learning and subject it, through computer simulations, to a battery of tests designed to probe cognitive abilities in animals. Our computer simulations show that this associative learning model can account well for how animals learn these various tasks. We conclude that a major challenge in understanding animal and machine intelligence lies in describing behavior systems. Specifically, how motor flexibility and perceptual systems together with internal factors allow animals and machines to navigate the world. As a consequence of our results, together with current progress in both animal- and machine learning, we cannot reject the idea that associative learning provides a general process for animal intelligence.

animal behavior and cognition↗

bioRxiv2018

Whatever You Want: Inconsistent Results Is The Rule, Not The Exception, In The Study Of Primate Brain Evolution

Primate brains differ in size and architecture. Hypotheses to explain this variation are numerous and many tests have been carried out. However, after body size has been accounted for there is little left to explain. The proposed explanatory variables for the residual variation are many and covary, both with each other and with body size. Further, the data sets used in analyses have been small, especially in light of the many proposed predictors. Here we report the complete list of models that results from exhaustively combining six commonly used predictors of brain and neocortex size. This provides an overview of how the output from standard statistical analyses changes when the inclusion of different predictors is altered. By using both the most commonly tested brain data set and a new, larger data set, we show that the choice of included variables fundamentally changes the conclusions as to what drives primate brain evolution. Our analyses thus reveal why studies have had troubles replicating earlier results and instead have come to such different conclusions. Although our results are somewhat disheartening, they highlight the importance of scientific rigor when trying to answer difficult questions. It is our position that there is currently no empirical justification to highlight any particular hypotheses, of those adaptive hypotheses we have examined here, as the main determinant of primate brain evolution.

evolutionary biology↗

bioRxiv2018

Social learning through associative processes: A computational theory

Social transmission of information is a key phenomenon in the evolution of behavior and in the establishment of traditions and culture. The diversity of social learning phenomena has engendered a diverse terminology and numerous ideas about underlying learning mechanisms, at the same time that some researchers have called for a unitary analysis of social learning in terms of associative processes. Leveraging previous attempts and a recent computational formulation of associative learning, we analyze the following learning scenarios in some generality: learning responses to social stimuli, including learning to imitate; learning responses to non-social stimuli; learning sequences of actions; learning to avoid danger. We conceptualize social learning as situations in which stimuli that arise from other individuals have an important role in learning. This role is supported by genetic predispositions that either cause responses to social stimuli or enable social stimuli to reinforce specific responses. Our explorations show that, when guided by such predispositions, associative processes can give rise to a wide variety of social learning phenomena, such as stimulus and local enhancement, contextual imitation and simple production imitation, observational conditioning, and social and response facilitation. In addition, we clarify how associative mechanisms can result in transfer of information and behavior from experienced to naive individuals.

animal behavior and cognition↗

bioRxiv2017

FGF4 Retrogene On CFA12 Is Responsible For Chondrodystrophy And Intervertebral Disc Disease In Dogs

Chondrodystrophy in dogs is defined by dysplastic, shortened long bones and premature degeneration and calcification of intervertebral discs. Independent genome-wide association analyses for skeletal dysplasia (short limbs) within a single breed (pBonferroni=0.0072) and intervertebral disc disease (IVDD) across breeds (pBonferroni=4.02x10-10) both identified a significant association to the same region on CFA12. Whole genome sequencing identified a highly expressed FGF4 retrogene within this shared region. The FGF4 retrogene segregated with limb length and had an odds ratio of 51.23 (95% CI = 46.69, 56.20) for IVDD. Long bone length in dogs is a unique example of multiple disease-causing retrocopies of the same parental gene in a mammalian species. FGF signaling abnormalities have been associated with skeletal dysplasia in humans, and our findings present opportunities for both selective elimination of a medically and financially devastating disease in dogs and further understanding of the ever-growing complexity of retrogene biology.

genetics↗