Biology subjects

Streich, J.

Publications and source records attributed to Streich, J..

5 recordsLinked to original sources

bioRxiv2024

Climate Adaptation and Genetic Differentiation in the Mosquito Species Culex tarsalis

The increasing prevalence of vector-borne diseases around the world highlights the pressing need for an in-depth exploration of the genetic and environmental factors that shape the adaptability and widespread distribution of mosquito populations. This research focuses on Culex tarsalis, a principal vector for various viral diseases including West Nile Virus (WNV). Through the development of a new reference genome and the examination of Restriction-Site Associated DNA sequencing (RAD-seq) data from over 300 individuals and 28 locations, we demonstrate that variables such as temperature, evaporation rates, and the density of vegetation significantly impact the genetic makeup of Cx. tarsalis populations. Among the alleles most strongly associated with environmental factors is a nonsynonymous mutation in a key gene related to circadian rhythms. These results offer new insights into the mechanisms of spread and adaptation in a key North American vector species, which is poised to become a growing health threat to both humans and animals in the face of ongoing climate change.

genomics↗

bioRxiv2023

Clustering the Planet: An Exascale Approach to Determining Global Climatype Zones

We present an exascale approach for producing global scale, high resolution, longitudinally based geoclimate classifications. Using a GPU implementation of the DUO Similarity Metric on the Summit supercomputer, we calculated the pairwise environmental similarity of 156,384,190 vectors of 414,640 encoded elements derived from 71 environmental variables over a 50-year time span at 1km2 resolution. GPU matrix-matrix (GEMM) kernels were optimized for the GPU architecture and their outputs were managed through aggressive concurrent MPI rank CPU communication, calculations, and transfers. Using vector transformation and highly optimized operations of generalized distributed dense linear algebra, calculation of all-vector-pairs similarity resulted in 5.07 x 1021 element comparisons and reached a peak performance of 2.31 exaflops. We demonstrated this method using existing and synthesized climate layers to show how geography can be parsed using high-performance computing. Geoclimate zones are important tools for understanding how environmental variables impact natural systems, particularly for agriculture and conservation with relevance to climate change. Historically, classification systems have been low resolution, based on limited variables, or subjective. To identify climate classes, we clustered DUO outputs at varying stringency, producing 69, 133, 340, and 717 global geoclimate zones. Our approach produced global scale, high resolution, longitudinally informed climate classifications that can be used in precision agriculture, cultivar breeding efforts, and conservation programs.

bioinformatics↗

bioRxiv2021

Climatic clustering and longitudinal analysis with impacts on food, bioenergy, and pandemics

Predicted growth in world population will put unparalleled stress on the need for sustainable energy and global food production, as well as increase the likelihood of future pandemics. In this work, we identify high-resolution environmental zones in the context of a changing climate and predict longitudinal processes relevant to these challenges. We do this using exhaustive vector comparison methods that measure the climatic similarity between all locations on earth at high geospatial resolution. The results are captured as networks, in which edges between geolocations are defined if their historical climates exceed a similarity threshold. We then apply Markov clustering and our novel Correlation of Correlations method to the resulting climatic networks, which provides unprecedented agglomerative and longitudinal views of climatic relationships across the globe. The methods performed here resulted in the fastest (9.37 x 1018 operations/sec) and one of the largest (168.7 x 1021 operations) scientific computations ever performed, with more than 100 quadrillion edges considered for a single climatic network. Correlation and network analysis methods of this kind are widely applicable across computational and predictive biology domains, including systems biology, ecology, carbon cycles, biogeochemistry, and zoonosis research.

systems biology↗

bioRxiv2020

Protoplast fusion in Bacillus species produces frequent, unbiased, genome-wide homologous recombination

In eukaryotes, fine-scale maps of meiotic recombination events have greatly advanced our understanding of the factors that affect genomic variation patterns and evolution of traits. However, in bacteria that lack natural systems for sexual reproduction, unbiased characterization of recombination landscapes has remained challenging due to variable rates of genetic exchange and influence of natural selection. Here, to overcome these limitations and to gain a genome-wide view on recombination, we crossed Bacillus strains with different genetic distances using protoplast fusion. The offspring displayed complex inheritance patterns with one of the parents consistently contributing the major part of the chromosome backbone and multiple unselected fragments originating from the second parent. Computational analyses suggested that this bias is due to the action of restriction-modification systems whereas genome features like GC content and local nucleotide identity did not affect distribution of recombination events around the chromosome. Furthermore, we found that the intensity of recombination is uniform across the genome without concentration into hotspots. Unexpectedly, our results revealed that large species-level genetic distance did not affect key recombination parameters. Our study provides a new insight into the dynamics of recombination in bacteria and a platform for studying recombination patterns in diverse bacterial species.

microbiology↗

bioRxiv2019

Extending the genotype in Brachypodium by including DNA methylation reveals a joint contribution with genetics on adaptive traits

Epigenomic changes have been considered a potential missing link underlying phenotypic variation in quantitative traits but is potentially confounded with the underlying DNA sequence variation. Although the concept of epigenetic inheritance has been discussed in depth, there have been few studies attempting to directly dissect the amount of epigenomic variation within inbred natural populations while also accounting for genetic diversity. By using known genetic relationships between Brachypodium lines, multiple sets of nearly identical accession families were selected for phenotypic studies and DNA methylome profiling to investigate the dual role of (epi)genetics under simulated natural seasonal climate conditions. Despite reduced genetic diversity, appreciable phenotypic variation was still observable in the measured traits (height, leaf width and length, tiller count, flowering time, ear count) between as well as within the inbred accessions. However, with reduced genetic diversity there was diminished variation in DNA methylation within families. Mixed-effects linear modelling revealed large genetic differences between families and a minor contribution of epigenomic variation on phenotypic variation in select traits. Taken together, this analysis suggests a limited but significant contribution of DNA methylation towards heritable phenotypic variation relative to genetic differences.

plant biology↗