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bioRxiv2023

Genetically Defined Subtypes of Layer 5 Somatostatin-Containing Cortical Interneurons

Inhibitory interneurons play a crucial role in proper development and function of the mammalian cerebral cortex. Of the different inhibitory subclasses, dendritic-targeting, somatostatin-containing (SOM) interneurons may be the most diverse. Earlier studies used transgenic mouse lines to identify and characterize subtypes of SOM interneurons by morphological, electrophysiological and neurochemical properties. More recently, large-scale studies classified SOM interneurons into 13 morpho-electro-transcriptomic (MET) types. It remains unclear, however, how these various classification schemes relate to each other, and experimental access to MET types has been limited by the scarcity of type-specific mouse driver lines. To begin to address these issues we crossed Flp and Cre driver mouse lines and a dual-color combinatorial reporter, allowing experimental access to genetically defined SOM subsets. Brains from adult mice of both sexes were retrogradely dye-labeled from the pial surface to identify layer 1-projecting neurons, and immunostained against several marker proteins, allowing correlation of genetic label, axonal target and marker protein expression in the same neurons. Using whole-cell recordings ex-vivo, we compared electrophysiological properties between intersectional and transgenic SOM subsets. We identified two layer 1-targeting intersectional subsets with non-overlapping marker protein expression and electrophysiological properties which, together with a previously characterized layer 4-targeting subtype, account for about half of all layer 5 SOM cells and >40% of all SOM cells, and appear to map onto 5 of the 13 MET types. Genetic access to these subtypes will allow researchers to determine their synaptic inputs and outputs and uncover their roles in cortical computations and animal behavior. SIGNIFICANCE STATEMENTInhibitory neurons are critically important for proper development and function of the cerebral cortex. Although a minority population, they are highly diverse, which poses a major challenge to investigating their contributions to cortical computations and animal and human behavior. As a step towards understanding this diversity we crossed genetically modified mouse lines to allow detailed examination of genetically-defined groups of the most diverse inhibitory subtype, somatostatin-containing interneurons. We identified and characterized three somatostatin subtypes in the deep cortical layers with distinct combinations of anatomical, neurochemical and electrophysiological properties. Future studies could now use these genetic tools to examine how these different subtypes are integrated into the cortical circuit and what roles they play during sensory, cognitive or motor behavior.

neuroscience↗

bioRxiv2023

Genetic variation of the HIV-1 subtype C transmitted/founder viruses long terminal repeat elements and the impact on transcription activation potential and clinical disease outcomes

A genetic bottleneck is a hallmark of HIV-1 transmission such that only very few viral strains, termed transmitted/founder (T/F) variants establish infection in a newly infected host. Phenotypic characteristics of these variants may determine the subsequent course of disease. The HIV-1 5 long terminal repeat (LTR) promoter drives viral gene transcription and is genetically identical to the 3 LTR. We hypothesized that HIV-1 subtype C (HIV-1C) T/F virus LTR genetic variation is a determinant of transcriptional activation potential and clinical disease outcome. The 3LTR was amplified from plasma samples of 41 study participants acutely infected with HIV-1C (Fiebig stages I and V/VI). Paired longitudinal samples were also available at one year post-infection for 31 of the 41 participants. 3 LTR amplicons were cloned into a pGL3-basic luciferase expression vector, and transfected alone or together with Transactivator of transcription (tat) into Jurkat cells in the absence or presence of cell activators (TNF-, PMA, Prostratin and SAHA). Inter-patient T/F LTR sequence diversity was 5.7% with subsequent intrahost viral evolution observed in 48.4% of the participants analyzed at 12 months post-infection. T/F LTR variants exhibited differential basal transcriptional activity, with significantly higher Tat-mediated transcriptional activity compared to basal (p<0.001). Basal and Tat-mediated T/F LTR transcriptional activity showed significant positive correlation with contemporaneous viral loads and negative correlation with CD4 T cell counts (p<0.05) during acute infection respectively. Furthermore, Tat-mediated T/F LTR transcriptional activity significanly correlated positively with viral load set point and viral load; and negatively with CD4 T cell counts at one year post infection (all p<0.05). Lastly, PMA, Prostratin, TNF- and SAHA cell stimulation resulted in enhanced yet heterologous transcriptional activation of different T/F LTR variants. Our data suggest that T/F LTR variants may influence viral transcriptional activity, disease outcomes and sensitivity to cell activation, with potentional implications for therapeutic interventions. Author summaryThere is heterogeneity in the rates of clinical disease progression in antiretroval therapy-naive people living with HIV (PLWH). In heterosexual HIV-1 transmission, only a single or very few viral strains, called transmitted/founder (T/F) viruses establish infection in a newly infected host. The long terminal repeat (LTR) is the viral promoter that drives viral gene transcription and is important for the HIV-1 life cycle. In this study we investigated the impact of HIV-1 subtype C T/F virus LTR genetic variation on transcriptional activity, clinical disease outcomes and response to cell activation. Our data show inter-patient T/F LTR genetic variation and limited intrahost evolution by 12 months post infection. T/F LTR variants exhibit differential basal LTR transcriptional activity, which is significanly increased in the presence of the Transactivator of transcription (Tat) protein. Furthermore, we show that T/F LTR transcription activity significanly correlates positively with viral load and viral load set point but negatively with CD4 T cell count. Lastly, we show that T/F LTR variants exhibit differential responses to cell activators PMA, TNF-, Prostratin and SAHA. Taken together our data suggest that T/F viruses LTR genetic variation and functional heterogeneity are important determinants of clinical outcomes and virus reactivation potential.

molecular biology↗

bioRxiv2023

Importance of genetic architecture in marker selection decisions for genomic prediction

Breeders commonly use genetic markers to predict the performance of untested individuals as a way to improve the efficiency of breeding programs. These genomic prediction models have almost exclusively used single nucleotide polymorphisms (SNPs) as their source of genetic information, even though other types of markers exist, such as structural variants (SVs). Given that SVs are associated with environmental adaptation and not all of them are in linkage disequilibrium to SNPs, SVs have the potential to bring additional information to multi-environment prediction models that are not captured by SNPs alone. Here, we evaluated different marker types (SNPs and/or SVs) on prediction accuracy across a range of genetic architectures for simulated traits across multiple environments. Our results show that SVs can improve prediction accuracy by up to 19%, but it is highly dependent on the genetic architecture of the trait. Differences in prediction accuracy across marker types were more pronounced for traits with high heritability, high number of QTLs, and SVs as causative variants. In these scenarios, using SV markers resulted in better prediction accuracies than SNP markers, especially when predicting untested genotypes across environments, likely due to more predictors being in linkage disequilibrium with causative variants. The simulations revealed little impact of different effect sizes between SNPs and SVs as causative variants on prediction accuracy. This study demonstrates the importance of knowing the genetic architecture of a trait in deciding what markers and marker types to use in large scale genomic prediction modeling in a breeding program. Key messageWe demonstrate potential for improved multi-environment genomic prediction accuracy using structural variant markers. However, the degree of observed improvement is highly dependent on the genetic architecture of the trait.

plant biology↗

bioRxiv2023

Mating systems and recombination landscape strongly shape genetic diversity and selection in wheat relatives

How and why genetic diversity varies among species is a long-standing question in evolutionary biology. Life history traits have been shown to explain a large part of observed diversity. Among them, mating systems have one of the strongest impacts on genetic diversity, with selfing species usually exhibiting much lower diversity than outcrossing relatives. Theory predicts that a high rate of selfing amplifies selection at linked sites, reducing genetic diversity genome wide, but frequent bottlenecks and rapid population turn-over could also explain low genetic diversity in selfers. However, how linked selection varies with mating systems and whether it is sufficient to explain the observed difference between selfers and outcrossers has never been tested. Here, we used the Aegilops/Triticum grass species, a group characterized by contrasted mating systems (from obligate out-crossing to high selfing) and marked recombination rate variation across the genome, to quantify the effects of mating system and linked selection on patterns of neutral and selected polymorphism. By analyzing phenotypic and transcriptomic data of 13 species, we show that selfing strongly affects genetic diversity and the efficacy of selection by amplifying the intensity of linked selection genome wide. In particular, signatures of adaptation were only found in the highly recombining regions in outcrossing species. These results bear implications for the evolution of mating systems and more generally for our understanding of the fundamental drivers of genetic diversity.

evolutionary biology↗

bioRxiv2023

An integrated map of genetic variation from 1,062 wheat genomes

The construction of a high-quality wheat genome variation map is important to wheat genetic studies and breeding. In this study, we developed the second-generation whole-genome genetic variation map of wheat (VMap 2.0) by integrating whole-genome sequencing data of 1,062 diverse wheat accessions from 20 species/subspecies. VMap 2.0 contains 195.96 million single nucleotide polymorphisms (SNPs), 2.22 million insertions, and 4.72 million deletions, achieving a high density of variation map in which one variant exists in every 73 base pairs on average. By systematically analyzing the phylogenetic relationships and genetic diversity of tetraploid wheat, hexaploid wheat, and diploid goatgrass (Aegilops tauschii), we found that the genetic diversity of wild emmer wheat was 2.4 times higher than that of common wheat. In contrast, the genetic diversity of diploid goatgrass is 7.8 times higher than the D subgenome of hexaploid wheat. With the high-density genetic variations, VMap 2.0 is anticipated to facilitate high-resolution trait dissection and expedite prediction-based breeding of wheat.

genomics↗

bioRxiv2023

Host genetic background regulates the capacity for anti-tumor antibody-dependent phagocytosis

BackgroundAntitumor antibody, or targeted immunotherapy, has revolutionized cancer treatment and markedly improved patient outcomes. A prime example is the monoclonal antibody (mAb) trastuzumab, which targets human epidermal growth factor receptor 2 (HER2). However, like many targeted immunotherapies, only a subset of patients benefit from trastuzumab long-term. In addition to tumor-intrinsic factors, we hypothesize that host genetics may influence subsequent immune activation. MethodsTo model the human population, we produced F1 crosses of genetically heterogeneous Diversity Outbred (DO) mice with BALB/c mice (DOCF1). Distinct DOCF1 mice were orthotopically implanted with the BALB/c-syngeneic TUBO mammary tumor line, which expresses the HER2 ortholog rat neu. Treatment with anti-neu mAb clone 7.16.4 began once tumors reached [~]200 mm3. Genetic linkage and quantitative trait locus (QTL) effects analyses in R/qtl2 identified loci associated with tumor growth rates. Locus validation was performed with BALB/c F1 crosses with recombinant-inbred Collaborative Cross (CC) strains selected for therapy-associated driver genetics (CCxCF1). The respective roles of natural killer (NK) cells and macrophages were investigated by selective depletion in vivo. Ex vivo macrophage antibody-dependent phagocytosis (ADCP) assays were evaluated by confocal microscopy using 7.16.4-opsonized E2Crimson-expressing TUBO tumor cells. ResultsWe observed a divergent response to anti-tumor antibody therapy in DOCF1 mice. Genetic linkage analysis detected a locus on chromosome 10 that correlates to a robust response to therapy, which was validated in CCxCF1 models. Single-cell RNA sequencing of tumors from responder and non-responder models identified key differences in tumor immune infiltrate composition, particularly within macrophage (M{varphi}) subsets. This is further supported by ex vivo analysis showing M{varphi} ADCP capacity correlates to in vivo treatment outcomes in both DOCF1 and CCxCF1 models. ConclusionsHost genetics play a key regulatory role in targeted immunotherapy outcomes, and putative causal genes are identified in murine chromosome 10 which may govern M{varphi} function during ADCP.

cancer biology↗

bioRxiv2023

Host obesity impacts genetic variation in influenza A viral populations

Obesity is a chronic health condition characterized by excess adiposity leading to a systemic increase in inflammation and dysregulation of metabolic hormones and immune cell populations. Obesity is well established as a risk factor for many noncommunicable diseases; however, its consequences for infectious disease are poorly understood. Influenza A virus (IAV) is a highly infectious pathogen responsible for seasonal and pandemic influenza. Host risk factors, including compromised immunity and pre-existing health conditions, can contribute to increased infection susceptibility and disease severity. During viral replication in a host, the negative sense single stranded RNA genome of IAV accumulates genetic diversity that may have important consequences for viral evolution and transmission. Here, we investigated the impact of host obesity on IAV genetic variation using a diet induced obesity ferret model. We infected obese and lean male ferrets with the A/Hong Kong/1073/1999 (H9N2) IAV strain. Using a co-caging study design, we investigated the maintenance, generation, and transmission of intrahost IAV genetic variation by sequencing viral genomic RNA obtained from nasal wash samples over multiple days of infection. We found evidence for an enhanced role of positive selection acting on de novo mutations in obese hosts that led to nonsynonymous changes that rose to high frequency. In addition, we identified numerous cases of recurrent low-frequency mutations throughout the genome that were specific to obese hosts. Despite these obese-specific variants, overall viral genetic diversity did not differ significantly between obese and lean hosts. This is likely due to the high supply rate of de novo variation and common evolutionary adaptations to the ferret host regardless of obesity status, which we show are mediated by variation in the hemagglutinin (HA) and polymerase genes (PB2 and PB1). As with single nucleotide variants, we identified a class of defective viral genomes (DVGs) that were found uniquely in either obese or lean hosts, but overall DVG diversity and dynamics did not differ between the two groups. Our study provides the first insight into the consequences of host obesity on viral genetic diversity and adaptation, suggesting that host factors associated with obesity alter the selective environment experienced by a viral population, thereby impacting the spectrum of genetic variation.

microbiology↗

bioRxiv2023

Genetic variability and vector competence of Aedes aegypti populations from Kisumu and Busia Counties, Western Kenya, for Chikungunya and Zika viruses

Aedes aegypti is the primary vector of several arboviruses, including dengue virus (DENV), chikungunya virus (CHIKV), yellow fever virus (YFV), and Zika virus (ZIKV). This vector is widespread globally in tropical and subtropical areas, but also found in temperate areas. Kenya experienced its first chikungunya outbreaks in Lamu County in 2004 and later in Mandera: 2016, and Mombasa: 2017. While there is yet to be a report of Zika outbreaks in Kenya, sero-surveillance studies indicate low-level transmission of this virus in coastal and northern parts of the country. Despite the presence of Ae. aegypti in Kisumu and Busia counties in sufficient densities, and free movement of people between the coast and the two western Kenya counties, no outbreaks of either disease have been reported in these regions. To investigate this phenomenon, we collected Ae. aegypti mosquitoes from county headquarter towns near railway stations connecting the coast and western Kenya and reared them under controlled laboratory conditions. The mosquitoes were then assessed for genetic variability using CO1 genes as well as their efficiency to transmit viruses using Laboratory colonies (F1) of the field mosquitoes challenged with an infectious blood meal containing CHIKV and ZIKV. Genetic analysis revealed the presence of both Ae. aegypti subspecies, (Ae. aegypti aegypti [Aaa] and Ae. aegypti formosus [Aaf]) in the two western Kenya counties, with Aaf being dominant (19:8 for Kisumu samples and 25:6 for Busia samples). Additionally, pairwise comparison revealed minimal genetic differentiation (0.62%) between the study populations, with a high genetic variation (99.38%) observed within each population, indicating significant diversity within individual populations. Ae. aegypti populations from Kisumu and Busia counties exhibited competence for CHIKV, with infection, dissemination, and transmission rates of 55.2%, 85.5%, and 27.1% for Kisumu; and 57.8%, 71.8%, and 25% for Busia populations, respectively. There was no significant difference in vector competence between these two populations. Interestingly, neither population was competent for ZIKV. In conclusion, the data shows that the Ae. aegypti populations in the two cities were homogeneous. This could explain the observed similarity in vector competence for CHIKV and ZIKV. Author SummaryOur study investigated the genetic variability and vector competence of Ae. aegypti mosquito populations in Kisumu and Busia Counties to CHIKV and ZIKV; revealing the presence and even distribution of both Aaa and Aaf subspecies. We also found that the Ae. aegypti populations from the two counties were not genetically differentiated. Furthermore, our study revealed that the Ae. aegypti mosquitoes from Kisumu and Busia counties were competent for CHIKV but may be refractory to ZIKV infection. These findings highlight the importance of continued monitoring of Ae. aegypti populations and their potential for arboviral disease transmission in the region.

microbiology↗

bioRxiv2023

Tillering structures the genetic variability of wheat vegetative growth and its plasticity under water deficit

Leaf expansion under drought drives the trade-off between water saving for later grain production and canopy photosynthesis. Fine-tuning leaf expansion could therefore become a target of genetic progress for drought-prone environments. However, its components (branching, leaf production and elongation) may have their own genetic variability and plasticity under drought, making hard to calibrate crop simulation models and specify breeding targets. In this study, we focused on the genetic diversity of bread wheat and durum wheat to determine the links and trade-offs between the underlying processes of leaf growth under drought and how it translates to leaf expansion at the whole plant and canopy level. For that, we used non-destructive imaging both in the field and controlled condition platforms to determine the dynamics of the components of shoot expansion and analyze their relative contribution to the genetic variability of whole-plant shoot expansion under drought. Results show that leaf expansion measured at plant level in controlled environment was associated with that measured at canopy level in the field, indicating that controlled phenotyping platforms can capture the genetic variability of growth in the field. Both whole-plant and canopy expansion were associated with tillering rate. In addition, the sensitivity of shoot growth and tillering to soil water deficit were correlated, indicating that both tillering ability and sensitivity to water deficit drive the genetic variability of shoot expansion. Overall, dissecting leaf expansion dynamics allowed determining the links between shoot expansion traits under drought, and provides key targets in phenotyping, modelling and breeding for drought environments.

plant biology↗

bioRxiv2023

Deciphering the Genetic Code of Neuronal Type Connectivity: A Bilinear Modeling Approach

Understanding how different neuronal types connect and communicate is critical to interpreting brain function and behavior. However, it has remained a formidable challenge to decipher the genetic underpinnings that dictate the specific connections formed between neuronal types. To address this, we propose a novel bilinear modeling approach that leverages the architecture similar to that of recommendation systems. Our model transforms the gene expressions of presynaptic and postsynaptic neuronal types, obtained from single-cell transcriptomics, into a covariance matrix. The objective is to construct this covariance matrix that closely mirrors a connectivity matrix, derived from connectomic data, reflecting the known anatomical connections between these neuronal types. When tested on a dataset of Caenorhabditis elegans, our model achieved a performance comparable to, if slightly better than, the previously proposed spatial connectome model (SCM) in reconstructing electrical synaptic connectivity based on gene expressions. Through a comparative analysis, our model not only captured all genetic interactions identified by the SCM but also inferred additional ones. Applied to a mouse retinal neuronal dataset, the bilinear model successfully recapitulated recognized connectivity motifs between bipolar cells and retinal ganglion cells, and provided interpretable insights into genetic interactions shaping the connectivity. Specifically, it identified unique genetic signatures associated with different connectivity motifs, including genes important to cell-cell adhesion and synapse formation, highlighting their role in orchestrating specific synaptic connections between these neurons. Our work establishes an innovative computational strategy for decoding the genetic programming of neuronal type connectivity. It not only sets a new benchmark for single-cell transcriptomic analysis of synaptic connections but also paves the way for mechanistic studies of neural circuit assembly and genetic manipulation of circuit wiring.

neuroscience↗

bioRxiv2023

Genetic crosses within and between species of Cryptosporidium

Parasites and their hosts are engaged in rapid coevolution that balances competing mechanisms of virulence, resistance, and evasion. This often leads to host specificity, but genomic reassortment between different strains can enable parasites to jump host barriers and conquer new niches. In the apicomplexan parasite Cryptosporidium genetic exchange has been hypothesized to play a prominent role in adaptation to humans. The sexual lifecycle of the parasite provides a potential mechanism for such exchange; however, the boundaries of Cryptosporidium sex are currently undefined. To explore this experimentally, we established a model for genetic crosses. Drug resistance was engineered using a mutated phenylalanyl tRNA synthetase gene and marking strains with this and the previously used Neo transgene enabled selection of recombinant progeny. This is highly efficient, and genomic recombination is evident and can be continuously monitored in real time by drug resistance, flow cytometry, and PCR mapping. Using this approach multiple loci can now be modified with ease. We demonstrate that essential genes can be ablated by crossing a Cre recombinase driver strain with floxed strains. We further find that genetic crosses are also feasible between species. Crossing C. parvum, a parasite of cattle and humans, and C. tyzzeri a mouse parasite resulted in progeny with a recombinant genome derived from both species that continues to vigorously replicate sexually. These experiments have important fundamental and translational implications for the evolution of Cryptosporidium and open the door to reverse- and forward-genetic analysis of parasite biology and host specificity. Significance statementThe parasite Cryptosporidium is a leading cause of diarrheal disease. While infection is common all around the world, young children experiencing malnutrition are impacted most profoundly, and the disease is an important contributor to early childhood mortality. This study experimentally demonstrates that different strains and even species of Cryptosporidium can recombine their genomes through sex. The progeny of such genetic crosses shows combined features of both parents, with resistance to multiple drugs being one example. Sex thus provides a critical mechanism for the parasite to rapidly adapt to changing environments and hosts. Genetic crosses as an experimental tool may also be harnessed in the future to discover the genes underlying differences in virulence, drug sensitivity, and immunogenicity between parasite isolates.

microbiology↗

bioRxiv2023

Inferring genetic structure of European beech from observations of spectral phenotypes

Biodiversity loss presents a growing threat to the global environment, and systematic and spatially contiguous monitoring is needed to inform mitigation strategies. Monitoring of genetic diversity within species, a key factor when assessing biodiversity loss, is laborious and could be supported by scalable phenotypic observations allowing inferences about genetic variation. We studied genetic and phenotypic variation in one of Europes most prevalent forest-forming trees, the common beech Fagus sylvatica L., using whole-genome sequence data and spectral phenotypes from 219 individuals at 23 sites across the species natural range. Spectral phenotypes were collected under standardized illumination and observation conditions from the same top-of-canopy leaves used for nuclear DNA extraction. We found that spectral and environmental information accounted for 77% of the variance along the first two principal coordinates representing genetic structure among sampled individuals, where spectral phenotypes contributed 12% to the prediction of genetic structure. Further, we identified 14 SNPs (single nucleotide polymorphisms), of which two were located within annotated genes, that showed significant associations with variation in leaf reflectance. Our study demonstrates how linking spectral and genomic variation in tree species may be upscaled to the remote observations to support monitoring, understanding and mitigating loss of genetic diversity within species.

plant biology↗

bioRxiv2023

Genetic and brain similarity independently predict childhood anthropometrics and socioeconomic markers

Linking the developing brain with individual differences in clinical and demographic traits is challenging due to the substantial interindividual heterogeneity of brain anatomy and organization. Here we employ a novel approach that parses individual differences in both cortical thickness and common genetic variants, and assess their effects on a wide set of childhood traits. The approach uses a linear mixed model framework to obtain the unique effects of each type of similarity, as well as their covariance, with the assumption that similarity in cortical thickness may in part be driven by similarity in genetic variants. We employ this approach in a sample of 7760 unrelated children in the ABCD cohort baseline sample (mean age 9.9, 46.8% female). In general, significant associations between cortical thickness similarity and traits were limited to anthropometrics such as height (r2 = 0.11, SE = 0.01), weight (r2 = 0.12, SE = 0.01), and birth weight (r2 = 0.19, SE = 0.01), as well as markers of socioeconomic status such as local area deprivation (r2 = 0.06, SE = 0.01). Analyses of the contribution from common genetic variants to traits revealed contributions across included outcomes, albeit somewhat lower than previous reports, possibly due to the young age of the sample. No significant covariance of the effects of genetic and cortical thickness similarity was found. The present findings highlight the connection between anthropometrics as well as socioeconomic factors and the developing brain, which appear to be independent from individual differences in common genetic variants in this population-based sample. The approach provides a promising framework for analyses of neuroimaging genetics cohorts, which can be further expanded by including imaging derived phenotypes beyond cortical thickness.

neuroscience↗

bioRxiv2023

A deep genetic analysis of banana Fusarium wilt pathogens of Cuba in a Latin American and Caribbean diversity landscape

O_LIFusarium wilt of bananas (FWB) is a devastating plant disease that causes significant economic losses in banana production worldwide and is one of the major concerns for Cuban banana cultivation. The disease is caused by members of the soil-borne Fusarium oxysporum species complex. However, the genetic diversity among Fusarium species infecting bananas in Cuba is currently unknown. C_LIO_LIWe conducted a comprehensive survey of symptomatic banana plants across all production zones of the country and assembled a collection of 170 Fusarium isolates. Using genotyping- by-sequencing and whole-genome comparisons, we investigated the genetic diversity across this suite of isolates and compared it with the genetic diversity of a global Fusarium panel. C_LIO_LITypical FWB symptoms were observed in varieties of the Bluggoe cooking banana and Pisang Awak subgroups in 14 provinces. Phylogenetic analysis revealed that F. purpurascens, F. phialophorum, and F. tardichlamydosporum cause FWB in Cuba, with the latter dominating the population. Furthermore, we identified between five and seven genetic clusters, with F. tardichlamydosporum isolates divided into at least two distinct subgroups, indicating a high genetic diversity of Fusarium spp. causing FWB in the Americas. C_LIO_LIOur study provides unprecedented insights into the population genetic structure and diversity of the FWB pathogen in Cuba and the Latin American and Caribbean regions. C_LI

microbiology↗

bioRxiv2023

High-throughput, microscopy-based screening, and quantification of genetic elements

Synthetic biology relies on the screening and quantification of genetic components to assemble sophisticated gene circuits with specific functions. Microscopy is powerful tool for characterizing complex cellular phenotypes with increasing spatial and temporal resolution to library screening of genetic elements. Microscopy-based assays are powerful tools for characterizing cellular phenotypes with spatial and temporal resolution, and can be applied to large-scale samples for library screening of genetic elements. However, strategies for high-throughput microscopy experiments remain limited. Here, we present a high-throughput, microscopy-based platform that can simultaneously complete the preparation of an 8x12-well agarose pads plate, allowing for the screening of 96 independent strains or experimental conditions in a single experiment. Using this platform, we screened a library of natural intrinsic promoters from Pseudomonas aeruginosa and identified a small subset of robust promoters that drives stable levels of gene expression under varying growth conditions. Additionally, the platform allowed for single-cell measurement of genetic elements over time, enabling the identification of complex and dynamic phenotypes to map genotype in high-throughput. We expected that the platform could be employed to accelerate the identification and characterization of genetic elements in various biological systems, as well as to understand the relationship between cellular phenotypes and internal states, including genotypes and gene expression programs. Impact statementThe high-throughput microscopy-based platform, presented in this study, enables efficient screening of 96 independent strains or experimental conditions in a single experiment, facilitating the rapid identification of genetic elements with desirable features, thereby advancing synthetic biology. The robust promoters identified through this platform, which provide predictable and consistent control over gene expression under varying growth conditions, can be utilized as reliable tools to regulate gene expression in various biological applications, including synthetic biology, metabolic engineering, and gene therapy, where consistent system performance is required.

molecular biology↗

bioRxiv2023

A large effect genetic trade-off is caused by a single mutation in CBF2

Identifying the genetic basis of local adaptation and fitness trade-offs across environments is a central goal of evolutionary biology. Cold acclimation is an adaptive plastic response for surviving seasonal freezing, and costs of acclimation may be a general mechanism for fitness trade-offs across environments in temperate zone species. Starting with locally adapted ecotypes of Arabidopsis thaliana from Italy and Sweden, we examined the fitness consequences of a naturally occurring functional polymorphism in CBF2. This gene encodes a transcription factor that is a major regulator of cold-acclimated freezing tolerance, and resides within a locus responsible for a genetic trade-off for long-term mean fitness. We estimated the consequences of alternate genotypes of CBF2 on 5-year mean fitness and fitness components at the native field sites by comparing near isogenic lines with alternate genotypes of CBF2 to their genetic background ecotypes. The effects of CBF2 were validated at the nucleotide level using gene edited lines in the native genetic backgrounds grown in simulated parental environments. The foreign CBF2 genotype in the local genetic background reduced long-term mean fitness in Sweden by more than 10%, primarily via effects on survival. In Italy, fitness was reduced by more than 20%, primarily via effects on fecundity. At both sites, the effects were temporally variable and much stronger in some years. The gene edited lines confirmed that CBF2 encodes the causal variant underlying this genetic trade-off. Additionally, we demonstrated a substantial fitness cost of cold acclimation, which has broad implications for potential maladaptive responses to climate change.

evolutionary biology↗

bioRxiv2023

Heteroscedastic Personalized Regression Unveils Genetic Basis of Alzheimer's Disease Stratified by Cognitive Level

In contemporary medical research, patient heterogeneity plays a pivotal role in comprehending intricate diseases such as Alzheimers disease and various forms of cancer. Specifically, in the genomic analysis of Alzheimers disease, individual patients may exhibit unique causal mutations that significantly influence their therapeutic trajectory. Conventional models that share numerous parameters across all individuals struggle to discern this heterogeneity and identify the influential factors for individuals. To tackle this challenge, we propose an innovative approach called Heteroscedastic Personalized Regression (Het-PR) to estimate the heterogeneity across samples and obtain personalized models for each sample. We demonstrate the effectiveness and robustness of Het-PR through both simulation and real data experiments. In the simulation experiment, we show that Het-PR outperforms other state-of-the-art models in capturing inter-sample heterogeneity. In the real data experiment, we apply Het-PR to Alzheimers data and show that it can identify persuasive selected genetic factors for each individual patient. Interestingly, our results suggest that there might be different associative SNPs for AD patients stratified by different cognitive levels. Author summaryIn medical research, it has been observed that causes of a disease vary significantly among individuals, especially when looking at complex diseases like Alzheimers disease and cancer. For Alzheimers disease, obesity, age, gender, and depression may play different roles across different patients. When studying the genes of Alzheimers patients, we find that each person might have their own unique genetic changes that can affect their treatment. For example, Alzheimers patients with different genetic mutations may respond differently to the same treatment. Traditional research methods often miss these individual differences and cant always pinpoint important personalized factors for each patient, because they usually use one model for all patients. To better understand these differences, weve introduced a new method, Heteroscedastic Personalized Regression (Het-PR), which generates a personalized model for each individual. Our experiments show that Het-PR is more effective than other leading methods in identifying these patient differences and recognizing Alzheimers genetic basis for each patient through both simulation and real data experiments. When we used Het-PR on real Alzheimers data, it helped us spot key genetic factors for each patient. Additionally, in our study, we excitedly find that different genetic markers in Alzheimers patients are possibly based on their cognitive abilities. Software for Heteroscedastic Personalized Regression is available in https://github.com/rong-hash/Het-PR.

bioinformatics↗

bioRxiv2023

Identification of Genetic and Genomic Influences on Progressive Ethanol Consumption in Diversity Outbred Mice

Genetic factors play a significant role in the risk for development of alcohol use disorder (AUD). Using 3-bottle choice intermittent access ethanol (IEA), we have employed the Diversity Outbred (DO) mouse panel as a model of alcohol use disorder in a genetically diverse population. Through use of gene expression network analysis techniques, in combination with expression quantitative trait loci (eQTL) mapping, we have completed an extensive analysis of the influence of genetic background on gene expression changes in the prefrontal cortex (PFC). This approach revealed that, in DO mice, genes whose expression was significantly disrupted by intermittent ethanol in the PFC also tended to be those whose expression correlated to intake. This finding is in contrast to previous studies of both mice and nonhuman primates. Importantly, these analyses identified genes involved in myelination in the PFC as significantly disrupted by IEA, correlated to ethanol intake, and having significant eQTLs. Genes that code for canonical components of the myelin sheath, such as Mbp, also emerged as key drivers of the gene expression response to intermittent ethanol drinking. Several regulators of myelination were also key drivers of gene expression, and had significant QTLs, indicating that genetic background may play an important role in regulation of brain myelination. These findings underscore the importance of disruption of normal myelination in the PFC in response to prolonged ethanol exposure, that genetic variation plays an important role in this response, and that this interaction between genetics and myelin disruption in the presence of ethanol may underlie previously observed behavioral changes under intermittent access ethanol drinking such as escalation of consumption.

genomics↗