Adaptive gene loss in the common bean pan-genome during range expansion and domestication.

The common bean (Phaseolus vulgaris L.) is a crucial legume crop and an ideal evolutionary model to study adaptive diversity in wild and domesticated populations. Here, we present a common bean pan-genome based on five high-quality genomes and whole-genome reads representing 339 genotypes. It reveals ~234 Mb of additional sequences containing 6,905 protein-coding genes missing from the reference, constituting 49% of all presence/absence variants (PAVs). More non-synonymous mutations are found in PAVs than core genes, probably reflecting the lower effective population size of PAVs and fitness advantages due to the purging effect of gene loss. Our results suggest pan-genome shrinkage occurred during wild range expansion. Selection signatures provide evidence that partial or complete gene loss was a key adaptive genetic change in common bean populations with major implications for plant adaptation. The pan-genome is a valuable resource for food legume research and breeding for climate change mitigation and sustainable agriculture.

The Effects of Preoperative Glycaemic Control (HbA1c) on Bariatric and Metabolic Surgery Outcomes: Data from a Tertiary-Referral Bariatric Centre in the UK.

BACKGROUND: Current recommendations advocate the achievement of an optimal glucose control (HbA1c < 69 mmol/mol) prior to elective surgery to reduce risks of peri- and post-operative complications, but the relevance for this glycaemic threshold prior to Bariatric Metabolic Surgery (BMS) following a specialist weight management programme remains unclear. METHODS: We undertook a retrospective cohort study of patients with type 2 diabetes mellitus (T2DM) who underwent BMS over a 6-year period (2016-2022) at a regional tertiary referral following completion of a specialist multidisciplinary weight management. Post-operative outcomes of interest included 30-day mortality, readmission rates, need for Intensive Care Unit (ICU) care and hospital length of stay (LOS) and were assessed according to HbA1c cut-off values of < 69 (N = 202) and > 69 mmol/mol (N = 67) as well as a continuous variable. RESULTS: A total of 269 patients with T2D were included in this study. Patients underwent primary Roux en-Y gastric bypass (RYGB, n = 136), Sleeve Gastrectomy (SG, n = 124), insertion of gastric band (n = 4) or one-anastomosis gastric bypass (OAGB, n = 4). No significant differences in the rates of complications were observed between the two groups of pre-operative HbA1c cut-off values. No HbA1c threshold was observed for glycaemic control that would affect the peri- and post-operative complications following BMS. CONCLUSIONS: We observed no associations between pre-operative HbA1C values and the risk of peri- and post-operative complications. In the context of a specialist multidisciplinary weight management programme, optimising pre-operative HbA1C to a recommended target value prior to BMS may not translate into reduced risks of peri- and post-operative complications.

Identification of candidate genes for LepR1 resistance against Leptosphaeria maculans in Brassica napus.

Utilising resistance (R) genes, such as LepR1, against Leptosphaeria maculans, the causal agent of blackleg in canola (Brassica napus), could help manage the disease in the field and increase crop yield. Here we present a genome wide association study (GWAS) in B. napus to identify LepR1 candidate genes. Disease phenotyping of 104 B. napus genotypes revealed 30 resistant and 74 susceptible lines. Whole genome re-sequencing of these cultivars yielded over 3 million high quality single nucleotide polymorphisms (SNPs). GWAS in mixed linear model (MLM) revealed a total of 2,166 significant SNPs associated with LepR1 resistance. Of these SNPs, 2108 (97%) were found on chromosome A02 of B. napus cv. Darmor bzh v9 with a delineated LepR1_mlm1 QTL at 15.11-26.08 Mb. In LepR1_mlm1, there are 30 resistance gene analogs (RGAs) (13 nucleotide-binding site-leucine rich repeats (NLRs), 12 receptor-like kinases (RLKs), and 5 transmembrane-coiled-coil (TM-CCs)). Sequence analysis of alleles in resistant and susceptible lines was undertaken to identify candidate genes. This research provides insights into blackleg resistance in B. napus and assists identification of the functional LepR1 blackleg resistance gene.

Urocortin-3 neurons in the perifornical area are critical mediators of chronic stress on female infant-directed behavior.

Infant avoidance and aggression are promoted by activation of the Urocortin-3 expressing neurons of the perifornical area of hypothalamus (PeFAUcn3) in male and female mice. PeFAUcn3 neurons have been implicated in stress, and stress is known to reduce maternal behavior. We asked how chronic restraint stress (CRS) affects infant-directed behavior in virgin and lactating females and what role PeFAUcn3 neurons play in this process. Here we show that infant-directed behavior increases activity in the PeFAUcn3 neurons in virgin and lactating females. Chemogenetic inhibition of PeFAUcn3 neurons facilitates pup retrieval in virgin females. CRS reduces pup retrieval in virgin females and increases activity of PeFAUcn3 neurons, while CRS does not affect maternal behavior in lactating females. Inhibition of PeFAUcn3 neurons blocks stress-induced deficits in pup-directed behavior in virgin females. Together, these data illustrate the critical role for PeFAUcn3 neuronal activity in mediating the impact of chronic stress on female infant-directed behavior.

Plant Genotype to Phenotype Prediction Using Machine Learning.

Genomic prediction tools support crop breeding based on statistical methods, such as the genomic best linear unbiased prediction (GBLUP). However, these tools are not designed to capture non-linear relationships within multi-dimensional datasets, or deal with high dimension datasets such as imagery collected by unmanned aerial vehicles. Machine learning (ML) algorithms have the potential to surpass the prediction accuracy of current tools used for genotype to phenotype prediction, due to their capacity to autonomously extract data features and represent their relationships at multiple levels of abstraction. This review addresses the challenges of applying statistical and machine learning methods for predicting phenotypic traits based on genetic markers, environment data, and imagery for crop breeding. We present the advantages and disadvantages of explainable model structures, discuss the potential of machine learning models for genotype to phenotype prediction in crop breeding, and the challenges, including the scarcity of high-quality datasets, inconsistent metadata annotation and the requirements of ML models.

Machine learning models outperform deep learning models, provide interpretation and facilitate feature selection for soybean trait prediction.

Recent growth in crop genomic and trait data have opened opportunities for the application of novel approaches to accelerate crop improvement. Machine learning and deep learning are at the forefront of prediction-based data analysis. However, few approaches for genotype to phenotype prediction compare machine learning with deep learning and further interpret the models that support the predictions. This study uses genome wide molecular markers and traits across 1110 soybean individuals to develop accurate prediction models. For 13/14 sets of predictions, XGBoost or random forest outperformed deep learning models in prediction performance. Top ranked SNPs by F-score were identified from XGBoost, and with further investigation found overlap with significantly associated loci identified from GWAS and previous literature. Feature importance rankings were used to reduce marker input by up to 90%, and subsequent models maintained or improved their prediction performance. These findings support interpretable machine learning as an approach for genomic based prediction of traits in soybean and other crops.

Basics of Bash.

Use of the Bash command shell and language is one of the fundamental skills of a bioinformatician. This language is required for accessing high performance computing (HPC) services and effectively using these resources to improve your analyses. Bash is completely text based, which is different from many graphic based operating systems, but this language is also highly powerful, allowing for significant automation and reproducibility within analysis pipelines. This chapter aims to teach the fundamentals of Bash, including how to create files and folders, how to sort and search through files, and how to use pipes and loops to automate processes. By the end of this chapter, readers should be ready to undertake their first simple bioinformatics analysis.

QTL Genetic Mapping Study for Traits Affecting Meal Quality in Winter Oilseed Rape (Brassica Napus L.).

Rapeseed (Brassica napus L.) meal is an important source of protein, but the presence of anti-nutritional compounds, such as fibre and glucosinolates, still limits its use as a livestock feed. Understanding the genetic basis of seed fibre biosynthesis would help to manipulate its content in seeds of oilseed rape. Here, we applied high-resolution skim genotyping by sequencing (SkimGBS) and characterised 187,835 single-nucleotide polymorphism (SNP) markers across a mapping population subsequently used for a genetic mapping study (R/qtl). This approach allowed the identification of 11 stable QTL related to seed quality traits and led to the identification of potential functional genes underlying these traits. Among these, key genes with a known role in carbohydrate metabolic process, cell wall, lignin, and flavonoid biosynthesis, including cellulase GH5, TT10/LAC15, TT4, and SUC2, were found. This study furthers the understanding of the molecular mechanisms underlying seed fibre content and provides new markers for molecular breeding in B. napus.

Modelling of gene loss propensity in the pangenomes of three Brassica species suggests different mechanisms between polyploids and diploids.

Plant genomes demonstrate significant presence/absence variation (PAV) within a species; however, the factors that lead to this variation have not been studied systematically in Brassica across diploids and polyploids. Here, we developed pangenomes of polyploid Brassica napus and its two diploid progenitor genomes B. rapa and B. oleracea to infer how PAV may differ between diploids and polyploids. Modelling of gene loss suggests that loss propensity is primarily associated with transposable elements in the diploids while in B. napus, gene loss propensity is associated with homoeologous recombination. We use these results to gain insights into the different causes of gene loss, both in diploids and following polyploidization, and pave the way for the application of machine learning methods to understanding the underlying biological and physical causes of gene presence/absence.

The application of pangenomics and machine learning in genomic selection in plants.

Genomic selection approaches have increased the speed of plant breeding, leading to growing crop yields over the last decade. However, climate change is impacting current and future yields, resulting in the need to further accelerate breeding efforts to cope with these changing conditions. Here we present approaches to accelerate plant breeding and incorporate nonadditive effects in genomic selection by applying state-of-the-art machine learning approaches. These approaches are made more powerful by the inclusion of pangenomes, which represent the entire genome content of a species. Understanding the strengths and limitations of machine learning methods, compared with more traditional genomic selection efforts, is paramount to the successful application of these methods in crop breeding. We describe examples of genomic selection and pangenome-based approaches in crop breeding, discuss machine learning-specific challenges, and highlight the potential for the application of machine learning in genomic selection. We believe that careful implementation of machine learning approaches will support crop improvement to help counter the adverse outcomes of climate change on crop production.

Status and advances in mining for blackleg (Leptosphaeria maculans) quantitative resistance (QR) in oilseed rape (Brassica napus).

KEY MESSAGE: Quantitative resistance (QR) loci discovered through genetic and genomic analyses are abundant in the Brassica napus genome, providing an opportunity for their utilization in enhancing blackleg resistance. Quantitative resistance (QR) has long been utilized to manage blackleg in Brassica napus (canola, oilseed rape), even before major resistance genes (R-genes) were extensively explored in breeding programmes. In contrast to R-gene-mediated qualitative resistance, QR reduces blackleg symptoms rather than completely eliminating the disease. As a polygenic trait, QR is controlled by numerous genes with modest effects, which exerts less pressure on the pathogen to evolve; hence, its effectiveness is more durable compared to R-gene-mediated resistance. Furthermore, combining QR with major R-genes has been shown to enhance resistance against diseases in important crops, including oilseed rape. For these reasons, there has been a renewed interest among breeders in utilizing QR in crop improvement. However, the mechanisms governing QR are largely unknown, limiting its deployment. Advances in genomics are facilitating the dissection of the genetic and molecular underpinnings of QR, resulting in the discovery of several loci and genes that can be potentially deployed to enhance blackleg resistance. Here, we summarize the efforts undertaken to identify blackleg QR loci in oilseed rape using linkage and association analysis. We update the knowledge on the possible mechanisms governing QR and the advances in searching for the underlying genes. Lastly, we lay out strategies to accelerate the genetic improvement of blackleg QR in oilseed rape using improved phenotyping approaches and genomic prediction tools.

Climate change and the need for agricultural adaptation.

Agriculture and food security are predicted to be significantly impacted by climate change, though the impact will vary by region and by crop. Combined with the increasing global population, there is an urgent need for agriculture to adapt to ensure future food security for this growing population. Adaptation strategies include changing land and cropping practices, the development of improved crop varieties and changing food consumption and waste. Recent advances in genomics and agronomy can help alleviate some of the impacts of climate change on food production; however, given the timeframe for crop improvement, significant investment is required to realise these changes. Ultimately, there is a limit as to how far agriculture can adapt to the changing climate, and a political will to reduce the impact of burning of fossil fuels on the global climate is essential for long term food security.

Method for Genome-Wide Association Study: A Soybean Example.

Genome-wide association studies (GWAS) are a valuable approach to identify single nucleotide polymorphisms (SNPs) associated with a phenotype of interest. There are now a variety of R-packages and command line tools available to perform GWAS. Here, we provide an example downloading and filtering SNP data, followed by GWAS analysis using the R-package rMVP.

Differential signaling pathway activation in 7,12-dimethylbenz[a] anthracene (DMBA)-treated mammary stem/progenitor cells from species with varying mammary cancer incidence.

A natural variation exists in the susceptibility to mammary cancer among wild and domestic mammalian species. Mammary stem/progenitor cells (MaSC) represent a primary target cell for transformation; however, little is known about the intrinsic response of these cells to carcinogenic insults. Polycyclic aromatic hydrocarbons (PAH), such as 7,12-dimethylbenz[a]anthracene (DMBA), are abundantly present in the environment and have been linked to the development of mammary cancer in humans and rodents. We treated MaSC from equine (mammary cancer-resistant) and canine (mammary cancer-susceptible) species with DMBA and assessed cytochrome P450 metabolic activity, DNA damage and viability. Our notable findings were that MaSC from both species showed DNA damage following DMBA treatment; however, equine MaSC initiated cell death whereas canine MaSC repaired this DNA damage. Follow-up studies, based on genome-wide transcriptome analyses, revealed that DMBA induced activation of both the intrinsic and extrinsic apoptotic pathways in equine, but not canine, MaSC. Based on these findings, we propose a hypothetical model in which undergoing apoptosis in response to an oncogenic event might contribute to a lower incidence of mammary cancer in certain mammalian species. Such a mechanism would allow for the elimination of DNA-damaged MaSC, and hence, reduce the risk of potential tumor-initiating mutations in these cells.

BB-Cl-Amidine as a novel therapeutic for canine and feline mammary cancer via activation of the endoplasmic reticulum stress pathway.

BACKGROUND: Mammary cancer is highly prevalent in dogs and cats and results in a poor prognosis due to critically lacking viable treatment options. Recent human and mouse studies have suggested that inhibiting peptidyl arginine deiminase enzymes (PAD) may be a novel breast cancer therapy. Based on the similarities between human breast cancer and mammary cancer in dogs and cats, we hypothesized that PAD inhibitors would also be an effective treatment for mammary cancer in these animals. METHODS: Canine and feline mammary cancer cell lines were treated with BB-Cl-Amidine (BB-CLA) and evaluated for viability and tumorigenicity. Endoplasmic reticulum stress was tested by western blot, immunofluorescence, and quantitative reverse transcriptase polymerase chain reaction (qRT-PCR). Canine and feline mammary cancer xenograft models were created using NOD scid gamma (NSG) mice, and were treated with BB-CLA for two weeks. RESULTS: We found that BB-CLA reduced viability and tumorigenicity of canine and feline mammary cancer cell lines in vitro. Additionally, we demonstrated that BB-CLA activates the endoplasmic reticulum stress pathway in these cells by downregulating 78 kDa Glucose-regulated Protein (GRP78), a potential target in breast cancer for molecular therapy, and upregulating the downstream target gene DNA Damage Inducible Transcript 3 (DDIT3). Finally, we established a mouse xenograft model of both canine and feline mammary cancer in which we preliminarily tested the effects of BB-CLA in vivo. CONCLUSION: We propose that our established mouse xenograft models will be useful for the study of mammary cancer in dogs and cats, and furthermore, that BB-CLA has potential as a novel therapeutic for mammary cancer in these species.

A1C predicts type 2 diabetes and impaired glucose tolerance in a population at risk: the community diabetes prevention project.

AIMS: In a population at risk for type 2 diabetes (T2DM), we assessed early physical and metabolic markers that predict progression from normal to impaired glucose tolerance (IGT) and T2DM. METHODS: A total of 388 individuals (22% male, age 46 + 11 years) at risk for T2DM were randomized to Standard (n = 182) or Intervention (n = 206) care and evaluated at baseline and 5 annual follow-up visits, including blood pressure, BMI, A1C, lipids, urine albumin/creatinine ratio, VO2max, fasting glucose, insulin and C-peptide. The Standard group received results of annual lab tests and quarterly newsletters, while the Intervention group received quarterly newsletters and detailed discussions of lab results, routine self-directed activities, semi-annual group meetings and monthly telephone calls for ongoing support. RESULTS: Overall, 359 (93%) returned for at least one follow-up visit and 272 (70%) completed the final 5-year assessment. Return rates, changes in measures and incidence of IGT/T2DM were similar between groups. Low cardiorespiratory fitness (VO2max) was the most prevalent baseline abnormality. A1C and BMI were significant predictors of IGT/T2DM after controlling for other factors. The risk of IGT/T2DM within 5 years was 17.16 (95% CL: 6.169, 47.736) times greater for those with baseline A1C>=5.8% as compared to those <5.8% (p < 0.0001). CONCLUSION: Baseline A1C>=5.8% was a significant predictor of IGT/T2DM within 5 years in a population at high risk for T2DM. A1C is routinely performed among patients with diabetes, however these data and other evidence suggest that it may also be a useful tool for risk assessment and screening.

Bringing adolescents into substance abuse treatment through community outreach and engagement: the Hartford Youth Project.

While outreach and case management services have been shown to improve retention of at-risk youth in behavioral health treatment, these important support services are challenging to implement. The Hartford Youth Project (HYP), established by the Connecticut Department of Children and Families as a pilot for the state adolescent substance abuse treatment system, made outreach and engagement integral to its system of care. HYPbrought together a network of stakeholders: referral sources (juvenile justice, schools, community agencies, child welfare, and families); community-based outreach agencies; treatment providers; and an administrative service organization responsible for project coordination. Culturally competent Engagement Specialists located in community agencies were responsible for: cultivation of referral sources; community outreach; screening and assessment; engagement of youth and families in treatment; case management; service planning; recovery support; and advocacy. This article describes HYP's approach to identifying and engaging youth in treatment, as well as its challenges. Use of family-based treatment models, expectations of referral sources, limited service capacity, youth and family problems, and staff turnover were all factors that affected the outreach and engagement process. Process, baseline assessment and case study data are used to describe the needs and issues specific to Hartford's substance-abusing Latino and African-American youth.

Evaluation of admission blood glucose levels in the intensive care unit.

OBJECTIVE: To assess the availability and clinical value of blood glucose (BG) testing at the time of admission to the intensive care unit (ICU) after such testing was implemented as routine care in the ICU. METHODS: We studied ICU admission BG testing rates and the prevalence of hyperglycemia. In this effort, we assessed the frequency of baseline BG testing in 330 consecutive patients during a period of 3 months and then implemented routine BG monitoring in 1,147 consecutive ICU patients during a 7-month period. RESULTS: Of the total study population, 25% had previously diagnosed diabetes (PDD). At baseline, 70% had BG measured within 4 hours before or after ICU admission (99% of patients with and 60% of patients without PDD). After implementation of routine BG monitoring, there was a significant increase in testing (70% before versus 87% after, P<0.001; 70% during the baseline 3-month period versus 93% in the final 3 months of the study, P<0.001). In patients without PDD, 41% had BG levels < or =140 mg/dL, and 8% had BG concentrations < or =200 mg/dL. Overall in the ICU setting, 57% of BG values < or =140 mg/dL and 33% of BG levels < or =200 mg/dL were in patients without PDD. Frequencies of BG testing by admission diagnosis included the following (at baseline and during the final 3 months after implementation of routine BG tests): postsurgical status (46%, 85%), peripheral vascular disease (51%, 90%), neurologic disease (52%, 83%), gastrointestinal disease (58%, 91%), infection (69%, 100%), and diabetes (100%, 100%). CONCLUSION: Rates of routine BG testing are low in ICU patients without PDD. Elevations in BG levels were detected in 41% of our study patients without PDD, suggesting that routine implementation of BG monitoring in an ICU will identify patients at increased risk for hyperglycemia-associated higher morbidity and mortality.