Adherence to Current Dyslipidemia Guideline in Patients Utilizing Statins According to Risk Groups and Gender Differences: The AIZANOI Study.

BACKGROUND: The aim of this study was to assess the adherence to the current European Society of Cardiology dyslipidemia guidelines, the ratio of reaching target values according to risk groups, and the reasons for not reaching LDL-cholesterol (LDL-C) goals in patients on already statin therapy in a cardiology outpatient population. METHODS: The AIZANOI study is a multi-center, cross-sectional observational study including conducted in 9 cardiology centers between August 1, 2021, and November 1, 2021. RESULTS: A total of 1225 patients (mean age 62 ± 11 years, 366 female) who were already on statin therapy for at least 3 months were included. More than half (58.2%) of the patients were using high-intensity statin regimens. Only 26.2% of patients had target LDL-C level according to their risk score. Despite 58.4% of very high-risk patients and 44.4% of high-risk patients have been using a high-intensity statin regimen, only 24.5% of very-high-risk patients and only 34.9% of high-risk patients have reached guideline-recommended LDL-C levels. Most prevalent reason for not using target dose statin was physician preference (physician inertia) (40.3%). CONCLUSION: The AIZANOI study showed that we achieved a target LDL-C level in only 26.2% of patients using statin therapy. Although 58.4% of patients with a very high SCORE risk and 44.4% of patients with a high SCORE risk were using a target dose statin regimen, we were only able to achieve guideline-recommended LDL-C levels in 24.5% and 34.9% of them, respectively, in cardiology outpatients clinics. Physician inertia is one of the major factors in non-adherence to guidelines. These findings highlight that combination therapy is needed in most of the patients.

Chromosome-scale genome assembly of bread wheat's wild relative Triticum timopheevii.

Wheat (Triticum aestivum) is one of the most important food crops with an urgent need for increase in its production to feed the growing world. Triticum timopheevii (2n = 4x = 28) is an allotetraploid wheat wild relative species containing the At and G genomes that has been exploited in many pre-breeding programmes for wheat improvement. In this study, we report the generation of a chromosome-scale reference genome assembly of T. timopheevii accession PI 94760 based on PacBio HiFi reads and chromosome conformation capture (Hi-C). The assembly comprised a total size of 9.35 Gb, featuring a contig N50 of 42.4 Mb and included the mitochondrial and plastid genome sequences. Genome annotation predicted 166,325 gene models including 70,365 genes with high confidence. DNA methylation analysis showed that the G genome had on average more methylated bases than the At genome. In summary, the T. timopheevii genome assembly provides a valuable resource for genome-informed discovery of agronomically important genes for food security.

Harnessing the predicted maize pan-interactome for putative gene function prediction and prioritization of candidate genes for important traits.

The recent assembly and annotation of the 26 maize nested association mapping population founder inbreds have enabled large-scale pan-genomic comparative studies. These studies have expanded our understanding of agronomically important traits by integrating pan-transcriptomic data with trait-specific gene candidates from previous association mapping results. In contrast to the availability of pan-transcriptomic data, obtaining reliable protein-protein interaction (PPI) data has remained a challenge due to its high cost and complexity. We generated predicted PPI networks for each of the 26 genomes using the established STRING database. The individual genome-interactomes were then integrated to generate core- and pan-interactomes. We deployed the PPI clustering algorithm ClusterONE to identify numerous PPI clusters that were functionally annotated using gene ontology (GO) functional enrichment, demonstrating a diverse range of enriched GO terms across different clusters. Additional cluster annotations were generated by integrating gene coexpression data and gene description annotations, providing additional useful information. We show that the functionally annotated PPI clusters establish a useful framework for protein function prediction and prioritization of candidate genes of interest. Our study not only provides a comprehensive resource of predicted PPI networks for 26 maize genomes but also offers annotated interactome clusters for predicting protein functions and prioritizing gene candidates. The source code for the Python implementation of the analysis workflow and a standalone web application for accessing the analysis results are available at https://github.com/eporetsky/PanPPI.

Turkish Real Life Atrial Fibrillation in Clinical Practice: TRAFFIC Study.

BACKGROUND: Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia worldwide and is associated with an increased risk of thromboembolism, ischemic stroke, impaired quality of life, and mortality. The latest research that shows the prevalence and incidence of AF patients in Türkiye was the Turkish Adults' Heart Disease and Risk Factors study, which included 3,450 patients and collected data until 2006/07.The Turkish Real Life Atrial Fibrillation in Clinical Practice (TRAFFIC) study is planned to present current prevalence data, reveal the reflection of new treatment and risk approaches in our country, and develop new prediction models in terms of outcomes. METHODS: The TRAFFIC study is a national, prospective, multicenter, observational registry. The study aims to collect data from at least 1900 patients diagnosed with atrial fibrillation, with the participation of 40 centers from Türkiye. The following data will be collected from patients: baseline demographic characteristics, medical history, vital signs, symptoms of AF, ECG and echocardiographic findings, CHADS2-VASC2 and HAS-BLED (1-year risk of major bleeding) risk scores, interventional treatments, antithrombotic and antiarrhythmic medications, or other medications used by the patients. For patients who use warfarin, international normalized ratio levels will be monitored. Follow-up data will be collected at 6, 12, 18, and 24 months. Primary endpoints are defined as systemic embolism or major safety endpoints (major bleeding, clinically relevant nonmajor bleeding, and minor bleeding as defined by the International Society on Thrombosis and Hemostasis). The main secondary endpoints include major adverse cardiovascular events (systemic embolism, myocardial infarction, and cardiovascular death), all-cause mortality, and hospitalizations due to all causes or specific reasons. RESULTS: The results of the 12-month follow-up of the study are planned to be shared by the end of 2023. CONCLUSION: The TRAFFIC study will reveal the prevalence and incidence, demographic characteristics, and risk profiles of AF patients in Türkiye. Additionally, it will provide insights into how current treatments are reflected in this population. Furthermore, risk prediction modeling and risk scoring can be conducted for patients with AF.

PhosBoost: Improved phosphorylation prediction recall using gradient boosting and protein language models.

Protein phosphorylation is a dynamic and reversible post-translational modification that regulates a variety of essential biological processes. The regulatory role of phosphorylation in cellular signaling pathways, protein-protein interactions, and enzymatic activities has motivated extensive research efforts to understand its functional implications. Experimental protein phosphorylation data in plants remains limited to a few species, necessitating a scalable and accurate prediction method. Here, we present PhosBoost, a machine-learning approach that leverages protein language models and gradient-boosting trees to predict protein phosphorylation from experimentally derived data. Trained on data obtained from a comprehensive plant phosphorylation database, qPTMplants, we compared the performance of PhosBoost to existing protein phosphorylation prediction methods, PhosphoLingo and DeepPhos. For serine and threonine prediction, PhosBoost achieved higher recall than PhosphoLingo and DeepPhos (.78, .56, and .14, respectively) while maintaining a competitive area under the precision-recall curve (.54, .56, and .42, respectively). PhosphoLingo and DeepPhos failed to predict any tyrosine phosphorylation sites, while PhosBoost achieved a recall score of .6. Despite the precision-recall tradeoff, PhosBoost offers improved performance when recall is prioritized while consistently providing more confident probability scores. A sequence-based pairwise alignment step improved prediction results for all classifiers by effectively increasing the number of inferred positive phosphosites. We provide evidence to show that PhosBoost models are transferable across species and scalable for genome-wide protein phosphorylation predictions. PhosBoost is freely and publicly available on GitHub.

Data sharing and ontology use among agricultural genetics, genomics, and breeding databases and resources of the Agbiodata Consortium.

Over the last couple of decades, there has been a rapid growth in the number and scope of agricultural genetics, genomics and breeding databases and resources. The AgBioData Consortium (https://www.agbiodata.org/) currently represents 44 databases and resources (https://www.agbiodata.org/databases) covering model or crop plant and animal GGB data, ontologies, pathways, genetic variation and breeding platforms (referred to as 'databases' throughout). One of the goals of the Consortium is to facilitate FAIR (Findable, Accessible, Interoperable, and Reusable) data management and the integration of datasets which requires data sharing, along with structured vocabularies and/or ontologies. Two AgBioData working groups, focused on Data Sharing and Ontologies, respectively, conducted a Consortium-wide survey to assess the current status and future needs of the members in those areas. A total of 33 researchers responded to the survey, representing 37 databases. Results suggest that data-sharing practices by AgBioData databases are in a fairly healthy state, but it is not clear whether this is true for all metadata and data types across all databases; and that, ontology use has not substantially changed since a similar survey was conducted in 2017. Based on our evaluation of the survey results, we recommend (i) providing training for database personnel in a specific data-sharing techniques, as well as in ontology use; (ii) further study on what metadata is shared, and how well it is shared among databases; (iii) promoting an understanding of data sharing and ontologies in the stakeholder community; (iv) improving data sharing and ontologies for specific phenotypic data types and formats; and (v) lowering specific barriers to data sharing and ontology use, by identifying sustainability solutions, and the identification, promotion, or development of data standards. Combined, these improvements are likely to help AgBioData databases increase development efforts towards improved ontology use, and data sharing via programmatic means. Database URL  https://www.agbiodata.org/databases.

The relationship of the hemoglobin to serum creatinine ratio with long-term mortality in patients with acute coronary syndrome: A retrospective study.

Acute coronary syndrome (ACS) is an urgent clinical condition of cardiovascular diseases. The present study evaluated the predictive efficacy of the hemoglobin to serum creatinine ratio (Hgb/Cr) on long-term mortality in patients with ACS. The ratio, representing the proportion of the 2 values, is cheap, practical, and very easy to calculate at the bedside. Our study included 475 patients who were admitted to the coronary intensive care unit with a diagnosis of ACS and who underwent coronary angiography. The Hgb/Cr ratio was calculated by dividing the admission hemoglobin by the admission serum creatinine. All patient data were collected from the electronic hospital information system, patient files, and the hospital's archive. A comparison of the patients laboratory findings revealed that the Hgb/Cr ratios differed significantly between the survivor and non-survivor group [16.6 (7.7-49) vs 13.8 (4.91-32.8), respectively; P < .001]. A univariate Cox regression analysis showed that the Hgb/Cr ratio was statistically significant in predicting long-term mortality (0.836; 95% confidence interval [CI]: 0.781-0.895; P < .001). After adjusting the model by adding clinically and statistically significant variables, the Hgb/Cr ratio was still an independent predictor of long-term mortality (0.886; 95% CI: 0.815-0.963; P = .004). The Hgb/Cr ratio's discriminant ability was tested with an receiver operating characteristic curve analysis. The Hgb/Cr ratio's area under the curve value was 0.679 (95% CI: 0.609-0.750; P < .001). A survival analysis using the Kaplan-Meier curve of the 2 Hgb/Cr ratio groups (according to cutoff value) revealed that the low-Hgb/Cr group had a significantly higher mortality rate than high-Hgb/Cr group. The Hgb/Cr ratio was found to be an independent predictor of long-term mortality in ACS patients.

Prognostic significance of medical therapy in patients with heart failure with reduced ejection fraction.

AIMS: The use of guideline-directed medical therapy (GDMT) among patients with heart failure (HF) with reduced ejection fraction (HFrEF) remains suboptimal. The SMYRNA study aims to identify the clinical factors for the non-use of GDMT and to determine the prognostic significance of GDMT in patients with HFrEF in a real-life setting. METHODS AND RESULTS: The SMYRNA study is a prospective, multicentre, and observational study that included outpatients with HFrEF. Patients were divided into three groups according to the status of GDMT at the time of enrolment: (i) patients receiving all classes of HF medications including renin-angiotensin system (RAS) inhibitors, beta-blockers, and mineralocorticoid receptor antagonists (MRAs); (ii) patients receiving any two classes of HF medications (RAS inhibitors and beta-blockers, or RAS inhibitors and MRAs, or beta-blockers and MRAs); and (iii) either patients receiving class of HF medications (only one therapy) or patients not receiving any class of HF medications. The primary outcome was a composite of hospitalization for HF or cardiovascular death. The study population consisted of 1062 patients with HFrEF, predominantly men (69.1%), with a median age of 68 (range: 20-96) years. RAS inhibitors, beta-blockers, and MRAs were prescribed in 76.0%, 89.4%, and 55.1% of the patients, respectively. The proportions of patients receiving target doses of guideline-directed medications were 24.4% for RAS inhibitors, 11.0% for beta-blockers, and 11.1% for MRAs. Overall, 491 patients (46.2%) were treated with triple therapy, 353 patients (33.2%) were treated with any two classes of HF medications, and 218 patients (20.6%) were receiving only one class of HF medication or not receiving any HF medication. Patient-related factors comprising older age, New York Heart Association functional class, rural living, presence of hypertension, and history of myocardial infarction were independently associated with the use or non-use of GDMT. During the median 24-month period, the primary composite endpoint occurred in 362 patients (34.1%), and 177 of 1062 (16.7%) patients died. Patients treated with two or three classes of HF medications had a decreased risk of hospitalization for HF or cardiovascular death compared with those patients receiving ≤1 class of HF medication [hazard ratio (HR): 0.65; 95% confidence interval (CI): 0.49-0.85; P = 0.002, and HR: 0.61; 95% CI: 0.47-0.79; P < 0.001, respectively]. CONCLUSIONS: The real-life SMYRNA study provided comprehensive data about the clinical factors associated with the non-use of GDMT and showed that suboptimal GDMT is associated with an increased risk of hospitalization for HF or cardiovascular death in patients with HFrEF.

The Definition of Sarcomeric and Non-Sarcomeric Gene Mutations in Hypertrophic Cardiomyopathy Patients: A Multicenter Diagnostic Study Across Türkiye.

BACKGROUND: Hypertrophic cardiomyopathy is a common genetic heart disease and up to 40%-60% of patients have mutations in cardiac sarcomere protein genes. This genetic diagnosis study aimed to detect pathogenic or likely pathogenic sarcomeric and non-sarcomeric gene mutations and to confirm a final molecular diagnosis in patients diagnosed with hypertrophic cardiomyopathy. METHODS: A total of 392 patients with hypertrophic cardiomyopathy were included in this nationwide multicenter study conducted at 23 centers across Türkiye. All samples were analyzed with a 17-gene hypertrophic cardiomyopathy panel using next-generation sequencing technology. The gene panel includes ACTC1, DES, FLNC, GLA, LAMP2, MYBPC3, MYH7, MYL2, MYL3, PLN, PRKAG2, PTPN11, TNNC1, TNNI3, TNNT2, TPM1, and TTR genes. RESULTS: The next-generation sequencing panel identified positive genetic variants (variants of unknown significance, likely pathogenic or pathogenic) in 12 genes for 121 of 392 samples, including sarcomeric gene mutations in 30.4% (119/392) of samples tested, galactosidase alpha variants in 0.5% (2/392) of samples and TTR variant in 0.025% (1/392). The likely pathogenic or pathogenic variants identified in 69 (57.0%) of 121 positive samples yielded a confirmed molecular diagnosis. The diagnostic yield was 17.1% (15.8% for hypertrophic cardiomyopathy variants) for hypertrophic cardiomyopathy and hypertrophic cardiomyopathy phenocopies and 0.5% for Fabry disease. CONCLUSIONS: Our study showed that the distribution of genetic mutations, the prevalence of Fabry disease, and TTR amyloidosis in the Turkish population diagnosed with hypertrophic cardiomyopathy were similar to the other populations, but the percentage of sarcomeric gene mutations was slightly lower.

The Impact of Timing of Fixed Dose Triple Antihypertensive Combinations on Ambulatory Blood Pressure Monitoring Parameters.

INTRODUCTION: Re-establishing "dipping" physiology significantly reduces cardiovascular events. The aim was to investigate the effect of timing of fixed dose triple antihypertensive combinations on blood pressure (BP) control. METHODS: One hundred sixteen consecutive patients (62.7 ± 10.7 years, 38 men) with grade II hypertension were randomized into four groups. Group 1 and Group 2 patients were given angiotensin converting enzyme inhibitor-based triple antihypertensive pills to be taken in the morning or evening, respectively while Group 3 and Group 4 patients were given angiotensin receptor blocker (ARB) based triple antihypertensive pills to be taken in the morning or evening, respectively. All patients underwent 24-h ambulatory BP monitoring 1 month after the initiation of treatment. RESULTS: There were not any significant differences in the characteristics, BP values and loads among groups. All patients in each group had good BP control. Dipping pattern in systolic BP was observed significantly less in Group 3 patients taking ARB in the morning (3 patients) compared to other groups (12 patients) in each group, [P = .025]. Similarly, dipping pattern in diastolic BP was observed significantly less in Group 3 patients (4 patients) compared to others (13 patients) in Group 1 and 15 patients in Group 2 and Group 4, [P = .008]. Nondipping pattern was significantly associated with taking ARB in the morning, even when adjusted by age, sex, and other comorbidities. CONCLUSION: Fixed dose triple antihypertensive drug combinations enable good BP control regardless of the timing of drug while ARB-based ones may be taken in the evening to ensure dipping physiology.

Real-World Evaluation of Anticoagulant Treatment Patterns in Patients with Atrial Fibrillation: Data from Multicenter ROTA Study.

OBJECTIVE: Oral anticoagulant therapy is the cornerstone of atrial fibrillation management to prevent stroke and systemic embolism. However, there is limited real-world information regarding stroke and systemic embolism prevention strategies in patients with atrial fibrillation. The aim of the ROTA study is to obtain the real-world data of anticoagulant treatment patterns in patients with atrial fibrillation. METHODS: The ROTA study is a prospective, multicenter, and observational study that included 2597 patients with atrial fibrillation. The study population was recruited from 41 cardiology outpatient clinics between January 2021 and May 2021. RESULTS: The median age of the study population was 72 years (range: 22-98 years) and 57.4% were female. The median CHA2DS2-VASc and HAS-BLED scores were 4 (range: 0-9) and 1 (range: 0-6), respectively. Vitamin K antagonists and direct oral anticoagulants were used in 15.9% and 79.4% of patients, respectively. The mean time in therapeutic range was 52.9% for patients receiving vitamin K antagonists, and 76% of those patients had an inadequate time in therapeutic range with <70%. The most common prescribed direct oral anticoagulants were rivaroxaban (38.1%), apixaban (25.5%), and edoxaban (11.2%). The rate of overuse of vitamin K antagonists and direct oral anticoagulants was high (76.1%) in patients with low stroke risk, and more than one-fourth of patients on direct oral anticoagulant therapy were receiving a reduced dose of direct oral anticoagulants. Among patients who were on direct oral anticoagulant treatment, patients with apixaban treatment were older, had higher CHA2DS2-VASc and HAS-BLED scores, and had lower creatinine clearance than the patients receiving other direct oral anticoagulants. CONCLUSIONS: The ROTA study provides important real-world information about anticoagulant treatment patterns in patients with atrial fibrillation.time in therapeutic range with <70%.

Influence of Intermittent Fasting During Ramadan on Circadian Variation of Symptom-Onset and Prehospital Time Delay in Acute ST-Segment Elevation Myocardial Infarction.

Ramadan interferes with circadian rhythms mainly by disturbing the routine patterns of feeding and smoking. The objective of this study was to investigate the circadian pattern of ST elevation acute myocardial infarction (STEMI) during the month of Ramadan. We studied consecutive STEMI patients 1 month before and after Ramadan (non-Ramadan group-NRG) and during Ramadan (Ramadan group-RG). The RG group was also divided into two groups, based on whether they chose to fast: fasting (FG) and non-fasting group (NFG). The time of STEMI onset was compared. A total of 742 consecutive STEMI patients were classified into 4 groups by 6 h intervals according to time-of-day at symptom onset. No consistent circadian variation in the onset of STEMI was observed both between the RG (P = .938) and NRG (P = .766) or between the FG (P = .232) and NFG (P = .523). When analyzed for subgroups of the study sample, neither smoking nor diabetes showed circadian rhythm. There was a trend towards a delay from symptom onset to hospital presentation, particularly at evening hours in the RG compared with the control group. In conclusion, there was no significant difference in STEMI onset time, but the time from symptom onset to hospital admission was significantly delayed during Ramadan.

PrevAleNce and Associated factors of inappropriaTe dosing of direct Oral anticoaguLants In pAtients with Atrial Fibrillation: the ANATOLIA-AF Study.

PURPOSE: Inappropriate dosing of direct oral anticoagulants is associated with an increased risk of stroke, systemic embolism, major bleeding, cardiovascular hospitalization, and death in patients with atrial fibrillation. The main goal of the study was to determine the prevalence and associated factors of inappropriate dosing of direct oral anticoagulants in real-life settings. METHODS: This study was a multicenter, cross-sectional, observational study that included 2004 patients with atrial fibrillation. The study population was recruited from 41 cardiology outpatient clinics between January and May 2021. The main criteria for inappropriate direct oral anticoagulant dosing were defined according to the recommendations of the European Heart Rhythm Association. RESULTS: The median age of the study population was 72 years and 58% were women. Nine-hundred and eighty-seven patients were prescribed rivaroxaban, 658 apixaban, 239 edoxaban, and 120 dabigatran. A total of 498 patients (24.9%) did not receive the appropriate dose of direct oral anticoagulants. In a logistic regression model, advanced age, presence of chronic kidney disease and permanent atrial fibrillation, prescription of reduced doses of direct oral anticoagulants or edoxaban treatment, concomitant use of amiodarone treatment, and non-use of statin treatment were significantly associated with potentially inappropriate dosing of direct oral anticoagulants. CONCLUSION: The study demonstrated that the prevalence of inappropriate direct oral anticoagulant dosing according to the European Heart Rhythm Association recommendations was 24.9% in patients with atrial fibrillation. Several demographic and clinical factors were associated with the inappropriate prescription of direct oral anticoagulants.

Co-expression pan-network reveals genes involved in complex traits within maize pan-genome.

BACKGROUND: With the advances in the high throughput next generation sequencing technologies, genome-wide association studies (GWAS) have identified a large set of variants associated with complex phenotypic traits at a very fine scale. Despite the progress in GWAS, identification of genotype-phenotype relationship remains challenging in maize due to its nature with dozens of variants controlling the same trait. As the causal variations results in the change in expression, gene expression analyses carry a pivotal role in unraveling the transcriptional regulatory mechanisms behind the phenotypes. RESULTS: To address these challenges, we incorporated the gene expression and GWAS-driven traits to extend the knowledge of genotype-phenotype relationships and transcriptional regulatory mechanisms behind the phenotypes. We constructed a large collection of gene co-expression networks and identified more than 2 million co-expressing gene pairs in the GWAS-driven pan-network which contains all the gene-pairs in individual genomes of the nested association mapping (NAM) population. We defined four sub-categories for the pan-network: (1) core-network contains the highest represented ~ 1% of the gene-pairs, (2) near-core network contains the next highest represented 1-5% of the gene-pairs, (3) private-network contains ~ 50% of the gene pairs that are unique to individual genomes, and (4) the dispensable-network contains the remaining 50-95% of the gene-pairs in the maize pan-genome. Strikingly, the private-network contained almost all the genes in the pan-network but lacked half of the interactions. We performed gene ontology (GO) enrichment analysis for the pan-, core-, and private- networks and compared the contributions of variants overlapping with genes and promoters to the GWAS-driven pan-network. CONCLUSIONS: Gene co-expression networks revealed meaningful information about groups of co-regulated genes that play a central role in regulatory processes. Pan-network approach enabled us to visualize the global view of the gene regulatory network for the studied system that could not be well inferred by the core-network alone.

Capturing Wheat Phenotypes at the Genome Level.

Recent technological advances in next-generation sequencing (NGS) technologies have dramatically reduced the cost of DNA sequencing, allowing species with large and complex genomes to be sequenced. Although bread wheat (Triticum aestivum L.) is one of the world's most important food crops, efficient exploitation of molecular marker-assisted breeding approaches has lagged behind that achieved in other crop species, due to its large polyploid genome. However, an international public-private effort spanning 9 years reported over 65% draft genome of bread wheat in 2014, and finally, after more than a decade culminated in the release of a gold-standard, fully annotated reference wheat-genome assembly in 2018. Shortly thereafter, in 2020, the genome of assemblies of additional 15 global wheat accessions was released. As a result, wheat has now entered into the pan-genomic era, where basic resources can be efficiently exploited. Wheat genotyping with a few hundred markers has been replaced by genotyping arrays, capable of characterizing hundreds of wheat lines, using thousands of markers, providing fast, relatively inexpensive, and reliable data for exploitation in wheat breeding. These advances have opened up new opportunities for marker-assisted selection (MAS) and genomic selection (GS) in wheat. Herein, we review the advances and perspectives in wheat genetics and genomics, with a focus on key traits, including grain yield, yield-related traits, end-use quality, and resistance to biotic and abiotic stresses. We also focus on reported candidate genes cloned and linked to traits of interest. Furthermore, we report on the improvement in the aforementioned quantitative traits, through the use of (i) clustered regularly interspaced short-palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9)-mediated gene-editing and (ii) positional cloning methods, and of genomic selection. Finally, we examine the utilization of genomics for the next-generation wheat breeding, providing a practical example of using in silico bioinformatics tools that are based on the wheat reference-genome sequence.

Predicting Tissue-Specific mRNA and Protein Abundance in Maize: A Machine Learning Approach.

Machine learning and modeling approaches have been used to classify protein sequences for a broad set of tasks including predicting protein function, structure, expression, and localization. Some recent studies have successfully predicted whether a given gene is expressed as mRNA or even translated to proteins potentially, but given that not all genes are expressed in every condition and tissue, the challenge remains to predict condition-specific expression. To address this gap, we developed a machine learning approach to predict tissue-specific gene expression across 23 different tissues in maize, solely based on DNA promoter and protein sequences. For class labels, we defined high and low expression levels for mRNA and protein abundance and optimized classifiers by systematically exploring various methods and combinations of k-mer sequences in a two-phase approach. In the first phase, we developed Markov model classifiers for each tissue and built a feature vector based on the predictions. In the second phase, the feature vector was used as an input to a Bayesian network for final classification. Our results show that these methods can achieve high classification accuracy of up to 95% for predicting gene expression for individual tissues. By relying on sequence alone, our method works in settings where costly experimental data are unavailable and reveals useful insights into the functional, evolutionary, and regulatory characteristics of genes.

Experimental and computational studies of cellulases as bioethanol enzymes.

Bioethanol industries and bioprocesses have many challenges that constantly impede commercialization of the end product. One of the bottlenecks in the bioethanol industry is the challenge of discovering highly efficient catalysts that can improve biomass conversion. The current promising bioethanol conversion catalysts are microorganism-based cellulolytic enzymes, but lack optimization for high bioethanol conversion, due to biological and other factors. A better understanding of molecular underpinnings of cellulolytic enzyme mechanisms and significant ways to improve them can accelerate the bioethanol commercial production process. In order to do this, experimental methods are the primary choice to evaluate and characterize cellulase's properties, but they are time-consuming and expensive. A time-saving, complementary approach involves computational methods that evaluate the same properties and improves our atomistic-level understanding of enzymatic mechanism of action. Theoretical methods in many cases have proposed research routes for subsequent experimental testing and validation, reducing the overall research cost. Having a plethora of tools to evaluate cellulases and the yield of the enzymatic process will aid in planning more optimized experimental setups. Thus, there is a need to connect the computational evaluation methods with the experimental methods to overcome the bottlenecks in the bioethanol industry. This review discusses various experimental and computational methods and their use in evaluating the multiple properties of cellulases.

G4Boost: a machine learning-based tool for quadruplex identification and stability prediction.

BACKGROUND: G-quadruplexes (G4s), formed within guanine-rich nucleic acids, are secondary structures involved in important biological processes. Although every G4 motif has the potential to form a stable G4 structure, not every G4 motif would, and accurate energy-based methods are needed to assess their structural stability. Here, we present a decision tree-based prediction tool, G4Boost, to identify G4 motifs and predict their secondary structure folding probability and thermodynamic stability based on their sequences, nucleotide compositions, and estimated structural topologies. RESULTS: G4Boost predicted the quadruplex folding state with an accuracy greater then 93% and an F1-score of 0.96, and the folding energy with an RMSE of 4.28 and R2 of 0.95 only by the means of sequence intrinsic feature. G4Boost was successfully applied and validated to predict the stability of experimentally-determined G4 structures, including for plants and humans. CONCLUSION: G4Boost outperformed the three machine-learning based prediction tools, DeepG4, Quadron, and G4RNA Screener, in terms of both accuracy and F1-score, and can be highly useful for G4 prediction to understand gene regulation across species including plants and humans.