Meta-QTLs, ortho-meta QTLs and related candidate genes for yield and its component traits under water stress in wheat (Triticum aestivum L.).

Meta-QTLs (MQTLs), ortho-MQTLs, and related candidate genes (CGs) for yield and its seven component traits evaluated under water deficit conditions were identified in wheat. For this purpose, a high density consensus map and 318 known QTLs were used for identification of 56 MQTLs. Confidence intervals (CIs) of the MQTLs were narrower (0.7-21 cM; mean = 5.95 cM) than the CIs of the known QTLs (0.4-66.6 cM; mean = 12.72 cM). Forty-seven MQTLs were co-located with marker trait associations reported in previous genome-wide association studies. Nine selected MQTLs were declared as 'breeders MQTLs' for use in marker-assisted breeding (MAB). Utilizing known MQTLs and synteny/collinearity among wheat, rice and maize, 12 ortho-MQTLs were also identified. A total of 1497 CGs underlying MQTLs were also identified, which were subjected to in-silico expression analysis, leading to identification of 64 differentially expressed CGs (DECGs) under normal and water deficit conditions. These DECGs encoded a variety of proteins, including the following: zinc finger, cytochrome P450, AP2/ERF domain-containing proteins, plant peroxidase, glycosyl transferase, glycoside hydrolase. The expression of 12 CGs at seedling stage (3 h stress) was validated using qRT-PCR in two wheat genotypes, namely Excalibur (drought tolerant) and PBW343 (drought sensitive). Nine of the 12 CGs were up-regulated and three down-regulated in Excalibur. The results of the present study should prove useful for MAB, for fine mapping of promising MQTLs and for cloning of genes across the three cereals studied. Supplementary Information: The online version contains supplementary material available at 10.1007/s12298-023-01301-z.

Intra-femoral tunnel graft lengths less than 20 mm do not predispose to early graft failure, inferior outcomes or poor function. A prospective clinico-radiological comparative study.

PURPOSE: Increasing demands on skills with mounting pressures from expectations from arthroscopic anterior cruciate ligament (ACL) reconstructions requires precise knowledge of technical details by surgeons. One such element is the minimum length of graft in femoral tunnel to allow for adequate tendon-to-bone healing and early return to activities and sports. This has, however, remained an unanswered question. PURPOSE: To study and compare clinico-radiological outcomes of ACL reconstructions in patients with < 20 mm of intra-femoral tunnel graft length with those measuring ≥ 20 mm. METHODS: All eligible patients undergoing arthroscopic ACL reconstruction were sequentially divided into two groups based on the intra-femoral tunnel graft lengths (A: < 20 mm, n = 27; and B: ≥ 20 mm, n = 25). Exclusions were made for those > 45 years of age, with chondral and/or multi-ligamentous injuries and with systemic pathologies. All patients were postoperatively evaluated in clinics by physical examination and functional scoring (Lysholm and modified Cincinnati scores) at 3, 6 and 12-month intervals. Graft vascularity was assessed by signal-to-noise quotient ratio (SNQR) using magnetic resonance imaging (MRI) at 3 and 12 months. RESULTS: No significant differences were noted in mean Lysholm and modified Cincinnati scores between the two groups at the end of 1 year. There were also no significant differences in graft maturation over time and SNQR at 3 and 12 months in the region of interest (ROI). CONCLUSIONS: Intra-femoral tunnel graft length of less than 20 mm does not compromise early clinical and functional outcomes of ACL reconstructions.

Leaf rust responsive miRNA and their target genes in wheat.

Small RNA sequencing (sRNA-seq) and degradome analysis were used for the identification of miRNAs and their target host genes in a pair of near-isogenic lines (NILs), which differed for the presence of leaf rust resistance gene Lr28. The study led to identification of (i) 506 known and 346 novel miRNAs; and (ii) 5054 target genes including 4557 in silico predicted and 497 degradome-based genes using 105 differentially expressed (DE) miRNAs. A subset of 128 targets (67 in silico + 61 degradome-based) was differentially expressed in RNA-seq data that was generated by us earlier using the same pair of NILs; among these 128 targets, 58 target genes exhibited an inverse relationship with the DE miRNAs (expression of miRNAs and activation/suppression of target genes). Eight miRNAs which belonged to the conserved miRNA families and were known to be induced in response to fungal diseases in plants included the following: miR156, miR158, miR159, miR168, miR169, miR172, miR319, miR396. The target genes belonged to the following classes of genes known to be involved in downstream disease resistance pathways; peroxidases, sugar transporters, auxin response signaling, oxidation-reduction, etc. It was also noticed that although a majority of miRNAs and target genes followed the above classical inverse relationship, there were also examples, where no such relationship was observed. Among the target genes, there were also 51 genes that were not only regulated by miRNAs, but were also differentially methylated at sequences including the following segments: promotors, introns, TSS, exons. The results of the present study suggest a complex interplay among miRNA genes, target genes, and various epigenetic controls, which regulate the expression of genes involved in downstream pathways for disease resistance.

Meta-QTLs for multiple disease resistance involving three rusts in common wheat (Triticum aestivum L.).

KEY MESSAGE: In wheat, multiple disease resistance meta-QTLs (MDR-MQTLs) and underlying candidate genes for the three rusts were identified which may prove useful for development of resistant cultivars. Rust diseases in wheat are a major threat to global food security. Therefore, development of multiple disease-resistant cultivars (resistant to all three rusts) is a major goal in all wheat breeding programs worldwide. In the present study, meta-QTLs and candidate genes for multiple disease resistance (MDR) involving all three rusts were identified using 152 individual QTL mapping studies for resistance to leaf rust (LR), stem rust (SR), and yellow rust (YR). From these 152 studies, a total of 1,146 QTLs for resistance to three rusts were retrieved, which included 368 QTLs for LR, 291 QTLs for SR, and 487 QTLs for YR. Of these 1,146 QTLs, only 718 QTLs could be projected onto the consensus map saturated with 2, 34,619 markers. Meta-analysis of the projected QTLs resulted in the identification of 86 MQTLs, which included 71 MDR-MQTLs. Ten of these MDR-MQTLs were referred to as the 'Breeders' MQTLs'. Seventy-eight of the 86 MQTLs could also be anchored to the physical map of the wheat genome, and 54 MQTLs were validated by marker-trait associations identified during earlier genome-wide association studies. Twenty MQTLs (including 17 MDR-MQTLs) identified in the present study were co-localized with 44 known R genes. In silico expression analysis allowed identification of several differentially expressed candidate genes (DECGs) encoding proteins carrying different domains including the following: NBS-LRR, WRKY domains, F-box domains, sugar transporters, transferases, etc. The introgression of these MDR loci into high-yielding cultivars should prove useful for developing high yielding cultivars with resistance to all the three rusts.

An Update on Resistance Genes and Their Use in the Development of Leaf Rust Resistant Cultivars in Wheat.

Wheat is one of the most important cereal crops in the world. The production and productivity of wheat is adversely affected by several diseases including leaf rust, which can cause yield losses, sometimes approaching >50%. In the present mini-review, we provide updated information on (i) all Lr genes including those derived from alien sources and 14 other novel resistance genes; (ii) a list of QTLs identified using interval mapping and MTAs identified using GWAS (particular those reported recently i.e., after 2018) and their association with known Lr genes; (iii) introgression/pyramiding of individual Lr genes in commercial/prominent cultivars from 18 different countries including India. Challenges and future perspectives of breeding for leaf rust resistance are also provided at the end of this mini-review. We believe that the information in this review will prove useful for wheat geneticists/breeders, not only in the development of leaf rust-resistant wheat cultivars, but also in the study of molecular mechanism of leaf rust resistance in wheat.

COVID-WideNet-A capsule network for COVID-19 detection.

Ever since the outbreak of COVID-19, the entire world is grappling with panic over its rapid spread. Consequently, it is of utmost importance to detect its presence. Timely diagnostic testing leads to the quick identification, treatment and isolation of infected people. A number of deep learning classifiers have been proved to provide encouraging results with higher accuracy as compared to the conventional method of RT-PCR testing. Chest radiography, particularly using X-ray images, is a prime imaging modality for detecting the suspected COVID-19 patients. However, the performance of these approaches still needs to be improved. In this paper, we propose a capsule network called COVID-WideNet for diagnosing COVID-19 cases using Chest X-ray (CXR) images. Experimental results have demonstrated that a discriminative trained, multi-layer capsule network achieves state-of-the-art performance on the COVIDx dataset. In particular, COVID-WideNet performs better than any other CNN based approaches for diagnosis of COVID-19 infected patients. Further, the proposed COVID-WideNet has the number of trainable parameters that is 20 times less than that of other CNN based models. This results in fast and efficient diagnosing COVID-19 symptoms and with achieving the 0.95 of Area Under Curve (AUC), 91% of accuracy, sensitivity and specificity respectively. This may also assist radiologists to detect COVID and its variant like delta.

Meta-QTLs, ortho-meta-QTLs and candidate genes for grain yield and associated traits in wheat (Triticum aestivum L.).

KEY MESSAGE: In wheat, 2852 major QTLs of 8998 QTLs available for yield and related traits were used for meta-analysis; 141 meta-QTLs were identified, which included 13 breeder's MQTLs and 24 ortho-MQTLs; 1202 candidate genes and 50 homologues of genes for yield from other cereals were also identified. Meta-QTL analysis was conducted using 2852 of the 8998 known QTLs, retrieved from 230 reports published during 1999-2020 (including 19 studies on tetraploid wheat) for grain yield (GY) and the following ten component traits: (i) grain weight (GWei), (ii) grain morphology-related traits (GMRTs), (iii) grain number (GN), (iv) spikes-related traits (SRTs), (v) plant height (PH), (vi) tiller number (TN), (vii) harvest index (HI), (viii) biomass yield (BY), (ix) days to heading/flowering and maturity (DTH/F/M), and (x) grain filling duration (GFD). The study resulted in the identification of 141 meta-QTLs (MQTLs), with an average confidence interval (CI) of 1.4 cM as against a CI of > 12.1 cM (8.8 fold reduction) in the QTLs that were used. The corresponding physical length of CI ranged from 0.01 Mb to 661.9 Mb (mean, 31.5 Mb). Seventy-seven (77) of these 141 MQTLs overlapped marker-trait associations (MTAs) reported in genome-wide association studies. Also, 63 MQTLs (each based on at least 10 QTLs) were considered stable and robust, with 13 MQTLs described as breeder's MQTLs (selected based on small CI, large LOD, and high level of phenotypic variation explained). Thirty-five yield-related genes from rice, barley, and maize were also utilized to identify 50 wheat homologues in MQTLs. Further, the use of synteny and collinearity allowed the identification of 24 ortho-MQTLs which were common among the wheat, barley, rice, and maize. The results of the present study should prove useful for wheat breeding and future basic research in cereals including wheat, barley, rice, and maize.

A novel bicistronic DNA vaccine with enhanced protective immune response against Bacillus anthracis through DNA prime-protein boost vaccination approach.

Anthrax, by Bacillus anthracis, remains a dreadful fatal hazard worldwide. The currently used anthrax vaccines are plagued by numerous issues that limit their widespread use. As an immunization approach targeting both extracellular antigens and toxins of B. anthracis may achieve better sterile immunity, the present investigation designed a bicistronic secretory anti-anthrax DNA vaccine targeting immune response against toxin and cells. The efficacy of the vaccine was compared with monocistronic DNA vaccines and the currently used anthrax vaccine. For this, mice were immunized with the developed vaccine containing pag (encoding protective antigen to block toxin) and eag genes (encoding EA1 to target cells) of B. anthracis through DNA-prime/Protein-boost (D/P) and DNA prime/DNA-boost (D/D) approaches. There was a >2 and > 5 fold increase in specific antibody level by D/D and D/P approaches respectively, on 42nd days post-immunization (dpi). Serum cytokine profiling showed that both Th1 and Th2 immune responses were elicited, with more Th2 responses in D/P strategy. More importantly, challenge with 100 times LD50 of B. anthracis at 42nd dpi exhibited maximum cumulative survival (83.33 %) by bicistronic D/P approach. Remarkably, immunization with EA1 delayed mortality onset in infection. The study forms the first report on complement-dependent bactericidal activity of antiEA1 antibodies. In short, co-immunization of PA and EA1 through the developed bicistronic DNA vaccine would be an effective immunization approach in anthrax vaccination. Further, D/P strategy could enhance vaccine-induced immunity against B. anthracis. Altogether, the study generates certain critical insights having direct applications in next-generation vaccine development against anthrax.

Preparation of alpha cellulose from sugarcane bagasse and its cationization: Synthesis, characterization, validation and application as wet-end additive.

Paper industry uses cationic polymers for imparting strong bonds with pulp furnish to enhance strength properties. Due to environmental reasons, emphasis is on utilization of biobased polymers in place of synthetic. Sugarcane bagasse, an agro-industrial waste, was processed for extraction of alpha cellulose and preparation of cationic derivative. Reaction conditions were optimized to achieve highly substituted cationic derivative with insertion of 2-hydroxy-3-(trimethylammonium) propyl group. Artificial neural network (ANN) was applied to analyze the experimental data for cationization modeling. Maximum degree of substitution 0.66, was achieved at 5.0 M NaOH/anhydro glucose unit (AGU), 20 °C alkalization temperature, 8 min alkalization time, 3.5 M/AGU etherification agent concentration, 45 min time and 60 °C etherification reaction temperature. The experimental results showed that mean square error values for input parameters were significantly low. The ANN based regression values of the output, and computed values of target were close to unity. ANN based fitting indicates better performance level to predict the degree of substitution. The synthesized cationic cellulose was characterized through FTIR, XRD, NMR, FESEM and TGA. The activity of cationized cellulose as wet-end additive was tested for bagasse, wheat straw and recycled pulps due to their shorten fiber and feeble pulp characters than wood pulp.

Application of Deep Learning on Student Engagement in e-learning environments.

The drastic impact of COVID-19 pandemic is visible in all aspects of our lives including education. With a distinctive rise in e-learning, teaching methods are being undertaken remotely on digital platforms due to COVID-19. To reduce the effect of this pandemic on the education sector, most of the educational institutions are already conducting online classes. However, to make these digital learning sessions interactive and comparable to the traditional offline classrooms, it is essential to ensure that students are properly engaged during online classes. In this paper, we have presented novel deep learning based algorithms that monitor the student's emotions in real-time such as anger, disgust, fear, happiness, sadness, and surprise. This is done by the proposed novel state-of-the-art algorithms which compute the Mean Engagement Score (MES) by analyzing the obtained results from facial landmark detection, emotional recognition and the weights from a survey conducted on students over an hour-long class. The proposed automated approach will certainly help educational institutions in achieving an improved and innovative digital learning method.

Development of a visible loop mediated isothermal amplification assay for rapid detection of Bacillus anthracis.

Distressing effects on animal and human health with lethal progression, being used as bioweapon and shared features with non-pathogenic bacteria demands sensitive, specific, safe, cost effective and rapid detection methods for anthrax causing organisms. Conventional microbiology based diagnostics for anthrax are time consuming and need sophisticated equipment, while molecular diagnostics require less time and labor. The Loop mediated isothermal amplification assay (LAMP) is rapid, sensitive and specific assay and requires no specialized equipment. In the present study, we developed a LAMP assay for rapid as well as specific detection of Bacillus anthracis. The optimized assay produced positive results with the Sterne strain and one field isolate of B. anthracis and, negative results with other bacteria of the same and different genera within 2 h. Sensitivity was 500 fg of total DNA of B. anthracis, which was 100 times more sensitive than conventional PCR. The present study also demonstrated that the simple method of total DNA extraction by repeated boiling and freezing will not adversely affect the LAMP results. In conclusion, the optimized LAMP assay is a promising tool for the specific, sensitive, less time-consuming diagnosis for anthrax causing bacteria and also, for detecting the virulence of suspected B. anthracis cultures.

Biofortification and bioavailability of Zn, Fe and Se in wheat: present status and future prospects.

KEY MESSAGE: Knowledge of genetic variation, genetics, physiology/molecular basis and breeding (including biotechnological approaches) for biofortification and bioavailability for Zn, Fe and Se will help in developing nutritionally improved wheat. Biofortification of wheat cultivars for micronutrients is a priority research area for wheat geneticists and breeders. It is known that during breeding of wheat cultivars for productivity and quality, a loss of grain micronutrient contents occurred, leading to decline in nutritional quality of wheat grain. Keeping this in view, major efforts have been made during the last two decades for achieving biofortification and bioavailability of wheat grain for micronutrients including Zn, Fe and Se. The studies conducted so far included evaluation of gene pools for contents of not only grain micronutrients as above, but also for phytic acid (PA) or phytate and phytase, so that, while breeding for the micronutrients, bioavailability is also improved. For this purpose, QTL interval mapping and GWAS were carried out to identify QTLs/genes and associated markers that were subsequently used for marker-assisted selection (MAS) during breeding for biofortification. Studies have also been conducted to understand the physiology and molecular basis of biofortification, which also allowed identification of genes for uptake, transport and storage of micronutrients. Transgenics using transgenes have also been produced. The breeding efforts led to the development of at least a dozen cultivars with improved contents of grain micronutrients, although land area occupied by these biofortified cultivars is still marginal. In this review, the available information on different aspects of biofortification and bioavailability of micronutrients including Zn, Fe and Se in wheat has been reviewed for the benefit of those, who plan to start work or already conducting research in this area.

Identification of modifiers of the plant height in wheat using an induced dwarf mutant controlled by RhtB4c allele.

In wheat, 25 Rht genes for dwarfness are known, which include both, GA-insensitive and GA-responsive genes. The GA-insensitive Rht genes have been widely used, although, their suitability under abiotic stress conditions has been questioned. This necessitated a search for alternative GA-responsive, spontaneous and induced dwarfing genes. We earlier reported an induced dwarf mutant ('dwarf mutant-3'; 44 cm), and the mutant allele was named Rht4c allele (2BL). This dwarf mutant was not suitable for cultivation due to its extra dwarf nature. Therefore, we searched for naturally occurring QTLs, which would modify the phenotype of 'dwarf-mutant-3' using 'mutant-assisted gene identification and characterization' (MAGIC) approach. For this purpose, the 'dwarf mutant-3' was crossed with a tall wheat cv. NP114 and homozygous mutant F2 plants (~ 25% of the progeny) were selected, which were phenotyped for plant height and genotyped using SSR markers. The data were utilized for QTL analysis and plant height. Six modifier QTLs were identified on chromosomes 2A, 2B and 4A. Two QTLs each on 2A and 2B were responsible for increase in plant height (described as 'enhancer modifiers'), whereas the remaining two QTLs located on 4A were responsible for reducing the plant height (described as 'suppressor modifiers'). It was hypothesized that the enhancer QTLs could be exploited for the development of semi-dwarf high yielding genotypes containing Rht4c allele. This is the first study of its kind in wheat demontsrating that the MAGIC approach could be used for identification of modifiers of the mutant phenotypes of other traits for wheat improvement.

A deep learning and grad-CAM based color visualization approach for fast detection of COVID-19 cases using chest X-ray and CT-Scan images.

The world is suffering from an existential global health crisis known as the COVID-19 pandemic. Countries like India, Bangladesh, and other developing countries are still having a slow pace in the detection of COVID-19 cases. Therefore, there is an urgent need for fast detection with clear visualization of infection is required using which a suspected patient of COVID-19 could be saved. In the recent technological advancements, the fusion of deep learning classifiers and medical images provides more promising results corresponding to traditional RT-PCR testing while making detection and predictions about COVID-19 cases with increased accuracy. In this paper, we have proposed a deep transfer learning algorithm that accelerates the detection of COVID-19 cases by using X-ray and CT-Scan images of the chest. It is because, in COVID-19, initial screening of chest X-ray (CXR) may provide significant information in the detection of suspected COVID-19 cases. We have considered three datasets known as 1) COVID-chest X-ray, 2) SARS-COV-2 CT-scan, and 3) Chest X-Ray Images (Pneumonia). In the obtained results, the proposed deep learning model can detect the COVID-19 positive cases in  ≤  2 seconds which is faster than RT-PCR tests currently being used for detection of COVID-19 cases. We have also established a relationship between COVID-19 patients along with the Pneumonia patients which explores the pattern between Pneumonia and COVID-19 radiology images. In all the experiments, we have used the Grad-CAM based color visualization approach in order to clearly interpretate the detection of radiology images and taking further course of action.

Meta-QTL analysis and identification of candidate genes for drought tolerance in bread wheat (Triticum aestivum L.).

Meta-QTL (MQTL) analysis for drought tolerance was undertaken in bread wheat to identify consensus and robust MQTLs using 340 known QTLs from 11 earlier studies; 13 MQTLs located on 6 chromosomes (1D, 3B, 5A, 6D, 7A and 7D) were identified, with maximum of 4 MQTLs on chromosome 5A. Mean confidence intervals for MQTLs were much narrower (mean, 6.01 cM; range 2.07-19.46 cM), relative to those in original QTLs (mean, 13.6 cM; range, 1.0-119.1 cM). Two MQTLs, namely MQTL4 and MQTL12, were major MQTLs with potential for use in marker-assisting breeding. As many as 228 candidate genes (CGs) were also identified using 6 of the 13 MQTLs. In-silico expression analysis of these 228 CGs allowed identification of 14 important CGs, with + 3 to - 8 fold change in expression under drought (relative to normal conditions) in a tolerant cv. named TAM107. These CGs encoded proteins belonging to the following families: NAD-dependent epimerase/dehydratase, protein kinase, NAD(P)-binding domain protein, heat shock protein 70 (Hsp70), glycosyltransferase 2-like, etc. Important MQTLs and CGs identified in the present study should prove useful for future molecular breeding and for the study of molecular basis of drought tolerance in cereals in general and wheat in particular.

Genome-wide analysis of H3K4me3 and H3K27me3 modifications due to Lr28 for leaf rust resistance in bread wheat (Triticum aestivum).

KEY MESSAGE: Present study revealed a complex relationship among histone H3 methylation (examined using H3K4/K27me3 marks), cytosine DNA methylation and differential gene expression during Lr28 mediated leaf rust resistance in wheat. During the present study, genome-wide histone modifications were examined in a pair of near isogenic lines (NILs) (with and without Lr28 in the background of cv. HD2329). The two histone marks used included H3K4me3 (an activation mark) and H3K27me3 (a repression mark). The results were compared with levels of expression (using RNA-seq) and DNA methylation (MeDIP) data obtained using the same pair of NILs. Some of the salient features of the present study include the following: (i) large scale differential binding sites (DBS) were available for only H3K4me3 in the susceptible cultivar, but for both H3K4me3 and H3K27me3 in its resistant NIL; (ii) DBSs for H3K27me3 mark were more abundant (> 80%) in intergenic regions, whereas DBSs for H3K4me3 were distributed in all genomic regions including exons, introns, intergenic, TTS (transcription termination sites) and promoters; (iii) fourteen (14) genes associated with DBSs showed co-localization for both the marks; (iv) only a small fraction (7% for H3K4me3 and 12% for H3K27me3) of genes associated with DBSs matched with the levels of gene expression inferred from RNA-seq data; (v) validation studies using qRT-PCR were conducted on 26 selected representative genes; results for only 11 genes could be validated. The proteins encoded by important genes involved in promoting infection included domains generally carried by R gene proteins such as Mlo like protein, protein kinases and purple acid phosphatase. Similarly, proteins encoded by genes involved in resistance included those carrying domains for lectin kinase, R gene, aspartyl protease, etc. Overall, the results suggest a very complex network of downstream genes that are expressed during compatible and incompatible interactions; some of the genes identified during the present study may be used in future validation studies involving RNAi/overexpression approaches.

Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet.

Presently, COVID-19 has posed a serious threat to researchers, scientists, health professionals, and administrations around the globe from its detection to its treatment. The whole world is witnessing a lockdown like situation because of COVID-19 pandemic. Persistent efforts are being made by the researchers to obtain the possible solutions to control this pandemic in their respective areas. One of the most common and effective methods applied by the researchers is the use of CT-Scans and X-rays to analyze the images of lungs for COVID-19. However, it requires several radiology specialists and time to manually inspect each report which is one of the challenging tasks in a pandemic. In this paper, we have proposed a deep learning neural network-based method nCOVnet, an alternative fast screening method that can be used for detecting the COVID-19 by analyzing the X-rays of patients which will look for visual indicators found in the chest radiography imaging of COVID-19 patients.

The progress of leaf rust research in wheat.

Leaf rust (also called brown rust) in wheat, caused by fungal pathogen Puccinia triticina Erikss. (Pt) is one of the major constraints in wheat production worldwide. Pt is widespread with diverse population structure and undergoes rapid evolution to produce new virulent races against resistant cultivars that are regularly developed to provide resistance against the prevailing races of the pathogen. Occasionally, the disease may also take the shape of an epidemic in some wheat-growing areas causing major economic losses. In the recent past, substantial progress has been made in characterizing the sources of leaf rust resistance including non-host resistance (NHR). Progress has also been made in elucidating the population biology of Pt and the mechanisms of wheat-Pt interaction. So far, ∼80 leaf rust resistance genes (Lr genes) have been identified and characterized; some of them have also been used for the development of resistant wheat cultivars. It has also been shown that a gene-for-gene relationship exists between individual wheat Lr genes and the corresponding Pt Avr genes so that no Lr gene can provide resistance unless the prevailing race of the pathogen carries the corresponding Avr gene. Several Lr genes have also been cloned and their products characterized, although no Avr gene corresponding a specific Lr gene has so far been identified. However, several candidate effectors for Pt have been identified and functionally characterized using genome-wide analyses, transcriptomics, RNA sequencing, bimolecular fluorescence complementation (BiFC), virus-induced gene silencing (VIGS), transient expression and other approaches. This review summarizes available information on different aspects of the pathogen Pt, genetics/genomics of leaf rust resistance in wheat including cloning and characterization of Lr genes and epigenetic regulation of disease resistance.

Complex relationship between DNA methylation and gene expression due to Lr28 in wheat-leaf rust pathosystem.

Differential DNA methylation due to Lr28 was examined in susceptible (S) wheat cv. HD2329 and its resistant (R) near isogenic line (NIL) (HD2329+Lr28) using two approaches: methylation sensitive amplified polymorphism (MSAP) and methylated DNA immunoprecipitation (MeDIP). S/R lines each had a large number of hypomethylated genes and relatively fewer hypermethylated genes at 96 hai (hours after inoculation) relative to 0 hbi (hours before inoculation), suggesting activation of many genes during the passage of time (96 hai), although identity of genes may differ in S and R lines. When R NIL was compared with S cultivar, there were many hypermethylated and fewer hypomethylated genes in R NIL relative to S cultivar, suggesting that many genes that are active in S cultivar are silenced in R NIL, both at 0 hbi and at 96 hai. Level of methylation was generally abundant in intergenic regions followed by that in promoters, transcription termination sites (TTSs) and exons/introns. Hypermethylation in promoter and gene body regions was not always associated with inhibition of gene expression and vice-versa, indicating that more than one regulatory mechanisms may control the expression of genes due to pathogen attack in presence and absence of Lr28. MSAP analysis also showed abundance of mCG methylation in S cultivar and that of mCCG methylation in R NIL (at 96 hai), suggesting differences in methylation context in NILs with and without Lr28. The results of the present study improved our understanding of the epigenetic control of leaf rust resistance in wheat.