Evolution, diversity, and function of the disease susceptibility gene Snn1 in wheat.

Septoria nodorum blotch (SNB), caused by Parastagonospora nodorum, is a disease of durum and common wheat initiated by the recognition of pathogen-produced necrotrophic effectors (NEs) by specific wheat genes. The wheat gene Snn1 was previously cloned, and it encodes a wall-associated kinase that directly interacts with the NE SnTox1 leading to programmed cell death and ultimately the development of SNB. Here, sequence analysis of Snn1 from 114 accessions including diploid, tetraploid, and hexaploid wheat species revealed that some wheat lines possess two copies of Snn1 (designated Snn1-B1 and Snn1-B2) approximately 120 kb apart. Snn1-B2 evolved relatively recently as a paralog of Snn1-B1, and both genes have undergone diversifying selection. Three point mutations associated with the formation of the first SnTox1-sensitive Snn1-B1 allele from a primitive wild wheat were identified. Four subsequent and independent SNPs, three in Snn1-B1 and one in Snn1-B2, converted the sensitive alleles to insensitive forms. Protein modeling indicated these four mutations could abolish Snn1-SnTox1 compatibility either through destabilization of the Snn1 protein or direct disruption of the protein-protein interaction. A high-throughput marker was developed for the absent allele of Snn1, and it was 100% accurate at predicting SnTox1-insensitive lines in both durum and spring wheat. Results of this study increase our understanding of the evolution, diversity, and function of Snn1-B1 and Snn1-B2 genes and will be useful for marker-assisted elimination of these genes for better host resistance.

A Novel and Efficient Digital Pathology Classifier for Predicting Cancer Biomarkers Using Sequencer Architecture.

In digital pathology tasks, transformers have achieved state-of-the-art results, surpassing convolutional neural networks (CNNs). However, transformers are usually complex and resource intensive. This study developed a novel and efficient digital pathology classifier called DPSeq to predict cancer biomarkers through fine-tuning a sequencer architecture integrating horizontal and vertical bidirectional long short-term memory networks. Using hematoxylin and eosin-stained histopathologic images of colorectal cancer from two international data sets (The Cancer Genome Atlas and Molecular and Cellular Oncology), the predictive performance of DPSeq was evaluated in a series of experiments. DPSeq demonstrated exceptional performance for predicting key biomarkers in colorectal cancer (microsatellite instability status, hypermutation, CpG island methylator phenotype status, BRAF mutation, TP53 mutation, and chromosomal instability), outperforming most published state-of-the-art classifiers in a within-cohort internal validation and a cross-cohort external validation. In addition, under the same experimental conditions using the same set of training and testing data sets, DPSeq surpassed four CNNs (ResNet18, ResNet50, MobileNetV2, and EfficientNet) and two transformer (Vision Transformer and Swin Transformer) models, achieving the highest area under the receiver operating characteristic curve and area under the precision-recall curve values in predicting microsatellite instability status, BRAF mutation, and CpG island methylator phenotype status. Furthermore, DPSeq required less time for both training and prediction because of its simple architecture. Therefore, DPSeq appears to be the preferred choice over transformer and CNN models for predicting cancer biomarkers.

The Hessian fly resistance gene HvRHF1 is localized in an NBS-LRR gene cluster in barley.

Hessian fly (Mayetiola destructor Say) is a significant pest in cereal crops, causing substantial yield losses worldwide. While host resistance is the most efficient method for pest control, research on genetic characterization of Hessian fly resistance in barley (Hordeum vulgare L.) has been limited, and the underlying resistance mechanism remains largely unknown. In this study, we conducted fine mapping of a crucial Hessian fly resistance locus, known as HvRHF1, using a biparental population. Assisted with genetic markers and robust phenotyping assay, we pinpointed the HvRHF1 gene to an ~ 82 kb region on chromosome 4H. Gene prediction and annotation revealed that the HvRHF1 locus comprises three complete NBS-LRR genes, which are characteristic of disease resistance genes. As a result, our study not only provides valuable resources for resistance in barley and genetic tools for breeding, but also identifies candidate genes that lay the foundation for cloning HvRHF1. This endeavor will significantly contribute to our understanding of the molecular mechanisms underlying cereal resistance to Hessian fly.

Identification of Sr67, a new gene for stem rust resistance in KU168-2 located close to the Sr13 locus in wheat.

KEY MESSAGE: Sr67 is a new stem rust resistance gene that represents a new resource for breeding stem rust resistant wheat cultivars Re-appearance of stem rust disease, caused by the fungal pathogen Puccinia graminis f. sp. tritici (Pgt), in different parts of Europe emphasized the need to develop wheat varieties with effective resistance to local Pgt populations and exotic threats. A Kyoto University wheat (Triticum aestivum L.) accession KU168-2 was reported to carry good resistance to leaf and stem rust. To identify the genomic region associated with the KU168-2 stem rust resistance, a genetic study was conducted using a doubled haploid (DH) population from the cross RL6071 × KU168-2. The DH population was phenotyped with three Pgt races (TTKSK, TPMKC, and QTHSF) and genotyped using the Illumina 90 K wheat SNP array. Linkage mapping showed the resistance to all three Pgt races was conferred by a single stem rust resistance (Sr) gene on chromosome arm 6AL, associated with Sr13. Presently, four Sr13 resistance alleles have been reported. Sr13 allele-specific KASP and STARP markers, and sequencing markers all showed null alleles in KU168-2. KU168-2 showed a unique combination of seedling infection types for five Pgt races (TTKSK, QTHSF, RCRSF, TMRTF, and TPMKC) compared to Sr13 alleles. The phenotypic uniqueness of the stem rust resistance gene in KU168-2 and null alleles for Sr13 allele-specific markers showed the resistance was conferred by a new gene, designated Sr67. Since Sr13 is less effective in hexaploid background, Sr67 will be a good source of stem rust resistance in bread wheat breeding programs.

Revealing the algicidal characteristics of Maribacter dokdonensis: An investigation into bacterial strain P4 isolated from Karenia mikimotoi bloom water.

Harmful algal blooms (HABs) are a global environmental concern, causing significant economic losses in fisheries and posing risks to human health. Algicidal bacteria have been suggested as a potential solution to control HABs, but their algicidal efficacy is influenced by various factors. This study aimed to characterize a novel algicidal bacterium, Maribacter dokdonensis (P4), isolated from a Karenia mikimotoi (Hong Kong strain, KMHK) HAB and assess the impact of P4 and KMHK's doses, growth phase, and algicidal mode and the axenicity of KMHK on P4's algicidal effect. Our results demonstrated that the algicidal effect of P4 was dose-dependent, with the highest efficacy at a dose of 25% v/v. The study also determined that P4's algicidal effect was indirect, with the P4 culture and the supernatant, but not the bacterial cells, showing significant effects. The algicidal efficacy was higher when both P4 and KMHK were in the stationary phase. Furthermore, the P4 culture at the log phase could effectively kill KMHK cells at the stationary phase, with higher algicidal efficacy in the bacterial culture than that of the supernatant alone. Interestingly, P4's algicidal efficacy was significantly higher when co-culturing with xenic KMHK (~90% efficacy at day 1) than that with the axenic KMHK (~50% efficacy at day 1), suggesting the presence of other bacteria could regulate P4's algicidal effect. The bacterial strain P4 also exhibited remarkable algicidal efficacy on four other dinoflagellate species, particularly the armored species. These results provide valuable insights into the algicidal effect of M. dokdonensis on K. mikimotoi and on their interactions.

SCEBE: an efficient and scalable algorithm for genome-wide association studies on longitudinal outcomes with mixed-effects modeling.

Genome-wide association studies (GWAS) using longitudinal phenotypes collected over time is appealing due to the improvement of power. However, computation burden has been a challenge because of the complex algorithms for modeling the longitudinal data. Approximation methods based on empirical Bayesian estimates (EBEs) from mixed-effects modeling have been developed to expedite the analysis. However, our analysis demonstrated that bias in both association test and estimation for the existing EBE-based methods remains an issue. We propose an incredibly fast and unbiased method (simultaneous correction for EBE, SCEBE) that can correct the bias in the naive EBE approach and provide unbiased P-values and estimates of effect size. Through application to Alzheimer's Disease Neuroimaging Initiative data with 6 414 695 single nucleotide polymorphisms, we demonstrated that SCEBE can efficiently perform large-scale GWAS with longitudinal outcomes, providing nearly 10 000 times improvement of computational efficiency and shortening the computation time from months to minutes. The SCEBE package and the example datasets are available at https://github.com/Myuan2019/SCEBE.

Linkage drag analysis in three Aegilops speltoides introgressions carrying Sr47 in modern durum and hard red spring wheat germplasm.

KEY MESSAGE: Yield and quality tests of wheat lines derived from RWG35 show they carry little, or no linkage drag and are the preferred source of Sr47 for stem rust resistance. Three durum wheat (Triticum turgidum L. subsp. durum) lines, RWG35, RWG36, and RWG37 carrying slightly different Aegilops speltoides introgressions, but each carrying the Sr47 stem rust resistance gene, were backcrossed to three durum and three hard red spring (HRS) wheat (Triticum aestivum L.) cultivars to produce 18 backcross populations. Each population was backcrossed to the recurrent parent six times and prepared for yield trials to test for linkage drag. Lines carrying the introgression (S-lines) were compared to euploid sibling lines (W-lines) and their parent. Yield trials were conducted from 2018 to 2021 at three locations. Three agronomic and several quality traits were studied. In durum, lines derived from RWG35 had little or no linkage drag. Lines derived from RWG36 and RWG37 still retained linkage drag, most notably involving yield and thousand kernel weight, but also test weight, falling number, kernel hardness index, semolina extract, semolina protein content, semolina brightness, and peak height. In HRS wheat, the results were more complex, though the general result of RWG35 lines having little or no linkage drag and RWG36 and RWG37 lines retaining linkage drag still applied. But there was heterogeneity in the Glenn35S lines, and Linkert lines had problems combining with the Ae. speltoides introgressions. We concluded that introgressions derived from RWG35 either had eliminated linkage drag or any negative effects were minor in nature. We recommend that breeders who wish to incorporate Sr47 into their cultivars should work exclusively with germplasm derived from RWG35.

Genome-wide association analyses of leaf rust resistance in cultivated emmer wheat.

KEY MESSAGE: Fifteen and eleven loci, with most loci being novel, were identified to associate with seedling and adult resistances, respectively, to the durum-specific races of leaf rust pathogen in cultivated emmer. Leaf rust, caused by Puccinia triticina (Pt), constantly threatens durum (Triticum turgidum ssp. durum) and bread wheat (Triticum aestivum) production worldwide. A Pt race BBBQD detected in California in 2009 poses a potential threat to durum production in North America because resistance source to this race is rare in durum germplasm. To find new resistance sources, we assessed a panel of 180 cultivated emmer wheat (Triticum turgidum ssp. dicoccum) accessions for seedling resistance to BBBQD and for adult resistance to a mixture of durum-specific races BBBQJ, CCMSS, and MCDSS in the field, and genotyped the panel using genotype-by-sequencing (GBS) and the 9 K SNP (Single Nucleotide Polymorphism) Infinium array. The results showed 24 and nine accessions consistently exhibited seedling and adult resistance, respectively, with two accessions providing resistance at both stages. We performed genome-wide association studies using 46,383 GBS and 4,331 9 K SNP markers and identified 15 quantitative trait loci (QTL) for seedling resistance located mostly on chromosomes 2B and 6B, and 11 QTL for adult resistance on 2B, 3B and 6A. Of these QTL, one might be associated with leaf rust resistance (Lr) gene Lr53, and two with the QTL previously reported in durum or hexaploid wheat. The remaining QTL are potentially associated with new Lr genes. Further linkage analysis and gene cloning are necessary to identify the causal genes underlying these QTL. The emmer accessions with high levels of resistance will be useful for developing mapping populations and adapted durum germplasm and varieties with resistance to the durum-specific races.

Bayesian nonparametric quantile process regression and estimation of marginal quantile effects.

Flexible estimation of multiple conditional quantiles is of interest in numerous applications, such as studying the effect of pregnancy-related factors on low and high birth weight. We propose a Bayesian nonparametric method to simultaneously estimate noncrossing, nonlinear quantile curves. We expand the conditional distribution function of the response in I-spline basis functions where the covariate-dependent coefficients are modeled using neural networks. By leveraging the approximation power of splines and neural networks, our model can approximate any continuous quantile function. Compared to existing models, our model estimates all rather than a finite subset of quantiles, scales well to high dimensions, and accounts for estimation uncertainty. While the model is arbitrarily flexible, interpretable marginal quantile effects are estimated using accumulative local effect plots and variable importance measures. A simulation study shows that our model can better recover quantiles of the response distribution when the data are sparse, and an analysis of birth weight data is presented.

GGNpTCR: A Generative Graph Structure Neural Network for Predicting Immunogenic Peptides for T-cell Immune Response.

Identifying the interactions between T-cell receptor (TCRs) and human antigens is a crucial step in developing new vaccines, diagnostics, and immunotherapy. Current methods primarily focus on learning binding patterns from known TCR binding repertoires by using sequence information alone without considering the binding specificity of new antigens or exogenous peptides that have not appeared in the training set. Furthermore, the spatial structure of antigens plays a critical role in immune studies and immunotherapy, which should be addressed properly in the identification of interacting TCR-antigen pairs. In this study, we introduced a novel deep learning framework based on generative graph structures, GGNpTCR, for predicting interactions between TCR and peptides from sequence information. Results of real data analysis indicate that our model achieved excellent prediction for new antigens unseen in the training data set, making significant improvements compared to existing methods. We also applied the model to a large COVID-19 data set with no antigens in the training data set, and the improvement was also significant. Furthermore, through incorporation of additional supervised mechanisms, GGNpTCR demonstrated the ability to precisely forecast the locations of peptide-TCR interactions within 3D configurations. This enhancement substantially improved the model's interpretability. In summary, based on the performance on multiple data sets, GGNpTCR has made significant progress in terms of performance, universality, and interpretability.

Association Mapping of Resistance to Tan Spot in the Global Durum Panel.

Tan spot, caused by the necrotrophic fungal pathogen Pyrenophora tritici-repentis (Ptr), is an important disease of durum and common wheat worldwide. Compared with common wheat, less is known about the genetics and molecular basis of tan spot resistance in durum wheat. We evaluated 510 durum lines from the Global Durum Wheat Panel (GDP) for sensitivity to the necrotrophic effectors (NEs) Ptr ToxA and Ptr ToxB and for reaction to Ptr isolates representing races 1 to 5. Overall, susceptible durum lines were most prevalent in South Asia, the Middle East, and North Africa. Genome-wide association analysis showed that the resistance locus Tsr7 was significantly associated with tan spot caused by races 2 and 3, but not races 1, 4, or 5. The NE sensitivity genes Tsc1 and Tsc2 were associated with susceptibility to Ptr ToxC- and Ptr ToxB-producing isolates, respectively, but Tsn1 was not associated with tan spot caused by Ptr ToxA-producing isolates, which further validates that the Tsn1-Ptr ToxA interaction does not play a significant role in tan spot development in durum. A unique locus on chromosome arm 2AS was associated with tan spot caused by race 4, a race once considered avirulent. A novel trait characterized by expanding chlorosis leading to increased disease severity caused by the Ptr ToxB-producing race 5 isolate DW5 was identified, and this trait was governed by a locus on chromosome 5B. We recommend that durum breeders select resistance alleles at the Tsr7, Tsc1, Tsc2, and the chromosome 2AS loci to obtain broad resistance to tan spot.

Upregulation of Peridinin-Chlorophyll A-Binding Protein in a Toxic Strain of Prorocentrum hoffmannianum under Normal and Phosphate-Depleted Conditions.

Some strains of the dinoflagellate species Prorocentrum hoffmannianum show contrasting ability to produce diarrhetic shellfish poisoning (DSP) toxins. We previously compared the okadaic acid (OA) production level between a highly toxic strain (CCMP2804) and a non-toxic strain (CCMP683) of P. hoffmannianum and revealed that the cellular concentration of OA in CCMP2804 would increase significantly under the depletion of phosphate. To understand the molecular mechanisms, here, we compared and analyzed the proteome changes of both strains growing under normal condition and at phosphate depletion using two-dimensional gel electrophoresis (2-DE). There were 41 and 33 differential protein spots observed under normal condition and phosphate depletion, respectively, of which most were upregulated in CCMP2804 and 22 were common to both conditions. Due to the lack of matched peptide mass fingerprints in the database, de novo peptide sequencing was applied to identify the differentially expressed proteins. Of those upregulated spots in CCMP2804, nearly 60% were identified as peridinin-chlorophyll a-binding protein (PCP), an important light-harvesting protein for photosynthesis in dinoflagellates. We postulated that the high expression of PCP encourages the production of DSP toxins by enhancing the yields of raw materials such as acetate, glycolate and glycine. Other possible mechanisms of toxicity related to PCP might be through triggering the transcription of non-ribosomal peptide synthetase/polyketide synthase genes and the transportation of dinophysistoxin-4 from chloroplast to vacuoles.

A protein kinase-major sperm protein gene hijacked by a necrotrophic fungal pathogen triggers disease susceptibility in wheat.

Septoria nodorum blotch (SNB), a disease caused by the necrotrophic fungal pathogen Parastagonospora nodorum, is a threat to wheat (Triticum aestivum) production worldwide. Multiple inverse gene-for-gene interactions involving the recognition of necrotrophic effectors (NEs) by wheat sensitivity genes play major roles in causing SNB. One interaction involves the wheat gene Snn3 and the P. nodorum NE SnTox3. Here, we used a map-based strategy to clone the Snn3-D1 gene from Aegilops tauschii, the D-genome progenitor of common wheat. Snn3-D1 contained protein kinase and major sperm protein domains, both of which were essential for function as confirmed by mutagenesis. As opposed to other characterized interactions in this pathosystem, a compatible Snn3-D1-SnTox3 interaction was light-independent, and Snn3-D1 transcriptional expression was downregulated by light and upregulated by darkness. Snn3-D1 likely emerged in Ae. tauschii due to an approximately 218-kb insertion that occurred along the west bank of the Caspian Sea. The identification of this new class of NE sensitivity genes combined with the previously cloned sensitivity genes demonstrates that P. nodorum can take advantage of diverse host targets to trigger SNB susceptibility in wheat.

Function and evolution of allelic variations of Sr13 conferring resistance to stem rust in tetraploid wheat (Triticum turgidum L.).

The resistance gene Sr13 is one of the most important genes in durum wheat for controlling stem rust caused by Puccinia graminis f. sp. tritici (Pgt). The Sr13 functional gene CNL13 has haplotypes R1, R2 and R3. The R1/R3 and R2 haplotypes were originally designated as alleles Sr13a and Sr13b, respectively. To detect additional Sr13 alleles, we developed Kompetitive allele specific PCR (KASP™) marker KASPSr13 and four semi-thermal asymmetric reverse PCR markers, rwgsnp37-rwgsnp40, based on the CNL13 sequence. These markers were shown to detect R1, R2 and R3 haplotypes in a panel of diverse tetraploid wheat accessions. We also observed the presence of Sr13 in durum line CAT-A1, although it lacked any of the known haplotypes. Sequence analysis revealed that CNL13 of CAT-A1 differed from the susceptible haplotype S1 by a single nucleotide (C2200T) in the leucine-rich repeat region and differed from the other three R haplotypes by one or two additional nucleotides, confirming that CAT-A1 carries a new (R4) haplotype. Stem rust tests on the monogenic, transgenic and mutant lines showed that R1 differed from R3 in its susceptibility to races TCMJC and THTSC, whereas R4 differed from all other haplotypes for susceptibility to TTKSK, TPPKC and TCCJC. Based on these differences, we designate the R1, R3 and R4 haplotypes as alleles Sr13a, Sr13c and Sr13d, respectively. This study indicates that Sr13d may be the primitive functional allele originating from the S1 haplotype via a point mutation, with the other three R alleles probably being derived from Sr13d through one or two additional point mutations.

A triple threat: the Parastagonospora nodorum SnTox267 effector exploits three distinct host genetic factors to cause disease in wheat.

Parastagonospora nodorum is a fungal pathogen of wheat. As a necrotrophic specialist, it deploys effector proteins that target dominant host susceptibility genes to elicit programmed cell death (PCD). Here we identify and functionally validate the effector targeting the host susceptibility genes Snn2, Snn6 and Snn7. We utilized whole-genome sequencing, association mapping, gene-disrupted mutants, gain-of-function transformants, virulence assays, bioinformatics and quantitative PCR to characterize these interactions. A single proteinaceous effector, SnTox267, targeted Snn2, Snn6 and Snn7 to trigger PCD. Snn2 and Snn6 functioned cooperatively to trigger PCD in a light-dependent pathway, whereas Snn7-mediated PCD functioned in a light-independent pathway. Isolates harboring 20 SnTox267 protein isoforms quantitatively varied in virulence. The diversity and distribution of isoforms varied between populations, indicating adaptation to local selection pressures. SnTox267 deletion resulted in the upregulation of effector genes SnToxA, SnTox1 and SnTox3. We validated a novel effector operating in an inverse-gene-for-gene manner to target three genetically distinct host susceptibility genes and elicit PCD. The discovery of the complementary gene action of Snn2 and Snn6 indicates their potential function in a guard or decoy model. Additionally, differences in light dependency in the elicited pathways and upregulation of unlinked effectors sheds new light onto a complex fungal necrotroph-host interaction.

The Parastagonospora nodorum necrotrophic effector SnTox5 targets the wheat gene Snn5 and facilitates entry into the leaf mesophyll.

Parastagonospora nodorum is an economically important necrotrophic fungal pathogen of wheat. Parastagonospora nodorum secretes necrotrophic effectors that target wheat susceptibility genes to induce programmed cell death (PCD). In this study, we cloned and functionally validated SnTox5 and characterized its role in pathogenesis. We used whole genome sequencing, genome-wide association study (GWAS) mapping, CRISPR-Cas9-based gene disruption, gain-of-function transformation, quantitative trait locus (QTL) analysis, haplotype and isoform analysis, protein modeling, quantitative PCR, and laser confocal microscopy to validate SnTox5 and functionally characterize SnTox5. SnTox5 is a mature 16.26 kDa protein with high structural similarity to SnTox3. Wild-type and mutant P. nodorum strains and wheat genotypes of SnTox5 and Snn5, respectively, were used to show that SnTox5 not only targets Snn5 to induce PCD but also facilitates the colonization of the mesophyll layer even in the absence of Snn5. Here we show that SnTox5 facilitates the efficient colonization of the mesophyll tissue and elicits PCD specific to host lines carrying Snn5. The homology to SnTox3 and the ability of SnTox5 to facilitate the colonizing of the mesophyll also suggest a role in the suppression of host defense before PCD induction.

Cytogenetic and genomic characterization of a novel tall wheatgrass-derived Fhb7 allele integrated into wheat B genome.

KEY MESSAGE: We identified and integrated the novel FHB-resistant Fhb7The2 allele into wheat B genome and made it usable in both common and durum wheat breeding programs without yellow flour linkage drag. A novel tall wheatgrass-derived (Thinopyrum elongatum, genome EE) Fhb7 allele, designated Fhb7The2, was identified and integrated into the wheat B genome through a small 7B-7E translocation (7BS·7BL-7EL) involving the terminal regions of the long arms. Fhb7The2 conditions significant Type II resistance to Fusarium head blight (FHB) in wheat. Integration of Fhb7The2 into the wheat B genome makes this wild species-derived FHB resistance gene usable for breeding in both common and durum wheat. By contrast, other Fhb7 introgression lines involving wheat chromosome 7D can be utilized only in common wheat breeding programs, not in durum wheat. Additionally, we found that Fhb7The2 does not have the linkage drag of the yellow flour pigment gene that is tightly linked to the decaploid Th. ponticum-derived Fhb7 allele Fhb7Thp. This will further improve the utility of Fhb7The2 in wheat breeding. DNA sequence analysis identified 12 single nucleotide polymorphisms (SNPs) in Fhb7The2, Fhb7Thp, and another Th. elongatum-derived Fhb7 allele Fhb7The1, which led to seven amino acid conversions in Fhb7The2, Fhb7Thp, and Fhb7The1, respectively. However, no significant variation was observed in their predicted protein configuration as a glutathione transferase. Diagnostic DNA markers were developed specifically for Fhb7The2. The 7EL segment containing Fhb7The2 in the translocation chromosome 7BS·7BL-7EL exhibited a monogenic inheritance pattern in the wheat genetic background. This will enhance the efficacy of marker-assisted selection for Fhb7The2 introgression, pyramiding, and deployment in wheat germplasm and varieties.

Haplotype variants of Sr46 in Aegilops tauschii, the diploid D genome progenitor of wheat.

KEY MESSAGE: Stem rust resistance genes, SrRL5271 and Sr672.1 as well as SrCPI110651, from Aegilops tauschii, the diploid D genome progenitor of wheat, are sequence variants of Sr46 differing by 1-2 nucleotides leading to non-synonymous amino acid substitutions. The Aegilops tauschii (wheat D-genome progenitor) accessions RL 5271 and CPI110672 were identified as resistant to multiple races (including the Ug99) of the wheat stem rust pathogen Puccinia graminis f. sp. tritici (Pgt). This study was conducted to identify the stem rust resistance (Sr) gene(s) in both accessions. Genetic analysis of the resistance in RL 5271 identified a single dominant allele (SrRL5271) controlling resistance, whereas resistance segregated at two loci (SR672.1 and SR672.2) for a cross of CPI110672. Bulked segregant analysis placed SrRL5271 and Sr672.1 in a region on chromosome arm 2DS that encodes Sr46. Molecular marker screening, mapping and genomic sequence analysis demonstrated SrRL5271 and Sr672.1 are alleles of Sr46. The amino acid sequence of SrRL5271 and Sr672.1 is identical but differs from Sr46 (hereafter referred to as Sr46_h1 by following the gene nomenclature in wheat) by a single amino acid (N763K) and is thus designated Sr46_h2. Screening of a panel of Ae. tauschii accessions identified an additional allelic variant that differed from Sr46_h2 by a different amino acid (A648V) and was designated Sr46_h3. By contrast, the protein encoded by the susceptible allele of Ae. tauschii accession AL8/78 differed from these resistance proteins by 54 amino acid substitutions (94% nucleotide sequence gene identity). Cloning and complementation tests of the three resistance haplotypes confirmed their resistance to Pgt race 98-1,2,3,5,6 and partial resistance to Pgt race TTRTF in bread wheat. The three Sr46 haplotypes, with no virulent races detected yet, represent a valuable source for improving stem resistance in wheat.

Colorectal Cancer Survival Prediction Using Deep Distribution Based Multiple-Instance Learning.

Most deep-learning algorithms that use Hematoxylin- and Eosin-stained whole slide images (WSIs) to predict cancer survival incorporate image patches either with the highest scores or a combination of both the highest and lowest scores. In this study, we hypothesize that incorporating wholistic patch information can predict colorectal cancer (CRC) cancer survival more accurately. As such, we developed a distribution-based multiple-instance survival learning algorithm (DeepDisMISL) to validate this hypothesis on two large international CRC WSIs datasets called MCO CRC and TCGA COAD-READ. Our results suggest that combining patches that are scored based on percentile distributions together with the patches that are scored as highest and lowest drastically improves the performance of CRC survival prediction. Including multiple neighborhood instances around each selected distribution location (e.g., percentiles) could further improve the prediction. DeepDisMISL demonstrated superior predictive ability compared to other recently published, state-of-the-art algorithms. Furthermore, DeepDisMISL is interpretable and can assist clinicians in understanding the relationship between cancer morphological phenotypes and a patient's cancer survival risk.

Transportability From Randomized Trials to Clinical Care: On Initial HIV Treatment With Efavirenz and Suicidal Thoughts or Behaviors.

In an analysis of randomized trials, use of efavirenz for treatment of human immunodeficiency virus (HIV) infection was associated with increased suicidal thoughts/behaviors. However, analyses of observational data have found no evidence of increased risk. To assess whether population differences might explain this divergence, we transported the effect of efavirenz use from these trials to a specific target population. Using inverse odds weights and multiple imputation, we transported the effect of efavirenz on suicidal thoughts/behaviors in these randomized trials (participants were enrolled in 2001-2007) to a trials-eligible cohort of US adults initiating antiretroviral therapy while receiving HIV clinical care at medical centers between 1999 and 2015. Overall, 8,291 cohort participants and 3,949 trial participants were eligible. Prescription of antidepressants (19% vs. 13%) and injection drug history (16% vs. 10%) were more frequent in the cohort than in the trial participants. Compared with the effect in trials, the estimated hazard ratio for efavirenz on suicidal thoughts/behaviors was attenuated in our target population (trials: hazard ratio (HR) = 2.3 (95% confidence interval (CI): 1.2, 4.4); transported: HR = 1.8 (95% CI: 0.9, 4.4)), whereas the incidence rate difference was similar (trials: HR = 5.1 (95% CI: 1.6, 8.7); transported: HR = 5.4 (95% CI: -0.4, 11.4)). In our target population, there was greater than 20% attenuation of the hazard ratio estimate as compared with the trials-only estimate. Transporting results from trials to a target population is informative for addressing external validity.