MSMTSeg: Multi-Stained Multi-Tissue Segmentation of Kidney Histology Images via Generative Self-Supervised Meta-Learning Framework.

Accurately diagnosing chronic kidney disease requires pathologists to assess the structure of multiple tissues under different stains, a process that is timeconsuming and labor-intensive. Current AI-based methods for automatic structure assessment, like segmentation, often demand extensive manual annotation and focus on single stain domain. To address these challenges, we introduce MSMTSeg, a generative self-supervised meta-learning framework for multi-stained multi-tissue segmentation in renal biopsy whole slide images (WSIs). MSMTSeg incorporates multiple stain transform models for style translation of inter-stain domains, a self-supervision module for obtaining pre-trained models with the domain-specific feature representation, and a meta-learning strategy that leverages generated virtual data and pre-trained models to learn the domain-invariant feature representation across multiple stains, thereby enhancing segmentation performance. Experimental results demonstrate that MSMTSeg achieves superior and robust performance, with mDSC of 0.836 and mIoU of 0.718 for multiple tissues under different stains, using only one annotated training sample for each stain. Our ablation study confirms the effectiveness of each component, positioning MSMTSeg ahead of classic advanced segmentation networks, recent few-shot segmentation methods, and unsupervised domain adaptation methods. In conclusion, our proposed few-shot cross-domain technology offers a feasible and cost-effective solution for multi-stained renal histology segmentation, providing convenient assistance to pathologists in clinical practice. The source code and conditionally accessible data are available at https://github.com/SnowRain510/MSMTSeg.

Severity-stratification of interstitial lung disease by deep learning enabled assessment and quantification of lesion indicators from HRCT images.

BACKGROUND: Interstitial lung disease (ILD) represents a group of chronic heterogeneous diseases, and current clinical practice in assessment of ILD severity and progression mainly rely on the radiologist-based visual screening, which greatly restricts the accuracy of disease assessment due to the high inter- and intra-subjective observer variability. OBJECTIVE: To solve these problems, in this work, we propose a deep learning driven framework that can assess and quantify lesion indicators and outcome the prediction of severity of ILD. METHODS: In detail, we first present a convolutional neural network that can segment and quantify five types of lesions including HC, RO, GGO, CONS, and EMPH from HRCT of ILD patients, and then we conduct quantitative analysis to select the features related to ILD based on the segmented lesions and clinical data. Finally, a multivariate prediction model based on nomogram to predict the severity of ILD is established by combining multiple typical lesions. RESULTS: Experimental results showed that three lesions of HC, RO, and GGO could accurately predict ILD staging independently or combined with other HRCT features. Based on the HRCT, the used multivariate model can achieve the highest AUC value of 0.755 for HC, and the lowest AUC value of 0.701 for RO in stage I, and obtain the highest AUC value of 0.803 for HC, and the lowest AUC value of 0.733 for RO in stage II. Additionally, our ILD scoring model could achieve an average accuracy of 0.812 (0.736 - 0.888) in predicting the severity of ILD via cross-validation. CONCLUSIONS: In summary, our proposed method provides effective segmentation of ILD lesions by a comprehensive deep-learning approach and confirms its potential effectiveness in improving diagnostic accuracy for clinicians.

Diagnosis of diabetic kidney disease in whole slide images via AI-driven quantification of pathological indicators.

Diagnosis of diabetic kidney disease (DKD) mainly relies on screening the morphological variations and internal lesions of glomeruli from pathological kidney biopsy. The prominent pathological alterations of glomeruli for DKD include glomerular hypertrophy and nodular mesangial sclerosis. However, the qualitative judgment of these alterations is inaccurate and inconstant due to the intra- and inter-subject variability of pathologists. It is necessary to design artificial intelligence (AI) methods for accurate quantification of these pathological alterations and outcome prediction of DKD. In this work, we present an AI-driven framework to quantify the volume of glomeruli and degree of nodular mesangial sclerosis, respectively, based on an instance segmentation module and a novel weakly supervised Macro-Micro Aggregation (MMA) module. Subsequently, we construct classic machine learning models to predict the degree of DKD based on three selected pathological indicators via factor analysis. These corresponding modules are trained and tested on a total of 281 whole slide images (WSIs) digitized from two hospitals with different scanners. Our designed AI framework achieved inspiring results with 0.926 mIoU for glomerulus segmentation, and 0.899 F1 score for glomerulus classification in the external testing dataset. Meantime, the visualized results of the MMA module could reflect the location of the lesions. The performance of predicting disease achieved the F1 score of 0.917, which further proved the effectiveness of our AI-driven quantification of pathological indicators. Additionally, the interpretation of the machine learning model with the SHAP method showed similar accordance with the development of DKD in pathology. In conclusion, the proposed auxiliary diagnostic technologies have the feasibility for quantitative analysis of glomerular pathological tissues and alterations in DKD. Pathological quantitative indicators will also make it more convenient to provide doctors with assistance in clinical practice.

[Chloroplast genomic characterization and phylogenetic analysis of Pellionia scabra].

Pellionia scabra belongs to the genus Pellionia in the family of Urticaceae, and is a high-quality wild vegetables with high nutritional value. In this study, high-throughput techniques were used to sequence, assemble and annotate the chloroplast genome. We also analyzed its structure, and construct the phylogenetic trees from the P. scabra to further study the chloroplast genome characteristics. The results showed that the chloroplast genome size was 153 220 bp, and the GC content was 36.4%, which belonged to the typical tetrad structure in P. scabra. The chloroplast genome encodes 130 genes, including 85 protein-coding genes, 37 tRNA genes, and 8 rRNA genes in P. scabra. Among them, 15 genes contained 1 intron, 2 genes contained 2 introns, and rps12 had trans-splicing, respectively. In P. scabra, chloroplast genomes could be divided into four categories, including 43 photosynthesis, 64 self-replication, other 7 coding proteins, and 4 unknown functions. A total of 51 073 codons were detected in the chloroplast genome, among which the codon encoding leucine (Leu) accounted for the largest proportion, and the codon preferred to use A and U bases. There were 72 simple sequence repeats (SSRs) in the chloroplast genome of P. scabra, containing 58 single nucleotides, 12 dinucleotides, 1 trinucleotide, and 1 tetranucleotide. The ycf1 gene expansion was present at the IRb/SSC boundary. The phylogenetic trees showed that P. scabra (OL800583) was most closely related to Elatostema stewardii (MZ292972), Elatostema dissectum (MK227819) and Elatostema laevissimum var. laevissimum (MN189961). Taken together, our results provide worthwhile information for understanding the identification, genetic evolution, and genomics research of P. scabra species.

[Characteristics and phylogenetic analysis of chloroplast genome of a new type of fruit Rubus rosaefolius].

The genomic DNA of Rubus rosaefolius was extracted and sequenced by Illumina NovaSeq platform to obtain the complete chloroplast genome sequence, and the sequence characteristics and phylogenetic analysis of chloroplast genes were carried out. The results showed that the complete chloroplast genome of the R. rosaefolius was 155 650 bp in length and had a typical tetrad structure, including two reverse repeats (25 748 bp each), a large copy region (85 443 bp) and a small copy region (18 711 bp). A total of 131 genes were identified in the whole genome of R. rosaefolius chloroplast, including 86 protein coding genes, 37 tRNA genes and 8 rRNA genes. The GC content of the whole genome was 36.9%. The genome of R. rosaefolius chloroplast contains 47 scattered repeats and 72 simple sequence repeating (SSR) loci. The codon preference is leucine codon, and the codon at the end of A/U is preferred. Phylogenetic analysis showed that R. rosaefolius had the closest relationship with R. taiwanicola, followed by R. rubraangustifolius and R. glandulosopunctatus. The chloroplast genome characteristics and phylogenetic analysis of R. rosaefolius provide a theoretical basis for its genetic diversity research and chloroplast development and utilization.

Microbial diversity and community composition of fecal microbiota in dual-purpose and egg type ducks.

Introduction: Ducks are important agricultural animals, which can be divided into egg and dual-purpose type ducks according to economic use. The gut microbiota of ducks plays an important role in their metabolism, immune regulation, and health maintenance. Methods: Here, we use 16S rDNA V4 hypervariable amplicon sequencing to investigate the compositions and community structures of fecal microbiota between egg (five breeds, 96 individuals) and dual-purpose type ducks (four breeds, 73 individuals) that were reared under the same conditions. Results: The alpha diversity of fecal microflora in egg type ducks was significantly higher than that in dual-type ducks. In contrast, there is no significant difference in the fecal microbial community richness between the two groups. MetaStat analysis showed that the abundance of Peptostreptococcaceae, Streptococcaceae, Lactobacillus, Romboutsia, and Campylobacter were significantly different between the two groups. The biomarkers associated with the egg and dual-purpose type ducks were identified using LEfSe analysis and IndVal index. Function prediction of the gut microbiota indicated significant differences between the two groups. The functions of environmental information processing, carbohydrate metabolism, lipid metabolism, xenobiotic biodegradation and metabolism, and metabolism of terpenoids and polyketides were more abundant in egg type ducks. Conversely, the genetic information processing, nucleotide metabolism, biosynthesis of amino acids and secondary metabolites, glycan biosynthesis and metabolism, fatty acid elongation, and insulin resistance were significantly enriched in dual-purpose type ducks. Discussion: This study explored the structure and diversity of the gut microbiota of ducks from different economic-use groups, and provides a reference for improving duck performance by using related probiotics in production.

Genomic characteristics and selection signals of Zhongshan ducks.

The Zhongshan duck (ZSP) is a duck breed indigenous to China and is known for its moderate body size, strong disease resistance, tender meat, and little subcutaneous fat. However, the genomic basis of such excellent breeding characteristics remains poorly understood. Therefore, we generated whole-genomes of 58 ZSPs and 180 other indigenous Chinese ducks (60 Jinding ducks, 60 Shan Partridge ducks, and 60 Liancheng white ducks) and identified 10 560 032 single nucleotide polymorphisms and 1 334 893 structural variants. Based on genetic diversity and population structure indices, our results confirm that the ZSP is a unique germplasm resource. In addition, three reproduction-related genes (i.e., OAZ, AMH, and RLF) were located in highly differentiated regions between the ZSP and the other three duck breeds (Jinding duck; Liancheng White duck; Shan Partridge duck), suggesting that these genes may have a strong influence on egg production. Among these genes, AMH may have introgressed from an unknown species of the Anatidae family. We also identified other significant genes in the significantly differentiated window (i.e., 1% cut-off), some of which are responsible for growth and development (SEMA5B and MIB1), metabolism (EDEM3 and Xylb), skeletal system morphogenesis (bglap and MGP), and egg shape (ITPR2). These findings highlight the genetic characteristics of the ZSP that shape an array of its morphological traits. Overall, this study should facilitate a more fine-scale approach towards improving the ZSP and other indigenous ducks in China and even all over the world.

Characterization of the complete chloroplast genome of Pellionia scabra (Urticaceae).

Pellionia scabra Benth. 1861 (Urticaceae) is distributed in east and southeast Asian countries, including China, Vietnam, and Japan, and has important applications in construction, medicine, and the food industry. We sequenced the genome using Illumina DNA sequencing technology. The genome was 153,220 bp long. Annotation of the genome showed that it encoded 130 genes, including 85 protein-coding genes, 37 tRNA genes, and eight rRNA genes. In addition, 15 of the genes contained a single intron and two contained two introns. Furthermore, rps12 consists of 3 exons that are expected to be trans-spliced together. Subsequent phylogenetic analysis revealed that P. scabra is closely related to the Elatostema species (i.e. E. stewardii [MZ292972], E. dissectum [MK227819], and E. laevissimum var. laevissimum [MN189961]).

IDA-MIL: Classification of Glomerular with Spike-like Projections via Multiple Instance Learning with Instance-level Data Augmentation.

BACKGROUND AND OBJECTIVE: Tiny spike-like projections on the basement membrane of glomeruli are the main pathological feature of membranous nephropathy at stage II (MN II), which is the most significant stage for the diagnosis and treatment of renal disease. Pathological technology is the gold standard in the diagnosis of spike-like and other MNs, and automatic classification of spike-like projection is a crucial step in assisting pathologists in their diagnosis. However, owing to hard-to-label spile-like projections and the scarcity of patient data, classification of glomeruli with spike-like projections based on supervised learning methods is a challenging task. METHOD: To overcome the aforementioned problems, the idea of integrating weakly-supervised learning and data augmentation methods is utilized in designing the classification framework. Specifically, a multiple instance learning with instance-level data augmentation (IDA-MIL) method for the classification of glomeruli with spike-like projections is established in this paper. The proposed classification framework first trains the MIL model on a dataset with image-level labels, and the well-trained MIL model is used to extract instances that include spike-like projections in the whole glomerular image. Then, rather than using an image-level generative adversarial network (ImgGAN), an instance-level generative adversarial network (InsGAN) based on the StyleGAN2-ADA model is trained on the spike-like instances obtained by the MIL model and synthesizes new spike-like projection instances. Finally, the synthesized spike-like instances are extended to the training dataset to further improve the classification performance of MIL. RESULTS: The designed IDA-MIL model is verified and evaluated from two aspects based on the in-house dataset. On the one hand, the performance comparisons between InsGAN and ImgGAN on five metrics, which involve FID, KID, Precision, Recall and IS, show that InsGAN obtains a better score and can synthesize effective spike-like projections. However, the proposed IDA-MIL model achieves the best classification performance with an accuracy of 0.9405. Then, to make nephrologists believe the inference result of the proposed model, we use heatmap technology to visualize the basis of the model inferences and show the top 4 probability spike-like instances per glomerulus. Furthermore, we analyze the correlation between the disease and the proportion of spike-like instances in bags from historical cases. CONCLUSION: Compared with the ImgGAN, the InsGAN can synthesize natural and varied spike-like projections, which results in the classification performance of the MIL model achieving great improvement by adding synthesized instance samples into the training dataset. The heatmap of spike-like inferences and the proportion of spike-like instances can help nephrologists to make a preliminary reliable diagnosis in clinical practice. This work provides a valuable reference for medical image classification with limited data and small-scale lesions based on deep learning.

Genomic signatures and evolutionary history of the endangered blue-crowned laughingthrush and other Garrulax species.

BACKGROUND: The blue-crowned laughingthrush (Garrulax courtoisi) is a critically endangered songbird endemic to Wuyuan, China, with population of ~323 individuals. It has attracted widespread attention, but the lack of a published genome has limited research and species protection. RESULTS: We report two laughingthrush genome assemblies and reveal the taxonomic status of laughingthrush species among 25 common avian species according to the comparative genomic analysis. The blue-crowned laughingthrush, black-throated laughingthrush, masked laughingthrush, white-browed laughingthrush, and rusty laughingthrush showed a close genetic relationship, and they diverged from a common ancestor between ~2.81 and 12.31 million years ago estimated by the population structure and divergence analysis using 66 whole-genome sequencing birds from eight laughingthrush species and one out group (Cyanopica cyanus). Population inference revealed that the laughingthrush species experienced a rapid population decline during the last ice age and a serious bottleneck caused by a cold wave during the Chinese Song Dynasty (960-1279 AD). The blue-crowned laughingthrush is still in a bottleneck, which may be the result of a cold wave together with human exploitation. Interestingly, the existing blue-crowned laughingthrush exhibits extremely rich genetic diversity compared to other laughingthrushes. These genetic characteristics and demographic inference patterns suggest a genetic heritage of population abundance in the blue-crowned laughingthrush. The results also suggest that fewer deleterious mutations in the blue-crowned laughingthrush genomes have allowed them to thrive even with a small population size. We believe that cooperative breeding behavior and a long reproduction period may enable the blue-crowned laughingthrush to maintain genetic diversity and avoid inbreeding depression. We identified 43 short tandem repeats that can be used as markers to identify the sex of the blue-crowned laughingthrush and aid in its genetic conservation. CONCLUSIONS: This study supplies the missing reference genome of laughingthrush, provides insight into the genetic variability, evolutionary potential, and molecular ecology of laughingthrush and provides a genomic resource for future research and conservation.

The complete chloroplast genome of Nageia fleuryi (Hickel) de Laub. (Podocarpaceae).

Nageia fleuryi (Hickel) de Laub. 1987 belongs to the genus Nageia in the family Podocarpaceae and is distributed throughout southeast Asia, including China, Vietnam, and Cambodia. It is a plant with high economic beneficial for food and construction industries. Here, we report on the complete chloroplast (cp) genome of N. fleuryi for the first time. The complete cp genome is similar to many gymnosperm plants, however, it lacks inverted repeat regions and does not possess a typical quadripartite structure. The complete cp genome is 133,870 bp in size and the overall guanine-cytosine (GC) content was found to be 37.27%. The total number of genes is 119, including 82 protein-coding genes, 33 tRNA genes, and 4 rRNA genes. Of these, 14 genes contain one intron, two genes contain two introns, and rps12 possessed a trans-splicing mechanism. Finally, the phylogenic tree demonstrated that N. fleuryi is closely related to Nageia nagi (AB830885.1 and LC572156.1).

The complete chloroplast genome of Rubus rosifolius (Rosaceae), an ornamental and medicinal plant.

Rubus rosifolius belongs to the genus Rubus in the family Rosaceae and is widely distributed globally. It has white flowers and red fruits. Moreover, it has medicinal value for diseases of the stomach and other areas. However, the complete chloroplast (cp) genome of R. rosifolius remains unclear. In the present study, we sequenced the complete cp genome of R. rosifolius (GenBank accession no. OL435124), which had a typical quadripartite structure and a size of 155,650 bp. Fifteen genes (trnK-UUU, rps16, trnG-UCC, atpF, rpoC1, trnL-UAA, trnV-UAC, petB, petD, rpl16, rpl2, ndhB, trnI-GAU, trnA-UGC, and ndhA) contained an intron, and two genes (clpP and ycf3) contained two introns. The gene rps12 showed trans-splicing. Phylogenetic analysis showed that R. rosifolius was closely related to Rubus taiwanicola, Rubus rubroangustifolius, and Rubus glandulosopunctatus.

Population structure of 3907 worldwide pigs and the introgression of Chinese indigenous pigs by European pigs.

With the improvement in sequencing technology and the decrease in sequencing cost, increasing amounts of genomic data for pigs have been uploaded to public databases. However, no researchers have to date integrated all currently available data to uncover the global genetic status of pigs. Meanwhile, little is known about the introgression from European to Chinese pigs and its underlying influences. Therefore, we integrated the effective genotype data of 3907 pigs from 193 populations worldwide using population genetic analysis, gene flow analysis and a sharing-IBD study. These findings illustrate not only the population structure of 59 Chinese native breeds and others but also the amounts of gene flow and introgression that have occurred between Western and Chinese pigs. In addition, we demonstrate the presence of introgressed European haplotypes in Chinese indigenous breeds and identify relevant introgressed regions that contain genes associated with growth and feed efficiency. Moreover, we compare the introgression patterns of Western and Chinese pigs and further discuss possible explanations for why the level of introgression differs between Chinese pig breeds and Western modern breeds. Collectively, this study provides a fine global population structure analysis of pigs and presents evidence of European pigs being interbred with local breeds in China.

Identification of Key Candidate Genes for Beak Length Phenotype by Whole-Genome Resequencing in Geese.

The domestic goose is an important economic animal in agriculture and its beak, a trait with high heritability, plays an important role in promoting food intake and defending against attacks. In this study, we sequenced 772 420-day-old Xingguo gray geese (XGG) using a low-depth (~1 ×) whole-genome resequencing strategy. We detected 12,490,912 single nucleotide polymorphisms (SNPs) using the standard GATK and imputed with STITCH. We then performed a genome-wide association study on the beak length trait in XGG. The results indicated that 57 SNPs reached genome-wide significance levels for the beak length trait and were assigned to seven genes, including TAPT1, DHX15, CCDC149, LGI2, SEPSECS, ANAPC4, and Slc34a2. The different genotypes of the most significant SNP (top SNP), which was located upstream of LGI2 and explained 7.24% of the phenotypic variation in beak length, showed significant differences in beak length. Priority-based significance analysis concluded that CCDC149, LGI2, and SEPSECS genes in the most significant quantitative trait locus interval were the most plausible positional and functional candidate genes for beak length development in the XGG population. These findings not only enhance our understanding of the genetic mechanism of the beak length phenotype in geese, but also lay the foundation for further studies to facilitate the genetic selection of traits in geese.

Investigation on the characteristics of gut microbiota in critically endangered blue-crowned laughingthrush (Garrulax courtoisi).

Blue-crowned laughingthrush (Garrulax courtoisi), passeriformes, is a critically endangered bird endemic to China. Gut microbiota is well known to play a pivotal role in host health and survival. Thus, the understanding of the microbial communities associated with Garrulax courtoisi could be beneficial to save this species from the brink of extinction. In this study, we used 16 s rDNA amplicon sequencing to investigate the gut community composition and microbial diversity of the Garrulax courtoisi population reared in Nanchang Zoo. The results showed that there were 31 phyla that were dominated by Firmicutes, Proteobacteria, Bacteroidetes, and Cyanobacteria in the intestine of Garrulax courtoisi. Compared with previous studies on birds, the Cyanobacteria exhibited an excessive abundance, which may be largely related to the personal lifestyle of Garrulax courtoisi. At the genus level, a total of 552 genera were identified, among which, 21 key genera constituted the core microbiome, including some culturable bacterial genera such as Lactobacillus, Acinetobacter, and Deinococcus. In the meanwhile, we found that there were remarkable intraspecific differences both in terms of microbial community structures, representative biomarkers and predicted functions between the parental generation and their offspring of the population investigated in this study. Furthermore, we also summarized their different eating behaviors and predicted its association with gut microbiota. This study provided the needed pieces of information about these extremely rare birds, Garrulax courtoisi, whose community composition and microbial diversity are hardly known. Importantly, these findings could contribute to our knowledge of the gut health of Garrulax courtoisi and advance the comprehensive conservation of this endangered bird.

Classification of renal biopsy direct immunofluorescence image using multiple attention convolutional neural network.

BACKGROUND AND OBJECTIVES: Direct immunofluorescence (DIF) is an important medical evaluation tool for renal pathology. In the DIF images, the deposition appearances and locations of immunoglobulin on glomeruli involve immunological characteristics of glomerulonephritis and thus can be used to aid in the identification of glomerulonephritis disease. Manual classification to such deposition patterns is time consuming and may lead to significant inter and intra operator variances. We wanted to automate the identification and fusion of deposition location and deposition appearance to assist physicians in achieving immunofluorescence reporting. METHODS: In this paper, we propose a framework that consists of a pre-segmentation module and a classification module for automatically segmenting glomerulus object and classifying the deposition pattern of immunoglobulin on glomerulus object. For the pre-segmentation module, the glomerulus object is segmented out from the acquired DIF images using a segmentation network, which excludes other tissues and makes the classification module focus on the glomerulus. For the classification module, two branches of classifying deposition region and appearance, respectively, are formed by using multiple attentions convolutional neural network (MANet) based on the segmented images, and the classification results of the two pre-trained classification networks are fused with labels. RESULTS: Experimental results show that the proposed framework achieves a high classification performance with an accuracy of 98% and 95% in terms of deposition region and appearance, respectively. The label fusion of deposition appearance and deposition classification is achieved with high accuracy based on well-trained classification. CONCLUSIONS: The data show that automated and accurate patterned immunofluorescence report generation is achieved, which can effectively help improve the diagnosis of autoimmune kidney disease.

Dietary purslane (Portulaca oleracea L.) promotes the growth performance of broilers by modulation of gut microbiota.

Purslane is a widespread wild vegetable with both medicinal and edible properties. It is highly appreciated for its high nutritional value and is also considered as a high-quality feed resource for livestock and poultry. In this study, Sanhuang broilers were used to investigate the effect of feeding purslane diets on the growth performance in broilers and their gut microbiota. A total of 48 birds with good growth and uniform weight were selected and randomly allocated to four treatment groups A (control), B, C and D. Dietary treatments were fed with basal diet without purslane and diets containing 1%, 2% and 3% purslane. The 16S rDNA was amplified by PCR and sequenced using the Illumina HiSeq platform to analyze the composition and diversity of gut microbiota in the four sets of samples. The results showed that dietary inclusion of 2% and 3% purslane could significantly improve the growth performance and reduce the feed conversion ratio. Microbial diversity analysis indicated that the composition of gut microbiota of Sanhuang broilers mainly included Gallibacterium, Bacteroides and Escherichia-Shigella, etc. As the content of purslane was increased, the abundance of Lactobacillus increased significantly, and Escherichia-Shigella decreased. LEfSe analysis revealed that Bacteroides_caecigallinarum, Lachnospiraceae, Lactobacillales and Firmicutes had significant differences compared with the control group. PICRUSt analysis revealed bacteria mainly enriched in carbohydrate metabolism pathway due to the additon of purslane in the diet. These results suggest that the addition of purslane to feed could increase the abundance of Lactobacillus in intestine, modulate the environment of gut microbiota and promote the metabolism of carbohydrates to improve its growth performance. This study indicates that the effect of purslane on the growth-promoting performance of broilers might depend on its modulation on gut microbiota, so as to provide a certain scientific basis for the application of purslane in the feed industry.

Cancer-Associated IDH1 Promotes Growth and Resistance to Targeted Therapies in the Absence of Mutation.

Oncogenic mutations in two isocitrate dehydrogenase (IDH)-encoding genes (IDH1 and IDH2) have been identified in acute myelogenous leukemia, low-grade glioma, and secondary glioblastoma (GBM). Our in silico and wet-bench analyses indicate that non-mutated IDH1 mRNA and protein are commonly overexpressed in primary GBMs. We show that genetic and pharmacologic inactivation of IDH1 decreases GBM cell growth, promotes a more differentiated tumor cell state, increases apoptosis in response to targeted therapies, and prolongs the survival of animal subjects bearing patient-derived xenografts (PDXs). On a molecular level, diminished IDH1 activity results in reduced α-ketoglutarate (αKG) and NADPH production, paralleled by deficient carbon flux from glucose or acetate into lipids, exhaustion of reduced glutathione, increased levels of reactive oxygen species (ROS), and enhanced histone methylation and differentiation marker expression. These findings suggest that IDH1 upregulation represents a common metabolic adaptation by GBMs to support macromolecular synthesis, aggressive growth, and therapy resistance.

Bcl2L13 is a ceramide synthase inhibitor in glioblastoma.

Therapy resistance is a major limitation to the successful treatment of cancer. Here, we identify Bcl2-like 13 (Bcl2L13), an atypical member of the Bcl-2 family, as a therapy susceptibility gene with elevated expression in solid and blood cancers, including glioblastoma (GBM). We demonstrate that mitochondria-associated Bcl2L13 inhibits apoptosis induced by a wide spectrum of chemo- and targeted therapies upstream of Bcl2-associated X protein activation and mitochondrial outer membrane permeabilization in vitro and promotes GBM tumor growth in vivo. Mechanistically, Bcl2L13 binds to proapoptotic ceramide synthases 2 (CerS2) and 6 (CerS6) via a unique C-terminal 250-aa sequence located between its Bcl-2 homology and membrane anchor domains and blocks homo- and heteromeric CerS2/6 complex formation and activity. Correspondingly, CerS2/6 activity and Bcl2L13 abundance are inversely correlated in GBM tumors. Thus, our genetic and functional studies identify Bcl2L13 as a regulator of therapy susceptibility and point to the Bcl2L13-CerS axis as a promising target to enhance responses of therapy-refractory cancers toward conventional and targeted regimens currently in clinical use.

LY303511 enhances TRAIL sensitivity of SHEP-1 neuroblastoma cells via hydrogen peroxide-mediated mitogen-activated protein kinase activation and up-regulation of death receptors.

We recently reported that LY294002 (LY29) and LY303511 (LY30) sensitized tumor cells to drug-induced apoptosis independent of the phosphoinositide 3-kinase/Akt pathway. Here, we investigated the mechanism of LY30-induced sensitization of human neuroblastoma cells to TRAIL-mediated apoptosis. We provide evidence that LY30-induced increase in intracellular H(2)O(2) up-regulates the expression of TRAIL receptors (DR4 and DR5) in SHEP-1 cells by activating mitogen-activated protein kinases, resulting in a significant amplification of TRAIL-mediated caspase-8 processing and activity, cytosolic translocation of cytochrome c, and cell death. Involvement of the death receptors was further confirmed by the ability of blocking antibodies against DR4 and/or DR5 to inhibit LY30-induced TRAIL sensitization. Pharmacologic inhibition of c-Jun NH(2) terminal kinase (JNK) and extracellular signal-regulated kinase (ERK) activation by SP600125 and PD98059, respectively, blocked LY30-induced increase in sensitization to TRAIL-mediated death. Finally, small interfering RNA-mediated gene silencing of JNK and ERK inhibited LY30-induced increase in surface expression of DR4 and DR5, respectively. These data show that JNK and ERK are two crucial players involved in H(2)O(2)-mediated increase in TRAIL sensitization of tumor cells upon exposure to LY30 and underscore a novel mode of action of this inactive analogue of LY29. Our findings could have implications for the use of LY30 and similar compounds for enhancing the apoptotic sensitivity of neuroblastoma cells that often become refractory to chemotherapy.