Search

Abstract Background Leukemia inhibitory factor (LIF) is a multifunctional member of the IL-6 cytokine family that activates downstream signaling pathways by binding to the heterodimer consisting of LIFR and gp130 on the cell surface. Previous research has shown that LIF is highly expressed in various tumor tissues (e.g. pancreatic cancer, breast cancer, prostate cancer, and colorectal cancer) and promotes cancer cell proliferation, migration, invasion, and differentiation. Moreover, the overexpression of LIF correlates with poor clinicopathological characteristics. Therefore, we hypothesized that LIF could be a promising target for the treatment of cancer. In this work, we developed the antagonist antibody 1G11 against LIF and investigated its anti-tumor mechanism and its therapeutic efficacy in mouse models. Results A series of single-chain variable fragments (scFvs) targeting LIF were screened from a naive human scFv phage library. These scFvs were reconstructed in complete IgG form and produced by the mammalian transient expression system. Among the antibodies, 1G11 exhibited the excellent binding activity to human, cynomolgus monkey and mouse LIF. Functional analysis demonstrated 1G11 could block LIF binding to LIFR and inhibit the intracellular STAT3 phosphorylation signal. Interestingly, 1G11 did not block LIF binding to gp130, another LIF receptor that is involved in forming the receptor complex together with LIFR. In vivo, intraperitoneal administration of 1G11 inhibited tumor growth in CT26 and MC38 models of colorectal cancer. IHC analysis demonstrated that p-STAT3 and Ki67 were decreased in tumor tissue, while c-caspase 3 was increased. Furthermore, 1G11 treatment improves CD3+, CD4 + and CD8 + T cell infiltration in tumor tissue. Conclusions We developed antagonist antibodies targeting LIF/LIFR signaling pathway from a naive human scFv phage library. Antagonist anti-LIF antibody exerts antitumor effects by specifically reducing p-STAT3. Further studies revealed that anti-LIF antibody 1G11 increased immune cell infiltration in tumor tissues.

Abstract Monocarboxylate transporter 1 (MCT1) exhibits essential roles in cellular metabolism and energy supply. Although MCT1 is highly expressed in activated B cells, it is not clear how MCT1-governed monocarboxylates transportation is functionally coupled to antibody production during the glucose metabolism. Here, we report that B cell-lineage deficiency of MCT1 significantly influences the class-switch recombination (CSR), rendering impaired IgG antibody responses in Mct1 f/f Mb1 Cre mice after immunization. Metabolic flux reveals that glucose metabolism is significantly reprogrammed from glycolysis to oxidative phosphorylation in Mct1-deficient B cells upon activation. Consistently, activation-induced cytidine deaminase (AID), is severely suppressed in Mct1-deficient B cells due to the decreased level of pyruvate metabolite. Mechanistically, MCT1 is required to maintain the optimal concentration of pyruvate to secure the sufficient acetylation of H3K27 for the elevated transcription of AID in activated B cells. Clinically, we found that MCT1 expression levels are significantly upregulated in systemic lupus erythematosus patients, and Mct1 deficiency can alleviate the symptoms of bm12-induced murine lupus model. Collectively, these results demonstrate that MCT1-mediated pyruvate metabolism is required for IgG antibody CSR through an epigenetic dependent AID transcription, revealing MCT1 as a potential target for vaccine development and SLE disease treatment.

Adeno-associated virus (AAV) serotypes from primates are being developed and clinically used as vectors for human gene therapy. However, the evolutionary mechanism of AAV variants is far from being understood, except that genetic recombination plays an important role. Furthermore, little is known about the interaction between AAV and its natural hosts, human and nonhuman primates. In this study, natural AAV capsid genes were subjected to systemic evolutionary analysis with a focus on selection drives during the diversification of AAV lineages. A number of positively selected sites were identified from these AAV lineages with functional relevance implied by their localization on the AAV structures. The selection drives of the two AAV2 capsid sites were further investigated in a series of biological experiments. These observations did not support the evolution of the site 410 of the AAV2 capsid driven by selection pressure from the human CD4+ T-cell response. However, positive selection on site 548 of the AAV2 capsid was directly related to host humoral immunity because of the profound effects of mutations at this site on the immune evasion of AAV variants from human neutralizing antibodies at both the individual and population levels. Overall, this work provides a novel interpretation of the genetic diversity and evolution of AAV lineages in their natural hosts, which may contribute to their further engineering and application in human gene therapy.

Abstract Urgent research into innovative severe acute respiratory coronavirus‐2 (SARS‐CoV‐2) vaccines that may successfully prevent various emerging emerged variants, particularly the Omicron variant and its subvariants, is necessary. Here, we designed a chimeric adenovirus‐vectored vaccine named Ad5‐Beta/Delta. This vaccine was created by incorporating the receptor‐binding domain from the Delta variant, which has the L452R and T478K mutations, into the complete spike protein of the Beta variant. Both intramuscular (IM) and intranasal (IN) vaccination with Ad5‐Beta/Deta vaccine induced robust broad‐spectrum neutralization against Omicron BA.5‐included variants. IN immunization with Ad5‐Beta/Delta vaccine exhibited superior mucosal immunity, manifested by higher secretory IgA antibodies and more tissue‐resident memory T cells (TRM) in respiratory tract. The combination of IM and IN delivery of the Ad5‐Beta/Delta vaccine was capable of synergically eliciting stronger systemic and mucosal immune responses. Furthermore, the Ad5‐Beta/Delta vaccination demonstrated more effective boosting implications after two dosages of mRNA or subunit recombinant protein vaccine, indicating its capacity for utilization as a booster shot in the heterologous vaccination. These outcomes quantified Ad5‐Beta/Delta vaccine as a favorable vaccine can provide protective immunity versus SARS‐CoV‐2 pre‐Omicron variants of concern and BA.5‐included Omicron subvariants.

Abstract Passiflora is a plant genus known for its extremely distinctive and colorful flowers and a wide range of genome size variation. However, how genome characteristics are related to flower traits among Passiflora species remains poorly understood. Here, we assembled a chromosome-scale genome of P. foetida, which belongs to the same subgenus as the commercial passionfruit P. edulis. The genome of P. foetida is smaller (424.16 Mb) and contains fewer copies of long terminal repeat retrotransposons (LTR-RTs). The disparity in LTR-RTs is one of the main contributors to the differences in genome sizes between these two species and possibly in floral traits. Additionally, we observed variation in insertion times and copy numbers of LTR-RTs across different transposable element (TE) lineages. Then, by integrating transcriptomic data from 33 samples (eight floral organs and flower buds at three developmental stages) with phylogenomic and metabolomic data, we conducted an in-depth analysis of the expression, phylogeny, and copy number of MIKC-type MADS-box genes and identified essential biosynthetic genes responsible for flower color and scent from glandular bracts and other floral organs. Our study pinpoints LRT-RTs as an important player in genome size variation in Passiflora species and provides insights into future genetic improvement. Graphical Abstract

Strigolactones are a group of small molecules that play critical roles in plant developmental processes and root biotic interactions. Strigolactones are agronomically important due to their role as a signal for the germination of a parasitic weed (Striga spp.) that reduces yields of cereal crops worldwide. To identify the genes encoding strigolactones in sorghum and their function, we characterized two CRISPR/Cas9-mediated gene knockouts of carotenoid cleavage dioxygenase 8 (CCD8) genes (SbCCD8a and SbCCD8b), which have been shown in other plant species to be involved in strigolactone biosynthesis. Although strigolactones are important for the parasitization of sorghum in Africa, the functions of members of the CCD8 family have not been characterized. The impact of the knockouts on strigolactone production, plant growth and development, resistance to the parasitic weed Striga, and the root-associated microbiomes were investigated in this study. The results revealed that knockout of SbCCD8 genes in sorghum significantly reduced orobanchol production and Striga germination. Strigolactone deficiency altered the shoot and root architecture and reduced grain yield of sorghum. The knockout of the SbCCD8b gene significantly affected the rhizosphere bacterial diversity and community composition at sorghum plant grain-fill stage due to the abolition of orobanchol exudation from roots. Reduced amounts of orobanchol in root exudates also influenced root-associated fungal taxa abundance. Our findings provide new insights into potentially sustainable approaches for the recruitment of beneficial microbes and for parasitic weed control through manipulation of strigolactone production in sorghum.

The sunflower (Helianthus annuus L.) is one of the most essential oil crops in the world. Several component traits, including flowering time, plant height, stem diameter, seed weight, and kernel weight, determine sunflower seed and oil yield. Although the genetic mechanisms governing the variation of these yield-related traits have been studied using various approaches, genome-wide association studies (GWAS) have not been widely applied to sunflowers. In this study, a set of 342 sunflower accessions was evaluated in 2019 and 2020 using an incomplete randomized block design, and GWAS was conducted utilizing two complementary approaches: the mixed linear model (MLM) and the fixed and random model circulating probability unification (farmCPU) model by fitting 226,779 high-quality SNPs. As a result, GWAS identified a number of trait-associated SNPs. Those SNPs were located close to several genes that may serve as a basis for further molecular characterization and provide promising targets for sunflower yield improvement.

Junctional adhesion molecule-A (JAM-A) is an adhesion molecule that exists on the surface of certain types of cells, including white blood cells, endothelial cells, and dendritic cells. In this study, the cDNA sequences of JAM-A-Fc were chemically synthesized with optimization for mammalian expression. Afterward, we analyzed JAM-A protein expression through transient transfection in HEK293 cell lines. Mice were immunized with JAM-A-Fc protein, and hybridoma was prepared by fusing myeloma cells and mouse spleen cells. Antibodies were purified from the hybridoma supernatant and four monoclonal strains were obtained and numbered 61H9, 70E5, 71A8, and 74H3 via enzyme-linked immunosorbent assay screening. Immunofluorescence staining assay showed 61H9 was the most suitable cell line for mAb production due to its fluorescence signal being the strongest. Flow cytometric analysis proved that 61H9 possessed high affinity. Moreover, antagonism of JAM-A mAb could attenuate the proliferative, migrative, and invasive abilities of ESCC cells and significantly inhibit tumor growth in mice. By examining hematoxylin-eosin staining mice tumor tissues, we found inflammatory cells infiltrated lightly in the anti-JAM-A group. The expression of BCL-2 and IκBα in the anti-JAM-A group were decreased in mice tumor tissues compared to the control group. Ultimately, a method for preparing high-yield JAM-A-Fc protein was created and a high affinity mAb against JAM-A with an antitumor effect was prepared.

Abstract Background Genetic variation in regulatory sequences that alter transcription factor (TF) binding is a major cause of phenotypic diversity. Brassinosteroid is a growth hormone that has major effects on plant phenotypes. Genetic variation in brassinosteroid-responsive cis-elements likely contributes to trait variation. Pinpointing such regulatory variations and quantitative genomic analysis of the variation in TF-target binding, however, remains challenging. How variation in transcriptional targets of signaling pathways such as the brassinosteroid pathway contributes to phenotypic variation is an important question to be investigated with innovative approaches. Results Here, we use a hybrid allele-specific chromatin binding sequencing (HASCh-seq) approach and identify variations in target binding of the brassinosteroid-responsive TF ZmBZR1 in maize. HASCh-seq in the B73xMo17 F1s identifies thousands of target genes of ZmBZR1. Allele-specific ZmBZR1 binding (ASB) has been observed for 18.3% of target genes and is enriched in promoter and enhancer regions. About a quarter of the ASB sites correlate with sequence variation in BZR1-binding motifs and another quarter correlate with haplotype-specific DNA methylation, suggesting that both genetic and epigenetic variations contribute to the high level of variation in ZmBZR1 occupancy. Comparison with GWAS data shows linkage of hundreds of ASB loci to important yield and disease-related traits. Conclusion Our study provides a robust method for analyzing genome-wide variations of TF occupancy and identifies genetic and epigenetic variations of the brassinosteroid response transcription network in maize.

Unmanned aerial vehicle (UAV)-based imagery has become widely used to collect time-series agronomic data, which are then incorporated into plant breeding programs to enhance crop improvements. To make efficient analysis possible, in this study, by leveraging an aerial photography dataset for a field trial of 233 different inbred lines from the maize diversity panel, we developed machine learning methods for obtaining automated tassel counts at the plot level. We employed both an object-based counting-by-detection (CBD) approach and a density-based counting-by-regression (CBR) approach. Using an image segmentation method that removes most of the pixels not associated with the plant tassels, the results showed a dramatic improvement in the accuracy of object-based (CBD) detection, with the cross-validation prediction accuracy (r2) peaking at 0.7033 on a detector trained with images with a filter threshold of 90. The CBR approach showed the greatest accuracy when using unfiltered images, with a mean absolute error (MAE) of 7.99. However, when using bootstrapping, images filtered at a threshold of 90 showed a slightly better MAE (8.65) than the unfiltered images (8.90). These methods will allow for accurate estimates of flowering-related traits and help to make breeding decisions for crop improvement.

The “do not eat me” signaling pathway is extremely active in tumor cells, providing a means for these cells to elude macrophage phagocytosis and escape immune surveillance. Representative markers of this pathway, such as CD47 and CD24, are highly expressed in numerous tumors. The interaction of SIRPα with CD47 reduces the accumulation of non-myosin ⅡA on the cell membrane. The combination of CD24 and Siglec10 ultimately leads to the recruitment of SHP-1 or SHP-2 to reduce signal transduction. Both of them weaken the ability of macrophages to engulf tumor cells. Blocking the mutual recognition between CD47-SIRPα or CD24-Siglec10 using large molecular proteins or small molecular drugs represents a promising avenue for tumor immunotherapy. Doing so can inhibit signal transduction and enhance macrophage clearance rates of cancer cells. In this paper, we summarize the characteristics of the drugs that affect the “do not eat me” signaling pathway via classical large molecular proteins and small molecule drugs, which target the CD47-SIRPα and CD24-Siglec10 signaling pathways, which target the CD47-SIRPα and CD24-Siglec10 signaling pathways. We expect it will offer insight into the development of new drugs centered on blocking the “do not eat me” signaling pathway.

Abstract Anti-CD38 monoclonal antibodies (mAbs), daratumumab, and isatuximab have represented a breakthrough in the treatment of multiple myeloma (MM). Recently, CD38-based mAbs were expected to achieve increasing potential beyond MM, which encouraged us to develop new anti-CD38 mAbs to meet clinical needs. In this study, we developed a novel humanized anti-CD38 antibody, FTL004, which exhibited enhanced pro-apoptotic ability and negligible binding to red blood cells (RBCs). FTL004 presented a better ability to induce direct apoptosis independent of Fc-mediated cross-linking against lymphoma and MM cell lines as well as primary myeloma cells derived from MM patients. For instance, FTL004 induced RPMI 8226 cells with 55% early apoptosis cells compared with 20% in the isatuximab-treated group. Of interest, FTL004 showed ignorable binding to CD38 on human RBCs in contrast to tumor cells, even at concentrations up to 30 μg/mL. Furthermore, with an engineered Fc domain, FTL004 displayed stronger antibody-dependent cellular cytotoxicity (ADCC) against CD38+ malignant cells. In vivo MM and non-Hodgkin lymphoma tumor xenograft models showed that FTL004 possessed an effective anti-tumor effect. Cryo-electron microscopy structure resolved two epitope centers of FTL004 on CD38: one of which was unique while the other partly overlapped with that of isatuximab. Taken together, FTL004 distinguishes it from other CD38 targeting mAbs and represents a potential candidate for the treatment of MM and non-Hodgkin lymphoma.

The SARS-CoV-2 Omicron variant has quickly become the predominant circulating variant, due to the high transmissibility and immune escape. Here, the authors develop a trimeric protein vaccine candidate and show a sustained humoral immune response, and protection from challenge (Omicron and Delta) in various animal models.

Abstract BA.4 and BA.5 (BA.4/5), the subvariants of Omicron, are more transmissible than BA.1 with more robust immune evasion capability because of its unique spike protein mutations. In light of such situation, the vaccination against severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) is in desperate need of the third booster. It has been reported that heterologous boosters might produce more effective immunity against wild‐type SARS‐CoV‐2 and the variants. Additionally, the third heterologous protein subunit booster should be considered potentially. In the present study, we prepared a Delta full‐length spike protein sequence‐based mRNA vaccine as the “priming” shot and developed a recombinant trimeric receptor‐binding domain (RBD) protein vaccine referred to as RBD–HR/trimer as a third heterologous booster. Compared to the homologous mRNA group, the heterologous group (RBD–HR/trimer vaccine primed with two mRNA vaccines) induced higher neutralizing antibody titers against BA.4/5‐included SARS‐CoV‐2 variants. In addition, heterologous vaccination exhibited stronger cellular immune response and long‐lasting memory response than the homologous mRNA vaccine. In conclusion, a third heterologous boosting with RBD–HR/trimer following two‐dose mRNA priming vaccination should be a superior strategy than a third homologous mRNA vaccine. The RBD–HR/trimer vaccine becomes an appropriate candidate for a booster immune injection.

Abstract Fibrosis is characterized by the excessive extracellular matrix deposition due to dysregulated wound and connective tissue repair response. Multiple organs can develop fibrosis, including the liver, kidney, heart, and lung. Fibrosis such as liver cirrhosis, idiopathic pulmonary fibrosis, and cystic fibrosis caused substantial disease burden. Persistent abnormal activation of myofibroblasts mediated by various signals, such as transforming growth factor, platelet-derived growth factor, and fibroblast growh factor, has been recongized as a major event in the occurrence and progression of fibrosis. Although the mechanisms driving organ-specific fibrosis have not been fully elucidated, drugs targeting these identified aberrant signals have achieved potent anti-fibrotic efficacy in clinical trials. In this review, we briefly introduce the aetiology and epidemiology of several fibrosis diseases, including liver fibrosis, kidney fibrosis, cardiac fibrosis, and pulmonary fibrosis. Then, we summarise the abnormal cells (epithelial cells, endothelial cells, immune cells, and fibroblasts) and their interactions in fibrosis. In addition, we also focus on the aberrant signaling pathways and therapeutic targets that regulate myofibroblast activation, extracellular matrix cross-linking, metabolism, and inflammation in fibrosis. Finally, we discuss the anti-fibrotic drugs based on their targets and clinical trials. This review provides reference for further research on fibrosis mechanism, drug development, and clinical trials.

Discovered On Gastrointestinal stromal tumors protein 1 (DOG1), a major calcium-activated chloride channel, has been used as a common diagnostic marker for gastrointestinal stromal tumors. However, the therapeutic application of DOG1 was not well defined. Here, we aim to investigate its potential as a therapeutic target for an antibody-drug conjugate (ADC) in various cancers of the alimentary tract and metastasis. The DOG1 expression profile was determined among TCGA samples and tissue microarrays. High levels of DOG1 expression were ubiquitously observed in multiple cancer samples from the alimentary tract determined by TCGA samples and tissue microarrays. Circulating tumor cells isolated from metastatic colon cancer patients were also positive for DOG1 expression. The mechanisms of anti-DOG1 antibody were investigated by dual-luciferase reporter assay. The anti-DOG1 antibody could inhibit proliferation and metastasis via p53 signaling in limited cancer cell lines. The anti-DOG1 antibody was conjugated with a microtubule inhibitor DM4, to construct a new anti-DOG1-DM4-ADC to strengthen its activity. The anti-DOG1-DM4-ADC showed cytotoxicity at the nanomolar level in vitro. In the murine xenograft tumor models, treatment of anti-DOG1-DM4-ADC achieved a significant tumor growth inhibition rate. Our study indicates that anti-DOG1-DM4-ADC may be promising therapeutic molecules for DOG1-positive alimentary tract tumors and may be effective in inhibiting recurrence after curative resection of liver metastases of colorectal origin.

DNA methylation mediated by DNA methyltransferase is an important epigenetic process that regulates gene expression in mammals, which plays a key role in silencing certain genes, such as tumor suppressor genes, in cancer, and it has become a promising therapeutic target for cancer treatment. Similar to other epigenetic targets, DNA methyltransferase can also be modulated by chemical agents. Four agents have already been approved to treat hematological cancers. In order to promote the development of a DNA methyltransferase inhibitor as an anti-tumor agent, in the current review, we discuss the relationship between DNA methylation and tumor, the anti-tumor mechanism, the research progress and pharmacological properties of DNA methyltransferase inhibitors, and the future research trend of DNA methyltransferase inhibitors.

The root-associated microbiome (rhizobiome) affects plant health, stress tolerance, and nutrient use efficiency. However, it remains unclear to what extent the composition of the rhizobiome is governed by intraspecific variation in host plant genetics in the field and the degree to which host plant selection can reshape the composition of the rhizobiome. Here, we quantify the rhizosphere microbial communities associated with a replicated diversity panel of 230 maize (Zea mays L.) genotypes grown in agronomically relevant conditions under high N (+N) and low N (-N) treatments. We analyze the maize rhizobiome in terms of 150 abundant and consistently reproducible microbial groups and we show that the abundance of many root-associated microbes is explainable by natural genetic variation in the host plant, with a greater proportion of microbial variance attributable to plant genetic variation in -N conditions. Population genetic approaches identify signatures of purifying selection in the maize genome associated with the abundance of several groups of microbes in the maize rhizobiome. Genome-wide association study was conducted using the abundance of microbial groups as rhizobiome traits, and n=622 plant loci were identified that are linked to the abundance of n=104 microbial groups in the maize rhizosphere. In 62/104 cases, which is more than expected by chance, the abundance of these same microbial groups was correlated with variation in plant vigor indicators derived from high throughput phenotyping of the same field experiment. We provide comprehensive datasets about the three-way interaction of host genetics, microbe abundance, and plant performance under two N treatments to facilitate targeted experiments toward harnessing the full potential of root-associated microbial symbionts in maize production.

Abstract Preeclampsia (PE) occurs only in humans and some non‐human primates, making its pathogenesis difficult to study. The aetiology of PE is mainly caused by a deficiency in trophoblast differentiation and spiral artery remodelling. The angiogenic factor Placental Growth Factor (PlGF) or the ratio of sFlt‐1 to PlGF and cell‐free RNA have been used as the main biomarkers for predicting PE. Now, Lee and colleagues show that the deficiency of cluster of differentiation 147 (CD147) results in preeclampsia pathophysiology. This contribution makes sheds light on PE development and unveils CD147 as a potential biomarker for clinical trials.

The study was carried out to explore the biomechanical properties of embracing and non-embracing rib plates. Forty-eight adult cadaver rib specimens were divided randomly into six groups: three fixation model groups were made using embracing plates (two pairs of equals on both sides of the broken end), and the other three groups were fixed with a pre-shaped anatomical plate (three locking screws on each side of the end were equally spaced). The biomechanical properties of these models were analyzed using non-destructive three-point bending tests, non-destructive torsion experiments, and destructive axial compression tests. In this study, the gap of fracture ends was widened in embracing plate group in the non-destructive three-point bending experiment. No change in the fracture ends was detected in the pre-shaped anatomical plate group. The bending stress of the pre-shaped anatomical plate group was significantly enhanced at the 2–12 mm displacement points (p

Leaf numbers are vital in estimating the yield of crops. Traditional manual leaf-counting is tedious, costly, and an enormous job. Recent convolutional neural network-based approaches achieve promising results for rosette plants. However, there is a lack of effective solutions to tackle leaf counting for monocot plants, such as sorghum and maize. The existing approaches often require substantial training datasets and annotations, thus incurring significant overheads for labeling. Moreover, these approaches can easily fail when leaf structures are occluded in images. To address these issues, we present a new deep neural network-based method that does not require any effort to label leaf structures explicitly and achieves superior performance even with severe leaf occlusions in images. Our method extracts leaf skeletons to gain more topological information and applies augmentation to enhance structural variety in the original images. Then, we feed the combination of original images, derived skeletons, and augmentations into a regression model, transferred from Inception-Resnet-V2, for leaf-counting. We find that leaf tips are important in our regression model through an input modification method and a Grad-CAM method. The superiority of the proposed method is validated via comparison with the existing approaches conducted on a similar dataset. The results show that our method does not only improve the accuracy of leaf-counting, with overlaps and occlusions, but also lower the training cost, with fewer annotations compared to the previous state-of-the-art approaches.The robustness of the proposed method against the noise effect is also verified by removing the environmental noises during the image preprocessing and reducing the effect of the noises introduced by skeletonization, with satisfactory outcomes.

Variation and evolution of DNA methylation during maize domestication remain largely unknown. Here, the authors generate genome and methylome sequencing data as well as HiChIP-based interactome data to investigate the adaptive and phenotypic consequences of methylation variations in maize.

Abstract Advancements in the use of genome‐wide markers have provided unprecedented opportunities for dissecting the genetic components that control phenotypic trait variation. However, cost‐effectively characterizing agronomically important phenotypic traits on a large scale remains a bottleneck. Unmanned aerial vehicle (UAV)‐based high‐throughput phenotyping has recently become a prominent method, as it allows large numbers of plants to be analyzed in a time‐series manner. In this experiment, 233 inbred lines from the maize (Zea mays L.) diversity panel were grown in the field under different nitrogen treatments. Unmanned aerial vehicle images were collected during different plant developmental stages throughout the growing season. A workflow for extracting plot‐level images, filtering images to remove nonfoliage elements, and calculating canopy coverage and greenness ratings based on vegetation indices (VIs) was developed. After applying the workflow, about 100,000 plot‐level image clips were obtained for 12 different time points. High correlations were detected between VIs and ground truth physiological and yield‐related traits. The genome‐wide association study was performed, resulting in n = 29 unique genomic regions associated with image extracted traits from two or more of the 12 total time points. A candidate gene Zm00001d031997, a maize homolog of the Arabidopsis HCF244 (high chlorophyll fluorescence 244), located underneath the leading single nucleotide polymorphisms of the canopy coverage associated signals were repeatedly detected under both nitrogen conditions. The plot‐level time‐series phenotypic data and the trait‐associated genes provide great opportunities to advance plant science and to facilitate plant breeding.

Angiokinases, such as vascular endothelial-, fibroblast- and platelet-derived growth factor receptors (VEGFRs, FGFRs and PDGFRs) play crucial roles in tumor angiogenesis. Anti-angiogenesis therapy using multi-angiokinase inhibitor has achieved great success in recent years. In this study, we presented the design, synthesis, target identification, molecular mechanism, pharmacodynamics (PD) and pharmacokinetics (PK) research of a novel triple-angiokinase inhibitor WXFL-152. WXFL-152, identified from a series of 4-oxyquinoline derivatives based on a structure–activity relationship study, inhibited the proliferation of vascular endothelial cells (ECs) and pericytes by blocking the angiokinase signals VEGF/VEGFR2, FGF/FGFRs and PDGF/PDGFRβ simultaneously in vitro. Significant anticancer effects of WXFL-152 were confirmed in multiple preclinical tumor xenograft models, including a patient-derived tumor xenograft (PDX) model. Pharmacokinetic studies of WXFL-152 demonstrated high favourable bioavailability with single-dose and continuous multi-dose by oral administration in rats and beagles. In conclusion, WXFL-152, which is currently in phase Ib clinical trials, is a novel and effective triple-angiokinase inhibitor with clear PD and PK in tumor therapy.

Inbreeding depression is the reduction in fitness and vigor resulting from mating of close relatives observed in many plant and animal species. The extent to which the genetic load of mutations contributing to inbreeding depression is due to large-effect mutations versus variants with very small individual effects is unknown and may be affected by population history. We compared the effects of outcrossing and self-fertilization on 18 traits in a landrace population of maize, which underwent a population bottleneck during domestication, and a neighboring population of its wild relative teosinte. Inbreeding depression was greater in maize than teosinte for 15 of 18 traits, congruent with the greater segregating genetic load in the maize population that we predicted from sequence data. Parental breeding values were highly consistent between outcross and selfed offspring, indicating that additive effects determine most of the genetic value even in the presence of strong inbreeding depression. We developed a novel linkage scan to identify quantitative trait loci (QTL) representing large-effect rare variants carried by only a single parent, which were more important in teosinte than maize. Teosinte also carried more putative juvenile-acting lethal variants identified by segregation distortion. These results suggest a mixture of mostly polygenic, small-effect partially recessive effects in linkage disequilibrium underlying inbreeding depression, with an additional contribution from rare larger-effect variants that was more important in teosinte but depleted in maize following the domestication bottleneck. Purging associated with the maize domestication bottleneck may have selected against some large effect variants, but polygenic load is harder to purge and overall segregating mutational burden increased in maize compared to teosinte.

Adsorption is an important technology for the separation of different tea components. The adsorption behavior of L-theanine onto adsorbents was comprehensively studied in this paper. Among tested adsorbents, cation exchange resin ZGSPC106Na and D001SD were suitable for separating L-theanine, PVPP and PA-6 for catechins and macroporous resin HPD-400 for caffeine. Adsorption of L-theanine onto the cation resins was significantly influenced by the acidity, contact time and temperature. The adsorption behavior could be described by the pseudo-second-order rate equation and fitted to Langmuir and Freundlich models. ZGSPC106Na exhibited higher adsorption capacity, while D001SD showed higher adsorption selectivity. These might be attributed to the distinctive structure of the two resins and different ionization of the adsorbates. A method for simultaneous preparation of decaffeinated polyphenols, caffeine-enriched extract and decaffeinated L-theanine was established through successive separation on the columns fulfilled with PA-6, HPD-400 and D001SD, respectively.

Background: Previous research suggested that Chinese Medicine (CM) Formula Huashibaidu granule might shorten the disease course in coronavirus disease 2019 (COVID-19) patients. This research aimed to investigate the early treatment effect of Huashibaidu granule in well-managed patients with mild COVID-19.Methods: An unblinded cluster-randomized clinical trial was conducted at the Dongxihu FangCang hospital. Two cabins were randomly allocated to a CM or control group, with 204 mild COVID-19 participants in each cabin. All participants received conventional treatment over a 7 day period, while the ones in CM group were additionally given Huashibaidu granule 10 g twice daily. Participants were followed up to their clinical endpoint. The primary outcome was worsening symptoms before the clinical endpoint. The secondary outcomes were cure and discharge before the clinical endpoint and alleviation of composite symptoms after the 7 days of treatment.Results: All 408 participants were followed up to their clinical endpoint and included in statistical analysis. Baseline characteristics were comparable between the two groups (P > 0.05). The number of worsening patients in the CM group was 5 (2.5%), and that in the control group was 16 (7.8%) with a significant difference between groups (P = 0.014). Eight foreseeable mild adverse events occurred without statistical difference between groups (P = 0.151).Conclusion: Seven days of early treatment with Huashibaidu granule reduced the likelihood of worsening symptoms in patients with mild COVID-19. Our study supports Huashibaidu granule as an active option for early treatment of mild COVID-19 in similar well-managed medical environments.Clinical Trial Registration:www.chictr.org.cn/showproj.aspx?proj=49408, identifier: ChiCTR2000029763.

[This corrects the article DOI: 10.1371/journal.pgen.1007019.].

Immune checkpoint inhibitors have achieved unprecedented success in cancer immunotherapy. However, the overall response rate to immune checkpoint inhibitor therapy for many cancers is only between 20 and 40%, and even less for colorectal cancer (CRC) patients. Thus, there is an urgent need to develop an efficient immunotherapeutic strategy for CRC. Here, we developed a novel CRC combination therapy consisting of a multiple receptor tyrosine kinase inhibitor (Foretinib) and anti-PD-1 antibody. The combination therapy significantly inhibited tumor growth in mice, led to improved tumor regression without relapse (83% for CT26 tumors and 50% for MC38 tumors) and prolonged overall survival. Mechanistically, Foretinib caused increased levels of PD-L1 via activating the JAK2-STAT1 pathway, which could improve the effectiveness of the immune checkpoint inhibitor. Moreover, the combination therapy remodeled the tumor microenvironment and enhanced anti-tumor immunity by further increasing the infiltration and improving the function of T cells, decreasing the percentage of tumor-associated macrophages (TAMs) and inhibiting their polarization toward the M2 phenotype. Furthermore, the combination therapy inhibited the metastasis of CT26-Luc tumors to the lung in BALB/c mouse by reducing proportions of regulatory T-cells, TAMs and M2 phenotype TAMs in their lungs. This study suggests that a novel combination therapy utilizing both Foretinib and anti-PD-1 antibody could be an effective combination strategy for CRC immunotherapy.

DDR1 has been identified as a cancer‐associated receptor tyrosine kinase that is highly expressed in several malignancies relative to normal tissues. Clinically approved multi‐kinase inhibitors, such as nilotinib, inhibit DDR1‐mediated tumor growth in xenograft models, suggesting DDR1 might be a potential target for cancer treatments. Here, we employed an antibody‐based strategy with a novel anti‐DDR1 antibody‐drug conjugate (ADC) for colon carcinoma treatment. We developed T4H11‐DM4, an ADC targeting DDR1 which carries the tubulin inhibitor payload DM4. Immunohistochemical analysis of a tissue microarray containing 100 colon cancer specimens revealed that DDR1 was highly expressed in 81% of tumor tissues. Meanwhile, high expression of DDR1 was associated with poor survival in patients. In vitro, T4H11‐DM4 exhibited potent anti‐proliferative activity with half maximal inhibitory concentration (IC50) values in the nanomolar range in a panel of colon cancer cell lines. In vivo, the antitumor efficacy of T4H11‐DM4 was evaluated in three colon cancer cell lines expressing different levels of DDR1. T4H11‐DM4 achieved complete tumor regression at doses of 5 and 10 mg·kg−1 in HT‐29 and HCT116 tumor models. Moreover, a correlation between in vivo efficacy of T4H11‐DM4 and the levels of DDR1 expression on the cell surface was observed. Tumor cell proliferation was caused by the induction of mitotic arrest, indicating that the antitumor effect in vivo was mediated by DM4. In addition, T4H11‐DM4 was efficacious in oxaliplatin‐resistant colon cancer models. In exploratory safety studies, T4H11‐DM4 exhibited no overt toxicities when multi‐doses were administered at 10 mg·kg−1 into BALB/c nude mice or when a single dose up to 50 mg·kg−1 was administered into BALB/c mice. Overall, our findings highlight the potential of DDR1‐targeted ADC and may facilitate the development of a new effective therapeutic strategy for colon cancer.

Abstract Background Hyperspectral reflectance data in the visible, near infrared and shortwave infrared range (VIS–NIR–SWIR, 400–2500 nm) are commonly used to nondestructively measure plant leaf properties. We investigated the usefulness of VIS–NIR–SWIR as a high-throughput tool to measure six leaf properties of maize plants including chlorophyll content (CHL), leaf water content (LWC), specific leaf area (SLA), nitrogen (N), phosphorus (P), and potassium (K). This assessment was performed using the lines of the maize diversity panel. Data were collected from plants grown in greenhouse condition, as well as in the field under two nitrogen application regimes. Leaf-level hyperspectral data were collected with a VIS–NIR–SWIR spectroradiometer at tasseling. Two multivariate modeling approaches, partial least squares regression (PLSR) and support vector regression (SVR), were employed to estimate the leaf properties from hyperspectral data. Several common vegetation indices (VIs: GNDVI, RENDVI, and NDWI), which were calculated from hyperspectral data, were also assessed to estimate these leaf properties. Results Some VIs were able to estimate CHL and N (R2 > 0.68), but failed to estimate the other four leaf properties. Models developed with PLSR and SVR exhibited comparable performance to each other, and provided improved accuracy relative to VI models. CHL were estimated most successfully, with R2 (coefficient of determination) > 0.94 and ratio of performance to deviation (RPD) > 4.0. N was also predicted satisfactorily (R2 > 0.85 and RPD > 2.6). LWC, SLA and K were predicted moderately well, with R2 ranging from 0.54 to 0.70 and RPD from 1.5 to 1.8. The lowest prediction accuracy was for P, with R2

Advances in next generation sequencing technologies and statistical approaches enable genome-wide dissection of phenotypic traits via genome-wide association studies (GWAS). Although multiple statistical approaches for conducting GWAS are available, the power and cross-validation rates of many approaches have been mostly tested using simulated data. Empirical comparisons of single variant (SV) and multi-variant (MV) GWAS approaches have not been conducted to test if a single approach or a combination of SV and MV is effective, through identification and cross-validation of trait-associated loci. In this study, kernel row number (KRN) data were collected from a set of 6,230 entries derived from the Nested Association Mapping (NAM) population and related populations. Three different types of GWAS analyses were performed: 1) single-variant (SV), 2) stepwise regression (STR) and 3) a Bayesian-based multi-variant (BMV) model. Using SV, STR, and BMV models, 257, 300, and 442 KRN-associated variants (KAVs) were identified in the initial GWAS analyses. Of these, 231 KAVs were subjected to genetic validation using three unrelated populations that were not included in the initial GWAS. Genetic validation results suggest that the three GWAS approaches are complementary. Interestingly, KAVs in low recombination regions were more likely to exhibit associations in independent populations than KAVs in recombinationally active regions, probably as a consequence of linkage disequilibrium. The KAVs identified in this study have the potential to enhance our understanding of the genetic basis of ear development.

HtrA-family proteases are commonly employed to sense the protein folding stress and activate the regulated intramembrane proteolysis (RIP) cascade in Gram-negative bacteria. Here, we reveal the unique dual-signal activation and dynamic regulation properties of AlgW, an HtrA-type protease triggering the AlgU stress-response pathway, which controls alginate production and mucoid conversion in Pseudomonas aeruginosa

Abstract Leaf number and leaf emergence rate are phenotypes of interest to plant breeders, plant geneticists, and crop modelers. Counting the extant leaves of an individual plant is straightforward even for an untrained individual, but manually tracking changes in leaf numbers for hundreds of individuals across multiple time points is logistically challenging. This study generated a dataset including over 150,000 maize and sorghum images for leaf counting projects. A subset of 17,783 images also includes annotations of the positions of individual leaf tips. With these annotated images, we evaluate two deep learning‐based approaches for automated leaf counting: the first based on counting‐by‐regression from whole image analysis and a second based on counting‐by‐detection. Both approaches can achieve root of mean square error (RMSE) smaller than one leaf, only moderately inferior to the RMSE between human annotators of between 0.57 and 0.73 leaves. The counting‐by‐regression approach based on convolutional neural networks (CNNs) exhibited lower accuracy and increased bias for plants with extreme leaf numbers which are underrepresented in this dataset. The counting‐by‐detection approach based on Faster R‐CNNs (region based convolutional neural networks) object detection models achieve near human performance for plants where all leaf tips are visible. The annotated image data and model performance metrics generated as part of this study provide large scale resources for the comparison and improvement of algorithms for leaf counting from image data in grain crops.

Peroxisome proliferator-activated receptor α/δ (PPAR α/δ), regulating glucolipid metabolism and immune inflammation, has been identified as an effective therapeutic target in non-alcoholic steatohepatitis (NASH). Dual PPAR α/δ agonist, such as GFT505 (also known as elafibranor), demonstrated potential therapeutic effect for NASH in clinical trials. To profile the regulatory network of PPAR α/δ agonist in NASH, the choline-deficient, L-amino acid-defined, high-fat diet (CDAHFD) induced NASH model was used to test the pharmacodynamics and transcriptome regulation of GFT505 in this study. The results showed that GFT505 ameliorated hepatic steatosis, inflammation and fibrosis in CDAHFD mice model. RNA-sequencing yielded 3995 up-regulated and 3576 down-regulated genes with GFT505 treatment. And the most significant differentialy expressed genes involved in glucolipid metabolism (Pparα, Acox1, Cpt1b, Fabp4, Ehhadh, Fabp3), inflammation (Ccl6, Ccl9, Cxcl14) and fibrosis (Timp1, Lamc3, Timp2, Col3a1, Col1a2, Col1a1, Hapln4, Timp3, Pik3r5, Pdgfα, Pdgfβ, Tgfβ1, Tgfβ2) were confirmed by RT-qPCR. The down-regulated genes were enriched in cytokine-cytokine receptor interaction pathway and ECM-receptor interaction pathway, while the up-regulated genes were enriched in PPAR signaling pathway and fatty acid degradation pathway. This study provides clues and basis for further understanding on the mechanism of PPAR α/δ agonist on NASH.

This study describes the evaluation of a range of approaches to semantic segmentation of hyperspectral images of sorghum plants, classifying each pixel as either nonplant or belonging to one of the three organ types (leaf, stalk, panicle). While many current methods for segmentation focus on separating plant pixels from background, organ-specific segmentation makes it feasible to measure a wider range of plant properties. Manually scored training data for a set of hyperspectral images collected from a sorghum association population was used to train and evaluate a set of supervised classification models. Many algorithms show acceptable accuracy for this classification task. Algorithms trained on sorghum data are able to accurately classify maize leaves and stalks, but fail to accurately classify maize reproductive organs which are not directly equivalent to sorghum panicles. Trait measurements extracted from semantic segmentation of sorghum organs can be used to identify both genes known to be controlling variation in a previously measured phenotypes (e.g., panicle size and plant height) as well as identify signals for genes controlling traits not previously quantified in this population (e.g., stalk/leaf ratio). Organ level semantic segmentation provides opportunities to identify genes controlling variation in a wide range of morphological phenotypes in sorghum, maize, and other related grain crops.

Background Non-alcoholic steatohepatitis (NASH) is a progressive liver disease characterized by hepatic steatosis, lobular inflammation and fibrosis. Acetyl-CoA carboxylase (ACC) isoform 1 and 2 involved in de novo lipogenesis (DNL) and fatty acid oxidation have been identified as a therapeutic target in NASH. GS-0976, the inhibitor of ACC1 and ACC2, has achieved favorable therapeutic effects in clinical trials with NASH. The purpose of this study was to explore the transcriptional alterations regulated by GS-0976 in NASH. Methods C57BL/6 mice were fed on a choline-deficient, L-amino acid-defined, high-fat diet (CDAHFD) or normal diet for 12 weeks. Mice were treated with or without GS-0976 (3 mg/kg per day) in the last 8 weeks. Oil Red O, Haematoxylin-eosin (H & E), and Sirius Red were used to evaluate hepatic steatosis, inflammation and fibrosis. The comparative RNA-sequencing was conducted to analyse the hepatic gene expression profiles in mice. Reverse transcription–polymerase chain reaction analysis was performed to validate the differential expression of representative genes. Results GS-0976 attenuated the steatosis, inflammation, and fibrosis of NASH in CDAHFD mouse model. High-throughput sequencing and differential gene expression analysis showed that there were 516 up-regulated genes and 525 down-regulated genes after GS-0976 treatment. Genes involved in the metabolic process, extracellular matrix formation, immune response, and angiogenesis were significantly enriched. The “Metabolic pathways” and “ECM-receptor interaction” pathways were the most significantly enriched KEGG pathways in the up-regulated and down-regulated differentially expressed genes (DEGs), respectively. Conclusions Transcriptome analysis showed that GS-0976 could regulate the expression of genes related to metabolism, inflammation and fibrosis in NASH. The global transcriptomic changes in gene expression promote the further understanding for the inhibition mechanisms of GS-0976 in NASH.

Pearl millet is a non-model grain and fodder crop adapted to extremely hot and dry environments globally. In India, a great deal of public and private sectors’ investment has focused on developing pearl millet single cross hybrids based on the cytoplasmic-genetic male sterility (CMS) system, while in Africa most pearl millet production relies on open pollinated varieties. Pearl millet lines were phenotyped for both the inbred parents and hybrids stage. Many breeding efforts focus on phenotypic selection of inbred parents to generate improved parental lines and hybrids. This study evaluated two genotyping techniques and four genomic selection schemes in pearl millet. Despite the fact that 6× more sequencing data were generated per sample for RAD-seq than for tGBS, tGBS yielded more than 2× as many informative SNPs (defined as those having MAF > 0.05) than RAD-seq. A genomic prediction scheme utilizing only data from hybrids generated prediction accuracies (median) ranging from 0.73-0.74 (1000-grain weight), 0.87-0.89 (days to flowering time), 0.48-0.51 (grain yield) and 0.72-0.73 (plant height). For traits with little to no heterosis, hybrid only and hybrid/inbred prediction schemes performed almost equivalently. For traits with significant mid-parent heterosis, the direct inclusion of phenotypic data from inbred lines significantly (P < 0.05) reduced prediction accuracy when all lines were analyzed together. However, when inbreds and hybrid trait values were both scored relative to the mean trait values for the respective populations, the inclusion of inbred phenotypic datasets moderately improved genomic predictions of the hybrid genomic estimated breeding values. Here we show that modern approaches to genotyping by sequencing can enable genomic selection in pearl millet. While historical pearl millet breeding records include a wealth of phenotypic data from inbred lines, we demonstrate that the naive incorporation of this data into a hybrid breeding program can reduce prediction accuracy, while controlling for the effects of heterosis per se allowed inbred genotype and trait data to improve the accuracy of genomic estimated breeding values for pearl millet hybrids.

Abstract The human CGB5 gene encodes chorionic gonadotropin (hCG)β 5, which is aberrantly expressed in trophoblastic neoplasm and in some non‐trophoblastic neoplasms. Fucntional studies observed that it involved tumor initiation, growth, and metastatic outgrowth. In this study, using data from the International Cancer Genome Consortium (ICGC) and the Cancer Genome Atlas (TCGA)‐stomach adenocarcinoma (STAD), we assessed the independent prognostic value of CGB5 expression in patients with primary gastric cancer (GC). Results showed that CGB5 expression was nearly not expressed in normal GC tissues. In comparison, its expression was detected in 214 of the 415 primary GC cases (51.6%) in TCGA‐STAD and was associated with poor response to primary therapy and a higher risk of recurrence and death. In early stages, CGB5 expression was not a prognostic factor in terms of OS (HR: 1.448; 95% CI: 0.811–2.588, P = 0.211) or RFS (HR: 1.659; 95% CI: 0.778–3.540, P = 0.190). However, its expression was independently associated with unfavorable OS (HR: 1.719; 95% CI: 1.115–2.651, P = 0.014) and RFS (HR: 3.602; 95% CI: 1.708–7.598, P = 0.001) in advanced stages. Using deep sequencing data from TCGA‐STAD, we found that CGB5 expression was not related to its genetic amplification or DNA methylation in GC. Based on these findings, we infer that CGB5 expression is common in GC patients and its expression might independently predict poor OS and RFS in advanced stages, but not in early stages of GC.

In molecular biology, protein synthesis is an essential biological process of generating specific proteins in living systems. Many mathematical models are designed to characterize the process of the flow of genetic information from deoxyribonucleic acid (DNA) to protein; however, most of them cannot provide detailed and observed steps to describe and analyze this fundamental biological process. Colored Petri Net (CPN), as a mathematical method widely applied in the process analysis of discrete event dynamic systems, has increasingly become an innovative and efficient theoretical approach for exploring the biological processes. Aiming at describing the entire process of protein synthesis, a CPN-based model has been designed that successfully and intuitively represents the flow of genetic information involving transcription and translation. DNA mutations are permanent alterations in the nucleotide sequence of the DNA strand, while different types of mutations have various effects on the synthesized proteins. Based on the proposed protein synthesis model, we put forward a new CPN model to identify the type of mutation, which is beneficial for analyzing whether the mutation has impacts on the structure or function of the produced protein. The mutation position and bases mutated rate are obtained by contrasting the nucleobases on DNA sequences, while the mutation type is determined via the alignment of the amino acids in the polypeptide chain. The model's effectiveness and accuracy are illustrated by biological mutation examples, indicating this method offers great superiority in modeling and analyzing the complex biological processes.

Abstract Background There are significant limitations in existing methods for the genome-wide identification of genes whose expression patterns affect traits. Results The transcriptomes of five tissues from 27 genetically diverse maize inbred lines were deeply sequenced to identify genes exhibiting high and low levels of expression variation across tissues or genotypes. Transcription factors are enriched among genes with the most variation in expression across tissues, as well as among genes with higher-than-median levels of variation in expression across genotypes. In contrast, transcription factors are depleted among genes whose expression is either highly stable or highly variable across genotypes. We developed a Bayesian-based method for genome-wide association studies (GWAS) in which RNA-seq-based measures of transcript accumulation are used as explanatory variables (eRD-GWAS). The ability of eRD-GWAS to identify true associations between gene expression variation and phenotypic diversity is supported by analyses of RNA co-expression networks, protein–protein interaction networks, and gene regulatory networks. Genes associated with 13 traits were identified using eRD-GWAS on a panel of 369 maize inbred lines. Predicted functions of many of the resulting trait-associated genes are consistent with the analyzed traits. Importantly, transcription factors are significantly enriched among trait-associated genes identified with eRD-GWAS. Conclusions eRD-GWAS is a powerful tool for associating genes with traits and is complementary to SNP-based GWAS. Our eRD-GWAS results are consistent with the hypothesis that genetic variation in transcription factor expression contributes substantially to phenotypic diversity.

Microtubule-stabilizing agents (MSAs) promote the polymerization of tubulin and are of great interest as anticancer drugs. Here the authors present the crystal structure of the MSA taccalonolide AJ bound to tubulin and give insights into its mode of action, which might help in the design of novel MSAs.

Abstract Background Preoperative imatinib mesylate (IM) treatment has not yet been standardized. Here, we aim to further explore such therapy on patients with gastrointestinal stromal tumors (GIST) retrospectively. Methods Patients experiencing preoperative IM were identified from January 2009 to February 2015. Results A total of 28 GIST patients were identified. The patients received preoperative IM treatment for a median length of 13.5 months, ranging from 5 to 37 months. PR and SD were observed in 24 (85.7%) and 4 (15.3%) patients, respectively. The tumor shrinkage occurred predominantly within 6 to 12 months, and slight tumor shrinkage could be observed after 12 months in certain patients. Nineteen patients (67.9%) received surgery, and R0 resection was acquired in 18 (94.7%) patients. The initial mean maximum diameter was 10.5 (5.2 to 19.0) cm and decreased to 5.9 (2.7 to 19.0) cm after preoperative treatment with a median length of 12 (ranging from 5 to 36) months (P

Microtubule assembly and disassembly is the target of many anticancer therapies, with β-tubulin the most-frequent target. Here, the authors used biochemical and biophysical techniques to demonstrate pironetin binds to α-tubulin and thereby inhibits microtubule polymerization providing a basis for the rational design of novel anticancer drugs.

Abstract SARI, also called as BATF2, belongs to the BATF family and has been implicated in cancer cell growth inhibition. However, the role and mechanism of SARI in tumour angiogenesis are elusive. Here we demonstrate that SARI deficiency facilitates AOM/DSS-induced colonic tumorigenesis in mice. We show that SARI is a novel inhibitor of colon tumour growth and angiogenesis in mice. Antibody array and HUVEC-related assays indicate that VEGF has an essential role in SARI-controlled inhibition of angiogenesis. Furthermore, Co-IP/PAGE/mass spectrometry indicates that SARI directly targets ceruloplasmin (Cp), and induces protease degradation of Cp, thereby inhibiting the activity of the HIF-1α/VEGF axis. Tissue microarray results indicate that SARI expression inversely correlates with poor clinical outcomes in colon cancer patients. Collectively, our results indicate that SARI is a potential target for therapy by inhibiting angiogenesis through the reduction of VEGF expression and is a prognostic indicator for patients with colon cancer.

While the vast majority of genome size variation in plants is due to differences in repetitive sequence, we know little about how selection acts on repeat content in natural populations. Here we investigate parallel changes in intraspecific genome size and repeat content of domesticated maize (Zea mays) landraces and their wild relative teosinte across altitudinal gradients in Mesoamerica and South America. We combine genotyping, low coverage whole-genome sequence data, and flow cytometry to test for evidence of selection on genome size and individual repeat abundance. We find that population structure alone cannot explain the observed variation, implying that clinal patterns of genome size are maintained by natural selection. Our modeling additionally provides evidence of selection on individual heterochromatic knob repeats, likely due to their large individual contribution to genome size. To better understand the phenotypes driving selection on genome size, we conducted a growth chamber experiment using a population of highland teosinte exhibiting extensive variation in genome size. We find weak support for a positive correlation between genome size and cell size, but stronger support for a negative correlation between genome size and the rate of cell production. Reanalyzing published data of cell counts in maize shoot apical meristems, we then identify a negative correlation between cell production rate and flowering time. Together, our data suggest a model in which variation in genome size is driven by natural selection on flowering time across altitudinal clines, connecting intraspecific variation in repetitive sequence to important differences in adaptive phenotypes.

[This corrects the article DOI: 10.1371/journal.pone.0187356.].