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Plant basic helix-loop-helix (bHLH) transcription factors (TFs) play central roles in various abiotic stresses. However, its role in plant cold resistance is largely unknown. Previously, we characterised CaNAC035 in pepper, which positively regulates tolerance to cold, salt and drought stresses tolerance. Here, we identified CabHLH035, a CaNAC035-interacting protein in pepper. To explore its functions in cold stress tolerance, we silenced the gene in pepper via virus-induced gene silencing (VIGS) and overexpressed the gene in Arabidopsis. The results showed that CabHLH035 expression was induced by cold treatment, and silencing of CabHLH035 decreased cold stress tolerance. Conversely, overexpression of CabHLH035 in Arabidopsis increased cold stress tolerance. To investigate homologs genes of C-repeat binding factor (CBF) pathway proteins and reactive oxygen species (ROS) marker gene expression blocking by CabHLH035, we performed yeast one-hybrid (Y1H), dual luciferase and electrophoretic mobility shift assay experiments. The results showed that CabHLH035 bound to the region upstream of the CaCBF1A and CaAPX promoters. Additionally, CaCBF1A bound to the CaDHN4 promoter. Taken together, our results showed that CabHLH035 plays a crucial role in cold stress tolerance and its potential as a target for breeding cold-resistant crops. The findings provide a basis for studying the functions and regulatory network of cold stress tolerance in pepper.

Low-molecular-weight polysaccharides (RLPs) extracted from Rosa laevigata fruits showed multiple biofunctions in Asia. This study aimed to investigate its anti-inflammatory obesity effect in high-fat diet-fed rats and further elucidated the underlying molecular mechanism by multi-omics methods. The results showed that RLPs administration had significantly restored immune organ indexes and reduced body weight gain. RNA-seq revealed that the effect of RLPs was partially attributed to its regulation on PPARs signaling by increasing the expressions of Scd, Acox3 and Hmgcs2, and on other redox-related pathways by decreasing the expressions of Cyp2e1, Il1-r1 and Lbp. Moreover, 16S rRNA sequencing coupled with metagenome sequencing showed that RLPs had significantly reduced the ratio of Firmicutes/Bacteroidetes from 8.01 to 2.37, and significantly increased the relative abundances of Alistipes, Prevotella, and Akkermansia from 0.36 %, 1.10 % and 2.61 % to 0.65 %, 2.37 % and 4.42 %, respectively. Spearman correlation analysis result indicated that the abundances of Lachnospiraceae, Prevotella and Bacteroidales were significantly negatively correlated with obesity phenotype, liver function and inflammatory factors. These results revealed that RLPs exerted significant anti-inflammatory obesity property partially via regulation on gut microbiota interface and the redox balance. Therefore, RLPs could be a promising functional food resource with the potential for redox imbalance-related diseases chemoprevention.

Abstract Background It remains an important challenge to predict the functional consequences or clinical impacts of genetic variants in human diseases, such as cancer. An increasing number of genetic variants in cancer have been discovered and documented in public databases such as COSMIC, but the vast majority of them have no functional or clinical annotations. Some databases, such as CiVIC are available with manual annotation of functional mutations, but the size of the database is small due to the use of human annotation. Since the unlabeled data (millions of variants) typically outnumber labeled data (thousands of variants), computational tools that take advantage of unlabeled data may improve prediction accuracy. Result To leverage unlabeled data to predict functional importance of genetic variants, we introduced a method using semi-supervised generative adversarial networks (SGAN), incorporating features from both labeled and unlabeled data. Our SGAN model incorporated features from clinical guidelines and predictive scores from other computational tools. We also performed comparative analysis to study factors that influence prediction accuracy, such as using different algorithms, types of features, and training sample size, to provide more insights into variant prioritization. We found that SGAN can achieve competitive performances with small labeled training samples by incorporating unlabeled samples, which is a unique advantage compared to traditional machine learning methods. We also found that manually curated samples can achieve a more stable predictive performance than publicly available datasets. Conclusions By incorporating much larger samples of unlabeled data, the SGAN method can improve the ability to detect novel oncogenic variants, compared to other machine-learning algorithms that use only labeled datasets. SGAN can be potentially used to predict the pathogenicity of more complex variants such as structural variants or non-coding variants, with the availability of more training samples and informative features.

Abstract Endogenous endophthalmitis caused by listeria monocytogenes is rare and serious, which is easily misdiagnosed initially. We present one case of endogenous endophthalmitis due to listeria monocytogenes in a cirrhotic patient, whose diagnosis was delayed 17 days after admission.

Abstract Background Knowledges graphs (KGs) serve as a convenient framework for structuring knowledge. A number of computational methods have been developed to generate KGs from biomedical literature and use them for downstream tasks such as link prediction and question answering. However, there is a lack of computational tools or web frameworks to support the exploration and visualization of the KG themselves, which would facilitate interactive knowledge discovery and formulation of novel biological hypotheses. Method We developed a web framework for Knowledge Graph Exploration and Visualization (KGEV), to construct and visualize KGs in five stages: triple extraction, triple filtration, metadata preparation, knowledge integration, and graph database preparation. The application has convenient user interface tools, such as node and edge search and filtering, data source filtering, neighborhood retrieval, and shortest path calculation, that work by querying a backend graph database. Unlike other KGs, our framework allows fast retrieval of relevant texts supporting the relationships in the KG, thus allowing human reviewers to judge the reliability of the knowledge extracted. Results We demonstrated a case study of using the KGEV framework to perform research on COVID-19. The COVID-19 pandemic resulted in an explosion of relevant literature, making it challenging to make full use of the vast and heterogenous sources of information. We generated a COVID-19 KG with heterogenous information, including literature information from the CORD-19 dataset, as well as other existing knowledge from eight data sources. We showed the utility of KGEV in three intuitive case studies to explore and query knowledge on COVID-19. A demo of this web application can be accessed at http://covid19nlp.wglab.org . Finally, we also demonstrated a turn-key adaption of the KGEV framework to study clinical phenotypic presentation of human diseases by Human Phenotype Ontology (HPO), illustrating the versatility of the framework. Conclusion In an era of literature explosion, the KGEV framework can be applied to many emerging diseases to support structured navigation of the vast amount of newly published biomedical literature and other existing biological knowledge in various databases. It can be also used as a general-purpose tool to explore and query gene-phenotype-disease-drug relationships interactively.

Background: The impact of COVID-19 on public health has mandated an ‘all hands on deck’ scientific response. The current clinical study and basic research on COVID-19 are mainly based on existing publications or our knowledge of coronavirus. However, efficiently retrieval of accurate, relevant knowledge on COVID-19 can pose significant challenges for researchers. Methods: To improve quality in accessing important literature findings, we developed a novel natural language processing (NLP) method to automatically recognize the associations among potential targeted host organ systems, associated clinical manifestations, and pathways. We further validated these associations through clinician experts' evaluations and prioritize candidate drug targets through bioinformatics network analysis. Results: We found that the angiotensin-converting enzyme 2 (ACE2), a receptor that SARS-CoV-2 required for cell entry, is associated with cardiovascular and endocrine organ system and diseases. Furthermore, we found SARS-CoV-2 is associated with some important pathways such as IL-6, TNF-alpha, and IL-1 beta-induced dyslipidemia, which are related to inflammation, lipogenesis, and oxidative stress mechanisms, suggesting potential drug candidates. Conclusion: We prioritized the list of therapeutic targets involved in antiviral and immune modulating drugs for experimental validation, rendering it valuable during public health crises marked by stresses on clinical and research capacity. Our automatic intelligence pipeline also contributes to other novel and emerging disease management and treatments in the future.

Abstract Background Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by restricted, repetitive behavior, and impaired social communication and interactions. However, significant challenges remain in diagnosing and subtyping ASD due in part to the lack of a validated, standardized vocabulary to characterize clinical phenotypic presentation of ASD. Although the human phenotype ontology (HPO) plays an important role in delineating nuanced phenotypes for rare genetic diseases, it is inadequate to capture characteristic of behavioral and psychiatric phenotypes for individuals with ASD. There is a clear need, therefore, for a well-established phenotype terminology set that can assist in characterization of ASD phenotypes from patients’ clinical narratives. Methods To address this challenge, we used natural language processing (NLP) techniques to identify and curate ASD phenotypic terms from high-quality unstructured clinical notes in the electronic health record (EHR) on 8499 individuals with ASD, 8177 individuals with non-ASD psychiatric disorders, and 8482 individuals without a documented psychiatric disorder. We further performed dimensional reduction clustering analysis to subgroup individuals with ASD, using nonnegative matrix factorization method. Results Through a note-processing pipeline that includes several steps of state-of-the-art NLP approaches, we identified 3336 ASD terms linking to 1943 unique medical concepts, which represents among the largest ASD terminology set to date. The extracted ASD terms were further organized in a formal ontology structure similar to the HPO. Clustering analysis showed that these terms could be used in a diagnostic pipeline to differentiate individuals with ASD from individuals with other psychiatric disorders. Conclusion Our ASD phenotype ontology can assist clinicians and researchers in characterizing individuals with ASD, facilitating automated diagnosis, and subtyping individuals with ASD to facilitate personalized therapeutic decision-making.

Polygonatum odoratum (Mill.) Druce possesses widespread medicinal properties; however, the continuous cropping (CC) often leads to a severe consecutive monoculture problem (CMP), ultimately causing a decline in yield and quality. Photosynthesis is the fundamental process for plant growth development. Improving photosynthesis is one of the most promising approaches to increase plant yields. To better understand how P. odoratum leaves undergo photosynthesis in response to CC, this study analyzed the physiochemical indexes and RNA-seq. The physiochemical indexes, such as the content of chlorophyll (chlorophyll a, b, and total chlorophyll), light response curves (LRCs), and photosynthetic parameters (Fv/Fm, Fv/F0, Fm/F0, Piabs, ABS/RC, TRo/RC, ETo/RC, and DIo/RC) were all changed in P. odoratum under the CC system. Furthermore, 13,798 genes that exhibited differential expression genes (DEGs) were identified in the P. odoratum leaves of CC and first cropping (FC) plants. Among them, 7932 unigenes were upregulated, while 5860 unigenes were downregulated. Here, the DEGs encoding proteins associated with photosynthesis and carbon assimilation showed a significant decrease in expression under the CC system, such as the PSII protein complex, PSI protein complex, Cytochorome b6/f complex, the photosynthetic electron transport chain, light-harvesting chlorophyll protein complex, and Calvin cycle, etc., -related gene. This study demonstrates that CC can suppress photosynthesis and carbon mechanism in P. odoratum, pinpointing potential ways to enhance photosynthetic efficiency in the CC of plants.

Abstract It is well established that epilepsy and autism spectrum disorder (ASD) commonly co-occur; however, the underlying biological mechanisms of the co-occurence from their genetic susceptibility are not well understood. Our aim in this study is to characterize genetic modules of subgroups of epilepsy and autism genes that have similar phenotypic manifestations and biological functions. We first integrate a large number of expert-compiled and well-established epilepsy- and ASD-associated genes in a multiplex network, where one layer is connected through protein–protein interaction (PPI) and the other layer through gene-phenotype associations. We identify two modules in the multiplex network, which are significantly enriched in genes associated with both epilepsy and autism as well as genes highly expressed in brain tissues. We find that the first module, which represents the Gene Ontology category of ion transmembrane transport, is more epilepsy-focused, while the second module, representing synaptic signaling, is more ASD-focused. However, because of their enrichment in common genes and association with both epilepsy and ASD phenotypes, these modules point to genetic etiologies and biological processes shared between specific subtypes of epilepsy and ASD. Finally, we use our analysis to prioritize new candidate genes for epilepsy (i.e. ANK2, CACNA1E, CACNA2D3, GRIA2, DLG4) for further validation. The analytical approaches in our study can be applied to similar studies in the future to investigate the genetic connections between different human diseases.

Abstract Background Natural language processing (NLP) tools can facilitate the extraction of biomedical concepts from unstructured free texts, such as research articles or clinical notes. The NLP software tools CLAMP, cTAKES, and MetaMap are among the most widely used tools to extract biomedical concept entities. However, their performance in extracting disease-specific terminology from literature has not been compared extensively, especially for complex neuropsychiatric disorders with a diverse set of phenotypic and clinical manifestations. Methods We comparatively evaluated these NLP tools using autism spectrum disorder (ASD) as a case study. We collected 827 ASD-related terms based on previous literature as the benchmark list for performance evaluation. Then, we applied CLAMP, cTAKES, and MetaMap on 544 full-text articles and 20,408 abstracts from PubMed to extract ASD-related terms. We evaluated the predictive performance using precision, recall, and F1 score. Results We found that CLAMP has the best performance in terms of F1 score followed by cTAKES and then MetaMap. Our results show that CLAMP has much higher precision than cTAKES and MetaMap, while cTAKES and MetaMap have higher recall than CLAMP. Conclusion The analysis protocols used in this study can be applied to other neuropsychiatric or neurodevelopmental disorders that lack well-defined terminology sets to describe their phenotypic presentations.

Long-standing radical species have raised noteworthy concerns in organic chemistry and but there remains a substantial challenge to produce long-standing radicals by light. Here, the authors demonstrate a stable dithienylethene derived photochromic radical for detection of peroxides and ozone.

Polygonatum odoratum (Mill.) Druce is an essential Chinese herb, but continuous cropping (CC) often results in a serious root rot disease, reducing the yield and quality. Phenolic acids, released through plant root exudation, are typical autotoxic substances that easily cause root rot in CC. To better understand the phenolic acid biosynthesis of P. odoratum roots in response to CC, this study performed a combined microRNA (miRNA)-seq and RNA-seq analysis. The phenolic acid contents of the first cropping (FC) soil and CC soil were determined by HPLC analysis. The results showed that CC soils contained significantly higher levels of p-coumaric acid, phenylacetate, and caffeic acid than FC soil, except for cinnamic acid and sinapic acid. Transcriptome identification and miRNA sequencing revealed 15,788 differentially expressed genes (DEGs) and 142 differentially expressed miRNAs (DEMs) in roots from FC and CC plants. Among them, 28 DEGs and eight DEMs were involved in phenolic acid biosynthesis. Meanwhile, comparative transcriptome and microRNA-seq analysis demonstrated that eight miRNAs corresponding to five target DEGs related to phenolic acid synthesis were screened. Among them, ath-miR172a, ath-miR172c, novel_130, sbi-miR172f, and tcc-miR172d contributed to phenylalanine synthesis. Osa-miR528-5p and mtr-miR2673a were key miRNAs that regulate syringyl lignin biosynthesis. Nta-miR156f was closely related to the shikimate pathway. These results indicated that the key DEGs and DEMs involved in phenolic acid anabolism might play vital roles in phenolic acid secretion from roots of P. odoratum under the CC system. As a result of the study, we may have a better understanding of phenolic acid biosynthesis during CC of roots of P. odoratum.

Digitaria sanguinalis (Linnaeus) Scopoli 1722 is an annual herbal plant that has important medicinal and ecological value. The chloroplast genome was 138,079 bp in length. In total, 129 genes were predicted, including 82 protein-coding genes, 39 tRNA genes, and 8 rRNA genes. The overall AT content of the genome was 61.39%. The phylogenetic analysis showed that D. sanguinalis and D. glauca formed a base clade in Panicoideae close to Thyridolepis xerophila. This study will help to understand the genetic diversity of the Digitaria plants.

New photoluminescent strategies are required to realize high-performance ratiometric sensors for bio-sensing applications. Here, the authors demonstrate a fluorescence-thermally activated delayed fluorescence dual emission strategy for ratiometric sensors with high precision.

It is increasingly appreciated that long non-coding RNAs (lncRNAs) associated with alternative splicing (AS) could be involved in aggressive hepatocellular carcinoma. Although many recent studies show the alteration of RNA alternative splicing by deregulated lncRNAs in cancer, the extent to which and how lncRNAs impact alternative splicing at the genome scale remains largely elusive. We analyzed RNA-seq data obtained from 369 hepatocellular carcinomas (HCCs) and 160 normal liver tissues, quantified 198,619 isoform transcripts, and identified a total of 1,375 significant AS events in liver cancer. In order to predict novel AS-associated lncRNAs, we performed an integration of co-expression, protein-protein interaction (PPI) and epigenetic interaction networks that links lncRNA modulators (such as splicing factors, transcript factors, and miRNAs) along with their targeted AS genes in HCC. We developed a random walk-based multi-graphic (RWMG) model algorithm that prioritizes functional lncRNAs with their associated AS targets to computationally model the heterogeneous networks in HCC. RWMG shows a good performance evaluated by the ROC curve based on cross-validation and bootstrapping strategies. As a conclusion, our robust network-based framework has derived 31 AS-related lncRNAs that not only validates known cancer-associated cases MALAT1 and HOXA11-AS, but also reveals new players such as DNM1P35 and DLX6-AS1with potential functional implications. Survival analysis further provides insights into the clinical significance of identified lncRNAs.

Efficiently learning representations of clinical concepts (i. e., symptoms, lab test, etc.) from unstructured clinical notes of electronic health record (EHR) data remain significant challenges, since each patient may have multiple visits at different times and each visit may contain different sequential concepts. Therefore, learning distributed representations from temporal patterns of clinical notes is an essential step for downstream applications on EHR data. However, existing methods for EHR representation learning can not adequately capture either contextual information per-visit or temporal information at multiple visits. In this study, we developed a new vector embedding method called EHR2Vec that can learn semantically-meaningful representations of clinical concepts. EHR2Vec incorporated the self-attention structure and showed its utility in accurately identifying relevant clinical concept entities considering time sequence information from multiple visits. Using EHR data from systemic lupus erythematosus (SLE) patients as a case study, we showed EHR2Vec outperforms in identifying interpretable representations compared to other well-known methods including Word2Vec and Med2Vec, according to clinical experts' evaluations.

To investigate the prognostic value of DGM-CM6 (Distant Genetic Model-Clinical variable Model 6) for endocrine-responsive breast cancer (ERBC) patients, we analyzed 752 operable breast cancer patients treated in a Taiwan cancer center from 2005 to 2014. Among them, 490 ERBC patients (identified by the PAM50 or immunohistochemistry method) were classified by DGM-CM6 into low- and high-risk groups (cutoff

Abstract Previous studies have reported that the aberrantly expressed AKR1B10 is associated with many cancer development, however the functional roles of AKR1B10 and its regulatory mechanisms in hepatocellular carcinoma (HCC) have been limited studied. In this project, we identified AKR1B10 functional as an oncogene in HCC through tumor/normal human tissue comparison from both GEO microarray and TCGA RNAseq dataset. Further experimental validations from three HCC cell lines (SMMC-7721, HePG2 and HeP3B) also suggested the ontogenetic functions of AKR1B10 in HCC tumor growth. By knocking down AKR1B10 through shRNA in HCC HeP3B cells, we showed it significantly induced cell cycle arrest and inhibited cell growth. Interestingly, integrative analysis of TCGA RNAseq data and miRNA-seq data predicted that miR-383-5p, a novel post-transcriptional tumor suppressor, is negatively associated with AKR1B10 expression. To further investigate the role of miR-383-5p in regulating AKR1B10 in HCC, we performed Dual-luciferase reporter assay experiments. Results showed that miR-383-5p is an upstream modulator targeting AKR1B10 in the post-transcriptional stage. Thus, we report AKR1B10 modulated regulated by miR-383-5p, promotes HCC tumor progress, and could be potentially a therapeutic target for precision medicine in HCC.

Claudin-low breast cancer (CLBC) is a poor prognosis molecular subtype showing stemness and mesenchymal features. We previously discovered that deletion of a Notch signaling modulator, Lunatic Fringe (Lfng), in the mouse mammary gland induced a subset of tumors resembling CLBC. Here we report that deletion of one copy of p53 on this background not only accelerated mammary tumor development but also led to a complete penetrance of the mesenchymal stem-like phenotype. All mammary tumors examined in the Lfng/p53 compound mutant mice displayed a mesenchymal/spindloid pathology. These tumors showed high level expressions of epithelial-to-mesenchymal transition (EMT) markers including Vimentin, Twist, and PDGFRα, a gene known to be enriched in CLBC. Prior to tumor onset, Lfng/p53 mutant mammary glands exhibited increased levels of Vimentin and E-cadherin, but decreased expressions of cytokeratin 14 and cytokeratin 8, accompanied by elevated basal cell proliferation and an expanded mammary stem cell-enriched population. Lfng/p53 mutant glands displayed increased accumulation of Notch3 intracellular fragment, up-regulation of Hes5 and down-regulation of Hes1. Analysis in human breast cancer datasets found the lowest HES1 and second lowest LFNG expressions in CLBC among molecular subtypes, and low level of LFNG is associated with poor survival. Immunostaining of human breast cancer tissue array found correlation between survival and LFNG immunoreactivity. Finally, patients carrying TP53 mutations express lower LFNG than patients with wild type TP53. Taken together, these data revealed genetic interaction between Lfng and p53 in mammary tumorigenesis, established a new mouse model resembling CLBC, and may suggest targeting strategy for this disease.

Although non-small cell lung cancer (NSCLC) patients benefit from standard taxane-platin chemotherapy, many relapse, developing drug resistance. We established preclinical taxane-platin-chemoresistance models and identified a 35-gene resistance signature, which was associated with poor recurrence-free survival in neoadjuvant-treated NSCLC patients and included upregulation of the JumonjiC lysine demethylase KDM3B. In fact, multi-drug-resistant cells progressively increased the expression of many JumonjiC demethylases, had altered histone methylation, and, importantly, showed hypersensitivity to JumonjiC inhibitors in vitro and in vivo. Increasing taxane-platin resistance in progressive cell line series was accompanied by progressive sensitization to JIB-04 and GSK-J4. These JumonjiC inhibitors partly reversed deregulated transcriptional programs, prevented the emergence of drug-tolerant colonies from chemo-naive cells, and synergized with standard chemotherapy in vitro and in vivo. Our findings reveal JumonjiC inhibitors as promising therapies for targeting taxane-platin-chemoresistant NSCLCs.

Recent studies have reported that surfaces and objects in the rooms of infected patients that are frequently touched by both medical staff and patients could be contaminated with SARS-CoV-2. In December 2019, Wuhan China suffered the earliest from this COVID-19 pandemic, and we took that opportunity to investigate whether the SARS-CoV-2 RNA exists in the ward environment after a long time from exposure. We found that on the 28th day following the discharge of COVID-19 patients, SARS-CoV-2 RNA could still be detected on the surfaces of pagers and in drawers in the isolation wards. Thorough disinfection of the ward environment was subsequently performed, after which these surfaces in the isolation wards tested negative for the presence of SARS-CoV-2 RNA. The findings remind us that the contaminated environment in the wards may become potential infectious resources and that despite a long time from exposure, the thorough disinfection in the COVID-10 units after is still necessary.

Background: Diffuse gliomas, grades II and III, hereafter called lower-grade gliomas (LGG), have variable, difficult to predict clinical courses, resulting in multiple studies to identify prognostic biomarkers. The purpose of this study was to assess expression or methylation of the homeobox family gene SHOX2 as independent markers for LGG survival. Methods: We downloaded publically available glioma datasets for gene expression and methylation. The Cancer Genome Atlas (TCGA) (LGG, n = 516) was used as a training set, and three other expression datasets (n = 308) and three other methylation datasets (n = 320), were used for validation. We performed Kaplan-Meier survival curves and univariate and multivariate Cox regression model analyses. Findings: SHOX2 expression and gene body methylation varied among LGG patients and highly significantly predicted poor overall survival. While they were tightly correlated, SHOX2 expression appeared more potent as a prognostic marker and was used for most further studies. The SHOX2 prognostic roles were maintained after analyses by histology subtypes or tumor grade. We found that the combination of SHOX2 expression and IDH genotype status identified a subset of LGG patients with IDH wild-type (IDHwt) and low SHOX2 expression with considerably favorable survival. We further investigated the combination of SHOX2 with other known clinically relevant markers of LGG (TERT expression, 1p/19q chromosome co-deletion, MGMT methylation, ATRX mutation and NES expression). When combined with SHOX2 expression, we identified subsets of LGG patients with significantly favorable survival outcomes, especially in the subgroup with worse prognosis for each individual marker. Finally, multivariate analysis demonstrated that SHOX2 was a potent independent survival marker. Interpretation: We have identified that SHOX2 expression or methylation are potent independent prognostic indicators for predicting LGG patient survival, and have potential to identify an important subset of LGG patients with IDHwt status with significantly better overall survival. The combination of IDH or other relevant markers with SHOX2 identified LGG subsets with significantly different survival outcomes, and further understanding of these subsets may benefit therapeutic target identification and therapy selections for glioma patients.

Background/Aims: Intradialytic hypertension (IH) occurs frequently in some hemodialysis patients and increases mortality risk. We simultaneously compared pre-dialysis, post-dialysis and changes in extracellular volume and hemodynamics in recurrent IH patients and controls. Methods: We performed a case-control study among prevalent hemodialysis patients with recurrent IH and hypertensive hemodialysis controls. We used bioimpedance spectroscopy and impedance cardiography to compare pre-dialysis, post-dialysis, and intradialytic change in total body water (TBW) and extracellular water (ECW), as well as cardiac index (CI) and total peripheral resistance index (TPRI). Results: The ECW/TBW was 0.453 (0.05) pre-dialysis and 0.427 (0.04) post-dialysis in controls vs. 0.478 (0.03) and 0.461 (0.03) in IH patients (p=0.01 post-dialysis). The ECW/TBW change was -0.027 (0.03) in controls and -0.013 (0.02) in IH patients (p=0.1). In controls, pre- and post-dialysis TPRI were 3254 (994) and 2469 (529) dynes/sec/cm2/m2 vs. 2983 (747) and 3408 (980) dynes/sec/cm2/m2 in IH patients (p=0.002 post-dialysis). There were between-group differences in TPRI change (0=0.0001), but not CI (p=0.09). Conclusions: Recurrent intradialytic hypertension is associated with higher post-dialysis extracellular volume and TPRI. Intradialytic TPRI surges account for the vasoconstrictive state post-dialysis, but intradialytic fluid shifts may contribute to post-hemodialysis volume expansion.

Both BRCA1 and Beclin 1 (BECN1) are tumor suppressor genes, which are in close proximity on the human chromosome 17q21 breast cancer tumor susceptibility locus and are often concurrently deleted. However, their importance in sporadic human breast cancer is not known. To interrogate the effects of BECN1 and BRCA1 in breast cancer, we studied their mRNA expression patterns in breast cancer patients from two large datasets: The Cancer Genome Atlas (TCGA) (n = 1067) and the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) (n = 1992). In both datasets, low expression of BECN1 was more common in HER2-enriched and basal-like (mostly triple-negative) breast cancers compared to luminal A/B intrinsic tumor subtypes, and was also strongly associated with TP53 mutations and advanced tumor grade. In contrast, there was no significant association between low BRCA1 expression and HER2-enriched or basal-like subtypes, TP53 mutations or tumor grade. In addition, low expression of BECN1 (but not low BRCA1) was associated with poor prognosis, and BECN1 (but not BRCA1) expression was an independent predictor of survival. These findings suggest that decreased mRNA expression of the autophagy gene BECN1 may contribute to the pathogenesis and progression of HER2-enriched, basal-like, and TP53 mutant breast cancers.

Thousands of whole-genome and whole-proteome sequences have been made available through advances in sequencing technology, and sequences of millions more organisms will become available in the coming years. This wealth of genetic information will provide numerous opportunities to enhance our understanding of these organisms including a greater understanding of relationships among species. Researchers have used 16S rRNA and other gene sequences to study the evolutionary origins of bacteria, but these strategies do not provide insight into the sharing of genes among bacteria via horizontal transfer. In this work we use an open source software program called pClust to cluster proteins from the complete proteomes of twelve species of Alphaproteobacteria and generate a dendrogram from the resulting orthologous protein clusters. We compare the results with dendrograms constructed using the 16S rRNA gene and multiple sequence alignment of seven housekeeping genes. Analysis of the whole proteomes of these pathogens grouped Rickettsia typhi with three other animal pathogens whereas conventional sequence analysis failed to group these pathogens together. We conclude that whole-proteome analysis can give insight into relationships among species beyond their phylogeny, perhaps reflecting the effects of horizontal gene transfer and potentially providing insight into the functions of shared genes by means of shared phenotypes.

SLC7A5, who is also named LAT-1, has been validated as a promoter regulated by miRNA-126 in our previous research for gastric cancer cells. However, the mechanisms driving SLC7A5 to affect the bio-function of gastric cancer cells are unclear, remaining us lots of to elucidate. The aim of this study is to investigate the regulating effect of CRKL, one of the critical genes involving with gastric cancer progression, on SLC7A5 expression. By studying the gastric cancer cell lines and clinical pathological specimens, we found that the expression of SLC7A5 was significantly correlated to CRKL. By depleting CRKL in gastric cancer SGC-7901 cells, the SLC7A5 expression was impaired, and the invasion and migration of SGC-7901 cells were suppressed. Ectopic expression of SLC7A5 could drastically rescue the phenotypes induced by CRKL depletion in this study. Accordingly, we conclude that SLC7A5 functions as a promoter in gastric cancer metastasis, and CRKL could be one of its regulators modulating the expression of SLC7A5 and consequentially affect the metastatic feature of SGC-7901 cells. The findings in this study indicate a regulation relationship between CRKL and SLC7A5, and provide useful evidence for gastric cancer therapeutic strategies.