Toll-like receptor-mediated innate immunity against herpesviridae infection: a current perspective on viral infection signaling pathways.

BACKGROUND: In the past decades, researchers have demonstrated the critical role of Toll-like receptors (TLRs) in the innate immune system. They recognize viral components and trigger immune signal cascades to subsequently promote the activation of the immune system. MAIN BODY: Herpesviridae family members trigger TLRs to elicit cytokines in the process of infection to activate antiviral innate immune responses in host cells. This review aims to clarify the role of TLRs in the innate immunity defense against herpesviridae, and systematically describes the processes of TLR actions and herpesviridae recognition as well as the signal transduction pathways involved. CONCLUSIONS: Future studies of the interactions between TLRs and herpesviridae infections, especially the subsequent signaling pathways, will not only contribute to the planning of effective antiviral therapies but also provide new molecular targets for the development of antiviral drugs.

Time-sensitive clinical concept embeddings learned from large electronic health records.

BACKGROUND: Learning distributional representation of clinical concepts (e.g., diseases, drugs, and labs) is an important research area of deep learning in the medical domain. However, many existing relevant methods do not consider temporal dependencies along the longitudinal sequence of a patient's records, which may lead to incorrect selection of contexts. METHODS: To address this issue, we extended three popular concept embedding learning methods: word2vec, positive pointwise mutual information (PPMI) and FastText, to consider time-sensitive information. We then trained them on a large electronic health records (EHR) database containing about 50 million patients to generate concept embeddings and evaluated them for both intrinsic evaluations focusing on concept similarity measure and an extrinsic evaluation to assess the use of generated concept embeddings in the task of predicting disease onset. RESULTS: Our experiments show that embeddings learned from information within one visit (time window zero) improve performance on the concept similarity measure and the FastText algorithm usually had better performance than the other two algorithms. For the predictive modeling task, the optimal result was achieved by word2vec embeddings with a 30-day sliding window. CONCLUSIONS: Considering time constraints are important in training clinical concept embeddings. We expect they can benefit a series of downstream applications.

Acid-base properties of non-protein nitrogen affect nutrients intake, rumen fermentation and antioxidant capacity of fattening Hu sheep.

This study conducted a comparison of the effects of non-protein nitrogen with different acid-base properties on feed intake, rumen fermentation, nutrient digestion and antioxidant capacity in fattening Hu sheep. Sixteen fattening male sheep (31.43 ± 2.41 kg) with permanent rumen cannulas were randomly assigned to two dietary treatments: 1% urea and 1.78% ammonium chloride (NH4Cl, AC). A 42 days experimental period was conducted, with 14 days for adaptation and 28 days for treatment. Daily feed intake was recorded and various samples including feed, feces, rumen fluid, and blood were collected at different time points during the final week. The results indicated that the urea group had significantly higher dry matter intake, average daily gain, and gain efficiency in comparison to the AC group (p < 0.01). There was no difference in rumen pH and concentration of ammonia nitrogen between different groups (p > 0.05), but the rumen pH of urea group was higher than that of the AC group at 1 and 3 h after feeding (p < 0.05). The urea group exhibited higher concentrations of total volatile fatty acids (VFA) and individual VFAs compared to the AC group at all-time points (p < 0.01). Compared to the urea group, the intake of all nutrients decreased in the AC group (p < 0.01), but the digestibility of dry matter and organic matter increased significantly (p < 0.01), and the digestibility of CP had an increasing trend (p = 0.06) in the AC group. Additionally, the urea group had lower levels of serum glucagon-like peptide-1, peptide YY, Cl, total protein and globulin than the AC group (p < 0.05). The overall levels of HCO3-, superoxide dismutase, glutathione peroxidase, catalase, albumin/globulin, blood urea nitrogen and total cholesterol in the urea group increased significantly compared to the AC group (p < 0.05). It was concluded that adding urea to the high-concentrate diet resulted in increased rumen pH and improved rumen fermentation and growth performance in fattening sheep compared to NH4Cl addition. Furthermore, urea addition improved sheep's antioxidant capacity and maintained their acid-base balance more effectively as compared to NH4Cl.

Extracting Drug-Protein Relation from Literature Using Ensembles of Biomedical Transformers.

Automatic extraction of relations between drugs/chemicals and proteins from ever-growing biomedical literature is required to build up-to-date knowledge bases in biomedicine. To promote the development of automated methods, BioCreative-VII organized a shared task - the DrugProt track, to recognize drug-protein entity relations from PubMed abstracts. We participated in the shared task and leveraged deep learning-based transformer models pre-trained on biomedical data to build ensemble approaches to automatically extract drug-protein relation from biomedical literature. On the main corpora of 10,750 abstracts, our best system obtained an F1-score of 77.60% (ranked 4th among 30 participating teams), and on the large-scale corpus of 2.4M documents, our system achieved micro-averaged F1-score of 77.32% (ranked 2nd among 9 system submissions). This demonstrates the effectiveness of domain-specific transformer models and ensemble approaches for automatic relation extraction from biomedical literature.

The inhibition of high ammonia to in vitro rumen fermentation is pH dependent.

Ammonia is an important rumen internal environment indicator. In livestock production, feeding a large amount of non-protein nitrogen to ruminants will create high ammonia stress to the animals, which increases the risk of ammonia toxicity. However, the effects of ammonia toxicity on rumen microbiota and fermentation are still unknown. In this study, an in vitro rumen fermentation technique was used to investigate the effects of different concentrations of ammonia on rumen microbiota and fermentation. To achieve the four final total ammonia nitrogen (TAN) concentrations of 0, 8, 32, and 128 mmol/L, ammonium chloride (NH4Cl) was added at 0, 42.8, 171.2, and 686.8 mg/100 mL, and urea was added at 0, 24, 96, and 384 mg/100 mL. Urea hydrolysis increased, while NH4Cl dissociation slightly reduced the pH. At similar concentrations of TAN, the increased pH of the rumen culture by urea addition resulted in a much higher free ammonia nitrogen (FAN) concentration compared to NH4Cl addition. Pearson correlation analysis revealed a strong negative correlation between FAN and microbial populations (total bacteria, protozoa, fungi, and methanogens) and in vitro rumen fermentation profiles (gas production, dry matter digestibility, total volatile fatty acid, acetate, propionate, etc.), and a much weaker correlation between TAN and the above indicators. Additionally, bacterial community structure changed differently in response to TAN concentrations. High TAN increased Gram-positive Firmicutes and Actinobacteria but reduced Gram-negative Fibrobacteres and Spirochaetes. The current study demonstrated that the inhibition of in vitro rumen fermentation by high ammonia was pH-dependent and was associated with variations of rumen microbial populations and communities.

Sulforaphene suppresses oesophageal cancer growth through mitogen- and stress-activated kinase 2 in a PDX mouse model.

BACKGROUND: Although sulforaphene has potential anticancer effects, little is known about its effect on oesophageal squamous cell carcinoma (ESCC) invasiveness. METHODS: To investigate whether sulforaphene inhibits the growth of oesophageal cancer cells, MTT and anchorage-independent cell growth assays were performed. Global changes in the proteome and phosphoproteome of oesophageal cancer cells after sulforaphene treatment were analysed by mass spectrometry (MS), and the underlying molecular mechanism was further verified by in vivo and in vitro experiments. RESULTS: Sulforaphene treatment markedly affected proteins that regulate several cellular processes in oesophageal cancer cells, and mitogen- and stress-activated kinase 2 (MSK2) was the main genetic target of sulforaphene in reducing the growth of oesophageal cancer cells. Sulforaphene significantly suppressed ESCC cell proliferation in vitro and reduced the tumour size in an oesophageal patient-derived xenograft (PDX) SCID mouse model. Furthermore, the binding of sulforaphane to MSK2 in vitro was verified using a cellular thermal dhift assay, and the effect of MSK2 knockdown on the ESCC phenotype was observed using a shMSK2 model. CONCLUSION: The results showed that sulforaphene suppresses ESCC growth in both human oesophageal squamous cells and PDX mouse model by inhibiting MSK2 expression, implicating sulforaphene as a promising candidate for ESCC treatment.

Curcuma Longa Induces the Transcription Factor FOXP3 to Downregulate Human Chemokine CCR5 Expression and Inhibit HIV-1 Infection.

HIV mutations occur frequently despite the substantial success of combination antiretroviral therapy, which significantly impairs HIV progression. Failure to develop specific vaccines, the occurrence of drug-resistant strains, and the high incidence of adverse effects due to combination antiviral therapy regimens call for novel and safer antivirals. Natural products are an important source of new anti-infective agents. For instance, curcumin inhibits HIV and inflammation in cell culture assays. Curcumin, the principal constituent of the dried rhizomes of Curcuma longa L. (turmeric), is known as a strong anti-oxidant and anti-inflammatory agent with different pharmacological effects. This work aims to assess curcumin's inhibitory effects on HIV in vitro and to explore the underpinning mechanism, focusing on CCR5 and the transcription factor forkhead box protein P3 (FOXP3). First, curcumin and the RT inhibitor zidovudine (AZT) were evaluated for their inhibitory properties. HIV-1 pseudovirus infectivity was determined by green fluorescence and luciferase activity measurements in HEK293T cells. AZT was used as a positive control that inhibited HIV-1 pseudoviruses dose-dependently, with IC50 values in the nanomolar range. Then, a molecular docking analysis was carried out to assess the binding affinities of curcumin for CCR5 and HIV-1 RNase H/RT. The anti-HIV activity assay showed that curcumin inhibited HIV-1 infection, and the molecular docking analysis revealed equilibrium dissociation constants of [Formula: see text]9.8[Formula: see text]kcal/mol and [Formula: see text]9.3[Formula: see text]kcal/mol between curcumin and CCR5 and HIV-1 RNase H/RT, respectively. To examine curcumin's anti-HIV effect and its mechanism in vitro, cell cytotoxicity, transcriptome sequencing, and CCR5 and FOXP3 amounts were assessed at different concentrations of curcumin. In addition, human CCR5 promoter deletion constructs and the FOXP3 expression plasmid pRP-FOXP3 (with an EGFP tag) were generated. Whether FOXP3 DNA binding to the CCR5 promoter was blunted by curcumin was examined using transfection assays employing truncated CCR5 gene promoter constructs, a luciferase reporter assay, and a chromatin immunoprecipitation (ChIP) assay. Furthermore, micromolar concentrations of curcumin inactivated the nuclear transcription factor FOXP3, which resulted in decreased expression of CCR5 in Jurkat cells. Moreover, curcumin inhibited PI3K-AKT activation and its downstream target FOXP3. These findings provide mechanistic evidence encouraging further assessment of curcumin as a dietary agent used to reduce the virulence of CCR5-tropic HIV-1. Curcumin-mediated FOXP3 degradation was also reflected in its functions, namely, CCR5 promoter transactivation and HIV-1 virion production. Furthermore, curcumin inhibition of CCR5 and HIV-1 might constitute a potential therapeutic strategy for reducing HIV progression.

Biomolecular Condensation: A New Phase in Cancer Research.

Multicellularity was a watershed development in evolution. However, it also meant that individual cells could escape regulatory mechanisms that restrict proliferation at a severe cost to the organism: cancer. From the standpoint of cellular organization, evolutionary complexity scales to organize different molecules within the intracellular milieu. The recent realization that many biomolecules can "phase-separate" into membraneless organelles, reorganizing cellular biochemistry in space and time, has led to an explosion of research activity in this area. In this review, we explore mechanistic connections between phase separation and cancer-associated processes and emerging examples of how these become deranged in malignancy. SIGNIFICANCE: One of the fundamental functions of phase separation is to rapidly and dynamically respond to environmental perturbations. Importantly, these changes often lead to alterations in cancer-relevant pathways and processes. This review covers recent advances in the field, including emerging principles and mechanisms of phase separation in cancer.

Exploiting induced vulnerability to overcome PARPi resistance and clonal heterogeneity in BRCA mutant triple-negative inflammatory breast cancer.

Acquired resistance and clonal heterogeneity are critical challenges in cancer treatment, and the lack of effective computational tools hampers the discovery of new treatments to overcome resistance. Using high-throughput transcriptomic databases of compound perturbation profiles, we have developed a bioinformatic strategy for identifying candidate drugs to overcome resistance with combinatorial therapy. We devised this strategy during an investigation into the acquired resistance against PARP inhibitors (PARPi) in a triple-negative inflammatory breast cancer cell line. In this study, we derived multiple PARPi-resistant clones and characterized their transcriptomic adaptations compared to the parental clone. The transcriptomes of the resistant clones showed substantial heterogeneity, highlighting the importance of characterizing multiple clones from the same tumour. Surprisingly, we found that these transcriptomic changes may not actually confer PARPi resistance, but they may nevertheless induce a shared secondary vulnerability. By modeling our data in relation to transcriptomic perturbation profiles of compounds, we uncovered deficiencies in Ras signaling that resulted from transcriptional adaptation to long-term PARPi treatment across multiple resistant clones. Due to these induced deficiencies, we predicted that the resistant clones would be sensitive to pharmacological reinforcement of PARPi-induced transcriptional adaptation. We then experimentally validated this predicted vulnerability that is shared by multiple resistant clones. Our results thus provide a promising paradigm for integrating transcriptomic data with compound perturbation profiles in order to identify drugs that can exploit an induced vulnerability and overcome therapeutic resistance, thus providing another strategy towards precision oncology.

Anticancer drug synergy prediction in understudied tissues using transfer learning.

OBJECTIVE: Drug combination screening has advantages in identifying cancer treatment options with higher efficacy without degradation in terms of safety. A key challenge is that the accumulated number of observations in in-vitro drug responses varies greatly among different cancer types, where some tissues are more understudied than the others. Thus, we aim to develop a drug synergy prediction model for understudied tissues as a way of overcoming data scarcity problems. MATERIALS AND METHODS: We collected a comprehensive set of genetic, molecular, phenotypic features for cancer cell lines. We developed a drug synergy prediction model based on multitask deep neural networks to integrate multimodal input and multiple output. We also utilized transfer learning from data-rich tissues to data-poor tissues. RESULTS: We showed improved accuracy in predicting synergy in both data-rich tissues and understudied tissues. In data-rich tissue, the prediction model accuracy was 0.9577 AUROC for binarized classification task and 174.3 mean squared error for regression task. We observed that an adequate transfer learning strategy significantly increases accuracy in the understudied tissues. CONCLUSIONS: Our synergy prediction model can be used to rank synergistic drug combinations in understudied tissues and thus help to prioritize future in-vitro experiments. Code is available at https://github.com/yejinjkim/synergy-transfer.

Human Endogenous Retroviruses in Glioblastoma Multiforme.

Glioblastoma multiforme (GBM) is the most aggressive and deadly brain tumor. It is primarily diagnosed in the elderly and has a 5-year survival rate of less than 6% even with the most aggressive therapies. The lack of biomarkers has made the development of immunotherapy for GBM challenging. Human endogenous retroviruses (HERVs) are a group of viruses with long terminal repeat (LTR) elements, which are believed to be relics from ancient viral infections. Recent studies have found that those repetitive elements play important roles in regulating various biological processes. The differentially expressed LTR elements from HERVs are potential biomarkers for immunotherapy to treat GBM. However, the understanding of the LTR element expression in GBM is greatly lacking. METHODS: We obtained 1077.4 GB of sequencing data from public databases. These data were generated from 111 GBM tissue studies, 30 GBM cell lines studies, and 45 normal brain tissues studies. We analyzed repetitive elements that were differentially expressed in GBM and normal brain samples. RESULTS: We found that 48 LTR elements were differentially expressed (p-value < 0.05) between GBM and normal brain tissues, of which 46 were HERV elements. Among these 46 elements, 34 significantly changed HERVs belong to the ERV1 superfamily. Furthermore, 43 out of the 46 differentially expressed HERV elements were upregulated. CONCLUSION: Our results indicate significant differential expression of many HERV LTR elements in GBM and normal brain tissues. Expression levels of these elements could be developed as biomarkers for GBM treatments.

Assessment of putative protein targets derived from the SARS genome.

The ability to rapidly and reliably develop hypotheses on the function of newly discovered protein sequences requires systematic and comprehensive analysis. Such an analysis, embodied within the DS GeneAtlas pipeline, has been used to critically evaluate the severe acute respiratory syndrome (SARS) genome with the goal of identifying new potential targets for viral therapeutic intervention. This paper discusses several new functional hypotheses on the roles played by the constituent gene products of SARS, and will serve as an example of how such assignments can be developed or extended on other systems of interest.