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Amifostine has been the only small molecule radio-protector approved by FDA for decades; however, the serious adverse effects limit its clinical use. To address the toxicity issues and maintain the good potency, a series of modified small polycysteine peptides had been prepared. Among them, compound 5 exhibited the highest radio-protective efficacy, the same as amifostine, but much better safety profile. To confirm the correlation between the radiation-protective efficacy and the DNA binding capability, each of the enantiomers of the polycysteine peptides had been prepared. As a result, the l -configuration compounds had obviously higher efficacy than the corresponding d -configuration enantiomers; among them, compound 5 showed the highest DNA binding capability and radiation-protective efficacy. To our knowledge, this is the first study that has proved their correlations using direct comparison. Further exploration of the mechanism revealed that the ionizing radiation (IR) triggered ferroptosis inhibition by compound 5 could be one of the pathways for the protection effect, which was different from amifostine. In summary, the preliminary result showed that compound 5 , a polycysteine as a new type of radio-protector, had been developed with good efficacy and safety profile. Further study of the compound for potential use is ongoing.

Background: Aberrant activation of RAS-RAF-MEK-ERK signaling pathway has been implicated in more than one-third of all malignancies. MEK inhibitors are promising therapeutic approaches to target this signaling pathway. Though four MEK inhibitors have been approved by FDA, these compounds possess either limited efficacy or unfavorable PK profiles with toxicity issues, hindering their broadly application in clinic. Our efforts were focused on the design and development of a novel MEK inhibitor, which subsequently led to the discovery of tunlametinib. Methods: This study verified the superiority of tunlametinib over the current MEK inhibitors in preclinical studies. The protein kinase selectivity activity of tunlametinib was evaluated against 77 kinases. Anti-proliferation activity was analyzed using the 3-(4,5-dimethylthiazole-2-yl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) or (3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium) (MTS) assay. ERK and phospho-ERK levels were evaluated by Western blot analysis. Flow cytometry analysis was employed to investigate cell cycle and arrest. Cell-derived xenograft (CDX) and Patient-derived xenograft (PDX) models were used to evaluate the tumor growth inhibition. The efficacy of tunlametinib as monotherapy treatment was evaluated in KRAS/BRAF mutant or wild type xenograft model. Furthermore, the combination studies of tunlametinib with BRAF/KRAS G12C /SHP2 inhibitors or chemotherapeutic agent were conducted by using the cell proliferation assay in vitro and xenograft models in vivo . Results: In vitro , tunlametinib demonstrated high selectivity with approximately 19-fold greater potency against MEK kinase than MEK162, and nearly 10–100-fold greater potency against RAS/RAF mutant cell lines than AZD6244. In vivo, tunlametinib resulted in dramatic tumor suppression and profound inhibition of ERK phosphorylation in tumor tissue. Mechanistic study revealed that tunlametinib induced cell cycle arrest at G0/G1 phase and apoptosis of cells in a dose-proportional manner. In addition, tunlametinib demonstrated a favorable pharmacokinetic profile with dose-proportionality and good oral bioavailability, with minimal drug exposure accumulation. Furthermore, tunlametinib combined with BRAF/KRAS G12C /SHP2 inhibitors or docetaxel showed synergistically enhanced response and marked tumor inhibition. Conclusion: Tunlametinib exhibited a promising approach for treating RAS/RAF mutant cancers alone or as combination therapies, supporting the evaluation in clinical trials. Currently, the first-in-human phase 1 study and pivotal clinical trial of tunlametinib as monotherapy have been completed and pivotal trials as combination therapy are ongoing.

Non-small cell lung cancer (NSCLC) has a poor prognosis and usually presents resistance against radiotherapy. MEK inhibitors have been proven to possess a radiosensitization effect. The compound KZ-001 as a particular MEK inhibitor is superior to the listed MEK inhibitor AZD6244.To investigate whether KZ-001 could enhance the radiosensitivity of NSCLC cell lines in vitro.MTT and colony formation assay were used to evaluate the radiosensitivity effect of KZ-001. Immunofluorescence, cell cycle, apoptosis staining, and western blot experiments were used to explore the radiosensitivity mechanism.KZ-001 significantly decreased A549 cell viability at 6 Gy and 8 Gy radiation doses and caused the radiosensitivity at 1 Gy, 4 Gy, and 6 Gy in colony formation experiments. The A549 apoptosis ratio induced by irradiation (IR) combined with KZ-001 increased significantly in comparison with that by IR monotherapy (10.57% vs. 6.23%, P = 0.0055). The anti-apoptosis marker Bcl-XL was found downregulated in KZ-001 and IR-treated A549/H460 cells, but apoptosis marker Bax was downregulated in H460. Extracellular regulated protein kinases (ERK1/2) phosphorylation of H460 cells could be blocked both by IR alone and IR combined with KZ-001. IR combined with KZ-001 is able to inhibit ERK activation of A549 cells apparently. KZ-001 increased the proportion of G2 phase in irradiated cells from 21.24% to 32.22%. KZ-001 could also significantly increase the double-strand break damage cell ratio to more than 30% compared to the irradiation alone group.MEK1/2 inhibitor KZ-001 is a potential radiosensitizer for clinical applications.

Microbiome samples with low microbial biomass or severe DNA degradation remain challenging for amplicon-based or whole-metagenome sequencing approaches. Here, we introduce 2bRAD-M, a highly reduced and cost-effective strategy which only sequences ~ 1% of metagenome and can simultaneously produce species-level bacterial, archaeal, and fungal profiles. 2bRAD-M can accurately generate species-level taxonomic profiles for otherwise hard-to-sequence samples with merely 1 pg of total DNA, high host DNA contamination, or severely fragmented DNA from degraded samples. Tests of 2bRAD-M on various stool, skin, environmental, and clinical FFPE samples suggest a successful reconstruction of comprehensive, high-resolution microbial profiles.

The number of metagenomes is increasing rapidly. However, current methods for metagenomic analysis are limited by their capability for in-depth data mining among a large number of microbiome each of which carries a complex community structure. Moreover, the complexity of configuring and operating computational pipeline also hinders efficient data processing for the end users. In this work we introduce Parallel-META 3, a comprehensive and fully automatic computational toolkit for rapid data mining among metagenomic datasets, with advanced features including 16S rRNA extraction for shotgun sequences, 16S rRNA copy number calibration, 16S rRNA based functional prediction, diversity statistics, bio-marker selection, interaction network construction, vector-graph-based visualization and parallel computing. Application of Parallel-META 3 on 5,337 samples with 1,117,555,208 sequences from diverse studies and platforms showed it could produce similar results as QIIME and PICRUSt with much faster speed and lower memory usage, which demonstrates its ability to unravel the taxonomical and functional dynamics patterns across large datasets and elucidate ecological links between microbiome and the environment. Parallel-META 3 is implemented in C/C++ and R, and integrated into an executive package for rapid installation and easy access under Linux and Mac OS X. Both binary and source code packages are available at http://bioinfo.single-cell.cn/parallel-meta.html .

A significant portion of world population still fails to brush teeth daily. As a result, the majority of the global adult population is afflicted with chronic gingivitis, and if it is left untreated, some of them will eventually suffer from periodontitis. Most adults experience episodes of gingivitis, which can progress to the irreversible, chronic state of periodontitis, yet roles of plaque in gingivitis onset and progression to periodontitis remain elusive. Here, we longitudinally profiled the plaque metagenome, the plaque metabolome, and salivary cytokines in 40 adults who transited from naturally occurring gingivitis (NG) to healthy gingivae (baseline) and then to experimental gingivitis (EG). During EG, rapid and consistent alterations in plaque microbiota, metabolites, and salivary cytokines emerged as early as 24 to 72 h after oral-hygiene pause, defining an asymptomatic suboptimal health (SoH) stage of the gingivae. SoH features a swift, full activation of 11 salivary cytokines but a steep synergetic decrease of plaque-derived betaine and Rothia spp., suggesting an anti-gum inflammation mechanism by health-promoting symbionts. Global, cross-cohort meta-analysis revealed, at SoH, a greatly elevated microbiome-based periodontitis index driven by its convergence of both taxonomical and functional profiles toward the periodontitis microbiome. Finally, post-SoH gingivitis development accelerates oral microbiota aging by over 1 year within 28 days, with Rothia spp. depletion and Porphyromonas gingivalis elevation as hallmarks. Thus, the microbiome-defined, transient gum SoH stage is a crucial link among gingivitis, periodontitis, and aging. IMPORTANCE A significant portion of world population still fails to brush teeth daily. As a result, the majority of the global adult population is afflicted with chronic gingivitis, and if it is left untreated, some of them will eventually suffer from periodontitis. Here, we identified periodontitis-like microbiome dysbiosis in an asymptomatic SoH stage as early as 24 to 72 h after oral-hygiene pause. SoH features a swift, full activation of multiple salivary cytokines but a steep synergetic decrease of plaque-derived betaine and Rothia spp. The microbial ecology during early gingivitis is highly similar to that in periodontitis under both taxonomical and functional contexts. Unexpectedly, exposures to gingivitis can accelerate over 10-fold the normal rate of oral microbiota aging. Our findings underscore the importance of intervening at the SoH stage of gingivitis via proper oral-hygiene practices on a daily basis, so as to maintain a periodontitis-preventive plaque and ensure the healthy aging of the oral ecosystem.

Massive microbiome sequencing data has been generated, which elucidates associations between microbes and their environmental phenotypes such as host health or ecosystem status. Outstanding bioinformatic tools are the basis to decipher the biological information hidden under microbiome data. However, most approaches placed difficulties on the accessibility to nonprofessional users. On the other side, the computing throughput has become a significant bottleneck of many analytical pipelines in processing large‐scale datasets. In this study, we introduce Parallel‐Meta Suite (PMS), an interactive software package for fast and comprehensive microbiome data analysis, visualization, and interpretation. It covers a wide array of functions for data preprocessing, statistics, visualization by state‐of‐the‐art algorithms in a user‐friendly graphical interface, which is accessible to diverse users. To meet the rapidly increasing computational demands, the entire procedure of PMS has been optimized by a parallel computing scheme, enabling the rapid processing of thousands of samples. PMS is compatible with multiple platforms, and an installer has been integrated for full‐automatic installation. Parallel‐Meta Suite (PMS) is an easy‐to‐use software package for fast and comprehensive microbiome data analysis on multiple platforms. It covers a wide array of functions for data pre‐processing, statistics, visualization by state‐of‐the‐art algorithms. The entire procedure of PMS is optimized by a parallel computing scheme that enables the rapid processing of thousands of microbiomes. Highlights Parallel‐Meta Suite (PMS) is an easy‐to‐use software package for fast and comprehensive microbiome data analysis on multiple platforms. PMS covers a wide array of functions for data preprocessing, statistics, visualization by state‐of‐the‐art algorithms. The entire procedure of PMS is optimized by a parallel computing scheme that enables the rapid processing of thousands of microbiomes.

Background Anti‐PD‐1/PD‐L1 therapeutics have been widely used in the clinic in various tumors, including advanced esophageal cancer, showing remarkable treatment efficacy. Factors determining the response to anti‐PD‐1/PD‐L1 therapeutics are numerous, including the tumor microenvironment, such as CD8+ T cells and expression of PD‐1/PD‐L1. Our study aimed to explore the effect of chemoimmunotherapy on the expression of CD8+ T cells, TIM‐3, and FOXP3+ in tumor, paratumor tissues, and the expression of PD‐L1, IDO, in tumor, paratumor tissues, and lymph nodes, and analyze the correlation among these markers. Methods A total of 18 patients were allocated into two treatment groups: a treatment group and a concurrent control group. A total of 38 tissue samples, 114 slides (tumor, paratumor, and lymph node) were collected in the treatment group, and 37 tissue samples, 111 slides (tumor, paratumor, and lymph node) were collected in the concurrent control group. Results The expression of PD‐L1, CD8+, FOXP3+, TIM‐3, and IDO in tumors, paratumor tissues, but not lymph nodes, was significantly affected by chemoimmunotherapy. Compared with patients without chemoimmunotherapy, the expression of CD8+ T cells, IDO, and PD‐L1 was significantly decreased in tumor and paratumor tissues after chemoimmunotherapy, while FOXP3+ expression was significantly decreased only in tumor tissues, and TIM‐3 expression was significantly decreased only in paratumor tissues. Moreover, the correlation between these markers was also completely altered after chemoimmunotherapy. In addition, N staging was associated with high expression of CD8 in advanced esophageal squamous cell carcinoma in the concurrent control group. Conclusion This study provides new insight into the effects of CI treatment on isolated CD8+ T cell infiltration, PD‐L1, IDO, FOXP3+ and TIM‐3 expression as well as their cross‐talk in different tissues enabling a better understanding of the impact of CI treatment on the immune microenvironment. The expression of PD‐L1, CD8+, FOXP3+, TIM‐3, and IDO in tumor, paratumor tissues, but not lymph nodes, was significantly affected by chemoimmunotherapy. Compared with patients without chemoimmunotherapy, the expression of CD8+ T cells, IDO, and PD‐L1 were significantly decreased in tumor and paratumor tissues after chemoimmunotherapy, while the expression of FOXP3+ was significantly decreased only in tumor tissues, and the expression of TIM‐3 was significantly decreased only in paratumor tissues. Moreover, the correlation between these markers was also completely altered after chemoimmunotherapy. In addition, N staging was associated with high expression of CD8 in advanced esophageal squamous cell carcinoma in the concurrent control group.

We introduce two concepts to quantify the novelty of a microbiome. The first, the microbiome novelty score (MNS), allows identification of microbiomes that are especially different from what is already sequenced. The second, the microbiome attention score (MAS), allows identification of microbiomes that have many close neighbors, implying that considerable scientific attention is devoted to their study. By computing a microbiome focus index based on the MNS and MAS, we objectively track and compare the novelty and attention scores of individual microbiome samples and projects over time and predict future trends in the field; i.e., we work toward yielding fundamentally new microbiomes rather than filling in the details. Therefore, MNS, MAS, and MFI can serve as “alt-metrics” for evaluating a microbiome project or prospective developments in the microbiome field, both of which are done in the context of existing microbiome big data. With the expansion of microbiome sequencing globally, a key challenge is to relate new microbiome samples to the existing space of microbiome samples. Here, we present Microbiome Search Engine (MSE), which enables the rapid search of query microbiome samples against a large, well-curated reference microbiome database organized by taxonomic similarity at the whole-microbiome level. Tracking the microbiome novelty score (MNS) over 8 years of microbiome depositions based on searching in more than 100,000 global 16S rRNA gene amplicon samples, we detected that the structural novelty of human microbiomes is approaching saturation and likely bounded, whereas that in environmental habitats remains 5 times higher. Via the microbiome focus index (MFI), which is derived from the MNS and microbiome attention score (MAS), we objectively track and compare the structural-novelty and attracted-attention scores of individual microbiome samples and projects, and we predict future trends in the field. For example, marine and indoor environments and mother-baby interactions are likely to receive disproportionate additional attention based on recent trends. Therefore, MNS, MAS, and MFI are proposed “alt-metrics” for evaluating a microbiome project or prospective developments in the microbiome field, both of which are done in the context of existing microbiome big data. IMPORTANCE We introduce two concepts to quantify the novelty of a microbiome. The first, the microbiome novelty score (MNS), allows identification of microbiomes that are especially different from what is already sequenced. The second, the microbiome attention score (MAS), allows identification of microbiomes that have many close neighbors, implying that considerable scientific attention is devoted to their study. By computing a microbiome focus index based on the MNS and MAS, we objectively track and compare the novelty and attention scores of individual microbiome samples and projects over time and predict future trends in the field; i.e., we work toward yielding fundamentally new microbiomes rather than filling in the details. Therefore, MNS, MAS, and MFI can serve as “alt-metrics” for evaluating a microbiome project or prospective developments in the microbiome field, both of which are done in the context of existing microbiome big data.

MiSH, which is based on the skin microbiome, can quantitatively assess pediatric skin health across cohorts from distinct countries over large geographic distances. Moreover, the index can identify a risk-prone skin state and compare treatment effect in children, suggesting applications in diagnosis and patient stratification. A quantitative and objective indicator for skin health via the microbiome is of great interest for personalized skin care, but differences among skin sites and across human populations can make this goal challenging. A three-city (two Chinese and one American) comparison of skin microbiota from atopic dermatitis (AD) and healthy pediatric cohorts revealed that, although city has the greatest effect size (the skin microbiome can predict the originated city with near 100% accuracy), a microbial index of skin health (MiSH) based on 25 bacterial genera can diagnose AD with 83 to ∼95% accuracy within each city and 86.4% accuracy across cities (area under the concentration-time curve [AUC], 0.90). Moreover, nonlesional skin sites across the bodies of AD-active children (which include shank, arm, popliteal fossa, elbow, antecubital fossa, knee, neck, and axilla) harbor a distinct but lesional state-like microbiome that features relative enrichment of Staphylococcus aureus over healthy individuals, confirming the extension of microbiome dysbiosis across body surface in AD patients. Intriguingly, pretreatment MiSH classifies children with identical AD clinical symptoms into two host types with distinct microbial diversity and treatment effects of corticosteroid therapy. These findings suggest that MiSH has the potential to diagnose AD, assess risk-prone state of skin, and predict treatment response in children across human populations. IMPORTANCE MiSH, which is based on the skin microbiome, can quantitatively assess pediatric skin health across cohorts from distinct countries over large geographic distances. Moreover, the index can identify a risk-prone skin state and compare treatment effect in children, suggesting applications in diagnosis and patient stratification.