An AT-barrier mechanically controls DNA reannealing under tension

Regulation of genomic activity occurs through the manipulation of DNA by competent mechanoenzymes. Force-clamp optical tweezers that allow the structural dynamics of the DNA molecule to be measured were used here to investigate the kinetics of mechanically-driven strand reannealing. When the force on the torsionally unconstrained lambda-phage DNA is decreased stepwise from above to below the overstretching transition, reannealing occurs via discrete shortening steps separated by exponentially distributed time intervals. Kinetic analysis reveals a transition barrier 0.58 nm along the reaction coordinate and an average reannealing-step size of approximately 750 bp, consistent with the average bp interval separating segments of more than 10 consecutive AT bases. In an AT-rich DNA construct, in which the distance between segments of more than 10 consecutive AT is reduced to approximately 210 bps, the reannealing step reduces accordingly without changes in the position of the transition barrier. Thus, the transition barrier for reannealing is determined by the presence of segments of more than 10 consecutive AT bps independent of changes in sequence composition, while the length of the reannealing strand changes according to the distance between poly-AT segments at least 10 bps long

Association of sperm-associated antigen 5 and treatment response in patients With estrogen receptor–positive breast cancer

Importance: There is no proven test that can guide the optimal treatment, either endocrine therapy or chemotherapy, for estrogen receptor–positive breast cancer. Objective: To investigate the associations of sperm-associated antigen 5 (SPAG5) transcript and SPAG5 protein expressions with treatment response in systemic therapy for estrogen receptor–positive breast cancer. Design, Settings, and Participants: This retrospective cohort study included patients with estrogen receptor–positive breast cancer who received 5 years of adjuvant endocrine therapy with or without neoadjuvant anthracycline-based combination chemotherapy (NACT) derived from 11 cohorts from December 1, 1986, to November 28, 2019. The associations of SPAG5 transcript and SPAG5 protein expression with pathological complete response to NACT were evaluated, as was the association of SPAG5 mRNA expression with response to neoadjuvant endocrine therapy. The associations of distal relapse–free survival with SPAG5 transcript or SPAG5 protein expressions were analyzed. Data were analyzed from September 9, 2015, to November 28, 2019. Main Outcomes and Measures: The primary outcomes were breast cancer–specific survival, distal relapse–free survival, pathological complete response, and clinical response. Outcomes were examined using Kaplan-Meier, multivariable logistic, and Cox regression models. Results: This study included 12 720 women aged 24 to 78 years (mean [SD] age, 58.46 [12.45] years) with estrogen receptor–positive breast cancer, including 1073 women with SPAG5 transcript expression and 361 women with SPAG5 protein expression of locally advanced disease stage IIA through IIIC. Women with SPAG5 transcript and SPAG5 protein expressions achieved higher pathological complete response compared with those without SPAG5 transcript or SPAG5 protein expressions (transcript: odds ratio, 2.45 [95% CI, 1.71-3.51]; P < .001; protein: odds ratio, 7.32 [95% CI, 3.33-16.22]; P < .001). Adding adjuvant anthracycline chemotherapy to adjuvant endocrine therapy for SPAG5 mRNA expression in estrogen receptor–positive breast cancer was associated with prolonged 5-year distal relapse–free survival in patients without lymph node involvement (hazard ratio, 0.34 [95% CI, 0.14-0.87]; P = .03) and patients with lymph node involvement (hazard ratio, 0.35 [95% CI, 0.18-0.68]; P = .002) compared with receiving 5-year endocrine therapy alone. Mean (SD) SPAG5 transcript was found to be downregulated after 2 weeks of neoadjuvant endocrine therapy compared with pretreatment levels in 68 of 92 patients (74%) (0.23 [0.18] vs 0.34 [0.24]; P < .001). Conclusions and Relevance: These findings suggest that SPAG5 transcript and SPAG5 protein expressions could be used to guide the optimal therapies for estrogen receptor–positive breast cancer. Retrospective and prospective clinical trials are warranted

Cell Dispersal Influences Tumor Heterogeneity and Introduces a Bias in NGS Data Interpretation

Short and long distance cell dispersal can have a marked effect on tumor structure, high cellular motility could lead to faster cell mixing and lower observable intratumor heterogeneity. Here we evaluated a model for cell mixing that investigates how short-range dispersal and cell turnover will account for mutational proportions. We show that cancer cells can penetrate neighboring and distinct areas in a matter of days. In next generation sequencing runs, higher proportions of a given cell line generated frequencies with higher precision, while mixtures with lower amounts of each cell line had lower precision manifesting in higher standard deviations. When multiple cell lines were co-cultured, cellular movement altered observed mutation frequency by up to 18.5%. We propose that some of the shared mutations detected at low allele frequencies represent highly motile clones that appear in multiple regions of a tumor owing to dispersion throughout the tumor. In brief, cell movement will lead to a significant technical (sampling) bias when using next generation sequencing to determine clonal composition. A possible solution to this drawback would be to radically decrease detection thresholds and increase coverage in NGS analyses. © 2017 The Author(s)

Co-Swarming and Local Collapse: Quorum Sensing Conveys Resilience to Bacterial Communities by Localizing Cheater Mutants in Pseudomonas aeruginosa

Background: Members of swarming bacterial consortia compete for nutrients but also use a co-operation mechanism called quorum sensing (QS) that relies on chemical signals as well as other secreted products (‘‘public goods’’) necessary for swarming. Deleting various genes of this machinery leads to cheater mutants impaired in various aspects of swarming cooperation. Methodology/Principal Findings: Pairwise consortia made of Pseudomonas aeruginosa, its QS mutants as well as B. cepacia cells show that a interspecies consortium can ‘‘combine the skills’ ’ of its participants so that the strains can cross together barriers that they could not cross alone. In contrast, deleterious mutants are excluded from consortia either by competition or by local population collapse. According to modeling, both scenarios are the consequence of the QS signalling mechanism itself. Conclusion/Significance: The results indirectly explain why it is an advantage for bacteria to maintain QS systems that can cross-talk among different species, and conversely, why certain QS mutants which can be abundant in isolated niches

Emergence of Collective Territorial Defense in Bacterial Communities: Horizontal Gene Transfer Can Stabilize Microbiomes

Multispecies bacterial communities such as the microbiota of the gastrointestinal tract can be remarkably stable and resilient even though they consist of cells and species that compete for resources and also produce a large number of antimicrobial agents. Computational modeling suggests that horizontal transfer of resistance genes may greatly contribute to the formation of stable and diverse communities capable of protecting themselves with a battery of antimicrobial agents while preserving a varied metabolic repertoire of the constituent species. In other words horizontal transfer of resistance genes makes a community compatible in terms of exoproducts and capable to maintain a varied and mature metagenome. The same property may allow microbiota to protect a host organism, or if used as a microbial therapy, to purge pathogens and restore a protective environment

Contemporary Challenges and Solutions

CA18131 CP16/00163 NIS-3317 NIS-3318 decision 295741 C18/BM/12585940The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 “ML4Microbiome” that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies.publishersversionpublishe

Novel Protocol for the Chemical Synthesis of Crustacean Hyperglycemic Hormone Analogues — An Efficient Experimental Tool for Studying Their Functions

The crustacean Hyperglycemic Hormone (cHH) is present in many decapods in different isoforms, whose specific biological functions are still poorly understood. Here we report on the first chemical synthesis of three distinct isoforms of the cHH of Astacus leptodactylus carried out by solid phase peptide synthesis coupled to native chemical ligation. The synthetic 72 amino acid long peptide amides, containing L- or D-Phe3 and (Glp1, D-Phe3) were tested for their biological activity by means of homologous in vivo bioassays. The hyperglycemic activity of the D-isoforms was significantly higher than that of the L-isoform, while the presence of the N-terminal Glp residue had no influence on the peptide activity. The results show that the presence of D-Phe3 modifies the cHH functionality, contributing to the diversification of the hormone pool

Predictors of Chemosensitivity in Triple Negative Breast Cancer: An Integrated Genomic Analysis

Background: Triple negative breast cancer (TNBC) is a highly heterogeneous and aggressive disease, and although no effective targeted therapies are available to date, about one-third of patients with TNBC achieve pathologic complete response (pCR) from standard-of-care anthracycline/taxane (ACT) chemotherapy. The heterogeneity of these tumors, however, has hindered the discovery of effective biomarkers to identify such patients. Methods and Findings: We performed whole exome sequencing on 29 TNBC cases from the MD Anderson Cancer Center (MDACC) selected because they had either pCR (n = 18) or extensive residual disease (n = 11) after neoadjuvant chemotherapy, with cases from The Cancer Genome Atlas (TCGA; n = 144) and METABRIC (n = 278) cohorts serving as validation cohorts. Our analysis revealed that mutations in the AR- and FOXA1-regulated networks, in which BRCA1 plays a key role, are associated with significantly higher sensitivity to ACT chemotherapy in the MDACC cohort (pCR rate of 94.1% compared to 16.6% in tumors without mutations in AR/FOXA1 pathway, adjusted p = 0.02) and significantly better survival outcome in the TCGA TNBC cohort (log-rank test, p = 0.05). Combined analysis of DNA sequencing, DNA methylation, and RNA sequencing identified tumors of a distinct BRCA-deficient (BRCA-D) TNBC subtype characterized by low levels of wild-type BRCA1/2 expression. Patients with functionally BRCA-D tumors had significantly better survival with standard-of-care chemotherapy than patients whose tumors were not BRCA-D (log-rank test, p = 0.021), and they had significantly higher mutation burden (p < 0.001) and presented clonal neoantigens that were associated with increased immune cell activity. A transcriptional signature of BRCA-D TNBC tumors was independently validated to be significantly associated with improved survival in the METABRIC dataset (log-rank test, p = 0.009). As a retrospective study, limitations include the small size and potential selection bias in the discovery cohort. Conclusions: The comprehensive molecular analysis presented in this study directly links BRCA deficiency with increased clonal mutation burden and significantly enhanced chemosensitivity in TNBC and suggests that functional RNA-based BRCA deficiency needs to be further examined in TNBC. © 2016 Jiang et al

Distinct cytokine patterns may regulate the severity of neonatal asphyxia

Abstract Background Neuroinflammation and a systemic inflammatory reaction are important features of perinatal asphyxia. Neuroinflammation may have dual aspects being a hindrance, but also a significant help in the recovery of the CNS. We aimed to assess intracellular cytokine levels of T-lymphocytes and plasma cytokine levels in moderate and severe asphyxia in order to identify players of the inflammatory response that may influence patient outcome. Methods We analyzed the data of 28 term neonates requiring moderate systemic hypothermia in a single-center observational study. Blood samples were collected between 3 and 6 h of life, at 24 h, 72 h, 1 week, and 1 month of life. Neonates were divided into a moderate (n = 17) and a severe (n = 11) group based on neuroradiological and amplitude-integrated EEG characteristics. Peripheral blood mononuclear cells were assessed with flow cytometry. Cytokine plasma levels were measured using Bioplex immunoassays. Components of the kynurenine pathway were assessed by high-performance liquid chromatography. Results The prevalence and extravasation of IL-1b + CD4 cells were higher in severe than in moderate asphyxia at 6 h. Based on Receiver operator curve analysis, the assessment of the prevalence of CD4+ IL-1β+ and CD4+ IL-1β+ CD49d+ cells at 6 h appears to be able to predict the severity of the insult at an early stage in asphyxia. Intracellular levels of TNF-α in CD4 cells were increased at all time points compared to 6 h in both groups. At 1 month, intracellular levels of TNF-α were higher in the severe group. Plasma IL-6 levels were higher at 1 week in the severe group and decreased by 1 month in the moderate group. Intracellular levels of IL-6 peaked at 24 h in both groups. Intracellular TGF-β levels were increased from 24 h onwards in the moderate group. Conclusions IL-1β and IL-6 appear to play a key role in the early events of the inflammatory response, while TNF-α seems to be responsible for prolonged neuroinflammation, potentially contributing to a worse outcome. The assessment of the prevalence of CD4+ IL-1β+ and CD4+ IL-1β+ CD49d+ cells at 6 h appears to be able to predict the severity of the insult at an early stage in asphyxia

Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions

The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 "ML4Microbiome" that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies