Bayesian adaptive selection of basis functions for functional data representation.

Considering the context of functional data analysis, we developed and applied a new Bayesian approach via the Gibbs sampler to select basis functions for a finite representation of functional data. The proposed methodology uses Bernoulli latent variables to assign zero to some of the basis function coefficients with a positive probability. This procedure allows for an adaptive basis selection since it can determine the number of bases and which ones should be selected to represent functional data. Moreover, the proposed procedure measures the uncertainty of the selection process and can be applied to multiple curves simultaneously. The methodology developed can deal with observed curves that may differ due to experimental error and random individual differences between subjects, which one can observe in a real dataset application involving daily numbers of COVID-19 cases in Brazil. Simulation studies show the main properties of the proposed method, such as its accuracy in estimating the coefficients and the strength of the procedure to find the true set of basis functions. Despite having been developed in the context of functional data analysis, we also compared the proposed model via simulation with the well-established LASSO and Bayesian LASSO, which are methods developed for non-functional data.

Acute toxicity and antitumor potential of 1,3,4-trisubstituted-1,2,3-triazole dhmtAc-loaded liposomes on a triple-negative breast cancer model.

For the first time, compounds developed from the 1,2,3-triazole scaffold were evaluated as novel drugs to treat triple-negative breast cancer (TNBC). Four organic salts were idealized as nonclassical bioisosteres of miltefosine, which is used in the topical treatment for skin metastasizing breast carcinoma. Among them, derivative dhmtAc displayed better solubility and higher cytotoxicity against the human breast adenocarcinoma cell line and mouse 4T1 cell lines, which are representatives of TNBC. In vitro assays revealed that dhmtAc interferes with cell integrity, confirmed by lactate dehydogenase leakage. Due to its human peripheral blood mononuclear cell (PBMC) toxicity, dhmtAc in vivo studies were carried out with the drug incorporated in a long-circulating and pH-sensitive liposome (SpHL-dhmtAc), and the acute toxicity in BALB/c mice was determined. Free dhmtAc displayed cardiac and pulmonary toxicity after the systemic administration of 5 mg/kg doses. On the other hand, SpHL-dhmtAc displayed no toxicity at 20 mg/kg. The in vivo antitumor effect of SpHL-dhmtAc was investigated using the 4T1 heterotopic murine model. Intravenous administration of SpHL-dhmtAc reduced the tumor volume and weight, without interfering with the body weight, compared with the control group and the dhmtAc free form. The incorporation of the triazole compound in the liposome allowed the demonstration of its anticancer potential. These findings evidenced 1,3,4-trisubstituted-1,2,3-triazole as a promising scaffold for the development of novel drugs with applicability for the treatment of patients with TNBC.

SomaticSiMu: a mutational signature simulator.

SUMMARY: SomaticSiMu is an in silico simulator of single and double base substitutions, and single base insertions and deletions in an input genomic sequence to mimic mutational signatures. SomaticSiMu outputs simulated DNA sequences and mutational catalogues with imposed mutational signatures. The tool is the first mutational signature simulator featuring a graphical user interface, control of mutation rates and built-in visualization tools of the simulated mutations. Simulated datasets are useful as a ground truth to test the accuracy and sensitivity of DNA sequence classification tools and mutational signature extraction tools under different experimental scenarios. The reliability of SomaticSiMu was affirmed by (i) supervised machine learning classification of simulated sequences with different mutation types and burdens, and (ii) mutational signature extraction from simulated mutational catalogues. AVAILABILITY AND IMPLEMENTATION: SomaticSiMu is written in Python 3.8.3. The open-source code, documentation and tutorials are available at https://github.com/HillLab/SomaticSiMu under the terms of the CreativeCommonsAttribution4.0InternationalLicense. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study.

The 2019 novel coronavirus (renamed SARS-CoV-2, and generally referred to as the COVID-19 virus) has spread to 184 countries with over 1.5 million confirmed cases. Such major viral outbreaks demand early elucidation of taxonomic classification and origin of the virus genomic sequence, for strategic planning, containment, and treatment. This paper identifies an intrinsic COVID-19 virus genomic signature and uses it together with a machine learning-based alignment-free approach for an ultra-fast, scalable, and highly accurate classification of whole COVID-19 virus genomes. The proposed method combines supervised machine learning with digital signal processing (MLDSP) for genome analyses, augmented by a decision tree approach to the machine learning component, and a Spearman's rank correlation coefficient analysis for result validation. These tools are used to analyze a large dataset of over 5000 unique viral genomic sequences, totalling 61.8 million bp, including the 29 COVID-19 virus sequences available on January 27, 2020. Our results support a hypothesis of a bat origin and classify the COVID-19 virus as Sarbecovirus, within Betacoronavirus. Our method achieves 100% accurate classification of the COVID-19 virus sequences, and discovers the most relevant relationships among over 5000 viral genomes within a few minutes, ab initio, using raw DNA sequence data alone, and without any specialized biological knowledge, training, gene or genome annotations. This suggests that, for novel viral and pathogen genome sequences, this alignment-free whole-genome machine-learning approach can provide a reliable real-time option for taxonomic classification.

Interfaces of Malignant and Immunologic Clonal Dynamics in Ovarian Cancer.

High-grade serous ovarian cancer (HGSC) exhibits extensive malignant clonal diversity with widespread but non-random patterns of disease dissemination. We investigated whether local immune microenvironment factors shape tumor progression properties at the interface of tumor-infiltrating lymphocytes (TILs) and cancer cells. Through multi-region study of 212 samples from 38 patients with whole-genome sequencing, immunohistochemistry, histologic image analysis, gene expression profiling, and T and B cell receptor sequencing, we identified three immunologic subtypes across samples and extensive within-patient diversity. Epithelial CD8+ TILs negatively associated with malignant diversity, reflecting immunological pruning of tumor clones inferred by neoantigen depletion, HLA I loss of heterozygosity, and spatial tracking between T cell and tumor clones. In addition, combinatorial prognostic effects of mutational processes and immune properties were observed, illuminating how specific genomic aberration types associate with immune response and impact survival. We conclude that within-patient spatial immune microenvironment variation shapes intraperitoneal malignant spread, provoking new evolutionary perspectives on HGSC clonal dispersion.

Engineered in-vitro cell line mixtures and robust evaluation of computational methods for clonal decomposition and longitudinal dynamics in cancer.

Characterization and quantification of tumour clonal populations over time via longitudinal sampling are essential components in understanding and predicting the response to therapeutic interventions. Computational methods for inferring tumour clonal composition from deep-targeted sequencing data are ubiquitous, however due to the lack of a ground truth biological data, evaluating their performance is difficult. In this work, we generate a benchmark data set that simulates tumour longitudinal growth and heterogeneity by in vitro mixing of cancer cell lines with known proportions. We apply four different algorithms to our ground truth data set and assess their performance in inferring clonal composition using different metrics. We also analyse the performance of these algorithms on breast tumour xenograft samples. We conclude that methods that can simultaneously analyse multiple samples while accounting for copy number alterations as a factor in allelic measurements exhibit the most accurate predictions. These results will inform future functional genomics oriented studies of model systems where time series measurements in the context of therapeutic interventions are becoming increasingly common. These studies will need computational models which accurately reflect the multi-factorial nature of allele measurement in cancer including, as we show here, segmental aneuploidies.

Cannabidiol disrupts the consolidation of specific and generalized fear memories via dorsal hippocampus CB1 and CB2 receptors.

Pharmacological interventions able to modulate a fear memory while it is consolidated could have therapeutic value in tempering those maladaptively overconsolidated. Animal and human studies have shown the intensity of unconditioned stimulus delivered during fear conditioning influences qualitative and quantitative aspects of the memory to be established. By varying the shock intensity used for contextual pairing in rats, here we induced specific and more generalized long-term fear memories to investigate whether, how and where in the brain the cannabidiol (CBD; 3.0-30 mg/kg i.p.) could impair their consolidation and related outcomes. When given immediately after their acquisition, it reduced respectively the conditioned fear expression, and fear generalization, ultrasonic vocalizations at 22-kHz and the relative resistance to extinction. CBD had no effects on short-term fear memory, and its delayed treatment no longer affected the consolidation process. As the dorsal hippocampus (DH) modulates fear memory specificity and generalization, and cannabinoid type-1 (CB1) and type-2 (CB2) receptors contribute to consolidation, we investigated their involvement in CBD effects. Both systemic and intra-DH treatment with the CB1 receptor antagonist/inverse agonist AM251 or the CB2 receptor antagonist/inverse agonist AM630 prevented the disrupting CBD effects on consolidation. Since the CBD effects on the endocannabinoid transmission are probably indirect, we investigated and demonstrated the FAAH inhibitor URB597 induced effects similar to those of CBD when given systemically or intra-DH. Altogether, the present results suggest the CBD disrupts the consolidation of different fear memories via anandamide-mediated activation of DH CB1 and CB2 receptors.

ReMixT: clone-specific genomic structure estimation in cancer.

Somatic evolution of malignant cells produces tumors composed of multiple clonal populations, distinguished in part by rearrangements and copy number changes affecting chromosomal segments. Whole genome sequencing mixes the signals of sampled populations, diluting the signals of clone-specific aberrations, and complicating estimation of clone-specific genotypes. We introduce ReMixT, a method to unmix tumor and contaminating normal signals and jointly predict mixture proportions, clone-specific segment copy number, and clone specificity of breakpoints. ReMixT is free, open-source software and is available at http://bitbucket.org/dranew/remixt .

Anandamide reverses depressive-like behavior, neurochemical abnormalities and oxidative-stress parameters in streptozotocin-diabetic rats: Role of CB1 receptors.

The pathophysiology associated with increased prevalence of depression in diabetics is not completely understood, although studies have pointed the endocannabinoid system as a possible target. Then, we aimed to investigate the role of this system in the pathophysiology of depression associated with diabetes. For this, diabetic (DBT) male Wistar rats were intraperitoneally treated with cannabinoid CB1 (AM251, 1mg/kg) or CB2 (AM630, 1mg/kg) receptor antagonists followed by anandamide (AEA, 0.005mg/kg) and then submitted to the forced swimming test (FST). Oxidative stress parameters, CB1 receptor expression and serotonin (5-HT) and noradrenaline levels in the hippocampus (HIP) and prefrontal cortex (PFC) were also performed. It was observed that DBT animals presented a more pronounced depressive-like behavior and increase of CB1 receptor expression in the HIP. AEA treatment induced a significant improvement in the depressive-like behavior, which was reversed by the CB1 antagonist AM251, without affecting the hyperglycemia or weight gain. AEA was also able to restore the elevated CB1 expression and also to elevate the reduced level of 5-HT in the HIP from DBT animals. In addition, AEA restored the elevated noradrenaline levels in the PFC and induced a neuroprotective effect by restoring the decreased reduced glutathione and increased lipid hydroperoxides levels along with the decreased superoxide dismutase activity observed in HIP or PFC. Together, our data suggest that in depression associated with diabetes, the endocannabinoid anandamide has a potential to induce neuroadaptative changes able to improve the depressive-like response by its action as a CB1 receptor agonist.

Increased oxidative stress in prefrontal cortex and hippocampus is related to depressive-like behavior in streptozotocin-diabetic rats.

Depression is a common comorbid in diabetic patients. The pathophysiologic mechanisms that relate this comorbidity is not completely elucidated yet, although several lines of evidence point out that increased oxidative stress resulting from hyperglycemia may have a crucial role. Thus, the effect of prolonged treatment with insulin (INS), the antioxidant vitamin E (VIT E) or the antidepressant imipramine (IMI) was evaluated in animals submitted to forced swimming test. Oxidative stress parameters (lipid peroxidation product levels, reduced gluthatione levels and catalase and superoxide dismutase activities) were also evaluated in brain areas related to depression, prefrontal cortex (PFC) and hippocampus (HIP). Our data show that treatment of streptozotocin-induced diabetic (DBT) rats with INS (6 UI/day, s.c.) prevented the blood glucose increase, reduced the immobility time, an antidepressant-like behavior, and normalized the reduced weight gain. Although the VIT E treatment (300 mg/kg, p.o.) had not altered the blood glucose levels, this treatment was able to reduce the immobility time and to reestablish the reduced weight gain in DBT rats. Differently, treatment with IMI (15 mg/kg, i.p.) induced antidepressant-like behavior in normoglycemic besides DBT animals. While VIT E and IMI treatments restored only specific oxidative stress parameters, INS was able to prevent all changed parameters evaluated in both PFC and HIP from DBT animals. Therefore, our data provide further evidence of the importance of oxidative stress in PFC and HIP in the pathophysiology of depression related to diabetes.