scholarly journals Co-Expression Networks in Chlamydomonas Reveal Significant Rhythmicity in Batch Cultures and Empower Gene Function Discovery

2021 ◽  
Author(s):  
Patrice A Salomé ◽  
Sabeeha S Merchant
Keyword(s):  
Gene Function ◽  
R Package ◽  
Data Sets ◽  
Sample Collection ◽  
Batch Cultures ◽  
Unicellular Green Alga ◽  
Cellular Pathways ◽  
Function Discovery ◽  
Expression Potential ◽  
And Function

Abstract The unicellular green alga Chlamydomonas reinhardtii is a choice reference system for the study of photosynthesis and chloroplast metabolism, cilium assembly and function, lipid and starch metabolism, and metal homeostasis. Despite decades of research, the functions of thousands of genes remain largely unknown, and new approaches are needed to categorically assign genes to cellular pathways. Growing collections of transcriptome and proteome data now allow a systematic approach based on integrative co-expression analysis. We used a dataset comprising 518 deep transcriptome samples derived from 58 independent experiments to identify potential co-expression relationships between genes. We visualized co-expression potential with the R package corrplot, to easily assess co-expression and anti-correlation between genes. We extracted several hundred high-confidence genes at the intersection of multiple curated lists involved in cilia, cell division, and photosynthesis, illustrating the power of our method. Surprisingly, Chlamydomonas experiments retained a significant rhythmic component across the transcriptome, suggesting an underappreciated variable during sample collection, even in samples collected in constant light. Our results therefore document substantial residual synchronization in batch cultures, contrary to assumptions of asynchrony. We provide step-by-step protocols for the analysis of co-expression across transcriptome data sets from Chlamydomonas and other species to help foster gene function discovery.

scholarly journals Co-Expression Networks in the Green Alga Chlamydomonas reinhardtii Empower Gene Discovery and Functional Exploration

2020 ◽  
Author(s):  
Patrice A. Salomé ◽  
Sabeeha S. Merchant
Keyword(s):  
Chlamydomonas Reinhardtii ◽  
Green Alga ◽  
R Package ◽  
Sample Collection ◽  
Simple Method ◽  
Batch Cultures ◽  
Unicellular Green Alga ◽  
Function Discovery ◽  
Expression Potential ◽  
And Function

ABSTRACTThe unicellular green alga Chlamydomonas reinhardtii is a choice reference system for the study of photosynthesis, cilium assembly and function, lipid and starch metabolism and metal homeostasis. Despite decades of research, the functions of thousands of genes remain largely unknown, and new approaches are needed to categorically assign genes to cellular pathways. Growing collections of transcriptome and proteome data now allow a systematic approach based on integrative co-expression analysis. We used a dataset comprising 518 deep transcriptome samples derived from 58 independent experiments to identify potential co-expression relationships between genes. We visualized co-expression potential with the R package corrplot, to easily assess co-expression and anti-correlation between genes from manually-curated and community-generated gene lists. We extracted 400 high-confidence cilia-related genes at the intersection of multiple co-expressed lists, illustrating the power of our simple method. Surprisingly, Chlamydomonas experiments did not cluster according to an obvious pattern, suggesting an underappreciated variable during sample collection. One possible source of variation may stem from the strong clustering of nuclear genes as a function of their diurnal phase, even in samples collected in constant conditions, indicating substantial residual synchronization in batch cultures. We provide a step-by-step guide into the analysis of co-expression across Chlamydomonas transcriptome datasets to help foster gene function discovery.One-sentence summarywe reveal co-expression potential between Chlamydomonas genes and describe strong synchronization of cells grown in batch cultures as a possible source of underappreciated variation.

Evaluation of Acoustic Analyses of Voice in Nonoptimized Conditions

2020 ◽  
Vol 63 (12) ◽  
pp. 3991-3999
Author(s):  
Benjamin van der Woerd ◽  
Min Wu ◽  
Vijay Parsa ◽  
Philip C. Doyle ◽  
Kevin Fung
Keyword(s):  
Repeated Measures ◽  
Voice Quality ◽  
Data Sets ◽  
Acoustic Measurements ◽  
Sample Collection ◽  
Experimental Conditions ◽  
Environment Analysis ◽  
Acoustic Measures ◽  
Recording Conditions ◽  
Cepstral Peak Prominence

Objectives This study aimed to evaluate the fidelity and accuracy of a smartphone microphone and recording environment on acoustic measurements of voice. Method A prospective cohort proof-of-concept study. Two sets of prerecorded samples (a) sustained vowels (/a/) and (b) Rainbow Passage sentence were played for recording via the internal iPhone microphone and the Blue Yeti USB microphone in two recording environments: a sound-treated booth and quiet office setting. Recordings were presented using a calibrated mannequin speaker with a fixed signal intensity (69 dBA), at a fixed distance (15 in.). Each set of recordings (iPhone—audio booth, Blue Yeti—audio booth, iPhone—office, and Blue Yeti—office), was time-windowed to ensure the same signal was evaluated for each condition. Acoustic measures of voice including fundamental frequency ( f o ), jitter, shimmer, harmonic-to-noise ratio (HNR), and cepstral peak prominence (CPP), were generated using a widely used analysis program (Praat Version 6.0.50). The data gathered were compared using a repeated measures analysis of variance. Two separate data sets were used. The set of vowel samples included both pathologic ( n = 10) and normal ( n = 10), male ( n = 5) and female ( n = 15) speakers. The set of sentence stimuli ranged in perceived voice quality from normal to severely disordered with an equal number of male ( n = 12) and female ( n = 12) speakers evaluated. Results The vowel analyses indicated that the jitter, shimmer, HNR, and CPP were significantly different based on microphone choice and shimmer, HNR, and CPP were significantly different based on the recording environment. Analysis of sentences revealed a statistically significant impact of recording environment and microphone type on HNR and CPP. While statistically significant, the differences across the experimental conditions for a subset of the acoustic measures (viz., jitter and CPP) have shown differences that fell within their respective normative ranges. Conclusions Both microphone and recording setting resulted in significant differences across several acoustic measurements. However, a subset of the acoustic measures that were statistically significant across the recording conditions showed small overall differences that are unlikely to have clinical significance in interpretation. For these acoustic measures, the present data suggest that, although a sound-treated setting is ideal for voice sample collection, a smartphone microphone can capture acceptable recordings for acoustic signal analysis.

scholarly journals Flavonoids in Skin Senescence Prevention and Treatment

2021 ◽  
Vol 22 (13) ◽  
pp. 6814
Author(s):  
Anna Domaszewska-Szostek ◽  
Monika Puzianowska-Kuźnicka ◽  
Alina Kuryłowicz
Keyword(s):  
Therapeutic Strategy ◽  
Cancer Susceptibility ◽  
Skin Aging ◽  
Structure And Function ◽  
Tissue Structure ◽  
Delayed Wound Healing ◽  
Secretory Phenotype ◽  
Cellular Pathways ◽  
And Function ◽  
Increased Susceptibility

Skin aging is associated with the accumulation of senescent cells and is related to many pathological changes, including decreased protection against pathogens, increased susceptibility to irritation, delayed wound healing, and increased cancer susceptibility. Senescent cells secrete a specific set of pro-inflammatory mediators, referred to as a senescence-associated secretory phenotype (SASP), which can cause profound changes in tissue structure and function. Thus, drugs that selectively eliminate senescent cells (senolytics) or neutralize SASP (senostatics) represent an attractive therapeutic strategy for age-associated skin deterioration. There is growing evidence that plant-derived compounds (flavonoids) can slow down or even prevent aging-associated deterioration of skin appearance and function by targeting cellular pathways crucial for regulating cellular senescence and SASP. This review summarizes the senostatic and senolytic potential of flavonoids in the context of preventing skin aging.

scholarly journals MUREN: a robust and multi-reference approach of RNA-seq transcript normalization

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yance Feng ◽  
Lei M. Li
Keyword(s):  
Biological Significance ◽  
Housekeeping Genes ◽  
R Package ◽  
Data Sets ◽  
Statistical Regression ◽  
Rna Seq ◽  
Least Trimmed Squares ◽  
Standard Data ◽  
Wide Range ◽  
Multiple References

Abstract Background Normalization of RNA-seq data aims at identifying biological expression differentiation between samples by removing the effects of unwanted confounding factors. Explicitly or implicitly, the justification of normalization requires a set of housekeeping genes. However, the existence of housekeeping genes common for a very large collection of samples, especially under a wide range of conditions, is questionable. Results We propose to carry out pairwise normalization with respect to multiple references, selected from representative samples. Then the pairwise intermediates are integrated based on a linear model that adjusts the reference effects. Motivated by the notion of housekeeping genes and their statistical counterparts, we adopt the robust least trimmed squares regression in pairwise normalization. The proposed method (MUREN) is compared with other existing tools on some standard data sets. The goodness of normalization emphasizes on preserving possible asymmetric differentiation, whose biological significance is exemplified by a single cell data of cell cycle. MUREN is implemented as an R package. The code under license GPL-3 is available on the github platform: github.com/hippo-yf/MUREN and on the conda platform: anaconda.org/hippo-yf/r-muren. Conclusions MUREN performs the RNA-seq normalization using a two-step statistical regression induced from a general principle. We propose that the densities of pairwise differentiations are used to evaluate the goodness of normalization. MUREN adjusts the mode of differentiation toward zero while preserving the skewness due to biological asymmetric differentiation. Moreover, by robustly integrating pre-normalized counts with respect to multiple references, MUREN is immune to individual outlier samples.

SAMPLING OF ODOUROUS AIR FOR OLFACTOMETRIC MEASUREMENT J. HARTUNG Institute for Animal Hygiene of the Hannover School of Veterinary Medicine, Blinteweg 17 p , 3000 Hannover 71, FRG Summary Both static and dynamic sampling procedures are used for olfactometric measurements. Care must be taken inorder to obtain a representative sample and to minimize sample losses due to condensation, adsorption and permeation, when using static sampling methods, particularly. Teflon or Tedlar bags and inert tubing materials help to diminish adsorption and desorption problems. Condensation can be avoided by heating the sampling tubes or by prediluting the sample with pure, odour-free air. Within the EEC guide lines exist for odour measurement in The Netherlands, France, Germany and the United Kingdom. The usefulness of dynamic sampling is agreed on. The opinions differ as far as static sampling is concerned. It seems that both sam­ pling methods can be applied successfully for olfactomet-ric measurements. However, it is necessary to define the details of the procedures aiming at a standardization of sampling which might be the first step for a harmoni­ zation of olfactometric measurements in the laboratories of the different countries. 1 . INTRODUCTION The method of measuring odour sensorily in general can be devided into the following basic steps (1): - sample collection - sample dilution and presentation - indication of response - interpretation of response Due to the fact that many different testing procedures exist in the different laboratories, results can only be com­ pared when knowing exactly - the conditions and procedures for sampling of the air to be i nvesti gated, - the design and function of the olfactonnetric apparatus, and - the physiological and physical status of the panel. The olfactometric apparatus and the panel are in close connection with each other as shown in Table I whereas the sam­ pling procedure is more or less apart from the apparatus and the panel and affects the olfactometric inlet, only. However, sample collection is the first step and can influence the re­ sults considerably; thus, valid sampling is the base for valid

1986 ◽  
pp. 58-58
Keyword(s):  
Veterinary Medicine ◽  
Sample Collection ◽  
Testing Procedures ◽  
The United Kingdom ◽  
Free Air ◽  
Sampling Procedures ◽  
And Function ◽  
Dynamic Sampling ◽  
Guide Lines ◽  
Sample Dilution

scholarly journals SLC26A2 gene expression levels in melanoma

2021 ◽  
Vol 31 (Supplement_2) ◽  
Author(s):  
Diana Assis ◽  
Ana Luísa De Sousa-Coelho
Keyword(s):  
Gene Expression ◽  
Cancer Cells ◽  
Normal Skin ◽  
Braf Inhibitor ◽  
Ovarian Cancer Cells ◽  
Data Sets ◽  
Dependent Manner ◽  
Therapeutic Decisions ◽  
Pharmacological Screening ◽  
And Function

Abstract Background A recent repurposing pharmacological screening revealed that vanadium-containing drugs anti-proliferative action in ovarian cancer cells was SLC26A2-dependent. SLC26A2/DTDST is a sulfate transporter, related to chondrodysplasia syndromes. Despite some reports on colon cancer, there are no studies on SLC26A2 performed in melanoma in the literature. Methods To better understand its potential use as biomarker for therapeutic decisions in melanoma, we performed gene expression analyses of the data available at GEO profiles (NCBI). Gene data sets that allowed analysis of SLC26A2 expression (1) in melanoma; (2) in response to drugs; (3) regulated by other proteins, were selected. Results Our results showed that, compared to normal skin or benign nevi, SLC26A2 expression was 2.5-fold higher in malignant melanoma (P = 0.019). Compared to the primary tumor, SLC26A2 expression tripled in melanoma (P = 0.022). We found a 6% decrease of SLC26A2 expression in A375 melanoma cells treated with BRAF inhibitor Vemurafenib (P < 0.001). After treatment of A375 cells with MLN4924, a selective inhibitor of the activating enzyme of Nedd8, SLC26A2 decreased in a time-dependent manner ( > 80% at 24 h; P < 0.001). In Sk-Mel-2 cells overexpressing E2F-1, a transcription factor that induces apoptosis in cancer cells, SLC26A2 levels were reduced by 76.4% (P = 0.067). In A375P cells depleted of PGC1α, an important metabolic co-activator in mitochondrial biogenesis and function, SLC26A2 levels increased 16% (P = 0.013). Conclusions From this work, we unveiled, for the first time, potential clues to better understand the regulation and role of SLC26A2 in melanoma. Though, it is still to be determined whether SLC26A2 is a driver or a passenger in the disease.

scholarly journals FORESEE: a tool for the systematic comparison of translational drug response modeling pipelines

2018 ◽  
Author(s):  
Lisa-Katrin Turnhoff ◽  
Ali Hadizadeh Esfahani ◽  
Maryam Montazeri ◽  
Nina Kusch ◽  
Andreas Schuppert
Keyword(s):  
Drug Response ◽  
Drug Efficacy ◽  
Response Prediction ◽  
R Package ◽  
Supplementary Information ◽  
Supplementary File ◽  
Data Sets ◽  
Training Algorithms ◽  
Model Training

Translational models that utilize omics data generated in in vitro studies to predict the drug efficacy of anti-cancer compounds in patients are highly distinct, which complicates the benchmarking process for new computational approaches. In reaction to this, we introduce the uniFied translatiOnal dRug rESponsE prEdiction platform FORESEE, an open-source R-package. FORESEE not only provides a uniform data format for public cell line and patient data sets, but also establishes a standardized environment for drug response prediction pipelines, incorporating various state-of-the-art preprocessing methods, model training algorithms and validation techniques. The modular implementation of individual elements of the pipeline facilitates a straightforward development of combinatorial models, which can be used to re-evaluate and improve already existing pipelines as well as to develop new ones. Availability and Implementation: FORESEE is licensed under GNU General Public License v3.0 and available at https://github.com/JRC-COMBINE/FORESEE . Supplementary Information: Supplementary Files 1 and 2 provide detailed descriptions of the pipeline and the data preparation process, while Supplementary File 3 presents basic use cases of the package. Contact: [email protected]

scholarly journals SpiderSeqR: an R package for crawling the web of high-throughput multi-omic data repositories for data-sets and annotation

2020 ◽  
Author(s):  
Anna M. Sozanska ◽  
Charles Fletcher ◽  
Dóra Bihary ◽  
Shamith A. Samarajiwa
Keyword(s):  
High Throughput ◽  
R Package ◽  
Data Reuse ◽  
Massively Parallel ◽  
Data Sets ◽  
Similar Data ◽  
Data Generation ◽  
Data Repositories ◽  
Public Data ◽  
Omic Data

AbstractMore than three decades ago, the microarray revolution brought about high-throughput data generation capability to biology and medicine. Subsequently, the emergence of massively parallel sequencing technologies led to many big-data initiatives such as the human genome project and the encyclopedia of DNA elements (ENCODE) project. These, in combination with cheaper, faster massively parallel DNA sequencing capabilities, have democratised multi-omic (genomic, transcriptomic, translatomic and epigenomic) data generation leading to a data deluge in bio-medicine. While some of these data-sets are trapped in inaccessible silos, the vast majority of these data-sets are stored in public data resources and controlled access data repositories, enabling their wider use (or misuse). Currently, most peer reviewed publications require the deposition of the data-set associated with a study under consideration in one of these public data repositories. However, clunky and difficult to use interfaces, subpar or incomplete annotation prevent discovering, searching and filtering of these multi-omic data and hinder their re-purposing in other use cases. In addition, the proliferation of multitude of different data repositories, with partially redundant storage of similar data are yet another obstacle to their continued usefulness. Similarly, interfaces where annotation is spread across multiple web pages, use of accession identifiers with ambiguous and multiple interpretations and lack of good curation make these data-sets difficult to use. We have produced SpiderSeqR, an R package, whose main features include the integration between NCBI GEO and SRA databases, enabling an integrated unified search of SRA and GEO data-sets and associated annotations, conversion between database accessions, as well as convenient filtering of results and saving past queries for future use. All of the above features aim to promote data reuse to facilitate making new discoveries and maximising the potential of existing data-sets.Availabilityhttps://github.com/ss-lab-cancerunit/SpiderSeqR

scholarly journals Mumame: a software tool for quantifying gene-specific point-mutations in shotgun metagenomic data

2019 ◽  
Vol 3 ◽  
Author(s):  
Shruthi Magesh ◽  
Viktor Jonsson ◽  
Johan Bengtsson-Palme
Keyword(s):  
Microbial Communities ◽  
Point Mutations ◽  
Software Tool ◽  
Metagenomic Data ◽  
Data Sets ◽  
Resistance Mutations ◽  
Shotgun Metagenomics ◽  
Key Factor ◽  
Detection Of Mutations ◽  
And Function

Metagenomics has emerged as a central technique for studying the structure and function of microbial communities. Often the functional analysis is restricted to classification into broad functional categories. However, important phenotypic differences, such as resistance to antibiotics, are often the result of just one or a few point mutations in otherwise identical sequences. Bioinformatic methods for metagenomic analysis have generally been poor at accounting for this fact, resulting in a somewhat limited picture of important aspects of microbial communities. Here, we address this problem by providing a software tool called Mumame, which can distinguish between wildtype and mutated sequences in shotgun metagenomic data and quantify their relative abundances. We demonstrate the utility of the tool by quantifying antibiotic resistance mutations in several publicly available metagenomic data sets. We also identified that sequencing depth is a key factor to detect rare mutations. Therefore, much larger numbers of sequences may be required for reliable detection of mutations than for most other applications of shotgun metagenomics. Mumame is freely available online (http://microbiology.se/software/mumame).

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