The Biology and Evolution of Calcite and Aragonite Mineralization in Octocorallia

Octocorallia (class Anthozoa, phylum Cnidaria) is a group of calcifying corals displaying a wide diversity of mineral skeletons. This includes skeletal structures composed of different calcium carbonate polymorphs (aragonite and calcite). This represents a unique feature among anthozoans, as scleractinian corals (subclass Hexacorallia), main reef builders and focus of biomineralization research, are all characterized by an aragonite exoskeleton. From an evolutionary perspective, the presence of aragonitic skeletons in Octocorallia is puzzling as it is observed in very few species and has apparently originated during a Calcite sea (i.e., time interval characterized by calcite-inducing seawater conditions). Despite this, octocorals have been systematically overlooked in biomineralization studies. Here we review what is known about octocoral biomineralization, focusing on the evolutionary and biological processes that underlie calcite and aragonite formation. Although differences in research focus between octocorals and scleractinians are often mentioned, we highlight how strong variability also exists between different octocoral groups. Different main aspects of octocoral biomineralization have been in fact studied in a small set of species, including the (calcitic) gorgonian Leptogorgia virgulata and/or the precious coral Corallium rubrum. These include descriptions of calcifying cells (scleroblasts), calcium transport and chemistry of the calcification fluids. With the exception of few histological observations, no information on these features is available for aragonitic octocorals. Availability of sequencing data is also heterogeneous between groups, with no transcriptome or genome available, for instance, for the clade Calcaxonia. Although calcite represents by far the most common polymorph deposited by octocorals, we argue that studying aragonite-forming could provide insight on octocoral, and more generally anthozoan, biomineralization. First and foremost it would allow to compare calcification processes between octocoral groups, highlighting homologies and differences. Secondly, similarities (exoskeleton) between Heliopora and scleractinian skeletons, would provide further insight on which biomineralization features are driven by skeleton characteristics (shared by scleractinians and aragonitic octocorals) and those driven by taxonomy (shared by octocorals regardless of skeleton polymorph). Including the diversity of anthozoan mineralization strategies into biomineralization studies remains thus essential to comprehensively study how skeletons form and evolved within this ecologically important group of marine animals.

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Metabolism and Functions of Inositol Pyrophosphates: Insights Gained from the Application of Synthetic Analogues

Molecules ◽

10.3390/molecules25194515 ◽

2020 ◽

Vol 25 (19) ◽

pp. 4515

Author(s):

Stephen B. Shears ◽

Huanchen Wang

Keyword(s):

Chemical Synthesis ◽

Molecular Level ◽

Biological Information ◽

Biological Processes ◽

Synthetic Analogues ◽

Metabolic Turnover ◽

Important Group ◽

Wide Range ◽

Inositol Pyrophosphates ◽

Low Levels

Inositol pyrophosphates (PP-InsPs) comprise an important group of intracellular, diffusible cellular signals that a wide range of biological processes throughout the yeast, plant, and animal kingdoms. It has been difficult to gain a molecular-level mechanistic understanding of the actions of these molecules, due to their highly phosphorylated nature, their low levels, and their rapid metabolic turnover. More recently, these obstacles to success are being surmounted by the chemical synthesis of a number of insightful PP-InsP analogs. This review will describe these analogs and will indicate the important chemical and biological information gained by using them.

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Effect of Inoculum Pretreatment on Alcohol Production from Volatile Fatty Acids through an Anaerobic Solventogenic Process

10.1101/2021.03.23.436570 ◽

2021 ◽

Author(s):

Gustavo Mockaitis ◽

Guillaume Bruant ◽

Eugenio Foresti ◽

Marcelo Zaiat ◽

Serge R. Guiot

Keyword(s):

Fatty Acids ◽

Volatile Fatty Acids ◽

Liquid Fuels ◽

Biological Processes ◽

Thermal Pretreatment ◽

Sequencing Data ◽

Balance Study ◽

Alcohol Production ◽

Acidic Pretreatment ◽

Mass Balance Study

1AbstractBackgroundProduction of alcohols from wastes through biological processes is environmentally and economically interesting, since they can be valorized as drop-in liquid fuels, which have a high market value. Using microbial mixed cultures in such processes is of great interest since it confers more stability, a higher resistance to both toxicity and contamination, and an increased substrate flexibility. However, there is still a lack of fundamental knowledge on such microbial populations used as inoculum in solventogenic processes. This work evaluates the effect of four different physicochemical pretreatments (acidic, thermal, acidic-thermal and thermal-acidic) on an anaerobic inoculum used for alcohols production from volatile fatty acids.ResultsAll experiments were conducted in single batches using acetate and butyrate as substrates, at 30°C and with a pressurized headspace of pure H2 at 2182 mBar. Higher productions of both ethanol and butanol were achieved with both thermal and acidic-thermal pretreatments of the inoculum. The highest concentrations of ethanol and butanol produced were respectively of 122 mg.L−1 and 97 mg.L−1 for the thermal pretreatment (after 710 hours), and of 87 mg.L−1 and 143 mg.L−1 for the acidic-thermal pretreatment (after 210 hours). Butyrate was consumed and acetate was produced in all assays. A mass balance study indicated that the inoculum provided part of the substrate. Thermodynamic data indicated that a high H2 partial pressure favored solventogenic metabolic pathways. Finally, sequencing data showed that both thermal and acidic-thermal pretreatments selected mainly the bacterial genera Pseudomonas, Brevundimonas and Clostridium.ConclusionThe acidic-thermal pretreatment selected a bacterial community more adapted to the conversion of acetate and butyrate into ethanol and butanol, respectively. A higher production of ethanol was achieved with the thermal pretreatment, but at a slower rate. The thermal-acidic pretreatment was unstable, showing a huge variability between replicates. The acidic pretreatment showed the lowest alcohol production, almost negligible as compared to the control assay.

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Inferring transcriptomic cell states and transitions only from time series transcriptome data

Scientific Reports ◽

10.1038/s41598-021-91752-9 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Kyuri Jo ◽

Inyoung Sung ◽

Dohoon Lee ◽

Hyuksoon Jang ◽

Sun Kim

Keyword(s):

Time Series ◽

Molecular Level ◽

Gaussian Process Regression ◽

Cell Types ◽

Biological Processes ◽

Transcriptome Data ◽

Sequencing Data ◽

Additional Information ◽

Cluster Networks ◽

Genetic Perturbations

AbstractCellular stages of biological processes have been characterized using fluorescence-activated cell sorting and genetic perturbations, charting a limited landscape of cellular states. Time series transcriptome data can help define new cellular states at the molecular level since the analysis of transcriptional changes can provide information on cell states and transitions. However, existing methods for inferring cell states from transcriptome data use additional information such as prior knowledge on cell types or cell-type-specific markers to reduce the complexity of data. In this study, we present a novel time series clustering framework to infer TRAnscriptomic Cellular States (TRACS) only from time series transcriptome data by integrating Gaussian process regression, shape-based distance, and ranked pairs algorithm in a single computational framework. TRACS determines patterns that correspond to hidden cellular states by clustering gene expression data. TRACS was used to analyse single-cell and bulk RNA sequencing data and successfully generated cluster networks that reflected the characteristics of key stages of biological processes. Thus, TRACS has a potential to help reveal unknown cellular states and transitions at the molecular level using only time series transcriptome data. TRACS is implemented in Python and available at http://github.com/BML-cbnu/TRACS/.

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Observed Survival Interval: A Supplement to TCGA Pan-Cancer Clinical Data Resource

Cancers ◽

10.3390/cancers11030280 ◽

2019 ◽

Vol 11 (3) ◽

pp. 280 ◽

Cited By ~ 5

Author(s):

Jie Xiong ◽

Zhitong Bing ◽

Shengyu Guo

Keyword(s):

Clinical Data ◽

The Cancer Genome Atlas ◽

Time Interval ◽

Omics Data ◽

Pathological Stage ◽

Sequencing Data ◽

Patient Death ◽

Data Resource ◽

Pan Cancer ◽

Survival Interval

To drive high-quality omics translational research using The Cancer Genome Atlas (TCGA) data, a TCGA Pan-Cancer Clinical Data Resource was proposed. However, there is an out-of-step issue between clinical outcomes and the omics data of TCGA for skin cutaneous melanoma (SKCM), due to the majority of metastatic samples. In clinical cases, the survival time started from the initial SKCM diagnosis, while the omics data were characterized at TCGA sampling. This study aimed to address this issue by proposing an observed survival interval (OBS), which was defined as the time interval from TCGA sampling to patient death or last follow-up. We compared the OBS with the usual recommended overall survival (OS) by associating them with both clinical data and microRNA sequencing data of TCGA-SKCM. We found that the OS of primary SKCM was significantly shorter than that of metastatic SKCM, while the opposite happened if OBS was compared. OS was associated with the pathological stage of both primary and metastatic SKCM, while OBS was associated with the pathological stage of primary SKCM but not that of metastatic SKCM. Five previously cross-validated survival-associated microRNAs were found to be associated with the OBS rather than OS in metastatic SKCM. Thus, the OBS was more appropriate for associating microRNA-omics data of TCGA-SKCM than OS, and it is a timely supplement to TCGA Pan-Cancer Clinical Data Resource.

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Sleep and its Interaction with Endocrine Rhythms

The British Journal of Psychiatry ◽

10.1192/bjp.142.3.215 ◽

1983 ◽

Vol 142 (3) ◽

pp. 215-219 ◽

Cited By ~ 19

Author(s):

Paul E. Mullen

Keyword(s):

Biological Rhythms ◽

Biological Processes ◽

Close Approximation ◽

Important Group ◽

Internal Oscillator ◽

Free Running ◽

The Individual

Rhythmic variations with frequences from fractions of seconds to years characterise a wide variety of biological processes (Aschoff, 1979). Biological rhythms can be observed, not only in the individual of the species, but also in the cells which comprise the organism and the populations of which it is a member. These regular fluctuations can be endogenously generated by some form of internal oscillator, or alternatively may passively reflect exogenous environmental alterations. An important group of rhythms combines both endogenous and exogenous inputs with an internal oscillator or oscillators which are capable of being influenced by some external change. In this situation, the internal rhythm is kept in harmony with an environmental cycle by a change in the outside world acting as a synchroniser or zeitgeber. In this type if the animal is artificially isolated from its normal external synchroniser, the rhythm will continue, but free running, with a periodicity which is a close approximation to the duration of the environmental cycle to which it is normally tied. These rhythms normally synchronised to an environmental cycle but capable of being self-sustaining at approximately the same rate, are termed circa rhythms: thus circadian, circannual and circalunar rhythms, according to the geophysical cycle by which they are normally entrained.

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TEfinder: A Bioinformatics Pipeline for Detecting New Transposable Element Insertion Events in Next-Generation Sequencing Data

Genes ◽

10.3390/genes12020224 ◽

2021 ◽

Vol 12 (2) ◽

pp. 224

Author(s):

Vista Sohrab ◽

Cristina López-Díaz ◽

Antonio Di Pietro ◽

Li-Jun Ma ◽

Dilay Hazal Ayhan

Keyword(s):

Next Generation Sequencing Data ◽

Biological Processes ◽

Sequencing Data ◽

Short Term ◽

Bioinformatics Pipeline ◽

Genetic Changes ◽

Bioinformatics Software ◽

External Software ◽

Short Term Adaptation ◽

Generation Sequencing

Transposable elements (TEs) are mobile elements capable of introducing genetic changes rapidly. Their importance has been documented in many biological processes, such as introducing genetic instability, altering patterns of gene expression, and accelerating genome evolution. Increasing appreciation of TEs has resulted in a growing number of bioinformatics software to identify insertion events. However, the application of existing tools is limited by either narrow-focused design of the package, too many dependencies on other tools, or prior knowledge required as input files that may not be readily available to all users. Here, we reported a simple pipeline, TEfinder, developed for the detection of new TE insertions with minimal software and input file dependencies. The external software requirements are BEDTools, SAMtools, and Picard. Necessary input files include the reference genome sequence in FASTA format, an alignment file from paired-end reads, existing TEs in GTF format, and a text file of TE names. We tested TEfinder among several evolving populations of Fusarium oxysporum generated through a short-term adaptation study. Our results demonstrate that this easy-to-use tool can effectively detect new TE insertion events, making it accessible and practical for TE analysis.

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DeepMP: a deep learning tool to detect DNA base modifications on Nanopore sequencing data

10.1101/2021.06.28.450135 ◽

2021 ◽

Author(s):

Jose Bonet ◽

Mandi Chen ◽

Marc Dabad ◽

Simon Heath ◽

Abel Gonzalez-Perez ◽

...

Keyword(s):

State Of The Art ◽

Direct Detection ◽

Methylation Status ◽

Low Frequency ◽

Biological Processes ◽

Sequencing Data ◽

E Coli ◽

Dna Base ◽

Long Read ◽

Free Position

DNA Methylation plays a key role in a variety of biological processes. Recently, Nanopore long-read sequencing has enabled direct detection of these modifications. As a consequence, a range of computational methods have been developed to exploit Nanopore data for methylation detection. However, current approaches rely on a human-defined threshold to detect the methylation status of a genomic position and are not optimized to detect sites methylated at low frequency. Furthermore, most methods employ either the Nanopore signals or the basecalling errors as the model input and do not take advantage of their combination. Here we present DeepMP, a convolutional neural network (CNN)-based model that takes information from Nanopore signals and basecalling errors to detect whether a given motif in a read is methylated or not. Besides, DeepMP introduces a threshold-free position modification calling model sensitive to sites methylated at low frequency across cells. We comprehensively benchmarked DeepMP against state-of-the-art methods on E. coli, human and pUC19 datasets. DeepMP outperforms current approaches at read-based and position-based methylation detection across sites methylated at different frequencies in the three datasets. DeepMP is implemented and freely available under MIT license at https://github.com/pepebonet/DeepMP

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Identification of Long Noncoding RNAs in Distinct Subtypes of Mouse Retinal Ganglion Cells

10.21203/rs.3.rs-147625/v1 ◽

2021 ◽

Author(s):

Ana Ayupe ◽

Felipe Beckedorff ◽

Konstantin Levay ◽

Ramin Shiekhattar ◽

Kevin Park

Keyword(s):

Retinal Ganglion Cells ◽

Ganglion Cells ◽

Axonal Injury ◽

Noncoding Rnas ◽

Small Population ◽

Long Noncoding Rnas ◽

Biological Processes ◽

Retinal Ganglion ◽

Sequencing Data ◽

Specific Expression

Abstract Background: Emerging evidence indicates that long noncoding RNAs (lncRNAs) are important regulators of various biological processes, and their expression can be altered following certain pathological conditions, including central nervous system injury. Retinal ganglion cells (RGCs), whose axons form the optic nerve, are a heterogeneous population of neurons with more than 20 molecularly distinct subtypes. While most RGCs, including the ON-OFF direction-selective RGCs (ooDSGCs), are vulnerable to axonal injury, a small population of RGCs, including the intrinsically photosensitive RGCs (ipRGCs), are more resilient. Results: By performing systematic analyses on RNA-sequencing data, here we identify lncRNAs that are expressed in ooDSGCs and ipRGCs with and without axonal injury. Our results reveal a repertoire of different classes of lncRNAs, including long intergenic noncoding RNAs and antisense ncRNAs that are differentially expressed between these RGC types. Strikingly, we also found dozens of lncRNAs whose expressions are altered markedly in response to axonal injury, some of which are expressed exclusively in either one of the subtypes. Moreover, analyses into these lncRNAs unraveled their neighboring coding genes, many of which encode transcription factors and signaling molecules, suggesting that these lncRNAs may act in cis to regulate important biological processes in these neurons. Lastly, guilt-by-association analysis showed that lncRNAs are correlated with apoptosis associated genes, suggesting potential roles for these lncRNAs in RGC survival.Conclusions: Overall, the results of this study reveal RGC type-specific expression of lncRNAs and provide a foundation for future investigation of the function of lncRNAs in regulating neuronal type specification and survival.

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TAPIOCA: Topological Attention and Predictive Inference of Chromatin Arrangement Using Epigenetic Features

10.1101/2021.05.16.444378 ◽

2021 ◽

Author(s):

Max R Highsmith ◽

Jianlin Cheng

Keyword(s):

Deep Learning ◽

Predictive Inference ◽

Chromatin Conformation ◽

Biological Processes ◽

Sequencing Data ◽

Prior Art ◽

Topologically Associated Domains ◽

A Cell ◽

Chromatin Arrangement

Chromatin conformation is an important characteristic of the genome which has been repeatedly demonstrated to play vital roles in many biological processes. Chromatin can be characterized by the presence or absence of structural motifs called topologically associated domains. The de facto strategy for determination of topologically associated domains within a cell line is the use of Hi-C sequencing data. However Hi-C sequencing data can be expensive or otherwise unavailable. Various epigenetic features have been hypothesized to contribute to the determination of chromatin conformation. Here we present TAPIOCA, a self-attention based deep learning transformer algorithm for the prediction of chromatin topology which circumvents the need for labeled Hi-C data and makes effective predictions of chromatin conformation organization using only epigenetic features. TAPIOCA outperforms prior art in established metrics of TAD prediction, while generalizing across cell lines beyond those used in training.

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Identification of long noncoding RNAs in injury-resilient and injury-susceptible mouse retinal ganglion cells

BMC Genomics ◽

10.1186/s12864-021-08050-x ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Ana C. Ayupe ◽

Felipe Beckedorff ◽

Konstantin Levay ◽

Benito Yon ◽

Yadira Salgueiro ◽

...

Keyword(s):

Retinal Ganglion Cells ◽

Ganglion Cells ◽

Axonal Injury ◽

Noncoding Rnas ◽

Long Noncoding Rnas ◽

Biological Processes ◽

Retinal Ganglion ◽

Sequencing Data ◽

Susceptible Mouse ◽

Specific Expression

Abstract Background Emerging evidence indicates that long noncoding RNAs (lncRNAs) are important regulators of various biological processes, and their expression can be altered following certain pathological conditions, including central nervous system injury. Retinal ganglion cells (RGCs), whose axons form the optic nerve, are a heterogeneous population of neurons with more than 40 molecularly distinct subtypes in mouse. While most RGCs, including the ON-OFF direction-selective RGCs (ooDSGCs), are vulnerable to axonal injury, a small population of RGCs, including the intrinsically photosensitive RGCs (ipRGCs), are more resilient. Results By performing systematic analyses on RNA-sequencing data, here we identify lncRNAs that are expressed in ooDSGCs and ipRGCs with and without axonal injury. Our results reveal a repertoire of different classes of lncRNAs, including long intergenic noncoding RNAs and antisense ncRNAs that are differentially expressed between these RGC types. Strikingly, we also found dozens of lncRNAs whose expressions are altered markedly in response to axonal injury, some of which are expressed exclusively in either one of the types. Moreover, analyses into these lncRNAs unraveled their neighboring coding genes, many of which encode transcription factors and signaling molecules, suggesting that these lncRNAs may act in cis to regulate important biological processes in these neurons. Lastly, guilt-by-association analysis showed that lncRNAs are correlated with apoptosis associated genes, suggesting potential roles for these lncRNAs in RGC survival. Conclusions Overall, the results of this study reveal RGC type-specific expression of lncRNAs and provide a foundation for future investigation of the function of lncRNAs in regulating neuronal type specification and survival.

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