Auto-regulation of Secretory Flux by Sensing and Responding to the Folded Cargo Protein Load in the Endoplasmic Reticulum.

Maintaining the optimal performance of cell processes and organelles is the task of auto-regulatory systems. Here we describe an auto-regulatory device that helps to maintain homeostasis of the endoplasmic reticulum (ER) by adjusting the secretory flux to the cargo load. The cargo-recruiting subunit of the coatomer protein II (COPII) coat, Sec24, doubles as a sensor of folded cargo and, upon cargo binding, acts as a guanine nucleotide exchange factor to activate the signaling protein Gα12 at the ER exit sites (ERESs). This step, in turn, activates a complex signaling network that activates and coordinates the ER export machinery and attenuates proteins synthesis, thus preventing large fluctuations of folded and potentially active cargo that could be harmful to the cell or the organism. We call this mechanism AREX (autoregulation of ER export) and expect that its identification will aid our understanding of human physiology and diseases that develop from secretory dysfunction.

Light from Afield: Fast, High-Resolution, and Layer-Free Deep Vat 3D Printing.

Harnessing light for cross-linking of photoresponsive materials has revolutionized the field of 3D printing. A wide variety of techniques leveraging broad-spectrum light shaping have been introduced as a way to achieve fast and high-resolution printing, with applications ranging from simple prototypes to biomimetic engineered tissues for regenerative medicine. Conventional light-based printing techniques use cross-linking of material in a layer-by-layer fashion to produce complex parts. Only recently, new techniques have emerged which deploy multidirection, tomographic, light-sheet or filamented light-based image projections deep into the volume of resin-filled vat for photoinitiation and cross-linking. These Deep Vat printing (DVP) approaches alleviate the need for layer-wise printing and enable unprecedented fabrication speeds (within a few seconds) with high resolution (>10 µm). Here, we elucidate the physics and chemistry of these processes, their commonalities and differences, as well as their emerging applications in biomedical and non-biomedical fields. Importantly, we highlight their limitations, and future scope of research that will improve the scalability and applicability of these DVP techniques in a wide variety of engineering and regenerative medicine applications.

Quantifying heterogeneity to drug response in cancer-stroma kinetics.

A crucial challenge in medicine is choosing which drug (or combination) will be the most advantageous for a particular patient. Usually, drug response rates differ substantially, and the reasons for this response unpredictability remain ambiguous. Consequently, it is central to classify features that contribute to the observed drug response variability. Pancreatic cancer is one of the deadliest cancers with limited therapeutic achievements due to the massive presence of stroma that generates an environment that enables tumor growth, metastasis, and drug resistance. To understand the cancer-stroma cross talk within the tumor microenvironment and to develop personalized adjuvant therapies, there is a necessity for effective approaches that offer measurable data to monitor the effect of drugs at the single-cell level. Here, we develop a computational approach, based on cell imaging, that quantifies the cellular cross talk between pancreatic tumor cells (L3.6pl or AsPC1) and pancreatic stellate cells (PSCs), coordinating their kinetics in presence of the chemotherapeutic agent gemcitabine. We report significant heterogeneity in the organization of cellular interactions in response to the drug. For L3.6pl cells, gemcitabine sensibly decreases stroma-stroma interactions but increases stroma-cancer interactions, overall enhancing motility and crowding. In the AsPC1 case, gemcitabine promotes the interactions among tumor cells, but it does not affect stroma-cancer interplay, possibly suggesting a milder effect of the drug on cell dynamics.

GRASP55 regulates intra-Golgi localization of glycosylation enzymes to control glycosphingolipid biosynthesis.

The Golgi apparatus, the main glycosylation station of the cell, consists of a stack of discontinuous cisternae. Glycosylation enzymes are usually concentrated in one or two specific cisternae along the cis-trans axis of the organelle. How such compartmentalized localization of enzymes is achieved and how it contributes to glycosylation are not clear. Here, we show that the Golgi matrix protein GRASP55 directs the compartmentalized localization of key enzymes involved in glycosphingolipid (GSL) biosynthesis. GRASP55 binds to these enzymes and prevents their entry into COPI-based retrograde transport vesicles, thus concentrating them in the trans-Golgi. In genome-edited cells lacking GRASP55, or in cells expressing mutant enzymes without GRASP55 binding sites, these enzymes relocate to the cis-Golgi, which affects glycosphingolipid biosynthesis by changing flux across metabolic branch points. These findings reveal a mechanism by which a matrix protein regulates polarized localization of glycosylation enzymes in the Golgi and controls competition in glycan biosynthesis.

Golgi maturation-dependent glycoenzyme recycling controls glycosphingolipid biosynthesis and cell growth via GOLPH3.

Glycosphingolipids are important components of the plasma membrane where they modulate the activities of membrane proteins including signalling receptors. Glycosphingolipid synthesis relies on competing reactions catalysed by Golgi-resident enzymes during the passage of substrates through the Golgi cisternae. The glycosphingolipid metabolic output is determined by the position and levels of the enzymes within the Golgi stack, but the mechanisms that coordinate the intra-Golgi localisation of the enzymes are poorly understood. Here, we show that a group of sequentially-acting enzymes operating at the branchpoint among glycosphingolipid synthetic pathways binds the Golgi-localised oncoprotein GOLPH3. GOLPH3 sorts these enzymes into vesicles for intra-Golgi retro-transport, acting as a component of the cisternal maturation mechanism. Through these effects, GOLPH3 controls the sub-Golgi localisation and the lysosomal degradation rate of specific enzymes. Increased GOLPH3 levels, as those observed in tumours, alter glycosphingolipid synthesis and plasma membrane composition thereby promoting mitogenic signalling and cell proliferation. These data have medical implications as they outline a novel oncogenic mechanism of action for GOLPH3 based on glycosphingolipid metabolism.

Metric Learning in Histopathological Image Classification: Opening the Black Box.

The application of machine learning techniques to histopathology images enables advances in the field, providing valuable tools that can speed up and facilitate the diagnosis process. The classification of these images is a relevant aid for physicians who have to process a large number of images in long and repetitive tasks. This work proposes the adoption of metric learning that, beyond the task of classifying images, can provide additional information able to support the decision of the classification system. In particular, triplet networks have been employed to create a representation in the embedding space that gathers together images of the same class while tending to separate images with different labels. The obtained representation shows an evident separation of the classes with the possibility of evaluating the similarity and the dissimilarity among input images according to distance criteria. The model has been tested on the BreakHis dataset, a reference and largely used dataset that collects breast cancer images with eight pathology labels and four magnification levels. Our proposed classification model achieves relevant performance on the patient level, with the advantage of providing interpretable information for the obtained results, which represent a specific feature missed by the all the recent methodologies proposed for the same purpose.

Glycosphingolipid metabolic reprogramming drives neural differentiation.

Neural development is accomplished by differentiation events leading to metabolic reprogramming. Glycosphingolipid metabolism is reprogrammed during neural development with a switch from globo- to ganglio-series glycosphingolipid production. Failure to execute this glycosphingolipid switch leads to neurodevelopmental disorders in humans, indicating that glycosphingolipids are key players in this process. Nevertheless, both the molecular mechanisms that control the glycosphingolipid switch and its function in neurodevelopment are poorly understood. Here, we describe a self-contained circuit that controls glycosphingolipid reprogramming and neural differentiation. We find that globo-series glycosphingolipids repress the epigenetic regulator of neuronal gene expression AUTS2. AUTS2 in turn binds and activates the promoter of the first and rate-limiting ganglioside-producing enzyme GM3 synthase, thus fostering the synthesis of gangliosides. By this mechanism, the globo-AUTS2 axis controls glycosphingolipid reprogramming and neural gene expression during neural differentiation, which involves this circuit in neurodevelopment and its defects in neuropathology.

Guiding Lights: Tissue Bioprinting Using Photoactivated Materials.

Photoactivated materials have found widespread use in biological and medical applications and are playing an increasingly important role in the nascent field of three-dimensional (3D) bioprinting. Light can be used as a trigger to drive the formation or the degradation of chemical bonds, leading to unprecedented spatiotemporal control over a material's chemical, physical, and biological properties. With resolution and construct size ranging from nanometers to centimeters, light-mediated biofabrication allows multicellular and multimaterial approaches. It promises to be a powerful tool to mimic the complex multiscale organization of living tissues including skin, bone, cartilage, muscle, vessels, heart, and liver, among others, with increasing organotypic functionality. With this review, we comprehensively discuss photochemical reactions, photoactivated materials, and their use in state-of-the-art deposition-based (extrusion and droplet) and vat polymerization-based (one- and two-photon) bioprinting. By offering an up-to-date view on these techniques, we identify emerging trends, focusing on both the chemistry and instrument aspects, thereby allowing the readers to select the best-suited approach. Starting with photochemical reactions and photoactivated materials, we then discuss principles, applications, and limitations of each technique. With a critical eye to the most recent achievements, the reader is guided through this exciting, emerging field, with special emphasis on cell-laden hydrogel constructs.

Sphingolipid metabolic flow controls phosphoinositide turnover at the trans-Golgi network.

Sphingolipids are membrane lipids globally required for eukaryotic life. The sphingolipid content varies among endomembranes with pre- and post-Golgi compartments being poor and rich in sphingolipids, respectively. Due to this different sphingolipid content, pre- and post-Golgi membranes serve different cellular functions. The basis for maintaining distinct subcellular sphingolipid levels in the presence of membrane trafficking and metabolic fluxes is only partially understood. Here, we describe a homeostatic regulatory circuit that controls sphingolipid levels at the trans-Golgi network (TGN). Specifically, we show that sphingomyelin production at the TGN triggers a signalling pathway leading to PtdIns(4)P dephosphorylation. Since PtdIns(4)P is required for cholesterol and sphingolipid transport to the trans-Golgi network, PtdIns(4)P consumption interrupts this transport in response to excessive sphingomyelin production. Based on this evidence, we envisage a model where this homeostatic circuit maintains a constant lipid composition in the trans-Golgi network and post-Golgi compartments, thus counteracting fluctuations in the sphingolipid biosynthetic flow.

Highly Sensitive Fluorescent pH Microsensors Based on the Ratiometric Dye Pyranine Immobilized on Silica Microparticles.

Pyranine (HPTS) is a remarkably interesting pH-sensitive dye that has been used for plenty of applications. Its high quantum yield and extremely sensitive ratiometric fluorescence against pH change makes it a very favorable for pH-sensing applications and the development of pH nano-/microsensors. However, its strong negative charge and lack of easily modifiable functional groups makes it difficult to use with charged substrates such as silica. This study reports a methodology for noncovalent HPTS immobilization on silica microparticles that considers the retention of pH sensitivity as well as the long-term stability of the pH microsensors. The study emphasizes the importance of surface charge for governing the sensitivity of the immobilized HPTS dye molecules on silica microparticles. The importance of the immobilization methodology, which preserves the sensitivity and stability of the microsensors, is also assessed.

Highly Sensitive Fluorescent pH Microsensors Based on the Ratiometric Dye Pyranine Immobilized on Silica Microparticles.

Invited for the cover of this issue are Anil Chandra, Loretta L. del Mercato and co-workers at the Institute of Nanotechnology of National Research Council and the University of Salento. The image depicts how negatively charged pH-sensitive pyranine (HPTS) molecules were successfully immobilized on silica microparticles (SMPs) without compromising the molecules' pH sensitivity. These resulting sensors can be used to measure pH in vitro and in vivo due to the cytocompatibility of HPTS molecules and SMPs. Read the full text of the article at 10.1002/chem.202101568.

CORENup: a combination of convolutional and recurrent deep neural networks for nucleosome positioning identification.

BACKGROUND: Nucleosomes wrap the DNA into the nucleus of the Eukaryote cell and regulate its transcription phase. Several studies indicate that nucleosomes are determined by the combined effects of several factors, including DNA sequence organization. Interestingly, the identification of nucleosomes on a genomic scale has been successfully performed by computational methods using DNA sequence as input data. RESULTS: In this work, we propose CORENup, a deep learning model for nucleosome identification. CORENup processes a DNA sequence as input using one-hot representation and combines in a parallel fashion a fully convolutional neural network and a recurrent layer. These two parallel levels are devoted to catching both non periodic and periodic DNA string features. A dense layer is devoted to their combination to give a final classification. CONCLUSIONS: Results computed on public data sets of different organisms show that CORENup is a state of the art methodology for nucleosome positioning identification based on a Deep Neural Network architecture. The comparisons have been carried out using two groups of datasets, currently adopted by the best performing methods, and CORENup has shown top performance both in terms of classification metrics and elapsed computation time.

miRTissue ce: extending miRTissue web service with the analysis of ceRNA-ceRNA interactions.

BACKGROUND: Non-coding RNAs include different classes of molecules with regulatory functions. The most studied are microRNAs (miRNAs) that act directly inhibiting mRNA expression or protein translation through the interaction with a miRNAs-response element. Other RNA molecules participate in the complex network of gene regulation. They behave as competitive endogenous RNA (ceRNA), acting as natural miRNA sponges to inhibit miRNA functions and modulate the expression of RNA messenger (mRNA). It became evident that understanding the ceRNA-miRNA-mRNA crosstalk would increase the functional information across the transcriptome, contributing to identify new potential biomarkers for translational medicine. RESULTS: We present miRTissue ce, an improvement of our original miRTissue web service. By introducing a novel computational pipeline, miRTissue ce provides an easy way to search for ceRNA interactions in several cancer tissue types. Moreover it extends the functionalities of previous miRTissue release about miRNA-target interaction in order to provide a complete insight about miRNA mediated regulation processes. miRTissue ce is freely available at http://tblab.pa.icar.cnr.it/mirtissue.html . CONCLUSIONS: The study of ceRNA networks and its dynamics in cancer tissue could be applied in many fields of translational biology, as the investigation of new cancer biomarker, both diagnostic and prognostic, and also in the investigation of new therapeutic strategies of intervention. In this scenario, miRTissue ce can offer a powerful instrument for the analysis and characterization of ceRNA-ceRNA interactions in different tissue types, representing a fundamental step in order to understand more complex regulation mechanisms.

BITS2019: the sixteenth annual meeting of the Italian society of bioinformatics.

The 16th Annual Meeting of the Bioinformatics Italian Society was held in Palermo, Italy, on June 26-28, 2019. More than 80 scientific contributions were presented, including 4 keynote lectures, 31 oral communications and 49 posters. Also, three workshops were organised before and during the meeting. Full papers from some of the works presented in Palermo were submitted for this Supplement of BMC Bioinformatics. Here, we provide an overview of meeting aims and scope. We also shortly introduce selected papers that have been accepted for publication in this Supplement, for a complete presentation of the outcomes of the meeting.

MiRNA therapeutics based on logic circuits of biological pathways.

BACKGROUND: In silico experiments, with the aid of computer simulation, speed up the process of in vitro or in vivo experiments. Cancer therapy design is often based on signalling pathway. MicroRNAs (miRNA) are small non-coding RNA molecules. In several kinds of diseases, including cancer, hepatitis and cardiovascular diseases, they are often deregulated, acting as oncogenes or tumor suppressors. miRNA therapeutics is based on two main kinds of molecules injection: miRNA mimics, which consists of injection of molecules that mimic the targeted miRNA, and antagomiR, which consists of injection of molecules inhibiting the targeted miRNA. Nowadays, the research is focused on miRNA therapeutics. This paper addresses cancer related signalling pathways to investigate miRNA therapeutics. RESULTS: In order to prove our approach, we present two different case studies: non-small cell lung cancer and melanoma. KEGG signalling pathways are modelled by a digital circuit. A logic value of 1 is linked to the expression of the corresponding gene. A logic value of 0 is linked to the absence (not expressed) gene. All possible relationships provided by a signalling pathway are modelled by logic gates. Mutations, derived according to the literature, are introduced and modelled as well. The modelling approach and analysis are widely discussed within the paper. MiRNA therapeutics is investigated by the digital circuit analysis. The most effective miRNA and combination of miRNAs, in terms of reduction of pathogenic conditions, are obtained. A discussion of obtained results in comparison with literature data is provided. Results are confirmed by existing data. CONCLUSIONS: The proposed study is based on drug discovery and miRNA therapeutics and uses a digital circuit simulation of a cancer pathway. Using this simulation, the most effective combination of drugs and miRNAs for mutated cancer therapy design are obtained and these results were validated by the literature. The proposed modelling and analysis approach can be applied to each human disease, starting from the corresponding signalling pathway.

The 2017 Network Tools and Applications in Biology (NETTAB) workshop: aims, topics and outcomes.

The 17th International NETTAB workshop was held in Palermo, Italy, on October 16-18, 2017. The special topic for the meeting was "Methods, tools and platforms for Personalised Medicine in the Big Data Era", but the traditional topics of the meeting series were also included in the event. About 40 scientific contributions were presented, including four keynote lectures, five guest lectures, and many oral communications and posters. Also, three tutorials were organised before and after the workshop. Full papers from some of the best works presented in Palermo were submitted for this Supplement of BMC Bioinformatics. Here, we provide an overview of meeting aims and scope. We also shortly introduce selected papers that have been accepted for publication in this Supplement, for a complete presentation of the outcomes of the meeting.

Deep learning architectures for prediction of nucleosome positioning from sequences data.

BACKGROUND: Nucleosomes are DNA-histone complex, each wrapping about 150 pairs of double-stranded DNA. Their function is fundamental for one of the primary functions of Chromatin i.e. packing the DNA into the nucleus of the Eukaryote cells. Several biological studies have shown that the nucleosome positioning influences the regulation of cell type-specific gene activities. Moreover, computational studies have shown evidence of sequence specificity concerning the DNA fragment wrapped into nucleosomes, clearly underlined by the organization of particular DNA substrings. As the main consequence, the identification of nucleosomes on a genomic scale has been successfully performed by computational methods using a sequence features representation. RESULTS: In this work, we propose a deep learning model for nucleosome identification. Our model stacks convolutional layers and Long Short-term Memories to automatically extract features from short- and long-range dependencies in a sequence. Using this model we are able to avoid the feature extraction and selection steps while improving the classification performances. CONCLUSIONS: Results computed on eleven data sets of five different organisms, from Yeast to Human, show the superiority of the proposed method with respect to the state of the art recently presented in the literature.

Deep learning models for bacteria taxonomic classification of metagenomic data.

BACKGROUND: An open challenge in translational bioinformatics is the analysis of sequenced metagenomes from various environmental samples. Of course, several studies demonstrated the 16S ribosomal RNA could be considered as a barcode for bacteria classification at the genus level, but till now it is hard to identify the correct composition of metagenomic data from RNA-seq short-read data. 16S short-read data are generated using two next generation sequencing technologies, i.e. whole genome shotgun (WGS) and amplicon (AMP); typically, the former is filtered to obtain short-reads belonging to a 16S shotgun (SG), whereas the latter take into account only some specific 16S hypervariable regions. The above mentioned two sequencing technologies, SG and AMP, are used alternatively, for this reason in this work we propose a deep learning approach for taxonomic classification of metagenomic data, that can be employed for both of them. RESULTS: To test the proposed pipeline, we simulated both SG and AMP short-reads, from 1000 16S full-length sequences. Then, we adopted a k-mer representation to map sequences as vectors into a numerical space. Finally, we trained two different deep learning architecture, i.e., convolutional neural network (CNN) and deep belief network (DBN), obtaining a trained model for each taxon. We tested our proposed methodology to find the best parameters configuration, and we compared our results against the classification performances provided by a reference classifier for bacteria identification, known as RDP classifier. We outperformed the RDP classifier at each taxonomic level with both architectures. For instance, at the genus level, both CNN and DBN reached 91.3% of accuracy with AMP short-reads, whereas RDP classifier obtained 83.8% with the same data. CONCLUSIONS: In this work, we proposed a 16S short-read sequences classification technique based on k-mer representation and deep learning architecture, in which each taxon (from phylum to genus) generates a classification model. Experimental results confirm the proposed pipeline as a valid approach for classifying bacteria sequences; for this reason, our approach could be integrated into the most common tools for metagenomic analysis. According to obtained results, it can be successfully used for classifying both SG and AMP data.

Design rules for combined label-free and fluorescence Bloch surface wave biosensors.

We report on the fabrication and physical characterization of optical biosensors implementing simultaneous label-free and fluorescence detection and taking advantage of the excitation of Bloch surface waves at a photonic crystal's truncation interface. Two types of purposely designed one-dimensional photonic crystals on molded organic substrates with micro-optics were fabricated. These crystals feature either high or low finesse of the Bloch surface wave resonances and were tested on the same optical readout system. The experimental results show that designing biochips with a large resonance quality factor does not necessarily lead in the real case to an improvement of the biosensor performance. The conditions for optimal biochip design and operation of the complete bio-sensing platform are established.

MiRNATIP: a SOM-based miRNA-target interactions predictor.

BACKGROUND: MicroRNAs (miRNAs) are small non-coding RNA sequences with regulatory functions to post-transcriptional level for several biological processes, such as cell disease progression and metastasis. MiRNAs interact with target messenger RNA (mRNA) genes by base pairing. Experimental identification of miRNA target is one of the major challenges in cancer biology because miRNAs can act as tumour suppressors or oncogenes by targeting different type of targets. The use of machine learning methods for the prediction of the target genes is considered a valid support to investigate miRNA functions and to guide related wet-lab experiments. In this paper we propose the miRNA Target Interaction Predictor (miRNATIP) algorithm, a Self-Organizing Map (SOM) based method for the miRNA target prediction. SOM is trained with the seed region of the miRNA sequences and then the mRNA sequences are projected into the SOM lattice in order to find putative interactions with miRNAs. These interactions will be filtered considering the remaining part of the miRNA sequences and estimating the free-energy necessary for duplex stability. RESULTS: We tested the proposed method by predicting the miRNA target interactions of both the Homo sapiens and the Caenorhbditis elegans species; then, taking into account validated target (positive) and non-target (negative) interactions, we compared our results with other target predictors, namely miRanda, PITA, PicTar, mirSOM, TargetScan and DIANA-microT, in terms of the most used statistical measures. We demonstrate that our method produces the greatest number of predictions with respect to the other ones, exhibiting good results for both species, reaching the for example the highest percentage of sensitivity of 31 and 30.5 %, respectively for Homo sapiens and for C. elegans. All the predicted interaction are freely available at the following url: http://tblab.pa.icar.cnr.it/public/miRNATIP/ . CONCLUSIONS: Results state miRNATIP outperforms or is comparable to the other six state-of-the-art methods, in terms of validated target and non-target interactions, respectively.