An innovative strategy to investigate microbial protein modifications in a reliable fast and sensitive way: A therapy oriented proof of concept based on UV-C irradiation of SARS-CoV-2 spike protein.

The characterization of modifications of microbial proteins is of primary importance to dissect pathogen lifecycle mechanisms and could be useful in identifying therapeutic targets. Attempts to solve this issue yielded only partial and non-exhaustive results. We developed a multidisciplinary approach by coupling in vitro infection assay, mass spectrometry (MS), protein 3D modelling, and surface plasma resonance (SPR). As a proof of concept, the effect of low UV-C (273 nm) irradiation on SARS-CoV-2 spike (S) protein was investigated. Following UV-C exposure, MS analysis identified, among other modifications, the disruption of a disulphide bond within the conserved S2 subunit of S protein. Computational analyses revealed that this bond breakage associates with an allosteric effect resulting in the generation of a closed conformation with a reduced ability to bind the ACE2 receptor. The UV-C-induced reduced affinity of S protein for ACE2 was further confirmed by SPR analyses and in vitro infection assays. This comprehensive approach pinpoints the S2 domain of S protein as a potential therapeutic target to prevent SARS-CoV-2 infection. Notably, this workflow could be used to screen a wide variety of microbial protein domains, resulting in a precise molecular fingerprint and providing new insights to adequately address future epidemics.

Devising novel performance measures for assessing the behavior of multilayer perceptrons trained on regression tasks.

This methodological article is mainly aimed at establishing a bridge between classification and regression tasks, in a frame shaped by performance evaluation. More specifically, a general procedure for calculating performance measures is proposed, which can be applied to both classification and regression models. To this end, a notable change in the policy used to evaluate the confusion matrix is made, with the goal of reporting information about regression performance therein. This policy, called generalized token sharing, allows to a) assess models trained on both classification and regression tasks, b) evaluate the importance of input features, and c) inspect the behavior of multilayer perceptrons by looking at their hidden layers. The occurrence of success and failure patterns at the hidden layers of multilayer perceptrons trained and tested on selected regression problems, together with the effectiveness of layer-wise training, is also discussed.

Framing Apache Spark in life sciences.

Advances in high-throughput and digital technologies have required the adoption of big data for handling complex tasks in life sciences. However, the drift to big data led researchers to face technical and infrastructural challenges for storing, sharing, and analysing them. In fact, this kind of tasks requires distributed computing systems and algorithms able to ensure efficient processing. Cutting edge distributed programming frameworks allow to implement flexible algorithms able to adapt the computation to the data over on-premise HPC clusters or cloud architectures. In this context, Apache Spark is a very powerful HPC engine for large-scale data processing on clusters. Also thanks to specialised libraries for working with structured and relational data, it allows to support machine learning, graph-based computation, and stream processing. This review article is aimed at helping life sciences researchers to ascertain the features of Apache Spark and to assess whether it can be successfully used in their research activities.

R2R3-MYBs in Durum Wheat: Genome-Wide Identification, Poaceae-Specific Clusters, Expression, and Regulatory Dynamics Under Abiotic Stresses.

MYB transcription factors (TFs) represent one of the biggest TF families in plants, being involved in various specific plant processes, such as responses to biotic and abiotic stresses. The implication of MYB TFs in the tolerance mechanisms to abiotic stress is particularly interesting for crop breeding, since environmental conditions can negatively affect growth and productivity. Wheat is a worldwide-cultivated cereal, and is a major source of plant-based proteins in human food. In particular, durum wheat plays an important role in global food security improvement, since its adaptation to hot and dry conditions constitutes the base for the success of wheat breeding programs in future. In the present study, a genome-wide identification of R2R3-MYB TFs in durum wheat was performed. MYB profile search and phylogenetic analyses based on homology with Arabidopsis and rice MYB TFs led to the identification of 233 R2R3-TdMYB (Triticum durum MYB). Three Poaceae-specific MYB clusters were detected, one of which had never been described before. The expression of eight selected genes under different abiotic stress conditions, revealed that most of them responded especially to salt and drought stress. Finally, gene regulatory network analyses led to the identification of 41 gene targets for three TdR2R3-MYBs that represent novel candidates for functional analyses. This study provides a detailed description of durum wheat R2R3-MYB genes and contributes to a deeper understanding of the molecular response of durum wheat to unfavorable climate conditions.

Transcriptomic analysis of Rhodococcus opacus R7 grown on polyethylene by RNA-seq.

Plastic waste management has become a global issue. Polyethylene (PE) is the most abundant synthetic plastic worldwide, and one of the most resistant to biodegradation. Indeed, few bacteria can degrade polyethylene. In this paper, the transcriptomic analysis unveiled for the first time Rhodococcus opacus R7 complex genetic system based on diverse oxidoreductases for polyethylene biodegradation. The RNA-seq allowed uncovering genes putatively involved in the first step of oxidation. In-depth investigations through preliminary bioinformatic analyses and enzymatic assays on the supernatant of R7 grown in the presence of PE confirmed the activation of genes encoding laccase-like enzymes. Moreover, the transcriptomic data allowed identifying candidate genes for the further steps of short aliphatic chain oxidation including alkB gene encoding an alkane monooxygenase, cyp450 gene encoding cytochrome P450 hydroxylase, and genes encoding membrane transporters. The PE biodegradative system was also validated by FTIR analysis on R7 cells grown on polyethylene.

A new strategy to map landslides with a generalized convolutional neural network.

Rapid mapping of event landslides is crucial to identify the areas affected by damages as well as for effective disaster response. Traditionally, such maps are generated with visual interpretation of remote sensing imagery (manned/unmanned airborne systems or spaceborne sensors) and/or using pixel-based and object-based methods exploiting data-intensive machine learning algorithms. Recent works have explored the use of convolutional neural networks (CNN), a deep learning algorithm, for mapping landslides from remote sensing data. These methods follow a standard supervised learning workflow that involves training a model using a landslide inventory covering a relatively small area. The trained model is then used to predict landslides in the surrounding regions. Here, we propose a new strategy, i.e., a progressive CNN training relying on combined inventories to build a generalized model that can be applied directly to a new, unexplored area. We first prove the effectiveness of CNNs by training and validating on event landslides inventories in four regions after earthquakes and/or extreme meteorological events. Next, we use the trained CNNs to map landslides triggered by new events spread across different geographic regions. We found that CNNs trained on a combination of inventories have a better generalization performance, with a bias towards high precision and low recall scores. In our tests, the combined training model achieved the highest (Matthews correlation coefficient) MCC score of 0.69 when mapping landslides in new unseen regions. The mapping was done on images from different optical sensors, resampled to a spatial resolution of 6 m, 10 m, and 30 m. Despite a slightly reduced performance, the main advantage of combined training is to overcome the requirement of a local inventory for training a new deep learning model. This implementation can facilitate automated pipelines providing fast response for the generation of landslide maps in the post-disaster phase. In this study, the study areas were selected from seismically active zones with a high hydrological hazard distribution and vegetation coverage. Hence, future works should also include regions from less vegetated geographic locations.

Transcriptomic Analysis of Rhodococcus opacus R7 Grown on o-Xylene by RNA-Seq.

Xylenes are considered one of the most common hazardous sources of environmental contamination. The biodegradation of these compounds has been often reported, rarer the ability to oxidize the ortho-isomer. Among few o-xylene-degrading bacteria, Rhodococcus opacus R7 is well known for its capability to degrade diverse aromatic hydrocarbons and toxic compounds, including o-xylene as only carbon and energy source. This work shows for the first time the RNA-seq approach to elucidate the genetic determinants involved in the o-xylene degradation pathway in R. opacus R7. Transcriptomic data showed 542 differentially expressed genes that are associated with the oxidation of aromatic hydrocarbons and stress response, osmotic regulation and central metabolism. Gene ontology (GO) enrichment and KEGG pathway analysis confirmed significant changes in aromatic compound catabolic processes, fatty acid metabolism, beta-oxidation, TCA cycle enzymes, and biosynthesis of metabolites when cells are cultured in the presence of o-xylene. Interestingly, the most up-regulated genes belong to the akb gene cluster encoding for the ethylbenzene (Akb) dioxygenase system. Moreover, the transcriptomic approach allowed identifying candidate enzymes involved in R7 o-xylene degradation for their likely participation in the formation of the metabolites that have been previously identified. Overall, this approach supports the identification of several oxidative systems likely involved in o-xylene metabolism confirming that R. opacus R7 possesses a redundancy of sequences that converge in o-xylene degradation through R7 peculiar degradation pathway. This work advances our understanding of o-xylene metabolism in bacteria belonging to Rhodococcus genus and provides a framework of useful enzymes (molecular tools) that can be fruitfully targeted for optimized o-xylene consumption.

Impacts drive lunar rockfalls over billions of years.

Past exploration missions have revealed that the lunar topography is eroded through mass wasting processes such as rockfalls and other types of landslides, similar to Earth. We have analyzed an archive of more than 2 million high-resolution images using an AI and big data-driven approach and created the first global map of 136.610 lunar rockfall events. Using this map, we show that mass wasting is primarily driven by impacts and impact-induced fracture networks. We further identify a large number of currently unknown rockfall clusters, potentially revealing regions of recent seismic activity. Our observations show that the oldest, pre-Nectarian topography still hosts rockfalls, indicating that its erosion has been active throughout the late Copernican age and likely continues today. Our findings have important implications for the estimation of the Moon's erosional state and other airless bodies as well as for the understanding of the topographic evolution of planetary surfaces in general.

The Punatsangchhu-I dam landslide illuminated by InSAR multitemporal analyses.

We use multitemporal analyses based on Synthetic Aperture Radar differential interferometry (DInSAR) to study the slope adjacent to the large Punatsangchhu-I hydropower plant, a concrete gravity dam under construction in Bhutan since 2009. Several slope failures affected the site since 2013, probably as a consequence of toe undercutting of a previously unrecognised active landslide. Our results indicate that downslope displacement, likely related to the natural instability, was already visible in 2007 on various sectors of the entire valley flank. Moreover, the area with active displacements impinging on the dam site has continuously increased in size since 2007 and into 2018, even though stabilization measures have been implemented since 2013. Stabilisation measures currently only focus on a small portion of the slope, however, the unstable area is larger than previously evaluated. Highly damaged rock is present across many areas of the entire valley flank, indicating that the volumes involved may be orders of magnitude higher than the area on which stabilisation efforts have been concentrated after the 2013 failure. The results highlight that satellite-based DInSAR could be systematically used to support decision making processes in the different phases of a complex hydropower project, from the feasibility study, to the dam site selection and construction phase.

Update on the Feasibility and Progress on Robotic Breast Surgery.

BACKGROUND: Robotic nipple-sparing mastectomy (RNSM) may allow for more precise anatomic dissection and improved cosmetic outcomes over conventional open nipple-sparing mastectomy; however, data regarding the feasibility and safety of the procedure are limited. OBJECTIVE: The aim of this study was to present and discuss perioperative surgical outcomes and early oncologic follow-up data on consecutive patients undergoing RNSM from June 2014 to January 2019. METHODS: Patients underwent RNSM and immediate robotic breast reconstruction through an axillary incision at a single institution. Perioperative data, complications at 3 months postoperatively, pathological data, and adjuvant therapies were recorded. Local recurrence-free, disease-free, and overall survival were analyzed. RESULTS: Overall, 73 women underwent 94 RNSM procedures. Indications were invasive breast cancer in 39 patients, ductal carcinoma in situ in 17 patients, and BRCA mutation in 17 patients. Mean surgery time was 3 h and 32 min. One-step reconstruction with implant occurred in 89.4% of procedures. The rate of complications requiring reoperation was 4.3%, and the rate of flap or nipple necrosis was 1.1%. Median follow-up was 19 months (range 3.1-44.8). No local recurrences occurred. Overall survival at 12, 24, or 60 months was 98% (95% confidence interval 86-100%). CONCLUSION: We observed a low complication rate in 94 consecutive RNSM procedures, demonstrating the procedure is technically feasible and safe. We found no early local failures at 19 months follow-up. Long-term follow-up is needed to confirm oncologic safety. Future clinical trials to study the advantages and disadvantages of RNSM are warranted.

Durum wheat genome highlights past domestication signatures and future improvement targets.

The domestication of wild emmer wheat led to the selection of modern durum wheat, grown mainly for pasta production. We describe the 10.45 gigabase (Gb) assembly of the genome of durum wheat cultivar Svevo. The assembly enabled genome-wide genetic diversity analyses revealing the changes imposed by thousands of years of empirical selection and breeding. Regions exhibiting strong signatures of genetic divergence associated with domestication and breeding were widespread in the genome with several major diversity losses in the pericentromeric regions. A locus on chromosome 5B carries a gene encoding a metal transporter (TdHMA3-B1) with a non-functional variant causing high accumulation of cadmium in grain. The high-cadmium allele, widespread among durum cultivars but undetected in wild emmer accessions, increased in frequency from domesticated emmer to modern durum wheat. The rapid cloning of TdHMA3-B1 rescues a wild beneficial allele and demonstrates the practical use of the Svevo genome for wheat improvement.

BITS 2017: the annual meeting of the Italian Society of Bioinformatics.

This preface introduces the content of the BioMed Central journal Supplement related to the 14th annual meeting of the Bioinformatics Italian Society, held in Cagliari, Italy, from the 5th to the 7th of July, 2017.

An infrastructure for precision medicine through analysis of big data.

BACKGROUND: Nowadays, the increasing availability of omics data, due to both the advancements in the acquisition of molecular biology results and in systems biology simulation technologies, provides the bases for precision medicine. Success in precision medicine depends on the access to healthcare and biomedical data. To this end, the digitization of all clinical exams and medical records is becoming a standard in hospitals. The digitization is essential to collect, share, and aggregate large volumes of heterogeneous data to support the discovery of hidden patterns with the aim to define predictive models for biomedical purposes. Patients' data sharing is a critical process. In fact, it raises ethical, social, legal, and technological issues that must be properly addressed. RESULTS: In this work, we present an infrastructure devised to deal with the integration of large volumes of heterogeneous biological data. The infrastructure was applied to the data collected between 2010-2016 in one of the major diagnostic analysis laboratories in Italy. Data from three different platforms were collected (i.e., laboratory exams, pathological anatomy exams, biopsy exams). The infrastructure has been designed to allow the extraction and aggregation of both unstructured and semi-structured data. Data are properly treated to ensure data security and privacy. Specialized algorithms have also been implemented to process the aggregated information with the aim to obtain a precise historical analysis of the clinical activities of one or more patients. Moreover, three Bayesian classifiers have been developed to analyze examinations reported as free text. Experimental results show that the classifiers exhibit a good accuracy when used to analyze sentences related to the sample location, diseases presence and status of the illnesses. CONCLUSIONS: The infrastructure allows the integration of multiple and heterogeneous sources of anonymized data from the different clinical platforms. Both unstructured and semi-structured data are processed to obtain a precise historical analysis of the clinical activities of one or more patients. Data aggregation allows to perform a series of statistical assessments required to answer complex questions that can be used in a variety of fields, such as predictive and precision medicine. In particular, studying the clinical history of patients that have developed similar pathologies can help to predict or individuate markers able to allow an early diagnosis of possible illnesses.

Adipose Progenitor Cell Secretion of GM-CSF and MMP9 Promotes a Stromal and Immunological Microenvironment That Supports Breast Cancer Progression.

A cell population with progenitor-like phenotype (CD45-CD34+) resident in human white adipose tissue (WAT) is known to promote the progression of local and metastatic breast cancer and angiogenesis. However, the molecular mechanisms of the interaction have not been elucidated. In this study, we identified two proteins that were significantly upregulated in WAT-derived progenitors after coculture with breast cancer: granulocyte macrophage colony-stimulating factor (GM-CSF) and matrix metallopeptidase 9 (MMP9). These proteins were released by WAT progenitors in xenograft and transgenic breast cancer models. GM-CSF was identified as an upstream modulator. Breast cancer-derived GM-CSF induced GM-CSF and MMP9 release from WAT progenitors, and GM-CSF knockdown in breast cancer cells neutralized the protumorigenic activity of WAT progenitors in preclinical models. GM-CSF neutralization in diet-induced obese mice significantly reduced immunosuppression, intratumor vascularization, and local and metastatic breast cancer progression. Similarly, MMP9 inhibition reduced neoplastic angiogenesis and significantly decreased local and metastatic tumor growth. Combined GM-CSF neutralization and MMP9 inhibition synergistically reduced angiogenesis and tumor progression. High-dose metformin inhibited GM-CSF and MMP9 release from WAT progenitors in in vitro and xenograft models. In obese syngeneic mice, metformin treatment mimicked the effects observed with GM-CSF neutralization and MMP9 inhibition, suggesting these proteins as new targets for metformin. These findings support the hypothesis that GM-CSF and MMP9 promote the protumorigenic effect of WAT progenitors on local and metastatic breast cancer. Cancer Res; 77(18); 5169-82. ©2017 AACR.

Total Breast Reconstruction With Fat Grafting After Internal Expansion and Expander Removal.

INTRODUCTION: The development of fat grafting techniques both in cosmetic and reconstructive breast surgery has highlighted the need to recreate a breast mound using similar tissues and without visible scarring. Our technique combines skin expansion by conventional expanders and breast volume replacement by serial fat grafting and final expander removal. MATERIALS AND METHODS: Fat grafting had been performed both by Coleman technique and Bodyjet system. We report a series of 12 cases enrolled between 2008 and 2013. Each case has been presented in this article, focusing on received treatment, observed complications, and results. RESULTS: Satisfactory results were observed 6 months after the last session in most of the concluded reconstructions with a low complication rate.

Robotic nipple-sparing mastectomy for the treatment of breast cancer: Feasibility and safety study.

BACKGROUND: We previously devised and reported on an innovative surgical technique of robotic nipple-sparing mastectomy and immediate robotic breast reconstruction. Here we describe the outcome of the first 29 such consecutive procedures performed on breast cancer patients to assess feasibility, reproducibility and safety. METHODS: The following morbidity factors were tested: operation time, conversion rate to open technique, length of hospitalization, registration of complications for 1 year postoperatively and their characterization as either minor, major, or multiple, depending on clinical severity and treatment required. RESULTS: The total duration of the final robotic surgeries of our series was around 3 h, showing a very rapid learning curve. The conversion rate due to technical problems was 2 of the 29 procedures (6,9%). No major complications, including hematoma, seroma, skin or nipple-areola injury or necrosis or infection were observed for any case. Two patients had a small degree of blistering from internal electrocautery in the breast skin flap, both of which resolved in one week without any specific therapy. No systemic complications were observed. CONCLUSION: The low conversion rate to open surgery, the rapid learning curve and the low rate of post-operative complications observed in this preliminary series lead us to endorse a prospective study aimed at evaluating patient satisfaction.