Electrospun poly(ε-caprolactone)/poly(glycerol sebacate) aligned fibers fabricated with benign solvents for tendon tissue engineering.

The electrospinning technique is a commonly employed approach to fabricate fibers intended for various tissue engineering applications. The aim of this study is to develop a novel strategy for tendon repair through the use of aligned poly(ε-caprolactone) (PCL) and poly(glycerol sebacate) (PGS) fibers fabricated in benign solvents, and further explore the potential application of PGS in tendon tissue engineering (TTE). The fibers were characterized for their morphological and physicochemical properties; amniotic epithelial stem cells (AECs) were used to assess the fibers teno-inductive and immunomodulatory potential due to their ability to teno-differentiate undergoing first a stepwise epithelial to mesenchymal transition, and due to their documented therapeutic role in tendon regeneration. The addition of PGS to PCL improved the spinnability of the polymer solution, as well as the uniformity and directionality of the so-obtained fibers. The mechanical properties were in the range of most TTE applications, specifically in the case of PCL/PGS 4:1 and 2:1 ratios. Compared to PCL alone, the same ratios also allowed a better AECs infiltration and growth over 7 days of culture, and triggered the activation of tendon-related genes (SCX, COL1, TNMD) and the expression of tenomodulin (TNMD) at the protein level. Concerning the immunomodulatory properties, both PCL and PCL/PGS fibers negatively affected the immunomodulatory profile of AECs, up-regulating both anti-inflammatory (IL-10) and pro-inflammatory (IL-12) cytokines over 7 days of culture. Overall, PCL/PGS 2:1 fibers fabricated with benign solvents proved to be the most suitable composition for TTE application based on their topographical cues, mechanical properties, biocompatibility, and teno-inductive properties.

An unbiased lncRNA dropout CRISPR-Cas9 screen reveals RP11-350G8.5 as a novel therapeutic target for multiple myeloma.

ABSTRACT: Multiple myeloma (MM) is an incurable malignancy characterized by altered expression of coding and noncoding genes promoting tumor growth and drug resistance. Although the crucial role of long noncoding RNAs (lncRNAs) in MM is clearly established, the function of the noncoding RNAome, which might allow the design of novel therapeutics, is largely unknown. We performed an unbiased CRISPR-Cas9 loss-of-function screen of 671 lncRNAs in MM cells and their bortezomib (BZB)-resistant derivative. To rank functionally and clinically relevant candidates, we designed and used a bioinformatic prioritization pipeline combining functional data from cellular screens with prognostic and transcriptional data from patients with MM. With this approach, we unveiled and prioritized 8 onco-lncRNAs essential for MM cell fitness, associated with high expression and poor prognosis in patients with MM. The previously uncharacterized RP11-350G8.5 emerged as the most promising target, irrespective of BZB resistance. We (1) demonstrated the anti-tumoral effect obtained by RP11-350G8.5 inhibition in vitro and in vivo; (2) highlighted a modulation of the unfolded protein response and the induction of immunogenic cell death triggered by the RP11-350G8.5 knockout, via RNA sequencing and molecular studies; (3) characterized its cytoplasmic homing through RNA fluorescence in situ hybridization; and (4) predicted its 2-dimensional structure and identified 2 G-quadruplex and 3 hairpin-forming regions by biophysical assays, including thioflavin T, 1H nuclear magnetic resonance, and circular dichroism, to pave the way to the development of novel targeted therapeutics. Overall, we provided innovative insights about unexplored lncRNAs in MM and identified RP11-350G8.5 as an oncogenic target for treatment-naïve and BZB-resistant patients with MM.

Impact of morphological features and chemical composition of tendon biomimetic scaffolds on immune recognition via Toll-like receptors.

Tendinopathies are a major worldwide clinical problem. The development of tendon biomimetic scaffolds is considered a promising, therapeutic approach. However, to be clinically effective, scaffolds should avoid immunological recognition. It has been well described that scaffolds composed of aligned fibers lead to a better tenocyte differentiation, vitality, proliferation and motility. However, little has been studied regarding the impact of fiber spatial distribution on the recognition by immune cells. Additionally, it has been suggested that higher hydrophilicity would reduce their immune recognition. Herein, polycaprolactone (PCL)-hyaluronic acid (HA)-based electrospun scaffolds were generated with different fiber diameters (in the nano- and micro-scales) and orientations as well as different grades of wettability and the impact of these properties on immunological recognition has been assessed, by means of Toll-like receptor (TLR) reporter cells. Our results showed that TLR 2/1 and TLR 2/6 were not triggered by the scaffolds. In addition, the TLR 4 signalling pathway seems to be triggered to a greater extent by higher PCL and HA concentrations, but the alignment of the fibers prevents the triggering of this receptor. Taken together, TLR reporter cells were shown to be a useful and effective tool to study the potential of scaffolds to induce immune responses and the results obtained can be used to inform the design of fibrous scaffolds for tendon repair.

Distinct genetic liability profiles define clinically relevant patient strata across common diseases.

Stratified medicine holds great promise to tailor treatment to the needs of individual patients. While genetics holds great potential to aid patient stratification, it remains a major challenge to operationalize complex genetic risk factor profiles to deconstruct clinical heterogeneity. Contemporary approaches to this problem rely on polygenic risk scores (PRS), which provide only limited clinical utility and lack a clear biological foundation. To overcome these limitations, we develop the CASTom-iGEx approach to stratify individuals based on the aggregated impact of their genetic risk factor profiles on tissue specific gene expression levels. The paradigmatic application of this approach to coronary artery disease or schizophrenia patient cohorts identified diverse strata or biotypes. These biotypes are characterized by distinct endophenotype profiles as well as clinical parameters and are fundamentally distinct from PRS based groupings. In stark contrast to the latter, the CASTom-iGEx strategy discovers biologically meaningful and clinically actionable patient subgroups, where complex genetic liabilities are not randomly distributed across individuals but rather converge onto distinct disease relevant biological processes. These results support the notion of different patient biotypes characterized by partially distinct pathomechanisms. Thus, the universally applicable approach presented here has the potential to constitute an important component of future personalized medicine paradigms.

A benchmark of computational methods for correcting biases of established and unknown origin in CRISPR-Cas9 screening data.

BACKGROUND: CRISPR-Cas9 dropout screens are formidable tools for investigating biology with unprecedented precision and scale. However, biases in data lead to potential confounding effects on interpretation and compromise overall quality. The activity of Cas9 is influenced by structural features of the target site, including copy number amplifications (CN bias). More worryingly, proximal targeted loci tend to generate similar gene-independent responses to CRISPR-Cas9 targeting (proximity bias), possibly due to Cas9-induced whole chromosome-arm truncations or other genomic structural features and different chromatin accessibility levels. RESULTS: We benchmarked eight computational methods, rigorously evaluating their ability to reduce both CN and proximity bias in the two largest publicly available cell-line-based CRISPR-Cas9 screens to date. We also evaluated the capability of each method to preserve data quality and heterogeneity by assessing the extent to which the processed data allows accurate detection of true positive essential genes, established oncogenetic addictions, and known/novel biomarkers of cancer dependency. Our analysis sheds light on the ability of each method to correct biases under different scenarios. AC-Chronos outperforms other methods in correcting both CN and proximity biases when jointly processing multiple screens of models with available CN information, whereas CRISPRcleanR is the top performing method for individual screens or when CN information is not available. In addition, Chronos and AC-Chronos yield a final dataset better able to recapitulate known sets of essential and non-essential genes. CONCLUSIONS: Overall, our investigation provides guidance for the selection of the most appropriate bias-correction method, based on its strengths, weaknesses and experimental settings.

The role of a mindful movement-based program (Movimento Biologico) in health promotion: results of a pre-post intervention study.

Introduction: Mindful movement is a comprehensive approach that integrates various bodily, emotional and cognitive aspects into physical activity, promoting overall well-being. This study assessed the impact of a mindful movement program, known as Movimento Biologico (MB), on participants psychological well-being (PWB), positive mental health (PMH), sense of coherence (SOC), and interoceptive awareness. Methods: MB program was conducted for students attending the bachelor's degree in Kinesiology and Sport Sciences of University of Perugia over 8 weeks (from October 16 to November 27, 2022). Participants were requested to fill in four questionnaires before and after the MB program: (1) 18-item PWB scale; (2) 9-item PMH scale; (3) 13-item SOC scale; (4) 32-item scale for Multidimensional Assessment of Interoceptive Awareness (MAIA). Wilcoxon signed-rank tests were used to assess changes, with significance set at p < 0.05. Results: Thirty-eight students (mean age 21.2, 60.5% male) participated. Several MAIA subscales, including noticing (p = 0.003), attention management (p = 0.002), emotional awareness (p = 0.007), self-regulation (p < 0.001), body listening (p = 0.001), and trusting (p = 0.001), showed significant improvements. PMH increased significantly (p = 0.015), and there was a significant enhancement in the autonomy subscale of PWB (p = 0.036). SOC and overall PWB also improved, though not significantly. Conclusion: The MB program significantly improved participants' positive mental health and interoceptive awareness. This likely resulted from better recognition and management of positive physiological sensations, a stronger link between physical sensations and emotions, enhanced confidence in one's body, and increased autonomy.

Benchmark Software and Data for Evaluating CRISPR-Cas9 Experimental Pipelines Through the Assessment of a Calibration Screen.

Genome-wide genetic screens using CRISPR-guide RNA libraries are widely performed in mammalian cells to functionally characterize individual genes and for the discovery of new anticancer therapeutic targets. As the effectiveness of such powerful and precise tools for cancer pharmacogenomics is emerging, tools and methods for their quality assessment are becoming increasingly necessary. Here, we provide an R package and a high-quality reference data set for the assessment of novel experimental pipelines through which a single calibration experiment has been executed: a screen of the HT-29 human colorectal cancer cell line with a commercially available genome-wide library of single-guide RNAs. This package and data allow experimental researchers to benchmark their screens and produce a quality-control report, encompassing several quality and validation metrics. The R code used for processing the reference data set, for its quality assessment, as well as to evaluate the quality of a user-provided screen, and to reproduce the figures presented in this article is available at https://github.com/DepMap-Analytics/HT29benchmark. The reference data is publicly available on FigShare.

A comprehensive clinically informed map of dependencies in cancer cells and framework for target prioritization.

Genetic screens in cancer cell lines inform gene function and drug discovery. More comprehensive screen datasets with multi-omics data are needed to enhance opportunities to functionally map genetic vulnerabilities. Here, we construct a second-generation map of cancer dependencies by annotating 930 cancer cell lines with multi-omic data and analyze relationships between molecular markers and cancer dependencies derived from CRISPR-Cas9 screens. We identify dependency-associated gene expression markers beyond driver genes, and observe many gene addiction relationships driven by gain of function rather than synthetic lethal effects. By combining clinically informed dependency-marker associations with protein-protein interaction networks, we identify 370 anti-cancer priority targets for 27 cancer types, many of which have network-based evidence of a functional link with a marker in a cancer type. Mapping these targets to sequenced tumor cohorts identifies tractable targets in different cancer types. This target prioritization map enhances understanding of gene dependencies and identifies candidate anti-cancer targets for drug development.

RAGE engagement by SARS-CoV-2 enables monocyte infection and underlies COVID-19 severity.

The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has fueled the COVID-19 pandemic with its enduring medical and socioeconomic challenges because of subsequent waves and long-term consequences of great concern. Here, we chart the molecular basis of COVID-19 pathogenesis by analyzing patients' immune responses at single-cell resolution across disease course and severity. This approach confirms cell subpopulation-specific dysregulation in COVID-19 across disease course and severity and identifies a severity-associated activation of the receptor for advanced glycation endproducts (RAGE) pathway in monocytes. In vitro THP1-based experiments indicate that monocytes bind the SARS-CoV-2 S1-receptor binding domain (RBD) via RAGE, pointing to RAGE-Spike interaction enabling monocyte infection. Thus, our results demonstrate that RAGE is a functional receptor of SARS-CoV-2 contributing to COVID-19 severity.

Processing and characterization of aligned electrospun gelatin/polycaprolactone nanofiber mats incorporating borate glass (13-93B3) microparticles.

Aligned biodegradable fibers incorporating bioactive glass particles are being highly investigated for tissue engineering applications. In this study, 5, 7 and 10 wt% melt-derived 1393B3 borate glass (BG) microparticles (average size: 3.15 µm) were incorporated in 83 wt% polycaprolactone (PCL) and 17 wt% gelatin (GEL) (83PCL/17GEL) solutions to produce aligned electrospun composite nanofiber mats. Addition of 5 wt% BG particles significantly increased the alignment of the nanofibers. However, further incorporation of BG particles led to reduced degree of alignment, likely due to an increase of viscosity. Mechanical tests indicated a tensile modulus and tensile strength of approximately 51 MPa and 3.4 MPa, respectively, for 5 wt% addition of 1393B3 BG microparticles, values considered suitable for soft tissue engineering applications. However, with the increasing amount of 1393B3 BG, the nanofiber mats became brittle. Contact angle was reduced after the addition of 5 wt% of 1393B3 BG particles from∼45° to∼39°. Cell culture studies with normal human dermal fibroblast (NHDF) cells indicated that 5 wt% 1393B3 BG incorporated nanofiber mats were cytocompatible whereas higher doping with 1393B3 BGs reduced biocompatibility. Overall, 5 wt% 1393B3 BG doped PCL/GEL nanofiber mats were aligned with high biocompatibility exhibiting desirable mechanical properties for soft tissue engineering, which indicates their potential for applications requiring aligned nanofibers, such as peripheral neural regeneration.

Highlights from the 1st European cancer dependency map symposium and workshop.

The systematic identification of tumour vulnerabilities through perturbational experiments on cancer models, including genome editing and drug screens, is playing a crucial role in combating cancer. This collective effort is known as the Cancer Dependency Map (DepMap). The 1st European Cancer Dependency Map Symposium (EuroDepMap), held in Milan last May, featured talks, a roundtable discussion, and a poster session, showcasing the latest discoveries and future challenges related to the DepMap. The symposium aimed to facilitate interactions among participants across Europe, encourage idea exchange with leading experts, and present their work and future projects. Importantly, it sparked discussions on future endeavours, such as screening more complex cancer models and accounting for tumour evolution.

Distinct genetic liability profiles define clinically relevant patient strata across common diseases.

Genome-wide association studies have unearthed a wealth of genetic associations across many complex diseases. However, translating these associations into biological mechanisms contributing to disease etiology and heterogeneity has been challenging. Here, we hypothesize that the effects of disease-associated genetic variants converge onto distinct cell type specific molecular pathways within distinct subgroups of patients. In order to test this hypothesis, we develop the CASTom-iGEx pipeline to operationalize individual level genotype data to interpret personal polygenic risk and identify the genetic basis of clinical heterogeneity. The paradigmatic application of this approach to coronary artery disease and schizophrenia reveals a convergence of disease associated variant effects onto known and novel genes, pathways, and biological processes. The biological process specific genetic liabilities are not equally distributed across patients. Instead, they defined genetically distinct groups of patients, characterized by different profiles across pathways, endophenotypes, and disease severity. These results provide further evidence for a genetic contribution to clinical heterogeneity and point to the existence of partially distinct pathomechanisms across patient subgroups. Thus, the universally applicable approach presented here has the potential to constitute an important component of future personalized medicine concepts.

An interactive web application for processing, correcting, and visualizing genome-wide pooled CRISPR-Cas9 screens.

A limitation of pooled CRISPR-Cas9 screens is the high false-positive rate in detecting essential genes arising from copy-number-amplified genomics regions. To solve this issue, we previously developed CRISPRcleanR: a computational method implemented as R/python package and in a dockerized version. CRISPRcleanR detects and corrects biased responses to CRISPR-Cas9 targeting in an unsupervised fashion, accurately reducing false-positive signals while maintaining sensitivity in identifying relevant genetic dependencies. Here, we present CRISPRcleanR WebApp , a web application enabling access to CRISPRcleanR through an intuitive interface. CRISPRcleanR WebApp removes the complexity of R/python language user interactions; provides user-friendly access to a complete analytical pipeline, not requiring any data pre-processing and generating gene-level summaries of essentiality with associated statistical scores; and offers a range of interactively explorable plots while supporting a more comprehensive range of CRISPR guide RNAs' libraries than the original package. CRISPRcleanR WebApp is available at https://crisprcleanr-webapp.fht.org/.

A heuristic algorithm solving the mutual-exclusivity-sorting problem.

MOTIVATION: Binary (or Boolean) matrices provide a common effective data representation adopted in several domains of computational biology, especially for investigating cancer and other human diseases. For instance, they are used to summarize genetic aberrations-copy number alterations or mutations-observed in cancer patient cohorts, effectively highlighting combinatorial relations among them. One of these is the tendency for two or more genes not to be co-mutated in the same sample or patient, i.e. a mutual-exclusivity trend. Exploiting this principle has allowed identifying new cancer driver protein-interaction networks and has been proposed to design effective combinatorial anti-cancer therapies rationally. Several tools exist to identify and statistically assess mutual-exclusive cancer-driver genomic events. However, these tools need to be equipped with robust/efficient methods to sort rows and columns of a binary matrix to visually highlight possible mutual-exclusivity trends. RESULTS: Here, we formalize the mutual-exclusivity-sorting problem and present MutExMatSorting: an R package implementing a computationally efficient algorithm able to sort rows and columns of a binary matrix to highlight mutual-exclusivity patterns. Particularly, our algorithm minimizes the extent of collective vertical overlap between consecutive non-zero entries across rows while maximizing the number of adjacent non-zero entries in the same row. Here, we demonstrate that existing tools for mutual-exclusivity analysis are suboptimal according to these criteria and are outperformed by MutExMatSorting. AVAILABILITY AND IMPLEMENTATION: https://github.com/AleVin1995/MutExMatSorting. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Phenotypic plasticity and genetic control in colorectal cancer evolution.

Genetic and epigenetic variation, together with transcriptional plasticity, contribute to intratumour heterogeneity1. The interplay of these biological processes and their respective contributions to tumour evolution remain unknown. Here we show that intratumour genetic ancestry only infrequently affects gene expression traits and subclonal evolution in colorectal cancer (CRC). Using spatially resolved paired whole-genome and transcriptome sequencing, we find that the majority of intratumour variation in gene expression is not strongly heritable but rather 'plastic'. Somatic expression quantitative trait loci analysis identified a number of putative genetic controls of expression by cis-acting coding and non-coding mutations, the majority of which were clonal within a tumour, alongside frequent structural alterations. Consistently, computational inference on the spatial patterning of tumour phylogenies finds that a considerable proportion of CRCs did not show evidence of subclonal selection, with only a subset of putative genetic drivers associated with subclone expansions. Spatial intermixing of clones is common, with some tumours growing exponentially and others only at the periphery. Together, our data suggest that most genetic intratumour variation in CRC has no major phenotypic consequence and that transcriptional plasticity is, instead, widespread within a tumour.

Reduced gene templates for supervised analysis of scale-limited CRISPR-Cas9 fitness screens.

Pooled genome-wide CRISPR-Cas9 screens are furthering our mechanistic understanding of human biology and have allowed us to identify new oncology therapeutic targets. Scale-limited CRISPR-Cas9 screens-typically employing guide RNA libraries targeting subsets of functionally related genes, biological pathways, or portions of the druggable genome-constitute an optimal setting for investigating narrow hypotheses and are easier to execute on complex models, such as organoids and in vivo models. Different supervised methods are used for computational analysis of genome-wide CRISPR-Cas9 screens; most are not well suited for scale-limited screens, as they require large sets of positive/negative control genes (gene templates) to be included among the screened ones. Here, we develop a computational framework identifying optimal subsets of known essential and nonessential genes (at different subsampling percentages) that can be used as templates for supervised analyses of scale-limited CRISPR-Cas9 screens, while having a reduced impact on the size of the employed library.

Computational estimation of quality and clinical relevance of cancer cell lines.

Immortal cancer cell lines (CCLs) are the most widely used system for investigating cancer biology and for the preclinical development of oncology therapies. Pharmacogenomic and genome-wide editing screenings have facilitated the discovery of clinically relevant gene-drug interactions and novel therapeutic targets via large panels of extensively characterised CCLs. However, tailoring pharmacological strategies in a precision medicine context requires bridging the existing gaps between tumours and in vitro models. Indeed, intrinsic limitations of CCLs such as misidentification, the absence of tumour microenvironment and genetic drift have highlighted the need to identify the most faithful CCLs for each primary tumour while addressing their heterogeneity, with the development of new models where necessary. Here, we discuss the most significant limitations of CCLs in representing patient features, and we review computational methods aiming at systematically evaluating the suitability of CCLs as tumour proxies and identifying the best patient representative in vitro models. Additionally, we provide an overview of the applications of these methods to more complex models and discuss future machine-learning-based directions that could resolve some of the arising discrepancies.

Negative-Pressure Wound Therapy for Prevention of Sternal Wound Infection after Adult Cardiac Surgery: Systematic Review and Meta-Analysis.

The results of current studies are not conclusive on the efficacy of incisional negative-pressure wound therapy (NPWT) for the prevention of sternal wound infection (SWI) after adult cardiac surgery. A systematic review of the literature was performed through PubMed, Scopus and Google to identify studies which investigated the efficacy of NPWT to prevent SWI after adult cardiac surgery. Available data were pooled using RevMan and Meta-analyst with random effect models. Out of 191 studies retrieved from the literature, ten fulfilled the inclusion criteria and were included in this analysis. The quality of these studies was judged fair for three of them and poor for seven studies. Only one study was powered to address the efficacy of NPWT for the prevention of postoperative SWI. Pooled analysis of these studies showed that NPWT was associated with lower risk of any SWI (4.5% vs. 9.0%, RR 0.54, 95% CI 0.34-0.84, I2 48%), superficial SWI (3.8% vs. 4.4%, RR 0.63, 95% CI 0.29-1.36, I2 65%), and deep SWI (1.8% vs. 4.7%, RR 0.46, 95% CI 0.26-0.74, I2 0%), but such a difference was not statistically significant for superficial SWI. When only randomized and alternating allocated studies were included, NPWT was associated with a significantly lower risk of any SWI (3.3% vs. 16.5%, RR 0.22, 95% CI 0.08-0.62, I2 0%), superficial SWI (2.6% vs. 12.4%, RR 0.21, 95% CI 0.06-0.69, I2 0%), and deep SWI (1.2% vs. 4.8%, RR 0.17, 95% CI 0.03-0.95, I2 0%). This pooled analysis showed that NPWT may prevent postoperative SWI after adult cardiac surgery. NPWT is expected to be particularly useful in patients at risk for surgical site infection and may significantly reduce the burden of resources needed to treat such a complication. However, the methodology of the available studies was judged as poor for most of them. Further studies are needed to obtain conclusive results on the potential benefits of this preventative strategy.