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.

Results and lessons learned from the sbv IMPROVER metagenomics diagnostics for inflammatory bowel disease challenge.

A growing body of evidence links gut microbiota changes with inflammatory bowel disease (IBD), raising the potential benefit of exploiting metagenomics data for non-invasive IBD diagnostics. The sbv IMPROVER metagenomics diagnosis for inflammatory bowel disease challenge investigated computational metagenomics methods for discriminating IBD and nonIBD subjects. Participants in this challenge were given independent training and test metagenomics data from IBD and nonIBD subjects, which could be wither either raw read data (sub-challenge 1, SC1) or processed Taxonomy- and Function-based profiles (sub-challenge 2, SC2). A total of 81 anonymized submissions were received between September 2019 and March 2020. Most participants' predictions performed better than random predictions in classifying IBD versus nonIBD, Ulcerative Colitis (UC) versus nonIBD, and Crohn's Disease (CD) versus nonIBD. However, discrimination between UC and CD remains challenging, with the classification quality similar to the set of random predictions. We analyzed the class prediction accuracy, the metagenomics features by the teams, and computational methods used. These results will be openly shared with the scientific community to help advance IBD research and illustrate the application of a range of computational methodologies for effective metagenomic classification.

Community Detection in Protein-Protein Interaction Networks and Applications.

The ability to identify and characterize not only the protein-protein interactions but also their internal modular organization through network analysis is fundamental for understanding the mechanisms of biological processes at the molecular level. Indeed, the detection of the network communities can enhance our understanding of the molecular basis of disease pathology, and promote drug discovery and disease treatment in personalized medicine. This work gives an overview of recent computational methods for the detection of protein complexes and functional modules in protein-protein interaction networks, also providing a focus on some of its applications. We propose a systematic reformulation of frequently adopted taxonomies for these methods, also proposing new categories to keep up with the most recent research. We review the literature of the last five years (2017-2021) and provide links to existing data and software resources. Finally, we survey recent works exploiting module identification and analysis, in the context of a variety of disease processes for biomarker identification and therapeutic target detection. Our review provides the interested reader with an up-to-date and self-contained view of the existing research, with links to state-of-the-art literature and resources, as well as hints on open issues and future research directions in complex detection and its applications.

TumorMet: A repository of tumor metabolic networks derived from context-specific Genome-Scale Metabolic Models.

Studies about the metabolic alterations during tumorigenesis have increased our knowledge of the underlying mechanisms and consequences, which are important for diagnostic and therapeutic investigations. In this scenario and in the era of systems biology, metabolic networks have become a powerful tool to unravel the complexity of the cancer metabolic machinery and the heterogeneity of this disease. Here, we present TumorMet, a repository of tumor metabolic networks extracted from context-specific Genome-Scale Metabolic Models, as a benchmark for graph machine learning algorithms and network analyses. This repository has an extended scope for use in graph classification, clustering, community detection, and graph embedding studies. Along with the data, we developed and provided Met2Graph, an R package for creating three different types of metabolic graphs, depending on the desired nodes and edges: Metabolites-, Enzymes-, and Reactions-based graphs. This package allows the easy generation of datasets for downstream analysis.

Early anteroposterior regionalisation of human neural crest is shaped by a pro-mesodermal factor.

The neural crest (NC) is an important multipotent embryonic cell population and its impaired specification leads to various developmental defects, often in an anteroposterior (A-P) axial level-specific manner. The mechanisms underlying the correct A-P regionalisation of human NC cells remain elusive. Recent studies have indicated that trunk NC cells, the presumed precursors of childhood tumour neuroblastoma, are derived from neuromesodermal-potent progenitors of the postcranial body. Here we employ human embryonic stem cell differentiation to define how neuromesodermal progenitor (NMP)-derived NC cells acquire a posterior axial identity. We show that TBXT, a pro-mesodermal transcription factor, mediates early posterior NC/spinal cord regionalisation together with WNT signalling effectors. This occurs by TBXT-driven chromatin remodelling via its binding in key enhancers within HOX gene clusters and other posterior regulator-associated loci. This initial posteriorisation event is succeeded by a second phase of trunk HOX gene control that marks the differentiation of NMPs toward their TBXT-negative NC/spinal cord derivatives and relies predominantly on FGF signalling. Our work reveals a previously unknown role of TBXT in influencing posterior NC fate and points to the existence of temporally discrete, cell type-dependent modes of posterior axial identity control.

Combining evidence from Mendelian randomization and colocalization: Review and comparison of approaches.

Mendelian randomization and colocalization are two statistical approaches that can be applied to summarized data from genome-wide association studies (GWASs) to understand relationships between traits and diseases. However, despite similarities in scope, they are different in their objectives, implementation, and interpretation, in part because they were developed to serve different scientific communities. Mendelian randomization assesses whether genetic predictors of an exposure are associated with the outcome and interprets an association as evidence that the exposure has a causal effect on the outcome, whereas colocalization assesses whether two traits are affected by the same or distinct causal variants. When considering genetic variants in a single genetic region, both approaches can be performed. While a positive colocalization finding typically implies a non-zero Mendelian randomization estimate, the reverse is not generally true: there are several scenarios which would lead to a non-zero Mendelian randomization estimate but lack evidence for colocalization. These include the existence of distinct but correlated causal variants for the exposure and outcome, which would violate the Mendelian randomization assumptions, and a lack of strong associations with the outcome. As colocalization was developed in the GWAS tradition, typically evidence for colocalization is concluded only when there is strong evidence for associations with both traits. In contrast, a non-zero estimate from Mendelian randomization can be obtained despite only nominally significant genetic associations with the outcome at the locus. In this review, we discuss how the two approaches can provide complementary information on potential therapeutic targets.

Netpro2vec: A Graph Embedding Framework for Biomedical Applications.

The ever-increasing importance of structured data in different applications, especially in the biomedical field, has driven the need for reducing its complexity through projections into a more manageable space. The latest methods for learning features on graphs focus mainly on the neighborhood of nodes and edges. Methods capable of providing a representation that looks beyond the single node neighborhood are kernel graphs. However, they produce handcrafted features unaccustomed with a generalized model. To reduce this gap, in this work we propose a neural embedding framework, based on probability distribution representations of graphs, named Netpro2vec. The goal is to look at basic node descriptions other than the degree, such as those induced by the Transition Matrix and Node Distance Distribution. Netpro2vec provides embeddings completely independent from the task and nature of the data. The framework is evaluated on synthetic and various real biomedical network datasets through a comprehensive experimental classification phase and is compared to well-known competitors.

Defining the signalling determinants of a posterior ventral spinal cord identity in human neuromesodermal progenitor derivatives.

The anteroposterior axial identity of motor neurons (MNs) determines their functionality and vulnerability to neurodegeneration. Thus, it is a crucial parameter in the design of strategies aiming to produce MNs from human pluripotent stem cells (hPSCs) for regenerative medicine/disease modelling applications. However, the in vitro generation of posterior MNs corresponding to the thoracic/lumbosacral spinal cord has been challenging. Although the induction of cells resembling neuromesodermal progenitors (NMPs), the bona fide precursors of the spinal cord, offers a promising solution, the progressive specification of posterior MNs from these cells is not well defined. Here, we determine the signals guiding the transition of human NMP-like cells toward thoracic ventral spinal cord neurectoderm. We show that combined WNT-FGF activities drive a posterior dorsal pre-/early neural state, whereas suppression of TGFß-BMP signalling pathways promotes a ventral identity and neural commitment. Based on these results, we define an optimised protocol for the generation of thoracic MNs that can efficiently integrate within the neural tube of chick embryos. We expect that our findings will facilitate the comparison of hPSC-derived spinal cord cells of distinct axial identities.

Correction to: Clustering analysis of tumor metabolic networks.

An amendment to this paper has been published and can be accessed via the original article.

Clustering analysis of tumor metabolic networks.

BACKGROUND: Biological networks are representative of the diverse molecular interactions that occur within cells. Some of the commonly studied biological networks are modeled through protein-protein interactions, gene regulatory, and metabolic pathways. Among these, metabolic networks are probably the most studied, as they directly influence all physiological processes. Exploration of biochemical pathways using multigraph representation is important in understanding complex regulatory mechanisms. Feature extraction and clustering of these networks enable grouping of samples obtained from different biological specimens. Clustering techniques separate networks depending on their mutual similarity. RESULTS: We present a clustering analysis on tissue-specific metabolic networks for single samples from three primary tumor sites: breast, lung, and kidney cancer. The metabolic networks were obtained by integrating genome scale metabolic models with gene expression data. We performed network simplification to reduce the computational time needed for the computation of network distances. We empirically proved that networks clustering can characterize groups of patients in multiple conditions. CONCLUSIONS: We provide a computational methodology to explore and characterize the metabolic landscape of tumors, thus providing a general methodology to integrate analytic metabolic models with gene expression data. This method represents a first attempt in clustering large scale metabolic networks. Moreover, this approach gives the possibility to get valuable information on what are the effects of different conditions on the overall metabolism.

Long Non-Coding RNA HAND2-AS1 Acts as a Tumor Suppressor in High-Grade Serous Ovarian Carcinoma.

Long non-coding RNAs (lncRNAs) are increasingly being identified as crucial regulators in pathologies like cancer. High-grade serous ovarian carcinoma (HGSC) is the most common subtype of ovarian cancer (OC), one of the most lethal gynecological malignancies. LncRNAs, especially in cancers such as HGSC, could play a valuable role in diagnosis and even therapy. From RNA-sequencing analysis performed between an OC cell line, SKOV3, and a Fallopian Tube (FT) cell line, FT194, an important long non-coding RNA, HAND2 Anti sense RNA 1 (HAND2-AS1), was observed to be significantly downregulated in OCs when compared to FT. Its downregulation in HGSC was validated in different datasets and in a panel of HGSC cell lines. Furthermore, this study shows that the downregulation of HAND2-AS1 is caused by promoter hypermethylation in HGSC and behaves as a tumor suppressor in HGSC cell lines. Since therapeutic relevance is of key importance in HGSC research, for the first time, HAND2-AS1 upregulation was demonstrated to be one of the mechanisms through which HDAC inhibitor Panobinostat could be used in a strategy to increase HGSC cells' sensitivity to chemotherapeutic agents currently used in clinical trials. To unravel the mechanism by which HAND2-AS1 exerts its role, an in silico mRNA network was constructed using mRNAs whose expressions were positively and negatively correlated with this lncRNA in HGSC. Finally, a putative ceRNA network with possible miRNA targets of HAND2-AS1 and their mRNA targets was constructed, and the enriched Gene Ontology (GO) biological processes and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were identified.

Long Non-Coding RNA MAGI2-AS3 is a New Player with a Tumor Suppressive Role in High Grade Serous Ovarian Carcinoma.

High-Grade Serous Ovarian Carcinoma (HGSC) is the most incidental and lethal subtype of epithelial ovarian cancer (EOC) with a high mortality rate of nearly 65%. Recent findings aimed at understanding the pathogenesis of HGSC have attributed its principal source as the Fallopian Tube (FT). To further comprehend the exact mechanism of carcinogenesis, which is still less known, we performed a transcriptome analysis comparing FT and HGSC. Our study aims at exploring new players involved in the development of HGSC from FT, along with their signaling network, and we chose to focus on non-coding RNAs. Non-coding RNAs (ncRNAs) are increasingly observed to be the major regulators of several cellular processes and could have key functions as biological markers, as well as even a therapeutic approach. The most physiologically relevant and significantly dysregulated non-coding RNAs were identified bioinformatically. After analyzing the trend in HGSC and other cancers, MAGI2-AS3 was observed to be an important player in EOC. We assessed its tumor-suppressive role in EOC by means of various assays. Further, we mapped its signaling pathway using its role as a miRNA sponge to predict the miRNAs binding to MAGI2AS3 and showed it experimentally. We conclude that MAGI2-AS3 acts as a tumor suppressor in EOC, specifically in HGSC by sponging miR-15-5p, miR-374a-5p and miR-374b-5p, and altering downstream signaling of certain mRNAs through a ceRNA network.

Exploiting single-cell RNA sequencing data to link alternative splicing and cancer heterogeneity: A computational approach.

Cell heterogeneity studies using single-cell sequencing are gaining great significance in the era of personalized medicine. In particular, characterization of tumor heterogeneity is an emergent issue to improve clinical oncology, since both inter- and intra-tumor level heterogeneity influence the utility and application of molecular classifications through specific biomarkers. Majority of studies have exploited gene expression to discriminate cell types. However, to provide a more nuanced view of the underlying differences, isoform expression and alternative splicing events have to be analyzed in detail. In this study, we utilize publicly available single cell and bulk RNA sequencing datasets of breast cancer cells from primary tumors and immortalized cell lines. Breast cancer is very heterogeneous with well defined molecular subtypes and was therefore chosen for this study. RNA-seq data were explored in terms of genes, isoforms abundance and splicing events. The study was conducted from an average based approach (gene level expression) to detailed and deeper ones (isoforms abundance/splicing events) to perform a comparative analysis, and, thus, highlight the importance of the splicing machinery in defining the tumor heterogeneity. Moreover, here we demonstrate how the investigation of gene isoforms expression can help to identify the appropriate in vitro models. We furthermore extracted marker isoforms, and alternatively spliced genes between and within the different single cell populations to improve the classification of the breast cancer subtypes.