hgtseq: A Standard Pipeline to Study Horizontal Gene Transfer.

Horizontal gene transfer (HGT) is well described in prokaryotes: it plays a crucial role in evolution, and has functional consequences in insects and plants. However, less is known about HGT in humans. Studies have reported bacterial integrations in cancer patients, and microbial sequences have been detected in data from well-known human sequencing projects. Few of the existing tools for investigating HGT are highly automated. Thanks to the adoption of Nextflow for life sciences workflows, and to the standards and best practices curated by communities such as nf-core, fully automated, portable, and scalable pipelines can now be developed. Here we present nf-core/hgtseq to facilitate the analysis of HGT from sequencing data in different organisms. We showcase its performance by analysing six exome datasets from five mammals. Hgtseq can be run seamlessly in any computing environment and accepts data generated by existing exome and whole-genome sequencing projects; this will enable researchers to expand their analyses into this area. Fundamental questions are still open about the mechanisms and the extent or role of horizontal gene transfer: by releasing hgtseq we provide a standardised tool which will enable a systematic investigation of this phenomenon, thus paving the way for a better understanding of HGT.

Prediction of Metabolic Profiles from Transcriptomics Data in Human Cancer Cell Lines.

The Metabolome and Transcriptome are mutually communicating within cancer cells, and this interplay is translated into the existence of quantifiable correlation structures between gene expression and metabolite abundance levels. Studying these correlations could provide a novel venue of understanding cancer and the discovery of novel biomarkers and pharmacological strategies, as well as laying the foundation for the prediction of metabolite quantities by leveraging information from the more widespread transcriptomics data. In the current paper, we investigate the correlation between gene expression and metabolite levels in the Cancer Cell Line Encyclopedia dataset, building a direct correlation network between the two molecular ensembles. We show that a metabolite/transcript correlation network can be used to predict metabolite levels in different samples and datasets, such as the NCI-60 cancer cell line dataset, both on a sample-by-sample basis and in differential contrasts. We also show that metabolite levels can be predicted in principle on any sample and dataset for which transcriptomics data are available, such as the Cancer Genome Atlas (TCGA).

HmtDB 2016: data update, a better performing query system and human mitochondrial DNA haplogroup predictor.

The HmtDB resource hosts a database of human mitochondrial genome sequences from individuals with healthy and disease phenotypes. The database is intended to support both population geneticists as well as clinicians undertaking the task to assess the pathogenicity of specific mtDNA mutations. The wide application of next-generation sequencing (NGS) has provided an enormous volume of high-resolution data at a low price, increasing the availability of human mitochondrial sequencing data, which called for a cogent and significant expansion of HmtDB data content that has more than tripled in the current release. We here describe additional novel features, including: (i) a complete, user-friendly restyling of the web interface, (ii) links to the command-line stand-alone and web versions of the MToolBox package, an up-to-date tool to reconstruct and analyze human mitochondrial DNA from NGS data and (iii) the implementation of the Reconstructed Sapiens Reference Sequence (RSRS) as mitochondrial reference sequence. The overall update renders HmtDB an even more handy and useful resource as it enables a more rapid data access, processing and analysis. HmtDB is accessible at http://www.hmtdb.uniba.it/.

A multi-parametric workflow for the prioritization of mitochondrial DNA variants of clinical interest.

Assigning a pathogenic role to mitochondrial DNA (mtDNA) variants and unveiling the potential involvement of the mitochondrial genome in diseases are challenging tasks in human medicine. Assuming that rare variants are more likely to be damaging, we designed a phylogeny-based prioritization workflow to obtain a reliable pool of candidate variants for further investigations. The prioritization workflow relies on an exhaustive functional annotation through the mtDNA extraction pipeline MToolBox and includes Macro Haplogroup Consensus Sequences to filter out fixed evolutionary variants and report rare or private variants, the nucleotide variability as reported in HmtDB and the disease score based on several predictors of pathogenicity for non-synonymous variants. Cutoffs for both the disease score as well as for the nucleotide variability index were established with the aim to discriminate sequence variants contributing to defective phenotypes. The workflow was validated on mitochondrial sequences from Leber's Hereditary Optic Neuropathy affected individuals, successfully identifying 23 variants including the majority of the known causative ones. The application of the prioritization workflow to cancer datasets allowed to trim down the number of candidate for subsequent functional analyses, unveiling among these a high percentage of somatic variants. Prioritization criteria were implemented in both standalone ( http://sourceforge.net/projects/mtoolbox/ ) and web version ( https://mseqdr.org/mtoolbox.php ) of MToolBox.

MToolBox: a highly automated pipeline for heteroplasmy annotation and prioritization analysis of human mitochondrial variants in high-throughput sequencing.

MOTIVATION: The increasing availability of mitochondria-targeted and off-target sequencing data in whole-exome and whole-genome sequencing studies (WXS and WGS) has risen the demand of effective pipelines to accurately measure heteroplasmy and to easily recognize the most functionally important mitochondrial variants among a huge number of candidates. To this purpose, we developed MToolBox, a highly automated pipeline to reconstruct and analyze human mitochondrial DNA from high-throughput sequencing data. RESULTS: MToolBox implements an effective computational strategy for mitochondrial genomes assembling and haplogroup assignment also including a prioritization analysis of detected variants. MToolBox provides a Variant Call Format file featuring, for the first time, allele-specific heteroplasmy and annotation files with prioritized variants. MToolBox was tested on simulated samples and applied on 1000 Genomes WXS datasets. AVAILABILITY AND IMPLEMENTATION: MToolBox package is available at https://sourceforge.net/projects/mtoolbox/.

Extraction and annotation of human mitochondrial genomes from 1000 Genomes Whole Exome Sequencing data.

BACKGROUND: Whole Exome Sequencing (WES) is one of the most used and cost-effective next generation technologies that allows sequencing of all nuclear exons. Off-target regions may be captured if they present high sequence similarity with baits. Bioinformatics tools have been optimized to retrieve a large amount of WES off-target mitochondrial DNA (mtDNA), by exploiting the aspecificity of probes, partially overlapping to Nuclear mitochondrial Sequences (NumtS). The 1000 Genomes project represents one of the widest resources to extract mtDNA sequences from WES data, considering the large effort the scientific community is undertaking to reconstruct human population history using mtDNA as marker, and the involvement of mtDNA in pathology. RESULTS: A previously published pipeline aimed at assembling mitochondrial genomes from off-target WES reads and further improved to detect insertions and deletions (indels) and heteroplasmy in a dataset of 1242 samples from the 1000 Genomes project, enabled to obtain a nearly complete mitochondrial genome from 943 samples (76% analyzed exomes). The robustness of our computational strategy was highlighted by the reduction of reads amount recognized as mitochondrial in the original annotation produced by the Consortium, due to NumtS filtering. CONCLUSIONS: To the best of our knowledge, this is likely the most extended population-scale mitochondrial genotyping in humans enriched with the estimation of heteroplasmies.

The promise of explainable deep learning for omics data analysis: Adding new discovery tools to AI.

Deep learning has already revolutionised the way a wide range of data is processed in many areas of daily life. The ability to learn abstractions and relationships from heterogeneous data has provided impressively accurate prediction and classification tools to handle increasingly big datasets. This has a significant impact on the growing wealth of omics datasets, with the unprecedented opportunity for a better understanding of the complexity of living organisms. While this revolution is transforming the way these data are analyzed, explainable deep learning is emerging as an additional tool with the potential to change the way biological data is interpreted. Explainability addresses critical issues such as transparency, so important when computational tools are introduced especially in clinical environments. Moreover, it empowers artificial intelligence with the capability to provide new insights into the input data, thus adding an element of discovery to these already powerful resources. In this review, we provide an overview of the transformative effects explainable deep learning is having on multiple sectors, ranging from genome engineering and genomics, from radiomics to drug design and clinical trials. We offer a perspective to life scientists, to better understand the potential of these tools, and a motivation to implement them in their research, by suggesting learning resources they can use to move their first steps in this field.

TGS1 mediates 2,2,7-trimethyl guanosine capping of the human telomerase RNA to direct telomerase dependent telomere maintenance.

Pathways that direct the selection of the telomerase-dependent or recombination-based, alternative lengthening of telomere (ALT) maintenance pathway in cancer cells are poorly understood. Using human lung cancer cells and tumor organoids we show that formation of the 2,2,7-trimethylguanosine (TMG) cap structure at the human telomerase RNA 5' end by the Trimethylguanosine Synthase 1 (TGS1) is central for recruiting telomerase to telomeres and engaging Cajal bodies in telomere maintenance. TGS1 depletion or inhibition by the natural nucleoside sinefungin impairs telomerase recruitment to telomeres leading to Exonuclease 1 mediated generation of telomere 3' end protrusions that engage in RAD51-dependent, homology directed recombination and the activation of key features of the ALT pathway. This indicates a critical role for 2,2,7-TMG capping of the RNA component of human telomerase (hTR) in enforcing telomerase-dependent telomere maintenance to restrict the formation of telomeric substrates conductive to ALT. Our work introduces a targetable pathway of telomere maintenance that holds relevance for telomere-related diseases such as cancer and aging.

The prolyl-isomerase PIN1 is essential for nuclear Lamin-B structure and function and protects heterochromatin under mechanical stress.

Chromatin organization plays a crucial role in tissue homeostasis. Heterochromatin relaxation and consequent unscheduled mobilization of transposable elements (TEs) are emerging as key contributors of aging and aging-related pathologies, including Alzheimer's disease (AD) and cancer. However, the mechanisms governing heterochromatin maintenance or its relaxation in pathological conditions remain poorly understood. Here we show that PIN1, the only phosphorylation-specific cis/trans prolyl isomerase, whose loss is associated with premature aging and AD, is essential to preserve heterochromatin. We demonstrate that this PIN1 function is conserved from Drosophila to humans and prevents TE mobilization-dependent neurodegeneration and cognitive defects. Mechanistically, PIN1 maintains nuclear type-B Lamin structure and anchoring function for heterochromatin protein 1α (HP1α). This mechanism prevents nuclear envelope alterations and heterochromatin relaxation under mechanical stress, which is a key contributor to aging-related pathologies.

Breast Cancer Organoids Model Patient-Specific Response to Drug Treatment.

Tumor organoids are tridimensional cell culture systems that are generated in vitro from surgically resected patients' tumors. They can be propagated in culture maintaining several features of the tumor of origin, including cellular and genetic heterogeneity, thus representing a promising tool for precision cancer medicine. Here, we established patient-derived tumor organoids (PDOs) from different breast cancer subtypes (luminal A, luminal B, human epidermal growth factor receptor 2 (HER2)-enriched, and triple negative). The established model systems showed histological and genomic concordance with parental tumors. However, in PDOs, the ratio of diverse cell populations was frequently different from that originally observed in parental tumors. We showed that tumor organoids represent a valuable system to test the efficacy of standard therapeutic treatments and to identify drug resistant populations within tumors. We also report that inhibitors of mechanosignaling and of Yes-associated protein 1 (YAP) activation can restore chemosensitivity in drug resistant tumor organoids.

A comprehensive characterization of mitochondrial DNA mutations in glioblastoma multiforme.

Glioblastoma multiforme (GBM) is the most malignant brain cancer in adults, with a poor prognosis, whose molecular stratification still represents a challenge in pathology and clinics. On the other hand, mitochondrial DNA (mtDNA) mutations have been found in most tumors as modifiers of the bioenergetics state, albeit in GBM a characterization of the mtDNA status is lacking to date. Here, a characterization of the burden of mtDNA mutations in GBM samples was performed. First, investigation of tumor-specific vs. non tumor-specific mutations was carried out with the MToolBox bioinformatics pipeline by analyzing 45 matched tumor/blood samples, from whole genome or whole exome sequencing datasets obtained from The Cancer Genome Atlas (TCGA) consortium. Additionally, the entire mtDNA sequence was obtained in a dataset of 104 fresh-frozen GBM samples. Mitochondrial mutations with potential pathogenic interest were prioritized based on heteroplasmic fraction, nucleotide variability, and in silico prediction of pathogenicity. A preliminary biochemical analysis of the activity of mitochondrial respiratory complexes was also performed on fresh-frozen GBM samples. Although a high number of mutations was detected, we report that the large majority of them does not pass the prioritization filters. Therefore, a relatively limited burden of pathogenic mutations is indeed carried by GBM, which did not appear to determine a general impairment of the respiratory chain. This article is part of a Directed Issue entitled: Energy Metabolism Disorders and Therapies.