Directional integration and pathway enrichment analysis for multi-omics data.

Omics techniques generate comprehensive profiles of biomolecules in cells and tissues. However, a holistic understanding of underlying systems requires joint analyses of multiple data modalities. We present DPM, a data fusion method for integrating omics datasets using directionality and significance estimates of genes, transcripts, or proteins. DPM allows users to define how the input datasets are expected to interact directionally given the experimental design or biological relationships between the datasets. DPM prioritises genes and pathways that change consistently across the datasets and penalises those with inconsistent directionality. To demonstrate our approach, we characterise gene and pathway regulation in IDH-mutant gliomas by jointly analysing transcriptomic, proteomic, and DNA methylation datasets. Directional integration of survival information in ovarian cancer reveals candidate biomarkers with consistent prognostic signals in transcript and protein expression. DPM is a general and adaptable framework for gene prioritisation and pathway analysis in multi-omics datasets.

Pan-cancer ion transport signature reveals functional regulators of glioblastoma aggression.

Ion channels, transporters, and other ion-flux controlling proteins, collectively comprising the "ion permeome", are common drug targets, however, their roles in cancer remain understudied. Our integrative pan-cancer transcriptome analysis shows that genes encoding the ion permeome are significantly more often highly expressed in specific subsets of cancer samples, compared to pan-transcriptome expectations. To enable target selection, we identified 410 survival-associated IP genes in 33 cancer types using a machine-learning approach. Notably, GJB2 and SCN9A show prominent expression in neoplastic cells and are associated with poor prognosis in glioblastoma, the most common and aggressive brain cancer. GJB2 or SCN9A knockdown in patient-derived glioblastoma cells induces transcriptome-wide changes involving neuron projection and proliferation pathways, impairs cell viability and tumor sphere formation in vitro, perturbs tunneling nanotube dynamics, and extends the survival of glioblastoma-bearing mice. Thus, aberrant activation of genes encoding ion transport proteins appears as a pan-cancer feature defining tumor heterogeneity, which can be exploited for mechanistic insights and therapy development.

Mutational processes of tobacco smoking and APOBEC activity generate protein-truncating mutations in cancer genomes.

Mutational signatures represent a genomic footprint of endogenous and exogenous mutational processes through tumor evolution. However, their functional impact on the proteome remains incompletely understood. We analyzed the protein-coding impact of single-base substitution (SBS) signatures in 12,341 cancer genomes from 18 cancer types. Stop-gain mutations (SGMs) (i.e., nonsense mutations) were strongly enriched in SBS signatures of tobacco smoking, APOBEC cytidine deaminases, and reactive oxygen species. These mutational processes alter specific trinucleotide contexts and thereby substitute serines and glutamic acids with stop codons. SGMs frequently affect cancer hallmark pathways and tumor suppressors such as TP53, FAT1, and APC. Tobacco-driven SGMs in lung cancer correlate with smoking history and highlight a preventable determinant of these harmful mutations. APOBEC-driven SGMs are enriched in YTCA motifs and associate with APOBEC3A expression. Our study exposes SGM expansion as a genetic mechanism by which endogenous and carcinogenic mutational processes directly contribute to protein loss of function, oncogenesis, and tumor heterogeneity.

Telomere-to-telomere genome assembly of Phaeodactylum tricornutum.

Phaeodactylum tricornutum is a marine diatom with a growing genetic toolbox available and is being used in many synthetic biology applications. While most of the genome has been assembled, the currently available genome assembly is not a completed telomere-to-telomere assembly. Here, we used Oxford Nanopore long reads to build a telomere-to-telomere genome for Phaeodactylum tricornutum. We developed a graph-based approach to extract all unique telomeres, and used this information to manually correct assembly errors. In total, we found 25 nuclear chromosomes that comprise all previously assembled fragments, in addition to the chloroplast and mitochondrial genomes. We found that chromosome 19 has filtered long-read coverage and a quality estimate that suggests significantly less haplotype sequence variation than the other chromosomes. This work improves upon the previous genome assembly and provides new opportunities for genetic engineering of this species, including creating designer synthetic chromosomes.

Human phospho-signaling networks of SARS-CoV-2 infection are rewired by population genetic variants.

SARS-CoV-2 infection hijacks signaling pathways and induces protein-protein interactions between human and viral proteins. Human genetic variation may impact SARS-CoV-2 infection and COVID-19 pathology; however, the genetic variation in these signaling networks remains uncharacterized. Here, we studied human missense single nucleotide variants (SNVs) altering phosphorylation sites modulated by SARS-CoV-2 infection, using machine learning to identify amino acid substitutions altering kinase-bound sequence motifs. We found 2,033 infrequent phosphorylation-associated SNVs (pSNVs) that are enriched in sequence motif alterations, potentially reflecting the evolution of signaling networks regulating host defenses. Proteins with pSNVs are involved in viral life cycle and host responses, including RNA splicing, interferon response (TRIM28), and glucose homeostasis (TBC1D4) with potential associations with COVID-19 comorbidities. pSNVs disrupt CDK and MAPK substrate motifs and replace these with motifs of Tank Binding Kinase 1 (TBK1) involved in innate immune responses, indicating consistent rewiring of signaling networks. Several pSNVs associate with severe COVID-19 and hospitalization (STARD13, ARFGEF2). Our analysis highlights potential genetic factors contributing to inter-individual variation of SARS-CoV-2 infection and COVID-19 and suggests leads for mechanistic and translational studies.

Modulating Mistranslation Potential of tRNASer in Saccharomyces cerevisiae.

Transfer RNAs (tRNAs) read the genetic code, translating nucleic acid sequence into protein. For tRNASer the anticodon does not specify its aminoacylation. For this reason, mutations in the tRNASer anticodon can result in amino acid substitutions, a process called mistranslation. Previously, we found that tRNASer with a proline anticodon was lethal to cells. However, by incorporating secondary mutations into the tRNA, mistranslation was dampened to a nonlethal level. The goal of this work was to identify second-site substitutions in tRNASer that modulate mistranslation to different levels. Targeted changes to putative identity elements led to total loss of tRNA function or significantly impaired cell growth. However, through genetic selection, we identified 22 substitutions that allow nontoxic mistranslation. These secondary mutations are primarily in single-stranded regions or substitute G:U base pairs for Watson-Crick pairs. Many of the variants are more toxic at low temperature and upon impairing the rapid tRNA decay pathway. We suggest that the majority of the secondary mutations affect the stability of the tRNA in cells. The temperature sensitivity of the tRNAs allows conditional mistranslation. Proteomic analysis demonstrated that tRNASer variants mistranslate to different extents with diminished growth correlating with increased mistranslation. When combined with a secondary mutation, other anticodon substitutions allow serine mistranslation at additional nonserine codons. These mistranslating tRNAs have applications in synthetic biology, by creating "statistical proteins," which may display a wider range of activities or substrate specificities than the homogenous form.