MATTE: a pipeline of transcriptome module alignment for anti-noise phenotype-gene-related analysis.

A phenotype may be associated with multiple genes that interact with each other in the form of a gene module or network. How to identify these relationships is one important aspect of comparative transcriptomics. However, it is still a challenge to align gene modules associated with different phenotypes. Although several studies attempted to address this issue in different aspects, a general framework is still needed. In this study, we introduce Module Alignment of TranscripTomE (MATTE), a novel approach to analyze transcriptomics data and identify differences in a modular manner. MATTE assumes that gene interactions modulate a phenotype and models phenotype differences as gene location changes. Specifically, we first represented genes by a relative differential expression to reduce the influence of noise in omics data. Meanwhile, clustering and aligning are combined to depict gene differences in a modular way robustly. The results show that MATTE outperformed state-of-the-art methods in identifying differentially expressed genes under noise in gene expression. In particular, MATTE could also deal with single-cell ribonucleic acid-seq data to extract the best cell-type marker genes compared to other methods. Additionally, we demonstrate how MATTE supports the discovery of biologically significant genes and modules, and facilitates downstream analyses to gain insight into breast cancer. The source code of MATTE and case analysis are available at https://github.com/zjupgx/MATTE.

Unraveling the Drivers of Tumorigenesis in the Context of Evolution: Theoretical Models and Bioinformatics Tools.

Cancer originates from somatic cells that have accumulated mutations. These mutations alter the phenotype of the cells, allowing them to escape homeostatic regulation that maintains normal cell numbers. The emergence of malignancies is an evolutionary process in which the random accumulation of somatic mutations and sequential selection of dominant clones cause cancer cells to proliferate. The development of technologies such as high-throughput sequencing has provided a powerful means to measure subclonal evolutionary dynamics across space and time. Here, we review the patterns that may be observed in cancer evolution and the methods available for quantifying the evolutionary dynamics of cancer. An improved understanding of the evolutionary trajectories of cancer will enable us to explore the molecular mechanism of tumorigenesis and to design tailored treatment strategies.

TSNAdb v2.0: The Updated Version of Tumor-specific Neoantigen Database.

In recent years, neoantigens have been recognized as ideal targets for tumor immunotherapy. With the development of neoantigen-based tumor immunotherapy, comprehensive neoantigen databases are urgently needed to meet the growing demand for clinical studies. We have built the tumor-specific neoantigen database (TSNAdb) previously, which has attracted much attention. In this study, we provide TSNAdb v2.0, an updated version of the TSNAdb. TSNAdb v2.0 offers several new features, including (1) adopting more stringent criteria for neoantigen identification, (2) providing predicted neoantigens derived from three types of somatic mutations, and (3) collecting experimentally validated neoantigens and dividing them according to the experimental level. TSNAdb v2.0 is freely available at https://pgx.zju.edu.cn/tsnadb/.

MODIG: integrating multi-omics and multi-dimensional gene network for cancer driver gene identification based on graph attention network model.

MOTIVATION: Identifying genes that play a causal role in cancer evolution remains one of the biggest challenges in cancer biology. With the accumulation of high-throughput multi-omics data over decades, it becomes a great challenge to effectively integrate these data into the identification of cancer driver genes. RESULTS: Here, we propose MODIG, a graph attention network (GAT)-based framework to identify cancer driver genes by combining multi-omics pan-cancer data (mutations, copy number variants, gene expression and methylation levels) with multi-dimensional gene networks. First, we established diverse types of gene relationship maps based on protein-protein interactions, gene sequence similarity, KEGG pathway co-occurrence, gene co-expression patterns and gene ontology. Then, we constructed a multi-dimensional gene network consisting of approximately 20 000 genes as nodes and five types of gene associations as multiplex edges. We applied a GAT to model within-dimension interactions to generate a gene representation for each dimension based on this graph. Moreover, we introduced a joint learning module to fuse multiple dimension-specific representations to generate general gene representations. Finally, we used the obtained gene representation to perform a semi-supervised driver gene identification task. The experiment results show that MODIG outperforms the baseline models in terms of area under precision-recall curves and area under the receiver operating characteristic curves. AVAILABILITY AND IMPLEMENTATION: The MODIG program is available at https://github.com/zjupgx/modig. The code and data underlying this article are also available on Zenodo, at https://doi.org/10.5281/zenodo.7057241. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

dN/dS-H, a New Test to Distinguish Different Selection Modes in Protein Evolution and Cancer Evolution.

One of the most popular measures in the analysis of protein sequence evolution is the ratio of nonsynonymous distance (dN) to synonymous distance (dS). Under the assumption that synonymous substitutions in the coding region are selectively neutral, the dN/dS ratio can be used to statistically detect the adaptive evolution (or purifying selection) if dN/dS > 1 (or dN/dS < 1) significantly. However, due to strong structural constraints and/or variable functional constraints imposed on amino acid sites, most encoding genes in most species have demonstrated dN/dS < 1. Consequently, the statistical power for testing dN/dS = 1 may be insufficient to distinguish between different selection modes. In this paper, we propose a more powerful test, called dN/dS-H, in which a new parameter H, a relative measure of rate variation among sites, was introduced. Given the condition of strong purifying selections at some sites, the dN/dS-H model predicts dN/dS = 1-H for neutral evolution, dN/dS < 1-H for nearly neutral selection, and dN/dS > 1-H for adaptive evolution. The potential of this new method for resolving the neutral-adaptive debates is illustrated by the protein sequence evolution in vertebrates, Drosophila and yeasts, as well as somatic cancer evolution (specialized as the CN/CS-H test).

MicroRNA-155-5p Targets SKP2, Activates IKKß, Increases Aß Aggregation, and Aggravates a Mouse Alzheimer Disease Model.

The nuclear factor kappa B (NF-κB) pathway and inhibitor of NF-κB kinase ß (IKKß) are involved in Alzheimer disease (AD) pathogenesis. This study explored the mechanisms underlying IKKß-mediated Aß aggregation and neuron regeneration in APP.PS1 mice. Adenoviral transduction particles were injected into the hippocampal CA1 region of the mice to knock down or inhibit target genes. Morris water maze was performed to evaluate the cognitive function of the mice. Aß deposition was determined by histological examination. sh-IKKß plasmids and microRNA (miR)-155-5p inhibitor were transfected into Aß1-42-induced N2a cells. The expressions of AD-related proteins were detected by Western blot. The interaction between S-phase kinase-associated protein 2 (SKP2) and IKKß was assessed by co-immunoprecipitation. IKKß knockdown (KD) and miR-155-5p inhibition ameliorated cognitive impairment, improved neuron regeneration, and attenuated Aß deposition in APP/PS1 mice. SKP2 KD aggravated cognitive impairment, inhibited neuron regeneration, and promoted Aß deposition in the mice. SKP2 regulated the stability of IKKß protein via ubiquitination. MiR-155-5p regulates Aß deposition and the expression of Aß generation-related proteins in N2a cells via targeting SKP2. These results indicate that the miR-155-5p/SKP2/IKKß axis was critical for pathogenesis in this AD model and suggest the potential of miR-155-5p as a target for AD treatment.

TSNAD v2.0: A one-stop software solution for tumor-specific neoantigen detection.

TSNAD is a one-stop software solution for predicting neoantigens from the whole genome/exome sequencing data of tumor-normal pairs. Here we present TSNAD v2.0 which provides several new features such as the function of RNA-Seq analysis including gene expression and gene fusion analysis, the support of different versions of the reference genome. Most importantly, we replace the NetMHCpan with DeepHLApan we developed previously, which considers both the binding between peptide and major histocompatibility complex (MHC) and the immunogenicity of the presented peptide-MHC complex (pMHC). TSNAD v2.0 achieves good performamce on a standard dataset. For better usage, we provide the Docker version and the web service of TSNAD v2.0. The source code of TSNAD v2.0 is freely available at https://github.com/jiujiezz/tsnad. And the web service of TSNAD v2.0 is available at http://biopharm.zju.edu.cn/tsnad/.

Evolution and functional divergence of the ERBB receptor family.

Aim: The ERBB gene family plays an important role in cell proliferation and differentiation, and aberrant activations could result in tumorigenesis, which makes this gene family an attractive drug target in the area of precision oncology. Materials & methods: Functional divergence analysis and conservation analysis were performed using ClustalW, MEGA7 and DIVERGE3 software. Results: One hundred and forty five functional divergence residues sites, 94 totally conserved sites and averagely 345 conserved sites of individual gene member were obtained. Some have been reported to play role in drug binding, tumorigenesis and drug resistance. Conclusion: Functional divergence residues with high posterior probabilities and conserved residues may possess certain functions, and aberrant alterations may confer drug resistance or contribute to tumorigenesis.

CanDriS: posterior profiling of cancer-driving sites based on two-component evolutionary model.

Current cancer genomics databases have accumulated millions of somatic mutations that remain to be further explored. Due to the over-excess mutations unrelated to cancer, the great challenge is to identify somatic mutations that are cancer-driven. Under the notion that carcinogenesis is a form of somatic-cell evolution, we developed a two-component mixture model: while the ground component corresponds to passenger mutations, the rapidly evolving component corresponds to driver mutations. Then, we implemented an empirical Bayesian procedure to calculate the posterior probability of a site being cancer-driven. Based on these, we developed a software CanDriS (Cancer Driver Sites) to profile the potential cancer-driving sites for thousands of tumor samples from the Cancer Genome Atlas and International Cancer Genome Consortium across tumor types and pan-cancer level. As a result, we identified that approximately 1% of the sites have posterior probabilities larger than 0.90 and listed potential cancer-wide and cancer-specific driver mutations. By comprehensively profiling all potential cancer-driving sites, CanDriS greatly enhances our ability to refine our knowledge of the genetic basis of cancer and might guide clinical medication in the upcoming era of precision medicine. The results were displayed in a database CandrisDB (http://biopharm.zju.edu.cn/candrisdb/).

Inhibitor Kappa B Kinase ß, Modulated by DJ-1/p-VHL, Reduces Phosphorylated Tau (p-Tau) Accumulation via Autophagy in Alzheimer's Disease Model.

It has been demonstrated Inhibitor Kappa B Kinase ß (IKKß) facilitates autophagy, which in turn mediates p-Tau protein clearance. However, the specific regulatory mechanism in Alzheimer's disease (AD) remains unclear. Firstly, AD model was generated by the intracerebroventricular (ICV) injection of the Β-amyloid 1-42 (Aß1-42) peptide. Subsequently, mice were injected with shRNA adenoviral transduction particles designed to target DJ-1 or Aß1-42 or Aß1-42 + shNC or Aß1-42 + shRNA against DJ-1. shRNA against DJ-1 were injected into hippocampus of mice (8 × 104 viral particles for each mice) for seven consecutive days. Immunohistochemistry was performed to detect the accumulation of Aß in the hippocampus of mice, and Hematoxylin-Eosin (HE) staining assay was carried to detect pathological changes in the hippocampus of mice. Further, sh-IKKß, shDJ-1, pcDNA-IKKß and pcDNA-DJ-1 plasmids were transfected into HT-22 cells, MTT assay, TUNEL staining and Hoechst staining were performed to detect cell viability and apoptosis, respectively. Western blotting was carried to measure the relative expression of proteins. Findings indicated that Aß1-42 inhibited autophagy and up-regulated p-Tau protein expression; Overexpression of IKKß and DJ-1 all rescued the autophagy inhibited by Aß1-42 and down-regulated p-Tau protein expression induced by Aß1-42; DJ-1 up-regulated IKKß via p-VHL, further promoted autophagy and reduced the expression of p-Tau protein; DJ-1 knockdown inhibited autophagy and up-regulated p-Tau protein expression, resulting in delayed behavior in mice. In conclusion, IKKß, modulated by DJ-1/p-VHL, reduces p-Tau accumulation via autophagy in AD's disease model. This study may provide theoretical basis for the treatment of AD.

DeepHLApan: A Deep Learning Approach for Neoantigen Prediction Considering Both HLA-Peptide Binding and Immunogenicity.

Neoantigens play important roles in cancer immunotherapy. Current methods used for neoantigen prediction focus on the binding between human leukocyte antigens (HLAs) and peptides, which is insufficient for high-confidence neoantigen prediction. In this study, we apply deep learning techniques to predict neoantigens considering both the possibility of HLA-peptide binding (binding model) and the potential immunogenicity (immunogenicity model) of the peptide-HLA complex (pHLA). The binding model achieves comparable performance with other well-acknowledged tools on the latest Immune Epitope Database (IEDB) benchmark datasets and an independent mass spectrometry (MS) dataset. The immunogenicity model could significantly improve the prediction precision of neoantigens. The further application of our method to the mutations with pre-existing T-cell responses indicating its feasibility in clinical application. DeepHLApan is freely available at https://github.com/jiujiezz/deephlapan and http://biopharm.zju.edu.cn/deephlapan.

Integrative Analysis of Somatic Mutations in Non-coding Regions Altering RNA Secondary Structures in Cancer Genomes.

RNA secondary structure may influence many cellular processes, including RNA processing, stability, localization, and translation. Single-nucleotide variations (SNVs) that alter RNA secondary structure, referred to as riboSNitches, are potentially causative of human diseases, especially in untranslated regions (UTRs) and noncoding RNAs (ncRNAs). The functions of somatic mutations that act as riboSNitches in cancer development remain poorly understood. In this study, we developed a computational pipeline called SNIPER (riboSNitch-enriched or depleted elements in cancer genomes), which employs MeanDiff and EucDiff to detect riboSNitches and then identifies riboSNitch-enriched or riboSNitch-depleted non-coding elements across tumors. SNIPER is available at github: https://github.com/suzhixi/SNIPER/ . We found that riboSNitches were more likely to be pathogenic. Moreover, we predicted several UTRs and lncRNAs (long non-coding RNA) that significantly enriched or depleted riboSNitches in cancer genomes, indicative of potential cancer driver or essential noncoding elements. Our study highlights the possibly neglected importance of RNA secondary structure in cancer genomes and provides a new strategy to identify new cancer-associated genes.

Olfactomedin domain-containing proteins: evolution, functional divergence, expression patterns and damaging SNPs.

Olfactomedin domain-containing proteins appear to facilitate neurodevelopment, cell adhesion, intercellular interactions, and protein-protein interactions, and the disruption of their expression will lead to dramatic developmental perturbations and lethality. The aim of the present work was to study how these genes evolved in metazoans and diverged after their duplication as well as to characterize their expression profiles and detrimental mutations. We conducted an exhaustive survey of olfactomedin domain-containing genes in genomic databases, identifying 235 olfactomedin-like (OLF) proteins in 29 representative species covering all the main metazoan lineages. Phylogenetic analyses allowed us to define nine different subfamilies of OLF genes, and subfamily IX, which specifically includes two immunoglobulin domains, was identified for the first time in arthropods. Functional divergence analysis suggested that the function of this arthropod-specific OLF subfamily might have diverged from that of other subfamilies. Expression pattern analysis of OLF genes in humans and rats showed that human OLF genes tended to be highly expressed in the brain, while rat OLF genes were inclined to be expressed in the ovary and brain. We used the SIFT and PolyPhen servers in dbNSFP to distinguish deleterious mutations from neutral mutations for each member of the OLF gene family. The results showed that OLFML2B contains the most destructive SNPs (up to 61), while none of the mutations in OLFM2, OLFM4 and LPHN2 were predicted to be harmful. Taken together, these findings may not only enhance understanding of the phylogenetic relationships of the OLF family but also aid future studies on OLF protein regulation of nervous system development and immune function.

TSNAdb: A Database for Tumor-specific Neoantigens from Immunogenomics Data Analysis.

Tumor-specific neoantigens have attracted much attention since they can be used as biomarkers to predict therapeutic effects of immune checkpoint blockade therapy and as potential targets for cancer immunotherapy. In this study, we developed a comprehensive tumor-specific neoantigen database (TSNAdb v1.0), based on pan-cancer immunogenomic analyses of somatic mutation data and human leukocyte antigen (HLA) allele information for 16 tumor types with 7748 tumor samples from The Cancer Genome Atlas (TCGA) and The Cancer Immunome Atlas (TCIA). We predicted binding affinities between mutant/wild-type peptides and HLA class I molecules by NetMHCpan v2.8/v4.0, and presented detailed information of 3,707,562/1,146,961 potential neoantigens generated by somatic mutations of all tumor samples. Moreover, we employed recurrent mutations in combination with highly frequent HLA alleles to predict potential shared neoantigens across tumor patients, which would facilitate the discovery of putative targets for neoantigen-based cancer immunotherapy. TSNAdb is freely available at http://biopharm.zju.edu.cn/tsnadb.

Evolutionary Origins of Pax6 Control of Crystallin Genes.

The birth of novel genes, including their cell-specific transcriptional control, is a major source of evolutionary innovation. The lens-preferred proteins, crystallins (vertebrates: α- and ß/γ-crystallins), provide a gateway to study eye evolution. Diversity of crystallins was thought to originate from convergent evolution through multiple, independent formation of Pax6/PaxB-binding sites within the promoters of genes able to act as crystallins. Here, we propose that αB-crystallin arose from a duplication of small heat shock protein (Hspb1-like) gene accompanied by Pax6-site and heat shock element (HSE) formation, followed by another duplication to generate the αA-crystallin gene in which HSE was converted into another Pax6-binding site. The founding ß/γ-crystallin gene arose from the ancestral Hspb1-like gene promoter inserted into a Ca2+-binding protein coding region, early in the cephalochordate/tunicate lineage. Likewise, an ancestral aldehyde dehydrogenase (Aldh) gene, through multiple gene duplications, expanded into a multigene family, with specific genes expressed in invertebrate lenses (Ω-crystallin/Aldh1a9) and both vertebrate lenses (η-crystallin/Aldh1a7 and Aldh3a1) and corneas (Aldh3a1). Collectively, the present data reconstruct the evolution of diverse crystallin gene families.

Mutation-profile-based methods for understanding selection forces in cancer somatic mutations: a comparative analysis.

Human genes exhibit different effects on fitness in cancer and normal cells. Here, we present an evolutionary approach to measure the selection pressure on human genes, using the well-known ratio of the nonsynonymous to synonymous substitution rate in both cancer genomes (CN /CS ) and normal populations (pN /pS ). A new mutation-profile-based method that adopts sample-specific mutation rate profiles instead of conventional substitution models was developed. We found that cancer-specific selection pressure is quite different from the selection pressure at the species and population levels. Both the relaxation of purifying selection on passenger mutations and the positive selection of driver mutations may contribute to the increased CN /CS values of human genes in cancer genomes compared with the pN /pS values in human populations. The CN /CS values also contribute to the improved classification of cancer genes and a better understanding of the onco-functionalization of cancer genes during oncogenesis. The use of our computational pipeline to identify cancer-specific positively and negatively selected genes may provide useful information for understanding the evolution of cancers and identifying possible targets for therapeutic intervention.

Identification and characterization of tyrosine kinases in anole lizard indicate the conserved tyrosine kinase repertoire in vertebrates.

The tyrosine kinases (TKs) play principal roles in regulation of multicellular aspects of the organism and are implicated in many cancer types and congenital disorders. The anole lizard has recently been introduced as a model organism for laboratory-based studies of organismal function and field studies of ecology and evolution. However, the TK family of anole lizard has not been systematically identified and characterized yet. In this study, we identified 82 TK-encoding genes in the anole lizard genome and classified them into 28 subfamilies through phylogenetic analysis, with no member from ROS and STYK1 subfamilies identified. Although TK domain sequences and domain organization in each subfamily were conserved, the total number of TKs in different species was much variable. In addition, extensive evolutionary analysis in metazoans indicated that TK repertoire in vertebrates tends to be remarkably stable. Phylogenetic analysis of Eph subfamily indicated that the divergence of EphA and EphB occurred prior to the whole genome duplication (WGD) but after the split of Urochordates and vertebrates. Moreover, the expression pattern analysis of lizard TK genes among 9 different tissues showed that 14 TK genes exhibited tissue-specific expression and 6 TK genes were widely expressed. Comparative analysis of TK expression suggested that the tissue specifically expressed genes showed different expression pattern but the widely expressed genes showed similar pattern between anole lizard and human. These results may provide insights into the evolutionary diversification of animal TK genes and would aid future studies on TK protein regulation of key growth and developmental processes.

Identification of trunk mutations in gastric carcinoma: a case study.

BACKGROUND: Intratumor heterogeneity (ITH) poses an urgent challenge for cancer precision medicine because it can cause drug resistance against cancer target therapy and immunotherapy. The search for trunk mutations that are present in all cancer cells is therefore critical for each patient. CASE PRESENTATION: In this study, we aimed to evaluate the efficiency of multiregional sequencing for the identification of trunk mutations present in all regions of a tumor as a case study. We applied multiregional whole-exome sequencing (WES) to investigate the genetic heterogeneity and homogeneity of a case of gastric carcinoma. Approximately 83% of common missense mutations present in two samples and approximately 89% of common missense mutations present in three samples were trunk mutations. Notably, trunk mutations appeared to have higher variant allele frequencies (VAFs) than non-trunk mutations. CONCLUSIONS: Our results indicate that small-scale multiregional sampling and subsequent screening of low VAF somatic mutations might be a cost-effective strategy for identifying the majority of trunk mutations in gastric carcinoma.

TSNAD: an integrated software for cancer somatic mutation and tumour-specific neoantigen detection.

Tumour antigens have attracted much attention because of their importance to cancer diagnosis, prognosis and targeted therapy. With the development of cancer genomics, the identification of tumour-specific neoantigens became possible, which is a crucial step for cancer immunotherapy. In this study, we developed software called the tumour-specific neoantigen detector for detecting cancer somatic mutations following the best practices of the genome analysis toolkit and predicting potential tumour-specific neoantigens, which could be either extracellular mutations of membrane proteins or mutated peptides presented by class I major histocompatibility complex molecules. This pipeline was beneficial to the biologist with little programmatic background. We also applied the software to the somatic mutations from the International Cancer Genome Consortium database to predict numerous potential tumour-specific neoantigens. This software is freely available from https://github.com/jiujiezz/tsnad.

Brownian model of transcriptome evolution and phylogenetic network visualization between tissues.

While phylogenetic analysis of transcriptomes of the same tissue is usually congruent with the species tree, the controversy emerges when multiple tissues are included, that is, whether species from the same tissue are clustered together, or different tissues from the same species are clustered together. Recent studies have suggested that phylogenetic network approach may shed some lights on our understanding of multi-tissue transcriptome evolution; yet the underlying evolutionary mechanism remains unclear. In this paper we develop a Brownian-based model of transcriptome evolution under the phylogenetic network that can statistically distinguish between the patterns of species-clustering and tissue-clustering. Our model can be used as a null hypothesis (neutral transcriptome evolution) for testing any correlation in tissue evolution, can be applied to cancer transcriptome evolution to study whether two tumors of an individual appeared independently or via metastasis, and can be useful to detect convergent evolution at the transcriptional level.