Prediction of trehalose-metabolic pathway and comparative analysis of KEGG, MetaCyc, and RAST databases based on complete genome of Variovorax sp. PAMC28711

Background Metabolism including anabolism and catabolism is a prerequisite phenomenon for all living organisms. Anabolism refers to the synthesis of the entire compound needed by a species. Catabolism refers to the breakdown of molecules to obtain energy. Many metabolic pathways are undisclosed and many organism-specific enzymes involved in metabolism are misplaced. When predicting a specific metabolic pathway of a microorganism, the first and foremost steps is to explore available online databases. Among many online databases, KEGG and MetaCyc pathway databases were used to deduce trehalose metabolic network for bacteria Variovorax sp. PAMC28711. Trehalose, a disaccharide, is used by the microorganism as an alternative carbon source. Results While using KEGG and MetaCyc databases, we found that the KEGG pathway database had one missing enzyme (maltooligosyl-trehalose synthase, EC 5.4.99.15). The MetaCyc pathway database also had some enzymes. However, when we used RAST to annotate the entire genome of Variovorax sp. PAMC28711, we found that all enzymes that were missing in KEGG and MetaCyc databases were involved in the trehalose metabolic pathway. Conclusions Findings of this study shed light on bioinformatics tools and raise awareness among researchers about the importance of conducting detailed investigation before proceeding with any further work. While such comparison for databases such as KEGG and MetaCyc has been done before, it has never been done with a specific microbial pathway. Such studies are useful for future improvement of bioinformatics tools to reduce limitations.

Review of the Geant4-DNA Simulation Toolkit for Radiobiological Applications at the Cellular and DNA Level

The Geant4-DNA low energy extension of the Geant4 Monte Carlo (MC) toolkit is a continuously evolving MC simulation code permitting mechanistic studies of cellular radiobiological effects. Geant4-DNA considers the physical, chemical, and biological stages of the action of ionizing radiation (in the form of x- and γ-ray photons, electrons and β±-rays, hadrons, α-particles, and a set of heavier ions) in living cells towards a variety of applications ranging from predicting radiotherapy outcomes to radiation protection both on earth and in space. In this work, we provide a brief, yet concise, overview of the progress that has been achieved so far concerning the different physical, physicochemical, chemical, and biological models implemented into Geant4-DNA, highlighting the latest developments. Specifically, the “dnadamage1” and “molecularDNA” applications which enable, for the first time within an open-source platform, quantitative predictions of early DNA damage in terms of single-strand-breaks (SSBs), double-strand-breaks (DSBs), and more complex clustered lesions for different DNA structures ranging from the nucleotide level to the entire genome. These developments are critically presented and discussed along with key benchmarking results. The Geant4-DNA toolkit, through its different set of models and functionalities, offers unique capabilities for elucidating the problem of radiation quality or the relative biological effectiveness (RBE) of different ionizing radiations which underlines nearly the whole spectrum of radiotherapeutic modalities, from external high-energy hadron beams to internal low-energy gamma and beta emitters that are used in brachytherapy sources and radiopharmaceuticals, respectively.

Comprehensive Analysis of G-Quadruplexes in African Swine Fever Virus Genome Reveals Potential Antiviral Targets by G-Quadruplex Stabilizers

African Swine Fever Virus (ASFV), a lethal hemorrhagic fever of the swine, poses a major threat to the world’s swine population and has so far resulted in devastating socio-economic consequences. The situation is further compounded by the lack of an approved vaccine or antiviral drug. Herein, we investigated a novel anti-ASFV approach by targeting G-Quadruplexes (G4s) in the viral genome. Bioinformatics analysis of putative G-quadruplex-forming sequences (PQSs) in the genome of ASFV BA71V strain revealed 317 PQSs on the forward strand and 322 PQSs on the reverse strand of the viral genome, translating to a density of 3.82 PQSs/kb covering 9.52% of the entire genome, which means that 85% of genes in the ASFV genome have at least 1 PQS on either strand. Biochemical characterization showed that 8 out of 13 conserved PQSs could form stable G4s in the presence of K+, and 4 of them could be stabilized by G4 ligands, N-Methyl Mesoporphyrin (NMM), and pyridostatin (PDS) in vitro. An enhanced green fluorescent protein (EGFP)-based reporter system revealed that the expression of two G4-containing genes, i.e., P1192R and D117L, could be significantly suppressed by NMM and PDS in 293T cells. In addition, a virus infection model showed that NMM could inhibit the replication of ASFV in Porcine Alveolar Macrophages (PAM) cells with an EC50 value of 1.16 μM. Altogether, the present study showed that functional PQSs existent in the promoters, CDS, 3′ and 5′ UTRs of the ASFV genome could be stabilized by G4 ligands, such as NMM and PDS, and could serve as potential targets for antivirals.

Genetic diversity patterns of human ethnic groups as inferred from the 1000 genomes

Human genetic diversity remains to be better understood. We here analyzed data from the 1000 Genomes Project and defined group specific fixed alleles (GSFAs) as those that are likely fixed in one ethnic group but non-fixed in at least one other group. The fraction of derived alleles in GSFAs indicates relative distance to apes because such alleles are absent in apes. Our results show that different groups differed in GSFA numbers consistent with known genetic diversity patterns, but also differed in the fraction of derived alleles in GSFAs throughout the entire genome, with East Asians having the largest fraction, followed by South Asians, Europeans, Native Americans, and Africans. Fast evolving sites such as intergenic regions were enriched with derived alleles and showed greater differences in GSFA numbers between East Asians and Africans. Furthermore, GSFAs in East Asians are mostly not fixed in other groups especially Africans, which was particularly more pronounced for fast evolving noncoding variants, while GSFAs in Africans are mostly also fixed in East Asians. Finally, variants that are likely non-neutral such as those leading to stop codon gain/loss and splice donor/acceptor gain/loss showed patterns similar to those of fast-evolving noncoding variants. These results can be accounted for by the maximum genetic diversity theory but not by the neutral theory or its inference that Eurasians suffered bottlenecks, and have implications for better management of group specific genetic diseases.

Transcription-Replication Collisions and Chromosome Fragility

Accurate replication of the entire genome is critical for cell division and propagation. Certain regions in the genome, such as fragile sites (common fragile sites, rare fragile sites, early replicating fragile sites), rDNA and telomeres, are intrinsically difficult to replicate, especially in the presence of replication stress caused by, for example, oncogene activation during tumor development. Therefore, these regions are particularly prone to deletions and chromosome rearrangements during tumorigenesis, rendering chromosome fragility. Although, the mechanism underlying their “difficult-to-replicate” nature and genomic instability is still not fully understood, accumulating evidence suggests transcription might be a major source of endogenous replication stress (RS) leading to chromosome fragility. Here, we provide an updated overview of how transcription affects chromosome fragility. Furthermore, we will use the well characterized common fragile sites (CFSs) as a model to discuss pathways involved in offsetting transcription-induced RS at these loci with a focus on the recently discovered atypical DNA synthesis repair pathway Mitotic DNA Synthesis (MiDAS).

A comprehensive gene-centric pleiotropic association analysis for 14 psychiatric disorders with GWAS summary statistics

Background Recent genome-wide association studies (GWASs) have revealed the polygenic nature of psychiatric disorders and discovered a few of single-nucleotide polymorphisms (SNPs) associated with multiple psychiatric disorders. However, the extent and pattern of pleiotropy among distinct psychiatric disorders remain not completely clear. Methods We analyzed 14 psychiatric disorders using summary statistics available from the largest GWASs by far. We first applied the cross-trait linkage disequilibrium score regression (LDSC) to estimate genetic correlation between disorders. Then, we performed a gene-based pleiotropy analysis by first aggregating a set of SNP-level associations into a single gene-level association signal using MAGMA. From a methodological perspective, we viewed the identification of pleiotropic associations across the entire genome as a high-dimensional problem of composite null hypothesis testing and utilized a novel method called PLACO for pleiotropy mapping. We ultimately implemented functional analysis for identified pleiotropic genes and used Mendelian randomization for detecting causal association between these disorders. Results We confirmed extensive genetic correlation among psychiatric disorders, based on which these disorders can be grouped into three diverse categories. We detected a large number of pleiotropic genes including 5884 associations and 2424 unique genes and found that differentially expressed pleiotropic genes were significantly enriched in pancreas, liver, heart, and brain, and that the biological process of these genes was remarkably enriched in regulating neurodevelopment, neurogenesis, and neuron differentiation, offering substantial evidence supporting the validity of identified pleiotropic loci. We further demonstrated that among all the identified pleiotropic genes there were 342 unique ones linked with 6353 drugs with drug-gene interaction which can be classified into distinct types including inhibitor, agonist, blocker, antagonist, and modulator. We also revealed causal associations among psychiatric disorders, indicating that genetic overlap and causality commonly drove the observed co-existence of these disorders. Conclusions Our study is among the first large-scale effort to characterize gene-level pleiotropy among a greatly expanded set of psychiatric disorders and provides important insight into shared genetic etiology underlying these disorders. The findings would inform psychiatric nosology, identify potential neurobiological mechanisms predisposing to specific clinical presentations, and pave the way to effective drug targets for clinical treatment.

Integrative multi-omics identifies high risk multiple myeloma subgroup associated with significant DNA loss and dysregulated DNA repair and cell cycle pathways

Background Despite significant therapeutic advances in improving lives of multiple myeloma (MM) patients, it remains mostly incurable, with patients ultimately becoming refractory to therapies. MM is a genetically heterogeneous disease and therapeutic resistance is driven by a complex interplay of disease pathobiology and mechanisms of drug resistance. We applied a multi-omics strategy using tumor-derived gene expression, single nucleotide variant, copy number variant, and structural variant profiles to investigate molecular subgroups in 514 newly diagnosed MM (NDMM) samples and identified 12 molecularly defined MM subgroups (MDMS1-12) with distinct genomic and transcriptomic features. Results Our integrative approach let us identify NDMM subgroups with transversal profiles to previously described ones, based on single data types, which shows the impact of this approach for disease stratification. One key novel subgroup is our MDMS8, associated with poor clinical outcome [median overall survival, 38 months (global log-rank p-value < 1 × 10−6)], which uniquely presents a broad genomic loss (> 9% of entire genome, t-test p value < 1e−5) driving dysregulation of various transcriptional programs affecting DNA repair and cell cycle/mitotic processes. This subgroup was validated on multiple independent datasets, and a master regulator analyses identified transcription factors controlling MDMS8 transcriptomic profile, including CKS1B and PRKDC among others, which are regulators of the DNA repair and cell cycle pathways. Conclusion Using multi-omics unsupervised clustering we were able to discover a new high-risk multiple myeloma patient segment. This high-risk group presents diverse previously known genetic markers, but also a new characteristic defined by accumulation of genomic loss which seems to drive transcriptional dysregulation of cell cycle, DNA repair and DNA damage. Finally, our work identified various master regulators, including E2F2 and CKS1B as the genes controlling these key biological pathways.

Assessment of mycoviral diversity in Pakistani fungal isolates revealed infection by 11 novel viruses of a single strain of Fusarium mangiferae isolate SP1

An extensive screening survey was conducted on Pakistani filamentous fungal isolates for the identification of viral infections. A total of 396 fungal samples were screened, of which 36 isolates were found double-stranded (ds) RNA positive with an overall frequency of 9% when analysed by a classical dsRNA isolation method. One of 36 dsRNA-positive strains, strain SP1 of a plant pathogenic fungus Fusarium mangiferae, was subjected to virome analysis. Next-generation sequencing and subsequent completion of the entire genome sequencing by a classical Sanger sequencing method showed the SP1 strain to be co-infected by 11 distinct viruses, at least seven of which should be described as new taxa at the species level according to the ICTV (International Committee on Taxonomy of Viruses) species demarcation criteria. The newly identified F. mangiferae viruses (FmVs) include two partitivirids, one betapartitivirus (FmPV1) and one gammapartitivirus (FmPV2); six mitovirids, three unuamitovirus (FmMV2, FmMV4, FmMV6), one duamitovirus (FmMV5), and two unclassified mitovirids (FmMV1, FmMV3); and three botourmiavirids, two magoulivirus (FmBOV1, FmBOV3) and one scleroulivirus (FmBOV2). The number of coinfecting viruses is among the largest ones of fungal coinfections. Their molecular features are thoroughly described here. This represents the first large virus survey in the Indian sub-continent.

Endogenous giant viruses shape intraspecies genomic variability in the model green alga Chlamydomonas reinhardtii

Chlamydomonas reinhardtii is an important eukaryotic alga that has been studied as a model organism for decades. Despite extensive history as a model system, phylogenetic and genetic characteristics of viruses infecting this alga have remained elusive. We analyzed high-throughput genome sequence data of numerous C. reinhardtii isolates, and in six strains we discovered endogenous genomes of giant viruses reaching over several hundred kilobases in length. In addition, we have also discovered the entire genome of a closely related giant virus that is endogenized within the genome of Chlamydomonas incerta, one of the closest sequenced phylogenetic relatives of C. reinhardtii. Endogenous giant viruses add hundreds of new gene families to the host strains, highlighting their contribution to the pangenome dynamics and inter-strain genomic variability of C. reinhardtii. Our findings suggest that endogenization of giant viruses can have profound implications in shaping the population dynamics and ecology of protists in the environment.

Predicting environmentally responsive transgenerational differential DNA methylated regions (epimutations) in the genome using a hybrid deep-machine learning approach

Background Deep learning is an active bioinformatics artificial intelligence field that is useful in solving many biological problems, including predicting altered epigenetics such as DNA methylation regions. Deep learning (DL) can learn an informative representation that addresses the need for defining relevant features. However, deep learning models are computationally expensive, and they require large training datasets to achieve good classification performance. Results One approach to addressing these challenges is to use a less complex deep learning network for feature selection and Machine Learning (ML) for classification. In the current study, we introduce a hybrid DL-ML approach that uses a deep neural network for extracting molecular features and a non-DL classifier to predict environmentally responsive transgenerational differential DNA methylated regions (DMRs), termed epimutations, based on the extracted DL-based features. Various environmental toxicant induced epigenetic transgenerational inheritance sperm epimutations were used to train the model on the rat genome DNA sequence and use the model to predict transgenerational DMRs (epimutations) across the entire genome. Conclusion The approach was also used to predict potential DMRs in the human genome. Experimental results show that the hybrid DL-ML approach outperforms deep learning and traditional machine learning methods.