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Various aspects of horizontal gene transfer among bacteria are considered: modeling of this phenomenon in microcosms and natural environments; influence of gene migration on the composition of natural bacterial populations; peculiarities of bacterial chromosome evolution.
MOTIVATION: The National Cancer Institute's Center for Bioinformatics (NCICB) has developed a Java based data management and information system called caCORE. One component of this software suite is the object oriented API (caBIO) used to access the rich biological datasets collected at the NCI. This API can access the data using native Java classes, SOAP requests or HTTP calls. Non-Java based clients wanting to use this API have to use the SOAP or HTTP interfaces with the data being returned from the NCI servers as an XML data stream. Although the XML can be read and manipulated using DOM or SAX parsers, one loses the convenience and usability of an object oriented programming paradigm. caBIONet is a set of .NET wrapper classes (managers, genes, chromosomes, sequences, etc.) capable of serializing the XML data stream into local .NET objects. The software is able to search NCICB databases and provide local objects representing the data that can be manipulated and used by other .NET programs. The software was written in C# and compiled as a .NET DLL.
The Na+/Ca2+ exchanger (NCX) is a member of the cation/Ca2+ antiporter (CaCA) family and plays a key role in maintaining cellular Ca2+ homeostasis in a variety of cell types. NCX is present in a diverse group of organisms and exhibits high overall identity across species. To date, three separate genes, i.e., NCX1, NCX2, and NCX3, have been identified in mammals. However, phylogenetic analysis of the exchanger has been hindered by the lack of nonmammalian NCX sequences. In this study, we expand and diversify the list of NCX sequences by identifying NCX homologs from whole-genome sequences accessible through the Ensembl Genome Browser. We identified and annotated 13 new NCX sequences, including 4 from zebrafish, 4 from Japanese pufferfish, 2 from chicken, and 1 each from honeybee, mosquito, and chimpanzee. Examination of NCX gene structure, together with construction of phylogenetic trees, provided novel insights into the molecular evolution of NCX and allowed us to more accurately annotate NCX gene names. For the first time, we report the existence of NCX2 and NCX3 in organisms other than mammals, yielding the hypothesis that two serial NCX gene duplications occurred around the time vertebrates and invertebrates diverged. In addition, we have found a putative new NCX protein, named NCX4, that is related to NCX1 but has been observed only in fish species genomes. These findings present a stronger foundation for our understanding of the molecular evolution of the NCX gene family and provide a framework for further NCX phylogenetic and molecular studies.
MOTIVATION: mRNA expression data obtained from high-throughput DNA microarrays exhibit strong departures from homogeneity of variances. Often a complex relationship between mean expression value and variance is seen. Variance stabilization of such data is crucial for many types of statistical analyses, while regularization of variances (pooling of information) can greatly improve overall accuracy of test statistics. RESULTS: A Classification and Regression Tree (CART) procedure is introduced for variance stabilization as well as regularization. The CART procedure adaptively clusters genes by variances. Using both local and cluster wide information leads to improved estimation of population variances which improves test statistics. Whereas making use of cluster wide information allows for variance stabilization of data. AVAILABILITY: Sufficient details for our CART procedure are given so that the interested reader can program the method for themselves. The algorithm is also accessible within the Java software package BAMarray(TM), which is freely available to non-commercial users at www.bamarray.com. CONTACT: hemant.ishwaran@gmail.com.
The long anticipated 'genetic revolution' in neuropsychiatry has yet to have an impact on the practice of clinical medicine. Excitement in the 1980s over major genetic breakthroughs in schizophrenia and manic depression, for example, has been replaced in the late 1990s by the sobering realization that most common neuropsychiatric disorders are multifactorial. Despite considerable effort and resources, no 'causative' genetic variation has been identified that plays a definitive major role in any common neuropsychiatric disorder.
Pathogen lineage nomenclature systems are a key component of effective communication and collaboration for researchers and public health workers. Since February 2021, the Pango dynamic lineage nomenclature for SARS-CoV-2 has been sustained by crowdsourced lineage proposals as new isolates were sequenced. This approach is vulnerable to time-critical delays as well as regional and personal bias. Here we developed a simple heuristic approach for dividing phylogenetic trees into lineages, including the prioritization of key mutations or genes. Our implementation is efficient on extremely large phylogenetic trees consisting of millions of sequences and produces similar results to existing manually curated lineage designations when applied to SARS-CoV-2 and other viruses including chikungunya virus, Venezuelan equine encephalitis virus complex and Zika virus. This method offers a simple, automated and consistent approach to pathogen nomenclature that can assist researchers in developing and maintaining phylogeny-based classifications in the face of ever-increasing genomic datasets.
The University of Minnesota Biocatalysis/Biodegradation Database (UM-BBD, http://umbbd.ahc.umn.edu/) provides curated information on microbial catabolism and related biotransformations, primarily for environmental pollutants. Currently, it contains information on over 130 metabolic pathways, 800 reactions, 750 compounds and 500 enzymes. In the past two years, it has increased its breath to include more examples of microbial metabolism of metals and metalloids; and expanded the types of information it includes to contain microbial biotransformations of, and binding interactions with many chemical elements. It has also increased the ways in which this data can be accessed (mined). Structure-based searching was added, for exact matches, similarity, or substructures. Analysis of UM-BBD reactions has lead to a prototype, guided, pathway prediction system. Guided prediction means that the user is shown all possible biotransformations at each step and guides the process to its conclusion. Mining the UM-BBD's data provides a unique view into how the microbial world recycles organic functional groups. UM-BBD users are encouraged to comment on all aspects of the database, including the information it contains and the tools by which it can be mined. The database and prediction system develop under the direction of the scientific community.
Relatively little is known about the evolutionary and demographic histories of gorillas, one of our closest living relatives. In this study, we used samples from both western (Gorilla gorilla) and eastern (Gorilla beringei) gorillas to infer the timing of the split between these geographically disjunct populations and to elaborate the demographic history of gorillas. Here we present DNA sequences from 16 noncoding autosomal loci from 15 western gorillas and 3 eastern gorillas, including 2 noninvasively sampled free-ranging individuals. We find that the genetic diversity of gorillas is similar to that of chimpanzees but almost twice as high as that of bonobos and humans. A significantly positive Fu & Li's D was observed for western gorillas, suggesting a complex demographic history with a constant, long-term population size and ancestral population structure. Among different population-split scenarios, our data suggest a complex history of western and eastern gorillas including an initial population split at around 0.9-1.6 MYA and subsequent, primarily male-mediated gene flow until approximately 80,000-200,000 years ago. Furthermore, simulations revealed that more gene flow took place from eastern to western gorilla populations than vice versa.
Genomes, functional genomics data and 3D structure reflect different aspects of protein function. Here, we combine these data to predict that BolA, a widely distributed protein family with unknown function, is a reductase that interacts with a glutaredoxin. Comparisons at the 3D structure level as well as at the sequence profile level indicate homology between BolA and OsmC, an enzyme that reduces organic peroxides. Complementary to this, comparative analyses of genomes and genomics data provide strong evidence of an interaction between BolA and the mono-thiol glutaredoxin family. The interaction between BolA and a mono-thiol glutaredoxin is of particular interest because BolA does not, in contrast to its homolog OsmC, have evolutionarily conserved cysteines to provide it with reducing equivalents. We propose that BolA uses the mono-thiol glutaredoxin as the source for these.
MOTIVATION: Most of diseases are caused by a set of gene defects, which occur in a complex association. The association scheme of expressed genes can be modelled by genetic networks. Genetic networks are efficiently facilities to understand the dynamic of pathogenic processes by modelling molecular reality of cell conditions. In this sense a genetic network consists of first, a set of genes of specified cells, tissues or species and second, causal relations between these genes determining the functional condition of the biological system, i. e. under disease. A relation between two genes will exist if they both are directly or indirectly associated with disease [8]. Our goal is to characterize diseases (especially autoimmune diseases like chronic pancreatitis CP, multiple sclerosis MS, rheumatoid arthritis RA) by genetic networks generated by a computer system. We want to introduce this practice as a bioinformatic approach for finding targets.
The human polyomavirus BK virus (BKV) is ubiquitous in humans, infecting children asymptomatically. BKV is the only primate polyomavirus that has subtypes (I-IV) distinguishable by immunological reactivity. Nucleotide (nt) variations in a major capsid protein (VP1) gene region (designated the epitope region), probably responsible for antigenic diversity, have been used to classify BKV isolates into subtypes. Here, with all the protein-encoding gene sequences, we attempted to elucidate the evolutionary relationships among 28 BKV isolates belonging to subtypes I, III, and IV (no isolate belonging to subtype II, a minor one, was included). First, using the GTR + Gamma + I model, maximum likelihood trees were reconstructed for individual viral genes as well as for concatenated viral genes. On the resultant trees, the 28 BKV isolates were consistently divided into three clades corresponding to subtypes I, III, and IV, although bootstrap probabilities are not always high. Then we used more sophisticated likelihood models, one of which takes account of codon structure, to elucidate the phylogenetic relationships among BKV subtypes, but the phylogeny of the deep branchings remained ambiguous. Furthermore, the possibility of positive selection in the evolution of BKV was examined using the nonsynonymous/synonymous rate ratio as a measure of selection. An analysis based on entire genes could not detect any strong evidence for positive selection, but that based on the epitope region identified a few sites potentially under positive selection (these sites were among those showing subtype linked polymorphisms).
Single-cell transcriptomics experiments provide gene expression snapshots of heterogeneous cell populations across cell states. These snapshots have been used to infer trajectories and dynamic information even without intensive, time-series data by ordering cells according to gene expression similarity. However, while single-cell snapshots sometimes offer valuable insights into dynamic processes, current methods for ordering cells are limited by descriptive notions of "pseudotime" that lack intrinsic physical meaning. Instead of pseudotime, we propose inference of "process time" via a principled modeling approach to formulating trajectories and inferring latent variables corresponding to timing of cells subject to a biophysical process. Our implementation of this approach, called Chronocell, provides a biophysical formulation of trajectories built on cell state transitions. The Chronocell model is identifiable, making parameter inference meaningful. Furthermore, Chronocell can interpolate between trajectory inference, when cell states lie on a continuum, and clustering, when cells cluster into discrete states. By using a variety of datasets ranging from cluster-like to continuous, we show that Chronocell enables us to assess the suitability of datasets and reveals distinct cellular distributions along process time that are consistent with biological process times. We also compare our parameter estimates of degradation rates to those derived from metabolic labeling datasets, thereby showcasing the biophysical utility of Chronocell. Nevertheless, based on performance characterization on simulations, we find that process time inference can be challenging, highlighting the importance of dataset quality and careful model assessment.
BACKGROUND: Palmitoylation, a post-translational lipid modification, has garnered increasing attention for its role in inflammatory processes and tumorigenesis. Emerging evidence suggests a potential association between palmitoylation and inflammatory responses in the pathogenesis of endometriosis. However, the precise mechanistic interplay remains elusive, necessitating further investigation. METHODS: This study integrated transcriptomic analysis and Mendelian randomization (MR) to identify a causal gene set implicated in endometriosis. Differentially expressed genes (DEGs) were first identified in the training dataset using the limma package in R. Weighted gene co-expression network analysis (WGCNA) was subsequently performed, leveraging Single Sample Gene Set Enrichment Analysis (ssGSEA)-derived scores of palmitoylation-related genes (PRGs) as phenotypic traits to identify key modular genes. The intersection of these key modular genes with DEGs yielded a refined gene set. Machine learning algorithms were then applied to further optimize gene selection, followed by external validation, immune infiltration analysis, RNA network construction, and exploration of potential targeted drug candidates. RESULTS: Through a rigorous screening process, VRK1, GALNT12, and RMI1 emerged as key genes associated with palmitoylation, exhibiting significant downregulation in endometriosis samples (P < 0.05), indicative of a potential protective role. Immune infiltration analysis further revealed strong correlations between these genes and M2 macrophages as well as resting Natural Killer (NK) cells. Additionally, investigations into the targeted RNA network and drug association profiling provided novel insights, laying the groundwork for future high-quality validation studies. CONCLUSIONS: This study employed a comprehensive analytical framework to identify palmitoylation-associated key genes in endometriosis. The integration of immunoinfiltration analysis, RNA network construction, and drug association profiling offers valuable insights for advancing clinical diagnostics, disease monitoring, and therapeutic development in endometriosis.
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Although positive selection has been detected in many genes, its overall contribution to protein evolution is debatable. If the bulk of molecular evolution is neutral, then the ratio of amino-acid (A) to synonymous (S) polymorphism should, on average, equal that of divergence. A comparison of the A/S ratio of polymorphism in Drosophila melanogaster with that of divergence from Drosophila simulans shows that the A/S ratio of divergence is twice as high---a difference that is often attributed to positive selection. But an increase in selective constraint owing to an increase in effective population size could also explain this observation, and, if so, all genes should be affected similarly. Here we show that the difference between polymorphism and divergence is limited to only a fraction of the genes, which are also evolving more rapidly, and this implies that positive selection is responsible. A higher A/S ratio of divergence than of polymorphism is also observed in other species, which suggests a rate of adaptive evolution that is far higher than permitted by the neutral theory of molecular evolution.
The identification of genes involved in health and disease remains a challenge. We describe a bioinformatics approach, together with a freely accessible, interactive and flexible software termed Endeavour, to prioritize candidate genes underlying biological processes or diseases, based on their similarity to known genes involved in these phenomena. Unlike previous approaches, ours generates distinct prioritizations for multiple heterogeneous data sources, which are then integrated, or fused, into a global ranking using order statistics. In addition, it offers the flexibility of including additional data sources. Validation of our approach revealed it was able to efficiently prioritize 627 genes in disease data sets and 76 genes in biological pathway sets, identify candidates of 16 mono- or polygenic diseases, and discover regulatory genes of myeloid differentiation. Furthermore, the approach identified a novel gene involved in craniofacial development from a 2-Mb chromosomal region, deleted in some patients with DiGeorge-like birth defects. The approach described here offers an alternative integrative method for gene discovery.