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Ancient gene duplications and the root(s) of the tree of life.

Tracing organismal histories on the timescale of the tree of life remains one of the challenging tasks in evolutionary biology. The hotly debated questions include the evolutionary relationship between the three domains of life (e.g., which of the three domains are sister domains, are the domains para-, poly-, or monophyletic) and the location of the root within the universal tree of life. For the latter, many different points of view have been considered but so far no consensus has been reached. The only widely accepted rationale to root the universal tree of life is to use anciently duplicated paralogous genes that are present in all three domains of life. To date only few anciently duplicated gene families useful for phylogenetic reconstruction have been identified. Here we present results from a systematic search for ancient gene duplications using twelve representative, completely sequenced, archaeal and bacterial genomes. Phylogenetic analyses of identified cases show that the majority of datasets support a root between Archaea and Bacteria; however, some datasets support alternative hypotheses, and all of them suffer from a lack of strong phylogenetic signal. The results are discussed with respect to the impact of horizontal gene transfer on the ability to reconstruct organismal evolution. The exchange of genetic information between divergent organisms gives rise to mosaic genomes, where different genes in a genome have different histories. Simulations show that even low rates of horizontal gene transfer dramatically complicate the reconstruction of organismal evolution, and that the different most recent common molecular ancestors likely existed at different times and in different lineages.

Carbamoyl-Phosphate Synthase (Ammonia)↗

Topochemical models for the prediction of voltage-gated sodium channel binding activity of hydantoins and related non-hydantoins.

The relationship of Wiener's topochemical index-a distance based topochemical index, molecular connectivity topochemical index-an adjacency based topochemical index and eccentric connectivity topochemical index-an adjacency-cum-distance based topochemical index with sodium channel binding activity has been investigated. A dataset comprising 50 hydantoins and related non-hydantoins was selected. The dataset was divided equally into training and test sets. The values of the three topochemical indices for all the compounds present in both the training and test sets were computed using an in-house computer program. The resulting data was analyzed and suitable models were developed after identification of the active ranges in the training set. Subsequently, a biological activity was assigned to each compound involved in the training set using these models, which was then compared with the reported sodium channel binding activity. An accuracy of prediction of the order of >99% was observed using the proposed models. Cross-validation of these models using the test set revealed an exceptionally high accuracy of approximately 95%.

Chemistry, Pharmaceutical↗

[Isolated fractures of the orbital floor].

BACKGROUND: The goal of this retrospective study was quantitative calculation of area and volume of isolated orbital floor fractures from computed tomography (CT) and correlation of these data with post-traumatic ophthalmologic findings. PATIENTS AND METHODS: A total of 76 patients with isolated orbital floor fractures were evaluated radiologically and clinically. CT scanning was performed in coronal sections (1.5-mm to 3.0-mm slice thickness) with contiguous table feed. Orbital floor and fracture area as well as volume of displaced tissue were measured and calculated from the CT dataset. The relation of quantitative CT data to ophthalmologic findings (motility, diplopia, and globe position) was assessed statistically. RESULTS: Calculation of the CT dataset revealed a mean orbital floor area of 6.33+/-1.05 cm(2), a mean fracture area of 2.60+/-1.14 cm(2), and a mean volume of displaced tissue of 1.16+/-0.80 cm(3). Volume of displaced tissue correlated significantly with ophthalmologic findings (p< or =0.01). Fracture area correlated significantly with globe position (p< or =0.01) and was less associated with diplopia and motility disturbances (p<0.10). CONCLUSION: Efficient evaluation of two-dimensional CT data enables quantitative assessment of orbital floor fractures. Position and function of the globe are mainly affected by the volume of displaced periorbital tissue.

Adult↗

Organ weights in human fetuses after formalin fixation: standards by gestational age and body weight.

This study provides new standards of fetal organ weights (brain, heart, liver, pancreas, spleen, lungs, kidneys, adrenals, thymus, and thyroid) and body dimensions (crown-heel and crown-rump lengths, head circumference, and foot length). Subjects came from a large dataset including more than 4,000 fetuses autopsied in fetopathology units of pediatric hospitals in Paris between 1986 and 2001. From this dataset, 673 subjects were carefully selected by exclusion of multiple pregnancies, macerated and malformed fetuses, subjects with abnormal karyotypes, and those with severe infections. Fetal age ranged from 9 to 42 gestational weeks, with a very large sample of fetuses in the first half of gestation. Each organ was weighed after fixation in formalin. The standards were computed in relation to age and body weight. The mathematical models used to fit the percentile growth curves were carefully selected for each organ or dimension. This study, based on reliable methodology, affords a whole set of accurate growth standards useful for pathologists.

Biometry↗

No association between alcohol consumption and hip osteoarthritis: a diverse national analysis of 87,585 adults from the "All of Us" research program.

INTRODUCTION: Hip osteoarthritis (OA) is estimated to affect 62.6 million individuals by 2050. A probable link exists between alcohol use and hip OA. However, the results are inconsistent, and the relationship between alcohol and hip OA remains speculative. To address these gaps, this study aimed to utilize the diverse, nationally representative All of Us Research Program dataset to explore the association between alcohol consumption and hip OA. METHODS: This retrospective case-control study utilized data from the All of Us Research Program Controlled Tier Dataset v8. 17,517 hip OA cases and 70,068 controls were identified. A 1:4 case-to-control matching ratio was applied based on age and sex. Alcohol use frequency was categorized into five levels: Never, Monthly or Less, Two to Four Times per Month, Two to Three Times per Week, and Four or More Times per Week. Multivariable logistic regression models evaluated the association between alcohol use frequency and hip OA after adjusting for demographic and clinical variables. RESULTS: Multivariable analysis found that alcohol use frequency was not significantly associated with hip OA. Compared to never users, participants with low (OR 0.98, 95% CI 0.93-1.04, P&#x2009;=&#x2009;0.583), moderate (OR 0.99-1.01, all P&#x2009;>&#x2009;0.05), and high (OR 1.02, 95% CI 0.95-1.09, P&#x2009;=&#x2009;0.599) levels of alcohol consumption had no statistically significant differences in odds of hip OA. Female sex, Asian race, diabetes,&#xa0;hypertension, hyperlipidemia, and nicotine dependence increased the odds of hip OA. CONCLUSION: Any level of alcohol consumption was not significantly associated with the odds of hip OA. This study adds valuable insight to the current body of conflicting evidence. Further prospective studies appear warranted to shed light on the long-term effects of different alcoholic beverages on different joints. Key Points &#x2022; This study found no significant association between any degree of alcohol consumption and the odds of developing hip osteoarthritis. &#x2022; Utilizing data from 87,585 adults in the NIH "All of Us" Research Program, this is the first study to analyze this relationship in a large, nationally representative population. &#x2022; The research provides clarity to previously conflicting literature by demonstrating that alcohol lacks a clear harmful or protective effect on the clinical course of the disease. &#x2022; The analysis highlights that independent risk factors such as Asian race, nicotine dependence, and components of metabolic syndrome increase the odds of hip osteoarthritis.

Humans↗

Low-frequency finite-element modeling of the gerbil middle ear.

The gerbil is a popular species for experimental middle-ear research. The goal of this study is to develop a 3D finite-element model to quantify the mechanics of the gerbil middle ear at low frequencies (up to about 1 kHz). The 3D reconstruction is based on a magnetic resonance imaging dataset with a voxel size of about 45 microm, and an x-ray micro-CT dataset with a voxel size of about 5.5 microm, supplemented by histological images. The eardrum model is based on moiré shape measurements. Each individual structure in the model was assumed to be homogeneous with isotropic, linear, and elastic material properties derived from a priori estimates in the literature. The behavior of the finite-element model in response to a uniform acoustic pressure on the eardrum of 1 Pa is analyzed. Sensitivity tests are done to evaluate the significance of the various parameters in the finite-element model. The Young's modulus and the thickness of the pars tensa have the most significant effect on the load transfer between the eardrum and the ossicles and, along with the Young's modulus of the pedicle and stapedial annular ligament, on the displacements of the stapes. Overall, the model demonstrates good agreement with low-frequency experimental data. For example, (1) the maximum footplate displacement is about 35 nm; (2) the umbo/stapes displacement ratio is found to be about 3.5; (3) the motion of the stapes is predominantly piston-like; and (4) the displacement pattern of the eardrum shows two points of maximum displacement, one in the posterior region and one in the anterior region. The effects of removing or stiffening the ligaments are comparable to those observed experimentally.

Animals↗

Anatomically based geometric modelling of the musculo-skeletal system and other organs.

Anatomically based finite element geometries are becoming increasingly popular in physiological modelling, owing to the demand for modelling that links organ function to spatially distributed properties at the protein, cell and tissue level. We present a collection of anatomically based finite element geometries of the musculo-skeletal system and other organs suitable for use in continuum analysis. These meshes are derived from the widely used Visible Human (VH) dataset and constitute a contribution to the world wide International Union of Physiological Sciences (IUPS) Physiome Project (www.physiome.org.nz). The method of mesh generation and fitting of tricubic Hermite volume meshes to a given dataset is illustrated using a least-squares algorithm that is modified with smoothing (Sobolev) constraints via the penalty method to account for sparse and scattered data. A technique ("host mesh" fitting) based on "free-form" deformation (FFD) is used to customise the fitted (generic) geometry. Lung lobes, the rectus femoris muscle and the lower limb bones are used as examples to illustrate these methods. Geometries of the lower limb, knee joint, forearm and neck are also presented. Finally, the issues and limitations of the methods are discussed.

Algorithms↗

Identification and analysis of metabolic reprogramming-related genes in triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is notorious for its rapid progression, tendency to metastasize, high recurrence rates, dismal outcomes, and limited treatment options, underscoring the urgent need to uncover new biomarkers and molecular pathways to enhance diagnosis, prognosis, and therapeutic strategies. Metabolic reprogramming continues to play a role throughout the life cycle of cancer, evolving and adapting. In this study, we aimed to identify specific genes associated with metabolic reprogramming in TNBC, which can potentially become unique biomarkers of this cancer. TNBC datasets retrieved from the Gene Expression Omnibus were employed to pinpoint genes exhibiting altered expression linked to tumor metabolic reprogramming. Key genes were accurately screened through machine learning algorithms, and then externally verified using the TBNC dataset based on the Cancer Genome Atlas database. Finally, immunohistochemical methods were used to clinically confirm the differential expression and trends of these key genes. Our analysis accurately identified four genes-CLEC7A, IRS1, RSPO3, and ALB-that are closely correlated with the metabolic reprogramming characteristics of cancer, and could be regarded as innovative biomarkers for TNBC. This opens a new avenue for further investigation into the mechanisms of metabolic reprogramming in TNBC and new treatment strategies.

Humans↗

The MTORC1 signaling pathway related gene POLR3G serves as a potential prognostic biomarker in Hepatocellular Carcinoma.

This study aims to investigate the prognostic significance and potential biological functions of the MTORC1 signaling pathway-associated gene POLR3G in Hepatocellular carcinoma (HCC). A prognostic risk model for HCC was developed by integrating HCC-related datasets and associated clinical data obtained from The Cancer Genome Atlas (TCGA) database. The GSVA website was employed to analyze the model genes across pan-cancer datasets, focusing on copy number variations (CNV), single nucleotide variations (SNV), methylation differences, drug sensitivity and immune cell infiltration profiles. Subsequently, we examined the expression levels and prognostic significance of POLR3G in HCC. Utilizing Spearman correlation analysis, we identified genes associated with POLR3G. Furthermore, Gene Set Enrichment Analysis (GSEA) was employed to elucidate the potential signaling pathways in which POLR3G may be involved. The relationship between POLR3G expression and immune cell abundance in HCC samples was assessed using the ssGSEA algorithm. Finally, the impact of POLR3G on HCC cell proliferation was validated through CCK-8 and EDU cell proliferation assays. Through univariate Cox regression analysis and LASSO regression analysis, we established a prognostic risk model for HCC comprising 13 genes. The analysis revealed that individuals categorized in the low-risk group had a markedly improved overall survival probability relative to those in the high-risk group. POLR3G exhibited a markedly elevated expression in HCC tissues when compared to adjacent normal tissues. The expression of POLR3G was correlated with tumor grade, and elevated POLR3G expression was associated with poor prognosis in HCC patients. Furthermore, the expression level of POLR3G was found to be correlated with the level of immune cell infiltration. Knockdown of POLR3G significantly inhibited the proliferative capacity of hepatocellular carcinoma cells. The findings suggest that POLR3G may serve as a potential biomarker influencing the prognosis of hepatocellular carcinoma patients by modulating the tumor immune microenvironment.

Humans↗

On the advantages of multi-input single-output parallel cascade classifiers.

Parallel Cascade Identification (PCI) has been successfully applied to build dynamic nonlinear systems that address diverse challenges in the field of bioinformatics. PCI may be used to identify either single-input single-output (SISO) or multi-input single-output (MISO) models. Although SISO PCI models have typically sufficed, it has been suggested that MISO PCI systems could also be used to form bioinformatics classifiers, and indeed they were successfully applied in one study. This paper reports on the first systematic comparison of MISO and SISO PCI classifiers. Motivation for using the MISO structure is given. The construction of MISO parallel cascade models is also briefly reviewed. In order to compare the accuracy of SISO and MISO PCI classifiers, genetic algorithms are applied to optimize the model architecture on a number of equivalent single-input and multi-input biological training datasets. Through evaluation of both model structures on independent test datasets, we establish that MISO PCI is capable of building classifiers of equal accuracy to those resulting from SISO PCI models. Moreover, we discuss and illustrate the benefits of the MISO approach, including significant reduction in training and testing times, and the ability to adjust automatically the weighting of individual inputs according to information content.

Algorithms↗

Developing and validating a conceptual model of recurring problems in teaching clinic.

Recurrent problems in medical teaching clinic are common and difficult to address because of complex interpersonal dynamics. To minimize this difficulty, we developed a conceptual model that simplifies problems and identifies the root cause of tension between groups in clinic. We used recursive analysis and modeling of the data from a larger multi-site, multi-method study of problems in teaching clinic. The first dataset from this study consisted of problem lists generated and prioritized by knowledgeable insiders from each site. The second dataset was a cultural consensus analysis independently performed at each site. The final model was checked for face validity and construct validity (using model predictions versus prior data and convergent/discriminant analysis). The study was performed in five Veterans Affairs teaching clinics in the U.S. Our final model, the Perception of Care Map, is a pentagram with five critical perspectives of the clinic visit. These five perspectives are structured care, educational care, relationship-based care, algorithmic care and efficient care. Each group emphasizes one or more of these perspectives, and group locations on the conceptual map explain the observed tensions between groups. Validity of the model is high. The Perception of Care Map may be a useful adjunct for understanding and addressing recurrent problems in teaching clinic.

Ambulatory Care↗

A machine learning approach to identify key epigenetic transcripts for ageing research in human blood (Epitage).

DNA methylation is an established biomarker of human ageing and is used by a variety of tools to identify meaningful epigenetic signals. We investigated whether analysing CpGs grouped by transcript as functional units could generate a ranked list of transcripts most correlated with age that might otherwise be overlooked in genome-wide CpG-based studies. Here we present Epitage ( https://github.com/a00s/epitage ), a continuously updated ranked list of transcripts built from the GSE87571 dataset (714 whole-blood samples, ages 14-94 years) through intensive testing with machine-learning models. To support reproducible analyses, we developed ugPlot ( https://github.com/a00s/ugplot ), an open-source R/Shiny tool with a graphical user interface that automates model training, testing, and comparison. Initially, we identified 48 transcripts across 13 genes, with some transcripts from the genes OBSCN, PRRT1, and SPTBN4 showing better predictive performance when multiple associated CpGs were analysed together rather than individually. In contrast, for the majority of transcripts, a dominant individual CpG still showed a higher Spearman correlation with age, as seen in established ageing genes such as ELOVL2, FHL2, and TRIM59. Epitage is a transcript-ranking list based on the methylation patterns observed in the analysed dataset. It provides a reproducible framework for prioritising transcripts associated with human ageing and for guiding future epigenetic studies.

Humans↗

Women with multiple primary breast cancers diagnosed within a five year period, 1994-1998.

INTRODUCTION: Women diagnosed with a primary breast cancer are at higher risk for a second primary. Few studies have focused on a comparison of women with single breast primary cancers and women with multiple primary breast cancers. The 1994-1998 NAACCR dataset aggregated from high quality registries representing more than one-third of the US population provides a unique opportunity to examine the incidence of multiple primary breast cancers in a large population. MATERIALS AND METHODS: Using this multi-registry dataset, we describe the incidence pattern of malignant synchronous (within 2 months) and short-term metachronous (from 3 to 60 months) multiple primaries and single primary breast cancers by demographic and tumor characteristics. RESULTS: Synchronous multiple primary tumors were more similar in age, stage, and tumor grade to single breast tumors than they were to short-term metachronous tumors. The short-term metachronous tumors did not resemble either the synchronous tumors or the single primaries. DISCUSSION: These findings may indicate that while synchronous multiple primaries may have treatment implications different from single primaries, their etiology may be similar to single breast primaries. Further, they may actually be multi-centric single primaries. The two-month interval between multiple primaries is arbitrary and may not distinguish between the synchronous tumors and those occurring within 12 months of the index tumor. The rules for defining and counting breast primaries have implications for interpretation of incidence rates and temporal trends. These data also suggest the need for standard definitions for multiple primary breast tumors among clinicians, pathologists, and surveillance epidemiologists.

Adolescent↗

Improvement of breast cancer relapse prediction in high risk intervals using artificial neural networks.

The objective of this study is to compare the predictive accuracy of a neural network (NN) model versus the standard Cox proportional hazard model. Data about the 3811 patients included in this study were collected within the 'El Alamo' Project, the largest dataset on breast cancer (BC) in Spain. The best prognostic model generated by the NN contains as covariates age, tumour size, lymph node status, tumour grade and type of treatment. These same variables were considered as having prognostic significance within the Cox model analysis. Nevertheless, the predictions made by the NN were statistically significant more accurate than those from the Cox model (p < 0.0001). Seven different time intervals were also analyzed to find that the NN predictions were much more accurate than those from the Cox model in particular in the early intervals between 1-10 and 11-20 months, and in the later one considered from 61 months to maximum follow-up time (MFT). Interestingly, these intervals contain regions of high relapse risk that have been observed in different studies and that are also present in the analyzed dataset.

Adult↗

Community analysis in stream biomonitoring: what we measure and what we don't.

Diatom assemblages from 83 epilithic samples taken from the Mesta River, Bulgaria, were regressed against three sets of predictor variables, i.e. environmental, spatial, and temporal. Redundancy analysis (RDA) of species and environmental data explained 36% of the diatom variance and extracted several important gradients of species distribution, associated with a downstream increase in nutrient levels, pH, temperature, and organic pollution. The inclusion of spatial and temporal variables in the RDA model captured additional 24% of the diatom variance and revealed three more gradients, a spatial gradient represented by higher order polynomial terms of latitude and longitude, and two temporal gradients of annual and seasonal variation. Partial RDAs demonstrated that the unique contribution of each predictor set to the explained diatom variance was the highest in the spatial dataset (16%), followed by the environmental (9%), and the temporal (7%) datasets. The remaining 28% of the variance was explained by the covariance of the predictor sets. This suggests that in biomonitoring of single stream basins, the cheap and simple account of space and time would explain most of the variance in assemblage composition obviating the necessity of expensive and time-consuming environmental assessments. The nature of the underlying environmental mechanisms can be easily inferred from the diatom composition itself.

Bulgaria↗

Comparison of commercially available genetic algorithms: gas as variable selection tool.

Many commercially available software programs claim similar efficiency and accuracy as variable selection tools. Genetic algorithms are commonly used variable selection methods where most relevant variables can be differentiated from 'less important' variables using evolutionary computing techniques. However, different vendors offer several algorithms, and the puzzling question is: which one is the appropriate method of choice? In this study, several genetic algorithm tools (e.g. GFA from Cerius2, QuaSAR-Evolution from MOE and Partek's genetic algorithm) were compared. Stepwise multiple linear regression models were generated using the most relevant variables identified by the above genetic algorithms. This procedure led to the successful generation of Quantitative Structure activity Relationship (QSAR) models for (a) proprietary datasets and (b) the Selwood dataset.

Algorithms↗

Linear analysis of carbon-13 chemical shift differences and its application to the detection and correction of errors in referencing and spin system identifications.

Statistical analysis reveals that the set of differences between the secondary shifts of the alpha- and beta-carbons for residues i of a protein (Deltadelta13C(alpha)i - Deltadelta13C(beta)i) provides the means to detect and correct referencing errors for 1H and 13C nuclei within a given dataset. In a correctly referenced protein dataset, linear regression plots of Deltadelta13C(alpha)i, Deltadelta13C(beta)i, or Deltadelta1H(alpha)i vs. (Deltadelta13C(alpha)i - Deltadelta13C(beta)i) pass through the origin from two directions, the helix-to-coil and strand-to-coil directions. Thus, linear analysis of chemical shifts (LACS) can be used to detect referencing errors and to recalibrate the 1H and 13C chemical shift scales if needed. The analysis requires only that the signals be identified with distinct residue types (intra-residue spin systems). LACS allows errors in calibration to be detected and corrected in advance of sequence-specific assignments and secondary structure determinations. Signals that do not fit the linear model (outliers) deserve scrutiny since they could represent errors in identifying signals with a particular residue, or interesting features such as a cis-peptide bond. LACS provides the basis for the automated detection of such features and for testing reassignment hypotheses. Early detection and correction of errors in referencing and spin system identifications can improve the speed and accuracy of chemical shift assignments and secondary structure determinations. We have used LACS to create a database of offset-corrected chemical shifts corresponding to nearly 1800 BMRB entries: 300 with and 1500 without corresponding three-dimensional (3D) structures. This database can serve as a resource for future analysis of the effects of amino acid sequence and protein secondary and tertiary structure on NMR chemical shifts.

Amino Acid Sequence↗

An automated assignment-free Bayesian approach for accurately identifying proton contacts from NOESY data.

The identification of proton contacts from NOE spectra remains the major bottleneck in NMR protein structure calculations. We describe an automated assignment-free system for deriving proton contact probabilities from NOESY peak lists that can be viewed as a quantitative extension of manual assignment techniques. Rather than assigning contacts to NOESY crosspeaks, a rigorous Bayesian methodology is used to transform initial proton contact probabilities derived from a set of 2992 protein structures into posterior probabilities using the observed crosspeaks as evidence. Given a target protein, the Bayesian approach is used to derive probabilities for all possible proton contacts. We evaluated the accuracy of this approach at predicting proton contacts on 60 (15)N separated NOESY and (13)C separated NOESY datasets simulated from experimentally determined NMR structures and compared it to CYANA, an established method for proton constraint assignment. On average, at the highest confidence level, our method accurately identifies 3.16/3.17 long range contacts per residue and 12.11/12.18 interresidue proton contacts per residue. These accuracies represent a significant increase over the performance of CYANA on the same data set. On a difficult real dataset that is publicly available, the coverage is lower but our method retains its advantage in accuracy over CANDID/CYANA. The algorithm is publicly available via the Protinfo NMR webserver http://protinfo.compbio.washington.edu/protinfo_nmr .

Algorithms↗