Human nonunion tissues display differential gene expression in comparison to physiological fracture callus.

The healing of bone fractures can become aberrant and lead to nonunions which in turn have a negative impact on patient health. Understanding why a bone fails to normally heal will enable us to make a positive impact in a patient's life. While we have a wealth of molecular data on rodent models of fracture repair, it is not the same with humans. As such, there is still a lack of information regarding the molecular differences between normal physiological repair and nonunions. This study was designed to address this gap in our molecular knowledge of the human repair process by comparing differentially expressed genes (DEGs) between physiological fracture callus and two different nonunion types, hypertrophic (HNU) and oligotrophic (ONU). RNA sequencing data revealed over ∼18,000 genes in each sample. Using the physiological callus as the control and the nonunion samples as the experimental groups, bioinformatic analyses identified 67 and 81 statistically significant DEGs for HNU and ONU, respectively. Out of the 67 DEGs for the HNU, 34 and 33 were up and down-regulated, respectively. Similarly, out of the 81 DEGs for the ONU, 48 and 33 were up and down-regulated, respectively. Additionally, we also identified common genes between the two nonunion samples; 8 (10.8 %) upregulated and 12 (22.2 %) downregulated. We further identified many biological processes, with several statistically significant ones. Some of these were related to muscle and were common between the two nonunion samples. This study represents the first comprehensive attempt to understand the global molecular events occurring in human nonunion biology. With further research, we can perhaps decipher new molecular pathways involved in aberrant healing of human bone fractures that can be therapeutically targeted.

Latent evolutionary signatures: a general framework for analysing music and cultural evolution.

Cultural processes of change bear many resemblances to biological evolution. The underlying units of non-biological evolution have, however, remained elusive, especially in the domain of music. Here, we introduce a general framework to jointly identify underlying units and their associated evolutionary processes. We model musical styles and principles of organization in dimensions such as harmony and form as following an evolutionary process. Furthermore, we propose that such processes can be identified by extracting latent evolutionary signatures from musical corpora, analogously to identifying mutational signatures in genomics. These signatures provide a latent embedding for each song or musical piece. We develop a deep generative architecture for our model, which can be viewed as a type of variational autoencoder with an evolutionary prior constraining the latent space; specifically, the embeddings for each song are tied together via an energy-based prior, which encourages songs close in evolutionary space to share similar representations. As illustration, we analyse songs from the McGill Billboard dataset. We find frequent chord transitions and formal repetition schemes and identify latent evolutionary signatures related to these features. Finally, we show that the latent evolutionary representations learned by our model outperform non-evolutionary representations in such tasks as period and genre prediction.

Phylogenetic and Phylodynamic Analysis of Delta Strains Circulating in Italy.

The hepatitis delta virus (HDV) exhibits high genetic and evolutionary variability and is classified into eight genotypes (HDV-1 to -8). HDV-1 is the most widespread genotype worldwide and includes several subtypes. It predominates mainly in Europe, the Middle East, North America, and Northern Africa, and is associated with both severe and mild forms of liver disease. In this study, we performed phylogenetic and phylodynamic analyses of HDV strains circulating in Regione Lazio, Italy, to understand when these strains were introduced into the Lazio region and to define their genetic variability in Italy. Fifty HDV RNA positive patient samples were amplified using a nested RT-PCR approach targeting the HDV R0 region and sequenced. A phylogenetic tree of patient-derived sequences and reference sequences representing HDV-1 to -8 was constructed using the GTRGAMMA model in RAxML v8. The results indicated that HDV-1 was the predominant genotype with HDV-1d being the most frequently inferred subtype. HDV-1 sequences clustering with subtypes 1b and 1e were also identified. A phylodynamic analysis of HDV-1 sequences employing a Bayesian birth-death model inferred a clock rate of 3.04 × 10-4 substitutions per site per million years, with a 95% Highest Posterior Density (HPD) interval of 3.45 × 10-5 to 5.72 × 10-4. A Bayesian birth-death analysis with tree calibration based on a sample dating approach indicated multiple original sources of infection (from the late 1950s to late 1980s). Overall, these results suggest that HDV sequences from the native Italian and non-Italian patients analyzed in this study represent multiple lineages introduced across a wide period. A common ancestral origin should be excluded.

Genetic determination of regional connectivity in modelling the spread of COVID-19 outbreak for more efficient mitigation strategies.

For the COVID-19 pandemic, viral transmission has been documented in many historical and geographical contexts. Nevertheless, few studies have explicitly modeled the spatiotemporal flow based on genetic sequences, to develop mitigation strategies. Additionally, thousands of SARS-CoV-2 genomes have been sequenced with associated records, potentially providing a rich source for such spatiotemporal analysis, an unprecedented amount during a single outbreak. Here, in a case study of seven states, we model the first wave of the outbreak by determining regional connectivity from phylogenetic sequence information (i.e. "genetic connectivity"), in addition to traditional epidemiologic and demographic parameters. Our study shows nearly all of the initial outbreak can be traced to a few lineages, rather than disconnected outbreaks, indicative of a mostly continuous initial viral flow. While the geographic distance from hotspots is initially important in the modeling, genetic connectivity becomes increasingly significant later in the first wave. Moreover, our model predicts that isolated local strategies (e.g. relying on herd immunity) can negatively impact neighboring regions, suggesting more efficient mitigation is possible with unified, cross-border interventions. Finally, our results suggest that a few targeted interventions based on connectivity can have an effect similar to that of an overall lockdown. They also suggest that while successful lockdowns are very effective in mitigating an outbreak, less disciplined lockdowns quickly decrease in effectiveness. Our study provides a framework for combining phylodynamic and computational methods to identify targeted interventions.

Identification of the miRNAome in human fracture callus and nonunion tissues.

Background: Nonunions remain a challenging post-traumatic complication that often leads to a financial and health burden that affects the patient's quality of life. Despite a wealth of knowledge about fracture repair, especially gene and more recently miRNA expression, much remains unknown about the molecular differences between normal physiological repair (callus tissue) and a nonunion. To probe this lack of knowledge, we embarked on a study that sought to identify and compare the human miRNAome of normal bone to that present in a normal fracture callus and those from two different classic nonunion types, hypertrophic and oligotrophic. Methods: Normal bone and callus tissue samples were harvested during revision surgery from patients with physiological fracture repair and nonunions (hypertrophic and oligotrophic) and analyzed using histology. Also, miRNAs were isolated and screened using microarrays followed by bioinformatic analyses, including, differential expression, pathways and biological processes, as well as elucidation of target genes. Results: Out of 30,424 mature miRNAs (from 203 organisms) screened via microarrays, 635 (∼2.1%) miRNAs were found to be upregulated and 855 (∼2.8%) downregulated in the fracture callus and nonunion tissues as compared to intact bone. As our tissue samples were derived from humans, we focused on the human miRNAs and out of the 4223 human miRNAs, 86 miRNAs (∼2.0%) were upregulated and 51 (∼1.2%) were downregulated. Although there were similarities between the three experimental samples, we also found specific miRNAs that were unique to individual samples. We further identified the predicted target genes from these differentially expressed miRNAs as well as the relevant biological processes, including specific signaling pathways that are activated in all three experimental samples. Conclusion: Collectively, this is the first comprehensive study reporting on the miRNAome of intact bone as compared to fracture callus and nonunion tissues. Further, we identify specific miRNAs involved in normal physiological fracture repair as well as those of nonunions. The translational potential of this article: The data generated from this study further increase our molecular understanding of the roles of miRNAs during normal and aberrant fracture repair and this knowledge can be used in the future in the development of miRNA-based therapeutics for skeletal regeneration.

Proteome-wide screening for mitogen-activated protein kinase docking motifs and interactors.

Essential functions of mitogen-activated protein kinases (MAPKs) depend on their capacity to selectively phosphorylate a limited repertoire of substrates. MAPKs harbor a conserved groove located outside of the catalytic cleft that binds to short linear sequence motifs found in substrates and regulators. However, the weak and transient nature of these "docking" interactions poses a challenge to defining MAPK interactomes and associated sequence motifs. Here, we describe a yeast-based genetic screening pipeline to evaluate large collections of MAPK docking sequences in parallel. Using this platform, we analyzed a combinatorial library based on the docking sequences from the MAPK kinases MKK6 and MKK7, defining features critical for binding to the stress-activated MAPKs JNK1 and p38α. Our screen of a library consisting of ~12,000 sequences from the human proteome revealed multiple MAPK-selective interactors, including many that did not conform to previously defined docking motifs. Analysis of p38α/JNK1 exchange mutants identified specific docking groove residues that mediate selective binding. Last, we verified that docking sequences identified in the screen functioned in substrate recruitment in vitro and in cultured cells. Together, these studies establish an approach to characterize MAPK docking sequences and provide a resource for future investigation of signaling downstream of p38 and JNK.

Baseline gene expression profiling determines long-term benefit to programmed cell death protein 1 axis blockade.

Treatment with immune checkpoint inhibitors has altered the course of malignant melanoma, with approximately half of the patients with advanced disease surviving for more than 5 years after diagnosis. Currently, there are no biomarker methods for predicting outcome from immunotherapy. Here, we obtained transcriptomic information from a total of 105 baseline tumor samples comprising two cohorts of patients with advanced melanoma treated with programmed cell death protein 1 (PD-1)-based immunotherapies. Gene expression profiles were correlated with progression-free survival (PFS) within consecutive clinical benefit intervals (i.e., 6, 12, 18, and 24 months). Elastic net binomial regression models with cross validation were utilized to compare the predictive value of distinct genes across time. Lasso regression was used to generate a signature predicting long-term benefit (LTB), defined as patients who remain alive and free of disease progression at 24 months post treatment initiation. We show that baseline gene expression profiles were consistently able to predict long-term immunotherapy outcomes with high accuracy. The predictive value of different genes fluctuated across consecutive clinical benefit intervals, with a distinct set of genes defining benefit at 24 months compared to earlier outcomes. A 12-gene signature was able to predict LTB following anti-PD-1 therapy with an area under the curve (AUC) equal to 0.92 and 0.74 in the training and validation set, respectively. Evaluation of LTB, via a unique signature may complement objective response classification and characterize the logistics of sustained antitumor immune responses.

Complete Genome Sequences of Arthrobacter Phages Eraser, Kaylissa, and Phives.

Bacteriophages Phives, Kaylissa, and Eraser are siphoviruses infecting Arthrobacter globiformis B-2880 that were isolated in fall 2019 in Long Island, New York, from soil samples collected in Old Westbury, New York. All three bacteriophages are assigned to phage cluster AZ based on gene content similarity. While many aspects of the genomes are similar across the three phages, the endolysin genes for the phages are different and are located in different locations within the genomes.

Phylogenetic and Phylodynamic Analyses of HCV Strains Circulating among Patients Using Injectable Drugs in Central Italy.

Approximately 71 million people worldwide are infected with the hepatitis C virus (HCV). Injectable drug use represents the most common route of transmission in Europe and other developed countries. We studied the molecular characteristics of the HCV infection among mono-infected people who used drugs (PWUD) in Italy. Among 208 PWUD with anti-HCV antibodies, 101 (48.6%) were HCV RNA-positive, the majority (47%) were infected with the HCV genotype (Gt)1a, followed by Gt3a (34.9%), Gt4 (9.1%), Gt1b (4.5%), and Gt2 (4.5%). Bayesian phylogenetic analyses of clustered HCV NS5B sequences from 66 HCV-positive PWUDs with available plasma samples indicated age and neighborhood proximity as the most common characteristics between closely related HCV strains. Population dynamics, as measured by a coalescent Bayesian skyline analysis, revealed an increase in HCV Gt1a infections from the mid-1980s to mid-1990s. While HCV Gt3a infections were first detected in the 1980s, patient numbers with this genotype subtype remained relatively constant. For both Gt1a and Gt3a, Birth-Death Bayesian Skyline analyses produced higher reproduction numbers post 2014. For earlier time intervals, slow growths were observed for both Gt1a and Gt3a with reproduction numbers (Re) of approximately 1. The evolutionary rates for Gt1a and Gt3a were estimated as 2.23 × 10-4 and 3.85 × 10-4, respectively.

Passenger Mutations in More Than 2,500 Cancer Genomes: Overall Molecular Functional Impact and Consequences.

The dichotomous model of "drivers" and "passengers" in cancer posits that only a few mutations in a tumor strongly affect its progression, with the remaining ones being inconsequential. Here, we leveraged the comprehensive variant dataset from the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) project to demonstrate that-in addition to the dichotomy of high- and low-impact variants-there is a third group of medium-impact putative passengers. Moreover, we also found that molecular impact correlates with subclonal architecture (i.e., early versus late mutations), and different signatures encode for mutations with divergent impact. Furthermore, we adapted an additive-effects model from complex-trait studies to show that the aggregated effect of putative passengers, including undetected weak drivers, provides significant additional power (∼12% additive variance) for predicting cancerous phenotypes, beyond PCAWG-identified driver mutations. Finally, this framework allowed us to estimate the frequency of potential weak-driver mutations in PCAWG samples lacking any well-characterized driver alterations.

Estimating growth patterns and driver effects in tumor evolution from individual samples.

Tumors accumulate thousands of mutations, and sequencing them has given rise to methods for finding cancer drivers via mutational recurrence. However, these methods require large cohorts and underperform for low recurrence. Recently, ultra-deep sequencing has enabled accurate measurement of VAFs (variant-allele frequencies) for mutations, allowing the determination of evolutionary trajectories. Here, based solely on the VAF spectrum for an individual sample, we report on a method that identifies drivers and quantifies tumor growth. Drivers introduce perturbations into the spectrum, and our method uses the frequency of hitchhiking mutations preceding a driver to measure this. As validation, we use simulation models and 993 tumors from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium with previously identified drivers. Then we apply our method to an ultra-deep sequenced acute myeloid leukemia (AML) tumor and identify known cancer genes and additional driver candidates. In summary, our framework presents opportunities for personalized driver diagnosis using sequencing data from a single individual.

Alpha, Beta, gamma human PapillomaViruses (HPV) detection with a different sets of primers in oropharyngeal swabs, anal and cervical samples.

BACKGROUND: Recent studies have shown a 13-fold increase of oropharyngeal cancer in the presence of HPV, while α-HPV detection seems to be rare in oral cavity in comparison to anal or cervical district, many novel ß and γ types have been isolated in this anatomical site suggesting a wide tropism range. Currently, there are no guidelines recommending HPV oral cavity screening as a mandatory test, and it remains unknown which HPV types should be included in HPV screening programs. Our goal was to assess HPV prevalence in oropharyngeal, anal, and cervical swabs using different sets of primers,which are able to amplify α, ß, γ HPV types. METHODS: We analysed the presence of HPV DNA in oropharyngeal (n = 124), anal (n = 186), cervical specimens (n = 43) from HIV positive and negative patients using FAP59/64 and MY09/11 primers. All untyped strains were genetically characterized through PCR amplification and direct sequencing of partial L1 region, and the resulting sequences were classified through phylogenetic analysis. RESULTS: HPV prevalence was 20.9% in 124 oropharyngeal swab samples, including infections with multiple HPV types (5.6%). HPV prevalence in this anatomical site was significantly associated with serostatus: 63.3%in HIV positive and 36.3% in HIV negative patients (p < 0.05). Unclassified types were detected in 6 specimens. In our analysis, we did not observe any difference in HPV (α, ß, γ) prevalence between men and women. Overall, ß species were the most frequently detected 69.7%. When using anal swabs, for HIV positive patients, ß genus prevalence was 1% and γ genus was 3.7% including 6 unclassified types. In cervical samples from 43 HIV positive women (18 HPV negative and 25 positive by MY09/11 PCR), only one sample was positivite for ß1 species (2.4%) using FAP primers. Six of the untyped strains clustered with sequences from species 7, 9, 10, 8,12 of γ genus. Four sequences remained unclassified. Finally, ß and γ HPV prevalence was significantly lower than their respective HPV prevalence as identified by the Luminex system in all anatomical sites that were analyzed in previous studies. CONCLUSION: This study provides new information about viral isolates present in oropharyngeal site and it will contribute to improve the monitoring of HPV infection.

A Genome-Scale Investigation of How Sequence, Function, and Tree-Based Gene Properties Influence Phylogenetic Inference.

Molecular phylogenetic inference is inherently dependent on choices in both methodology and data. Many insightful studies have shown how choices in methodology, such as the model of sequence evolution or optimality criterion used, can strongly influence inference. In contrast, much less is known about the impact of choices in the properties of the data, typically genes, on phylogenetic inference. We investigated the relationships between 52 gene properties (24 sequence-based, 19 function-based, and 9 tree-based) with each other and with three measures of phylogenetic signal in two assembled data sets of 2,832 yeast and 2,002 mammalian genes. We found that most gene properties, such as evolutionary rate (measured through the percent average of pairwise identity across taxa) and total tree length, were highly correlated with each other. Similarly, several gene properties, such as gene alignment length, Guanine-Cytosine content, and the proportion of tree distance on internal branches divided by relative composition variability (treeness/RCV), were strongly correlated with phylogenetic signal. Analysis of partial correlations between gene properties and phylogenetic signal in which gene evolutionary rate and alignment length were simultaneously controlled, showed similar patterns of correlations, albeit weaker in strength. Examination of the relative importance of each gene property on phylogenetic signal identified gene alignment length, alongside with number of parsimony-informative sites and variable sites, as the most important predictors. Interestingly, the subsets of gene properties that optimally predicted phylogenetic signal differed considerably across our three phylogenetic measures and two data sets; however, gene alignment length and RCV were consistently included as predictors of all three phylogenetic measures in both yeasts and mammals. These results suggest that a handful of sequence-based gene properties are reliable predictors of phylogenetic signal and could be useful in guiding the choice of phylogenetic markers.

The real cost of sequencing: scaling computation to keep pace with data generation.

As the cost of sequencing continues to decrease and the amount of sequence data generated grows, new paradigms for data storage and analysis are increasingly important. The relative scaling behavior of these evolving technologies will impact genomics research moving forward.

Computing the Internode Certainty and Related Measures from Partial Gene Trees.

We present, implement, and evaluate an approach to calculate the internode certainty (IC) and tree certainty (TC) on a given reference tree from a collection of partial gene trees. Previously, the calculation of these values was only possible from a collection of gene trees with exactly the same taxon set as the reference tree. An application to sets of partial gene trees requires mathematical corrections in the IC and TC calculations. We implement our methods in RAxML and test them on empirical datasets. These tests imply that the inclusion of partial trees does matter. However, in order to provide meaningful measurements, any dataset should also include trees containing the full species set.

Novel information theory-based measures for quantifying incongruence among phylogenetic trees.

Phylogenies inferred from different data matrices often conflict with each other necessitating the development of measures that quantify this incongruence. Here, we introduce novel measures that use information theory to quantify the degree of conflict or incongruence among all nontrivial bipartitions present in a set of trees. The first measure, internode certainty (IC), calculates the degree of certainty for a given internode by considering the frequency of the bipartition defined by the internode (internal branch) in a given set of trees jointly with that of the most prevalent conflicting bipartition in the same tree set. The second measure, IC All (ICA), calculates the degree of certainty for a given internode by considering the frequency of the bipartition defined by the internode in a given set of trees in conjunction with that of all conflicting bipartitions in the same underlying tree set. Finally, the tree certainty (TC) and TC All (TCA) measures are the sum of IC and ICA values across all internodes of a phylogeny, respectively. IC, ICA, TC, and TCA can be calculated from different types of data that contain nontrivial bipartitions, including from bootstrap replicate trees to gene trees or individual characters. Given a set of phylogenetic trees, the IC and ICA values of a given internode reflect its specific degree of incongruence, and the TC and TCA values describe the global degree of incongruence between trees in the set. All four measures are implemented and freely available in version 8.0.0 and subsequent versions of the widely used program RAxML.

Inferring ancient divergences requires genes with strong phylogenetic signals.

To tackle incongruence, the topological conflict between different gene trees, phylogenomic studies couple concatenation with practices such as rogue taxon removal or the use of slowly evolving genes. Phylogenomic analysis of 1,070 orthologues from 23 yeast genomes identified 1,070 distinct gene trees, which were all incongruent with the phylogeny inferred from concatenation. Incongruence severity increased for shorter internodes located deeper in the phylogeny. Notably, whereas most practices had little or negative impact on the yeast phylogeny, the use of genes or internodes with high average internode support significantly improved the robustness of inference. We obtained similar results in analyses of vertebrate and metazoan phylogenomic data sets. These results question the exclusive reliance on concatenation and associated practices, and argue that selecting genes with strong phylogenetic signals and demonstrating the absence of significant incongruence are essential for accurately reconstructing ancient divergences.

The evolutionary imprint of domestication on genome variation and function of the filamentous fungus Aspergillus oryzae.

The domestication of animals, plants, and microbes fundamentally transformed the lifestyle and demography of the human species [1]. Although the genetic and functional underpinnings of animal and plant domestication are well understood, little is known about microbe domestication [2-6]. Here, we systematically examined genome-wide sequence and functional variation between the domesticated fungus Aspergillus oryzae, whose saccharification abilities humans have harnessed for thousands of years to produce sake, soy sauce, and miso from starch-rich grains, and its wild relative A. flavus, a potentially toxigenic plant and animal pathogen [7]. We discovered dramatic changes in the sequence variation and abundance profiles of genes and wholesale primary and secondary metabolic pathways between domesticated and wild relative isolates during growth on rice. Our data suggest that, through selection by humans, an atoxigenic lineage of A. flavus gradually evolved into a "cell factory" for enzymes and metabolites involved in the saccharification process. These results suggest that whereas animal and plant domestication was largely driven by Neolithic "genetic tinkering" of developmental pathways, microbe domestication was driven by extensive remodeling of metabolism.

Transcriptome-wide analysis of small RNA expression in early zebrafish development.

During early vertebrate development, a large number of noncoding RNAs are maternally inherited or expressed upon activation of zygotic transcription. The exact identity, expression levels, and function for most of these noncoding RNAs remain largely unknown. miRNAs (microRNAs) and piRNAs (piwi-interacting RNAs) are two classes of small noncoding RNAs that play important roles in gene regulation during early embryonic development. Here, we utilized next-generation sequencing technology to determine temporal expression patterns for both miRNAs and piRNAs during four distinct stages of early vertebrate development using zebrafish as a model system. For miRNAs, the expression patterns for 198 known miRNAs within 122 different miRNA families and eight novel miRNAs were determined. Significant sequence variation was observed at the 5' and 3'ends of miRNAs, with most extra nucleotides added at the 3' end in a nontemplate directed manner. For the miR-430 family, the addition of adenosine and uracil residues is developmentally regulated and may play a role in miRNA stability during the maternal zygotic transition. Similar modification at the 3' ends of a large number of miRNAs suggests widespread regulation of stability during early development. Beside miRNAs, we also identified a large and unexpectedly diverse set of piRNAs expressed during early development.