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Whole-genome assemblies of 19 placental mammals and two outgroup species were used to reconstruct the order and orientation of syntenic fragments in chromosomes of the eutherian ancestor and six other descendant ancestors leading to human. For ancestral chromosome reconstructions, we developed an algorithm (DESCHRAMBLER) that probabilistically determines the adjacencies of syntenic fragments using chromosome-scale and fragmented genome assemblies. The reconstructed chromosomes of the eutherian, boreoeutherian, and euarchontoglires ancestor each included >80% of the entire length of the human genome, whereas reconstructed chromosomes of the most recent common ancestor of simians, catarrhini, great apes, and humans and chimpanzees included >90% of human genome sequence. These high-coverage reconstructions permitted reliable identification of chromosomal rearrangements over ∼105 My of eutherian evolution. Orangutan was found to have eight chromosomes that were completely conserved in homologous sequence order and orientation with the eutherian ancestor, the largest number for any species. Ruminant artiodactyls had the highest frequency of intrachromosomal rearrangements, and interchromosomal rearrangements dominated in murid rodents. A total of 162 chromosomal breakpoints in evolution of the eutherian ancestral genome to the human genome were identified; however, the rate of rearrangements was significantly lower (0.80/My) during the first ∼60 My of eutherian evolution, then increased to greater than 2.0/My along the five primate lineages studied. Our results significantly expand knowledge of eutherian genome evolution and will facilitate greater understanding of the role of chromosome rearrangements in adaptation, speciation, and the etiology of inherited and spontaneously occurring diseases.

Conjugated estrogens (CE) and Bazedoxifene (BZA) combination is used to alleviate menopause-associated symptoms in women. CE+BZA undergo first-pass-metabolism in the liver and deconjugation by gut microbiome via β-glucuronidase (GUS) enzyme inside the distal gut. To date, the impact of long-term exposure to CE+BZA on the gut microbiome or GUS activity has not been examined. Our study using an ovariectomized mouse model showed that CE+BZA administration did not affect the overall cecal or fecal microbiome community except that it decreased the abundance of Akkermansia , which was identified as a fecal biomarker correlated with weight gain. The fecal GUS activity was reduced significantly and was positively correlated with the abundance of Lactobacillaceae in the fecal microbiome. We further confirmed in Escherichia coli K12 and Lactobacillus gasseri ADH that Tamoxifen-, 4-hydroxy-Tamoxifen- and Estradiol-Glucuronides competed for GUS activity. Our study for the first time demonstrated that long-term estrogen supplementation directly modulated gut microbial GUS activity. Our findings implicate that long-term estrogen supplementation impacts composition of gut microbiota and microbial activity, which affects estrogen metabolism in the gut. Thus, it is possible to manipulate such activity to improve the efficacy and safety of long-term administered estrogens for postmenopausal women or breast cancer patients.

Epithelial ovarian cancer (OC) is the most deadly cancer of the female reproductive system. To date, there is no effective screening method for early detection of OC and current diagnostic armamentarium may include sonographic grading of the tumor and analyzing serum levels of tumor markers, Cancer Antigen 125 (CA-125) and Human epididymis protein 4 (HE4). Microorganisms (bacterial, archaeal, and fungal cells) residing in mucosal tissues including the gastrointestinal and urogenital tracts can be altered by different disease states, and these shifts in microbial dynamics may help to diagnose disease states. We hypothesized that the peritoneal microbial environment was altered in patients with OC and that inclusion of selected peritoneal microbial features with current clinical features into prediction analyses will improve detection accuracy of patients with OC. Blood and peritoneal fluid were collected from consented patients that had sonography confirmed adnexal masses and were being seen at SIU School of Medicine Simmons Cancer Institute. Blood was processed and serum HE4 and CA-125 were measured. Peritoneal fluid was collected at the time of surgery and processed for Next Generation Sequencing (NGS) using 16S V4 exon bacterial primers and bioinformatics analyses. We found that patients with OC had a unique peritoneal microbial profile compared to patients with a benign mass. Using ensemble modeling and machine learning pathways, we identified 18 microbial features that were highly specific to OC pathology. Prediction analyses confirmed that inclusion of microbial features with serum tumor marker levels and control features (patient age and BMI) improved diagnostic accuracy compared to currently used models. We conclude that OC pathogenesis alters the peritoneal microbial environment and that these unique microbial features are important for accurate diagnosis of OC. Our study warrants further analyses of the importance of microbial features in regards to oncological diagnostics and possible prognostic and interventional medicine.

One of the most difficult problems in modern genomics is the assembly of full-length chromosomes using next generation sequencing (NGS) data. To address this problem, we developed “reference-assisted chromosome assembly” (RACA), an algorithm to reliably order and orient sequence scaffolds generated by NGS and assemblers into longer chromosomal fragments using comparative genome information and paired-end reads. Evaluation of results using simulated and real genome assemblies indicates that our approach can substantially improve genomes generated by a wide variety of de novo assemblers if a good reference assembly of a closely related species and outgroup genomes are available. We used RACA to reconstruct 60 Tibetan antelope (Pantholops hodgsonii) chromosome fragments from 1,434 SOAPdenovo sequence scaffolds, of which 16 chromosome fragments were homologous to complete cattle chromosomes. Experimental validation by PCR showed that predictions made by RACA are highly accurate. Our results indicate that RACA will significantly facilitate the study of chromosome evolution and genome rearrangements for the large number of genomes being sequenced by NGS that do not have a genetic or physical map.

The genome organizations of eight phylogenetically distinct species from five mammalian orders were compared in order to address fundamental questions relating to mammalian chromosomal evolution. Rates of chromosome evolution within mammalian orders were found to increase since the Cretaceous-Tertiary boundary. Nearly 20% of chromosome breakpoint regions were reused during mammalian evolution; these reuse sites are also enriched for centromeres. Analysis of gene content in and around evolutionary breakpoint regions revealed increased gene density relative to the genome-wide average. We found that segmental duplications populate the majority of primate-specific breakpoints and often flank inverted chromosome segments, implicating their role in chromosomal rearrangement.

In our analysis, we examine whether the labeling of social media posts as misinformation affects the subsequent sharing of them by social media users. Conventional understandings of the presentation of self and work in cognitive psychology provide different understandings of whether labeling misinformation in social media posts will reduce sharing behavior. Part of the problem with understanding whether interventions will work hinges on how closely social media interactions mirror other interpersonal interactions with friends and associates in the offline world. Our analysis looks at rates of misinformation labeling during the height of the COVID-19 pandemic on Facebook and Twitter, and then assesses whether sharing behavior is deterred by misinformation labels applied to social media posts. Our results suggest that labeling is relatively successful at lowering sharing behavior. We discuss how our results contribute to a larger understanding of the role of existing inequalities and government responses to crises such as the COVID-19 pandemic.

Screening agrochemicals and pharmaceuticals for potential liver toxicity is required for regulatory approval and is an expensive and time-consuming process. The identification and utilization of early exposure gene signatures and robust predictive models in regulatory toxicity testing has the potential to reduce time and costs substantially. In this study, comparative supervised machine learning approaches were applied to the rat liver TG-GATEs dataset to develop feature selection and predictive testing. We identified ten gene biomarkers using three different feature selection methods that predicted liver necrosis with high specificity and selectivity in an independent validation dataset from the Microarray Quality Control (MAQC)-II study. Nine of the ten genes that were selected with the supervised methods are involved in metabolism and detoxification (Car3, Crat, Cyp39a1, Dcd, Lbp, Scly, Slc23a1, and Tkfc) and transcriptional regulation (Ablim3). Several of these genes are also implicated in liver carcinogenesis, including Crat, Car3 and Slc23a1. Our biomarker gene signature provides high statistical accuracy and a manageable number of genes to study as indicators to potentially accelerate toxicity testing based on their ability to induce liver necrosis and, eventually, liver cancer.

Estimating the individualized treatment effect has become one of the most popular topics in statistics and machine learning communities in recent years. Most existing methods focus on modeling the heterogeneous treatment effects for univariate outcomes. However, many biomedical studies are interested in studying multiple highly correlated endpoints at the same time. We propose a random forest model that simultaneously estimates individualized treatment effects of multivariate outcomes. We consider a popular study design where covariates and outcomes are measured both before and after the intervention. The proposed model uses oblique splitting rules to partition population space to the neighborhood that experiences distinct treatment effects. An extensive simulation study suggests that the proposed method outperforms existing methods in various nonlinear settings. We further apply the proposed method to two nutrition studies investigating the effects of food consumption on gastrointestinal microbiota composition and clinical biomarkers. The method has been implemented in a freely available R package MOTE.RF at https://github.com/boyiguo1/MOTE.RF .

Influenza A viruses are significant human pathogens that caused 83,000 deaths in the United States during 2017 to 2018, and there is need to understand the molecular correlates of illness and to identify prognostic markers of viral infection, symptom severity, and disease course. Preexisting antibodies against viral neuraminidase (NA) and hemagglutinin (HA) proteins play a critical role in lessening disease severity. We performed global gene expression profiling of peripheral blood leukocytes collected during acute and convalescent phases from a large cohort of people infected with A/H1N1pdm virus. Using statistical and machine-learning approaches, populations of genes were identified early in infection that correlated with active viral shedding, predicted length of shedding, or disease severity. Finally, these gene expression responses were differentially affected by increased levels of preexisting influenza antibodies, which could mask detection of these markers of contagiousness and disease severity in people with active clinical disease. In this study, we examined the relationships between anti-influenza virus serum antibody titers, clinical disease, and peripheral blood leukocyte (PBL) global gene expression during presymptomatic, acute, and convalescent illness in 83 participants infected with 2009 pandemic H1N1 virus in a human influenza challenge model. Using traditional statistical and logistic regression modeling approaches, profiles of differentially expressed genes that correlated with active viral shedding, predicted length of viral shedding, and predicted illness severity were identified. These analyses further demonstrated that challenge participants fell into three peripheral blood leukocyte gene expression phenotypes that significantly correlated with different clinical outcomes and prechallenge serum titers of antibodies specific for the viral neuraminidase, hemagglutinin head, and hemagglutinin stalk. Higher prechallenge serum antibody titers were inversely correlated with leukocyte responsiveness in participants with active disease and could mask expression of peripheral blood markers of clinical disease in some participants, including viral shedding and symptom severity. Consequently, preexisting anti-influenza antibodies may modulate PBL gene expression, and this must be taken into consideration in the development and interpretation of peripheral blood diagnostic and prognostic assays of influenza infection. IMPORTANCE Influenza A viruses are significant human pathogens that caused 83,000 deaths in the United States during 2017 to 2018, and there is need to understand the molecular correlates of illness and to identify prognostic markers of viral infection, symptom severity, and disease course. Preexisting antibodies against viral neuraminidase (NA) and hemagglutinin (HA) proteins play a critical role in lessening disease severity. We performed global gene expression profiling of peripheral blood leukocytes collected during acute and convalescent phases from a large cohort of people infected with A/H1N1pdm virus. Using statistical and machine-learning approaches, populations of genes were identified early in infection that correlated with active viral shedding, predicted length of shedding, or disease severity. Finally, these gene expression responses were differentially affected by increased levels of preexisting influenza antibodies, which could mask detection of these markers of contagiousness and disease severity in people with active clinical disease.

The hippocampus is involved in learning and memory and undergoes significant growth and maturation during the neonatal period. Environmental insults during this developmental timeframe can have lasting effects on brain structure and function. This study assessed hippocampal DNA methylation and gene transcription from two independent studies reporting reduced cognitive development stemming from early life environmental insults (iron deficiency and porcine reproductive and respiratory syndrome virus (PRRSv) infection) using porcine biomedical models. In total, 420 differentially expressed genes (DEGs) were identified between the reduced cognition and control groups, including genes involved in neurodevelopment and function. Gene ontology (GO) terms enriched for DEGs were associated with immune responses, angiogenesis, and cellular development. In addition, 116 differentially methylated regions (DMRs) were identified, which overlapped 125 genes. While no GO terms were enriched for genes overlapping DMRs, many of these genes are known to be involved in neurodevelopment and function, angiogenesis, and immunity. The observed altered methylation and expression of genes involved in neurological function suggest reduced cognition in response to early life environmental insults is due to altered cholinergic signaling and calcium regulation. Finally, two DMRs overlapped with two DEGs, VWF and LRRC32, which are associated with blood brain barrier permeability and regulatory T-cell activation, respectively. These results support the role of altered hippocampal DNA methylation and gene expression in early life environmentally-induced reductions in cognitive development across independent studies.