Chloroplast genomes: diversity, evolution, and applications in genetic engineering

Chloroplasts play a crucial role in sustaining life on earth. The availability of over 800 sequenced chloroplast genomes from a variety of land plants has enhanced our understanding of chloroplast biology, intracellular gene transfer, conservation, diversity, and the genetic basis by which chloroplast transgenes can be engineered to enhance plant agronomic traits or to produce high-value agricultural or biomedical products. In this review, we discuss the impact of chloroplast genome sequences on understanding the origins of economically important cultivated species and changes that have taken place during domestication. We also discuss the potential biotechnological applications of chloroplast genomes.

Phase separation in biology; functional organization of a higher order

Inside eukaryotic cells, macromolecules are partitioned into membrane-bounded compartments and, within these, some are further organized into non-membrane-bounded structures termed membrane-less organelles. The latter structures are comprised of heterogeneous mixtures of proteins and nucleic acids and assemble through a phase separation phenomenon similar to polymer condensation. Membrane-less organelles are dynamic structures maintained through multivalent interactions that mediate diverse biological processes, many involved in RNA metabolism. They rapidly exchange components with the cellular milieu and their properties are readily altered in response to environmental cues, often implicating membrane-less organelles in responses to stress signaling. In this review, we discuss: (1) the functional roles of membrane-less organelles, (2) unifying structural and mechanistic principles that underlie their assembly and disassembly, and (3) established and emerging methods used in structural investigations of membrane-less organelles.

A novel Ruminococcus gnavus clade enriched in inflammatory bowel disease patients

BACKGROUND

Inflammatory bowel disease (IBD) is characterized by chronic inflammation of the gastrointestinal tract that is associated with changes in the gut microbiome. Here, we sought to identify strain-specific functional correlates with IBD outcomes.

METHODS

We performed metagenomic sequencing of monthly stool samples from 20 IBD patients and 12 controls (266 total samples). These were taxonomically profiled with MetaPhlAn2 and functionally profiled using HUMAnN2. Differentially abundant species were identified using MaAsLin and strain-specific pangenome haplotypes were analyzed using PanPhlAn.

RESULTS

We found a significantly higher abundance in patients of facultative anaerobes that can tolerate the increased oxidative stress of the IBD gut. We also detected dramatic, yet transient, blooms of Ruminococcus gnavus in IBD patients, often co-occurring with increased disease activity. We identified two distinct clades of R. gnavus strains, one of which is enriched in IBD patients. To study functional differences between these two clades, we augmented the R. gnavus pangenome by sequencing nine isolates from IBD patients. We identified 199 IBD-specific, strain-specific genes involved in oxidative stress responses, adhesion, iron-acquisition, and mucus utilization, potentially conferring an adaptive advantage for this R. gnavus clade in the IBD gut.

CONCLUSIONS

This study adds further evidence to the hypothesis that increased oxidative stress may be a major factor shaping the dysbiosis of the microbiome observed in IBD and suggests that R. gnavus may be an important member of the altered gut community in IBD.

Brain and blood metabolite signatures of pathology and progression in Alzheimer disease: A targeted metabolomics study

BACKGROUND

The metabolic basis of Alzheimer disease (AD) is poorly understood, and the relationships between systemic abnormalities in metabolism and AD pathogenesis are unclear. Understanding how global perturbations in metabolism are related to severity of AD neuropathology and the eventual expression of AD symptoms in at-risk individuals is critical to developing effective disease-modifying treatments. In this study, we undertook parallel metabolomics analyses in both the brain and blood to identify systemic correlates of neuropathology and their associations with prodromal and preclinical measures of AD progression.

METHODS AND FINDINGS

Quantitative and targeted metabolomics (Biocrates AbsoluteIDQ [identification and quantification] p180) assays were performed on brain tissue samples from the autopsy cohort of the Baltimore Longitudinal Study of Aging (BLSA) (N = 44, mean age = 81.33, % female = 36.36) from AD (N = 15), control (CN; N = 14), and "asymptomatic Alzheimer's disease" (ASYMAD, i.e., individuals with significant AD pathology but no cognitive impairment during life; N = 15) participants. Using machine-learning methods, we identified a panel of 26 metabolites from two main classes-sphingolipids and glycerophospholipids-that discriminated AD and CN samples with accuracy, sensitivity, and specificity of 83.33%, 86.67%, and 80%, respectively. We then assayed these 26 metabolites in serum samples from two well-characterized longitudinal cohorts representing prodromal (Alzheimer's Disease Neuroimaging Initiative [ADNI], N = 767, mean age = 75.19, % female = 42.63) and preclinical (BLSA) (N = 207, mean age = 78.68, % female = 42.63) AD, in which we tested their associations with magnetic resonance imaging (MRI) measures of AD-related brain atrophy, cerebrospinal fluid (CSF) biomarkers of AD pathology, risk of conversion to incident AD, and trajectories of cognitive performance. We developed an integrated blood and brain endophenotype score that summarized the relative importance of each metabolite to severity of AD pathology and disease progression (Endophenotype Association Score in Early Alzheimer's Disease [EASE-AD]). Finally, we mapped the main metabolite classes emerging from our analyses to key biological pathways implicated in AD pathogenesis. We found that distinct sphingolipid species including sphingomyelin (SM) with acyl residue sums C16:0, C18:1, and C16:1 (SM C16:0, SM C18:1, SM C16:1) and hydroxysphingomyelin with acyl residue sum C14:1 (SM (OH) C14:1) were consistently associated with severity of AD pathology at autopsy and AD progression across prodromal and preclinical stages. Higher log-transformed blood concentrations of all four sphingolipids in cognitively normal individuals were significantly associated with increased risk of future conversion to incident AD: SM C16:0 (hazard ratio [HR] = 4.430, 95% confidence interval [CI] = 1.703-11.520, p = 0.002), SM C16:1 (HR = 3.455, 95% CI = 1.516-7.873, p = 0.003), SM (OH) C14:1 (HR = 3.539, 95% CI = 1.373-9.122, p = 0.009), and SM C18:1 (HR = 2.255, 95% CI = 1.047-4.855, p = 0.038). The sphingolipid species identified map to several biologically relevant pathways implicated in AD, including tau phosphorylation, amyloid-β (Aβ) metabolism, calcium homeostasis, acetylcholine biosynthesis, and apoptosis. Our study has limitations: the relatively small number of brain tissue samples may have limited our power to detect significant associations, control for heterogeneity between groups, and replicate our findings in independent, autopsy-derived brain samples.

CONCLUSIONS

We present a novel framework to identify biologically relevant brain and blood metabolites associated with disease pathology and progression during the prodromal and preclinical stages of AD. Our results show that perturbations in sphingolipid metabolism are consistently associated with endophenotypes across preclinical and prodromal AD, as well as with AD pathology at autopsy. Sphingolipids may be biologically relevant biomarkers for the early detection of AD, and correcting perturbations in sphingolipid metabolism may be a plausible and novel therapeutic strategy in AD.

Next-generation probes, particles, and proteins for neural interfacing

Bidirectional interfacing with the nervous system enables neuroscience research, diagnosis, and therapy. This two-way communication allows us to monitor the state of the brain and its composite networks and cells as well as to influence them to treat disease or repair/restore sensory or motor function. To provide the most stable and effective interface, the tools of the trade must bridge the soft, ion-rich, and evolving nature of neural tissue with the largely rigid, static realm of microelectronics and medical instruments that allow for readout, analysis, and/or control. In this Review, we describe how the understanding of neural signaling and material-tissue interactions has fueled the expansion of the available tool set. New probe architectures and materials, nanoparticles, dyes, and designer genetically encoded proteins push the limits of recording and stimulation lifetime, localization, and specificity, blurring the boundary between living tissue and engineered tools. Understanding these approaches, their modality, and the role of cross-disciplinary development will support new neurotherapies and prostheses and provide neuroscientists and neurologists with unprecedented access to the brain.

The early infant gut microbiome varies in association with a maternal high-fat diet

BACKGROUND

Emerging evidence suggests that the in utero environment is not sterile as once presumed. Work in the mouse demonstrated transmission of commensal bacteria from mother to fetus during gestation, though it is unclear what modulates this process. We have previously shown in the nonhuman primate that, independent of obesity, a maternal high-fat diet during gestation and lactation persistently shapes the juvenile gut microbiome. We therefore sought to interrogate in a population-based human longitudinal cohort whether a maternal high-fat diet similarly alters the neonatal and infant gut microbiome in early life.

METHODS

A representative cohort was prospectively enrolled either in the early third trimester or intrapartum (n = 163), with a subset consented to longitudinal sampling through the postpartum interval (n = 81). Multiple body site samples, including stool and meconium, were collected from neonates at delivery and by 6 weeks of age. A rapid dietary questionnaire was administered to estimate intake of fat, added sugars, and fiber over the past month (National Health and Examination Survey). DNA was extracted from each infant meconium/stool sample (MoBio) and subjected to 16S rRNA gene sequencing and analysis.

RESULTS

On average, the maternal dietary intake of fat ranged from 14.0 to 55.2 %, with an average intake of 33.1 % (σ = 6.1 %). Mothers whose diets significantly differed from the mean (±1 standard deviation) were separated into two distinct groups, a control group (n = 13, μ = 24.4 %) and a high-fat group (n = 13, μ = 43.1 %). Principal coordinate analysis revealed that the microbiome of the neonatal stool at birth (meconium) clustered differently by virtue of maternal gestational diet (PERMANOVA p = 0.001). LEfSe feature selection identified several taxa that discriminated the groups, with a notable relative depletion of Bacteroides in the neonates exposed to a maternal high-fat gestational diet (Student's t-test, p < 0.05) that persisted to 6 weeks of age.

CONCLUSIONS

Similar to the primate, independent of maternal body mass index, a maternal high-fat diet is associated with distinct changes in the neonatal gut microbiome at birth which persist through 4-6 weeks of age. Our findings underscore the importance of counseling pregnant mothers on macronutrient consumption during pregnancy and lactation.

An integrated genetic-epigenetic analysis of schizophrenia: evidence for co-localization of genetic associations and differential DNA methylation

BACKGROUND

Schizophrenia is a highly heritable, neuropsychiatric disorder characterized by episodic psychosis and altered cognitive function. Despite success in identifying genetic variants associated with schizophrenia, there remains uncertainty about the causal genes involved in disease pathogenesis and how their function is regulated.

RESULTS

We performed a multi-stage epigenome-wide association study, quantifying genome-wide patterns of DNA methylation in a total of 1714 individuals from three independent sample cohorts. We have identified multiple differentially methylated positions and regions consistently associated with schizophrenia across the three cohorts; these effects are independent of important confounders such as smoking. We also show that epigenetic variation at multiple loci across the genome contributes to the polygenic nature of schizophrenia. Finally, we show how DNA methylation quantitative trait loci in combination with Bayesian co-localization analyses can be used to annotate extended genomic regions nominated by studies of schizophrenia, and to identify potential regulatory variation causally involved in disease.

CONCLUSIONS

This study represents the first systematic integrated analysis of genetic and epigenetic variation in schizophrenia, introducing a methodological approach that can be used to inform epigenome-wide association study analyses of other complex traits and diseases. We demonstrate the utility of using a polygenic risk score to identify molecular variation associated with etiological variation, and of using DNA methylation quantitative trait loci to refine the functional and regulatory variation associated with schizophrenia risk variants. Finally, we present strong evidence for the co-localization of genetic associations for schizophrenia and differential DNA methylation.

Propagating annotations of molecular networks using in silico fragmentation

The annotation of small molecules is one of the most challenging and important steps in untargeted mass spectrometry analysis, as most of our biological interpretations rely on structural annotations. Molecular networking has emerged as a structured way to organize and mine data from untargeted tandem mass spectrometry (MS/MS) experiments and has been widely applied to propagate annotations. However, propagation is done through manual inspection of MS/MS spectra connected in the spectral networks and is only possible when a reference library spectrum is available. One of the alternative approaches used to annotate an unknown fragmentation mass spectrum is through the use of in silico predictions. One of the challenges of in silico annotation is the uncertainty around the correct structure among the predicted candidate lists. Here we show how molecular networking can be used to improve the accuracy of in silico predictions through propagation of structural annotations, even when there is no match to a MS/MS spectrum in spectral libraries. This is accomplished through creating a network consensus of re-ranked structural candidates using the molecular network topology and structural similarity to improve in silico annotations. The Network Annotation Propagation (NAP) tool is accessible through the GNPS web-platform https://gnps.ucsd.edu/ProteoSAFe/static/gnps-theoretical.jsp.

Epigenetic therapy for ovarian cancer: promise and progress

Ovarian cancer is the deadliest gynecologic malignancy, with a 5-year survival rate of approximately 47%, a number that has remained constant over the past two decades. Early diagnosis improves survival, but unfortunately only 15% of ovarian cancers are diagnosed at an early or localized stage. Most ovarian cancers are epithelial in origin and treatment prioritizes surgery and cytoreduction followed by cytotoxic platinum and taxane chemotherapy. While most tumors will initially respond to this treatment, recurrence is likely to occur within a median of 16 months for patients who present with advanced stage disease. New treatment options separate from traditional chemotherapy that take advantage of advances in understanding of the pathophysiology of ovarian cancer are needed to improve outcomes. Recent work has shown that mutations in genes encoding epigenetic regulators are mutated in ovarian cancer, driving tumorigenesis and resistance to treatment. Several of these epigenetic modifiers have emerged as promising drug targets for ovarian cancer therapy. In this article, we delineate epigenetic abnormalities in ovarian cancer, discuss key scientific advances using epigenetic therapies in preclinical ovarian cancer models, and review ongoing clinical trials utilizing epigenetic therapies in ovarian cancer.

Exploring the emerging role of the microbiome in cancer immunotherapy

The activity of the commensal microbiota significantly impacts human health and has been linked to the development of many diseases, including cancer. Gnotobiotic animal models have shown that the microbiota has many effects on host physiology, including on the development and regulation of immune responses. More recently, evidence has indicated that the microbiota can more specifically influence the outcome of cancer immunotherapy. Therapeutic interventions to optimize microbiota composition to improve immunotherapy outcomes have shown promise in mouse studies. Ongoing endeavors are translating these pre-clinical findings to early stage clinical testing. In this review we summarize 1) basic methodologies and considerations for studies of host-microbiota interactions; 2) experimental evidence towards a causal link between gut microbiota composition and immunotherapeutic efficacy; 3) possible mechanisms governing the microbiota-mediated impact on immunotherapy efficacy. Moving forward, there is need for a deeper understanding of the underlying biological mechanisms that link specific bacterial strains to host immunity. Integrating microbiome effects with other tumor and host factors regulating immunotherapy responsiveness versus resistance could facilitate optimization of therapeutic outcomes.

Sources of airborne microorganisms in the built environment

Each day people are exposed to millions of bioaerosols, including whole microorganisms, which can have both beneficial and detrimental effects. The next chapter in understanding the airborne microbiome of the built environment is characterizing the various sources of airborne microorganisms and the relative contribution of each. We have identified the following eight major categories of sources of airborne bacteria, viruses, and fungi in the built environment: humans; pets; plants; plumbing systems; heating, ventilation, and air-conditioning systems; mold; dust resuspension; and the outdoor environment. Certain species are associated with certain sources, but the full potential of source characterization and source apportionment has not yet been realized. Ideally, future studies will quantify detailed emission rates of microorganisms from each source and will identify the relative contribution of each source to the indoor air microbiome. This information could then be used to probe fundamental relationships between specific sources and human health, to design interventions to improve building health and human health, or even to provide evidence for forensic investigations.

Sex differences in cancer mechanisms

We now know that cancer is many different diseases, with great variation even within a single histological subtype. With the current emphasis on developing personalized approaches to cancer treatment, it is astonishing that we have not yet systematically incorporated the biology of sex differences into our paradigms for laboratory and clinical cancer research. While some sex differences in cancer arise through the actions of circulating sex hormones, other sex differences are independent of estrogen, testosterone, or progesterone levels. Instead, these differences are the result of sexual differentiation, a process that involves genetic and epigenetic mechanisms, in addition to acute sex hormone actions. Sexual differentiation begins with fertilization and continues beyond menopause. It affects virtually every body system, resulting in marked sex differences in such areas as growth, lifespan, metabolism, and immunity, all of which can impact on cancer progression, treatment response, and survival. These organismal level differences have correlates at the cellular level, and thus, males and females can fundamentally differ in their protections and vulnerabilities to cancer, from cellular transformation through all stages of progression, spread, and response to treatment. Our goal in this review is to cover some of the robust sex differences that exist in core cancer pathways and to make the case for inclusion of sex as a biological variable in all laboratory and clinical cancer research. We finish with a discussion of lab- and clinic-based experimental design that should be used when testing whether sex matters and the appropriate statistical models to apply in data analysis for rigorous evaluations of potential sex effects. It is our goal to facilitate the evaluation of sex differences in cancer in order to improve outcomes for all patients.

Four alpha ganglion cell types in mouse retina: Function, structure, and molecular signatures

The retina communicates with the brain using ≥30 parallel channels, each carried by axons of distinct types of retinal ganglion cells. In every mammalian retina one finds so-called "alpha" ganglion cells (αRGCs), identified by their large cell bodies, stout axons, wide and mono-stratified dendritic fields, and high levels of neurofilament protein. In the mouse, three αRGC types have been described based on responses to light steps: On-sustained, Off-sustained, and Off-transient. Here we employed a transgenic mouse line that labels αRGCs in the live retina, allowing systematic targeted recordings. We characterize the three known types and identify a fourth, with On-transient responses. All four αRGC types share basic aspects of visual signaling, including a large receptive field center, a weak antagonistic surround, and absence of any direction selectivity. They also share a distinctive waveform of the action potential, faster than that of other RGC types. Morphologically, they differ in the level of dendritic stratification within the IPL, which accounts for their response properties. Molecularly, each type has a distinct signature. A comparison across mammals suggests a common theme, in which four large-bodied ganglion cell types split the visual signal into four channels arranged symmetrically with respect to polarity and kinetics.

Integrating molecular QTL data into genome-wide genetic association analysis: Probabilistic assessment of enrichment and colocalization

We propose a novel statistical framework for integrating the result from molecular quantitative trait loci (QTL) mapping into genome-wide genetic association analysis of complex traits, with the primary objectives of quantitatively assessing the enrichment of the molecular QTLs in complex trait-associated genetic variants and the colocalizations of the two types of association signals. We introduce a natural Bayesian hierarchical model that treats the latent association status of molecular QTLs as SNP-level annotations for candidate SNPs of complex traits. We detail a computational procedure to seamlessly perform enrichment, fine-mapping and colocalization analyses, which is a distinct feature compared to the existing colocalization analysis procedures in the literature. The proposed approach is computationally efficient and requires only summary-level statistics. We evaluate and demonstrate the proposed computational approach through extensive simulation studies and analyses of blood lipid data and the whole blood eQTL data from the GTEx project. In addition, a useful utility from our proposed method enables the computation of expected colocalization signals using simple characteristics of the association data. Using this utility, we further illustrate the importance of enrichment analysis on the ability to discover colocalized signals and the potential limitations of currently available molecular QTL data. The software pipeline that implements the proposed computation procedures, enloc, is freely available at https://github.com/xqwen/integrative.

Genome-wide association studies (GWAS) have been tremendously successful in identifying genetic variants that impact complex diseases. However, the roles of such studies in disease etiology remain poorly understood, primarily because a large proportion of the GWAS findings are located in the non-coding region of the genome. Recent advancements in high-throughput sequencing technology enable the systematic investigation of molecular quantitative trait loci (QTLs), which are genetic variants that directly affect molecular phenotypes (e.g., gene expression, transcription factor binding and DNA methylation). Linking molecular QTLs to GWAS findings intuitively represents an important step for interpreting the biological and clinical relevance of the GWAS results. In this paper, we describe a rigorous and efficient computational approach that assesses the enrichment and overlap between the GWAS findings and molecular QTLs. Importantly, we illustrate that the accurate quantification of overlapping between molecular QTL and GWAS signals requires reliable enrichment estimation. Our proposed approach fully accounts for the intrinsic uncertainty embedded in the association analyses of GWAS and molecular QTL mapping, and it outperforms the existing state-of-the-art approaches. Applying the proposed approach to the GWAS data of blood lipid traits and the whole blood expression QTLs (eQTLs) yields some novel biological insights and also illustrates the potential limitations of the currently available molecular QTL data.

Intracoronary Delivery of Mitochondria to the Ischemic Heart for Cardioprotection

We have previously shown that transplantation of autologously derived, respiration-competent mitochondria by direct injection into the heart following transient ischemia and reperfusion enhances cell viability and contractile function. To increase the therapeutic potential of this approach, we investigated whether exogenous mitochondria can be effectively delivered through the coronary vasculature to protect the ischemic myocardium and studied the fate of these transplanted organelles in the heart. Langendorff-perfused rabbit hearts were subjected to 30 minutes of ischemia and then reperfused for 10 minutes. Mitochondria were labeled with ¹⁸F-rhodamine 6G and iron oxide nanoparticles. The labeled mitochondria were either directly injected into the ischemic region or delivered by vascular perfusion through the coronary arteries at the onset of reperfusion. These hearts were used for positron emission tomography, microcomputed tomography, and magnetic resonance imaging with subsequent microscopic analyses of tissue sections to confirm the uptake and distribution of exogenous mitochondria. Injected mitochondria were localized near the site of delivery; while, vascular perfusion of mitochondria resulted in rapid and extensive dispersal throughout the heart. Both injected and perfused mitochondria were observed in interstitial spaces and were associated with blood vessels and cardiomyocytes. To determine the efficacy of vascular perfusion of mitochondria, an additional group of rabbit hearts were subjected to 30 minutes of regional ischemia and reperfused for 120 minutes. Immediately following regional ischemia, the hearts received unlabeled, autologous mitochondria delivered through the coronary arteries. Autologous mitochondria perfused through the coronary vasculature significantly decreased infarct size and significantly enhanced post-ischemic myocardial function. In conclusion, the delivery of mitochondria through the coronary arteries resulted in their rapid integration and widespread distribution throughout the heart and provided cardioprotection from ischemia-reperfusion injury.

Scaffolding of long read assemblies using long range contact information

BACKGROUND

Long read technologies have revolutionized de novo genome assembly by generating contigs orders of magnitude longer than that of short read assemblies. Although assembly contiguity has increased, it usually does not reconstruct a full chromosome or an arm of the chromosome, resulting in an unfinished chromosome level assembly. To increase the contiguity of the assembly to the chromosome level, different strategies are used which exploit long range contact information between chromosomes in the genome.

METHODS

We develop a scalable and computationally efficient scaffolding method that can boost the assembly contiguity to a large extent using genome-wide chromatin interaction data such as Hi-C.

RESULTS

we demonstrate an algorithm that uses Hi-C data for longer-range scaffolding of de novo long read genome assemblies. We tested our methods on the human and goat genome assemblies. We compare our scaffolds with the scaffolds generated by LACHESIS based on various metrics.

CONCLUSION

Our new algorithm SALSA produces more accurate scaffolds compared to the existing state of the art method LACHESIS.

Social and Genetic Networks of HIV-1 Transmission in New York City

Background

Sexually transmitted infections spread across contact networks. Partner elicitation and notification are commonly used public health tools to identify, notify, and offer testing to persons linked in these contact networks. For HIV-1, a rapidly evolving pathogen with low per-contact transmission rates, viral genetic sequences are an additional source of data that can be used to infer or refine transmission networks.

Methods and Findings

The New York City Department of Health and Mental Hygiene interviews individuals newly diagnosed with HIV and elicits names of sexual and injection drug using partners. By law, the Department of Health also receives HIV sequences when these individuals enter healthcare and their physicians order resistance testing. Our study used both HIV sequence and partner naming data from 1342 HIV-infected persons in New York City between 2006 and 2012 to infer and compare sexual/drug-use named partner and genetic transmission networks. Using these networks, we determined a range of genetic distance thresholds suitable for identifying potential transmission partners. In 48% of cases, named partners were infected with genetically closely related viruses, compatible with but not necessarily representing or implying, direct transmission. Partner pairs linked through the genetic similarity of their HIV sequences were also linked by naming in 53% of cases. Persons who reported high-risk heterosexual contact were more likely to name at least one partner with a genetically similar virus than those reporting their risk as injection drug use or men who have sex with men.

Conclusions

We analyzed an unprecedentedly large and detailed partner tracing and HIV sequence dataset and determined an empirically justified range of genetic distance thresholds for identifying potential transmission partners. We conclude that genetic linkage provides more reliable evidence for identifying potential transmission partners than partner naming, highlighting the importance and complementarity of both epidemiological and molecular genetic surveillance for characterizing regional HIV-1 epidemics.

Understanding the path over which viruses such as HIV have been transmitted may be crucial for directing public health resources and guiding policy decisions. Contact tracing of named sexual and injection drug-use partners of people recently diagnosed with HIV is an indispensible tool for reconstructing this transmission network. Viral genetic sequence data—routinely collected by public health agencies—can also be used to infer the dynamics of HIV transmission. We analyzed partner naming and viral genetic sequence data in 1342 people living with HIV in New York City reported to the New York City Department of Health and Mental Hygiene between 2006 and 2012. Genetically linked partners were more likely to be named partners than named partners were to be genetically linked. This finding indicates that genetic sequence data are better than partner naming data for reconstructing this viral transmission network. Importantly, the success rate in naming a genetically linked partner varied by transmission risk category (e.g., men who have sex with men, heterosexuals, and injection drug users). This study validates the use viral genetic sequences in reconstructing these viral transmission networks in a public health surveillance setting.

Muscle-specific loss of Bmal1 leads to disrupted tissue glucose metabolism and systemic glucose homeostasis

BACKGROUND

Diabetes is the seventh leading cause of death in the USA, and disruption of circadian rhythms is gaining recognition as a contributing factor to disease prevalence. This disease is characterized by hyperglycemia and glucose intolerance and symptoms caused by failure to produce and/or respond to insulin. The skeletal muscle is a key insulin-sensitive metabolic tissue, taking up ~80 % of postprandial glucose. To address the role of the skeletal muscle molecular clock to insulin sensitivity and glucose tolerance, we generated an inducible skeletal muscle-specific Bmal1 (-/-) mouse (iMSBmal1 (-/-)).

RESULTS

Progressive changes in body composition (decreases in percent fat) were seen in the iMSBmal1 (-/-) mice from 3 to 12 weeks post-treatment as well as glucose intolerance and non-fasting hyperglycemia. Ex vivo analysis of glucose uptake revealed that the extensor digitorum longus (EDL) muscles did not respond to either insulin or 5-aminoimidazole-4-carboxamide ribonucleotide (AICAR) stimulation. RT-PCR and Western blot analyses demonstrated a significant decrease in mRNA expression and protein content of the muscle glucose transporter (Glut4). We also found that both mRNA expression and activity of two key rate-limiting enzymes of glycolysis, hexokinase 2 (Hk2) and phosphofructokinase 1 (Pfk1), were significantly reduced in the iMSBmal1 (-/-) muscle. Lastly, results from metabolomics analyses provided evidence of decreased glycolytic flux and uncovered decreases in some tricarboxylic acid (TCA) intermediates with increases in amino acid levels in the iMSBmal1 (-/-) muscle. These findings suggest that the muscle is relying predominantly on fat as a fuel with increased protein breakdown to support the TCA cycle.

CONCLUSIONS

These data support a fundamental role for Bmal1, the endogenous circadian clock, in glucose metabolism in the skeletal muscle. Our findings have implicated altered molecular clock dictating significant changes in altered substrate metabolism in the absence of feeding or activity changes. The changes in body composition in our model also highlight the important role that changes in skeletal muscle carbohydrate, and fat metabolism can play in systemic metabolism.

Alevin efficiently estimates accurate gene abundances from dscRNA-seq data

We introduce alevin, a fast end-to-end pipeline to process droplet-based single-cell RNA sequencing data, performing cell barcode detection, read mapping, unique molecular identifier (UMI) deduplication, gene count estimation, and cell barcode whitelisting. Alevin's approach to UMI deduplication considers transcript-level constraints on the molecules from which UMIs may have arisen and accounts for both gene-unique reads and reads that multimap between genes. This addresses the inherent bias in existing tools which discard gene-ambiguous reads and improves the accuracy of gene abundance estimates. Alevin is considerably faster, typically eight times, than existing gene quantification approaches, while also using less memory.

Indole-3-lactic acid associated with Bifidobacterium-dominated microbiota significantly decreases inflammation in intestinal epithelial cells

BACKGROUND

Bifidobacterium longum subsp. infantis (B. infantis) is a commensal bacterium that colonizes the gastrointestinal tract of breast-fed infants. B. infantis can efficiently utilize the abundant supply of oligosaccharides found in human milk (HMO) to help establish residence. We hypothesized that metabolites from B. infantis grown on HMO produce a beneficial effect on the host.

RESULTS

In a previous study, we demonstrated that B. infantis routinely dominated the fecal microbiota of a breast fed Bangladeshi infant cohort (1). Characterization of the fecal metabolome of binned samples representing high and low B. infantis populations from this cohort revealed higher amounts of the tryptophan metabolite indole-3-lactic acid (ILA) in feces with high levels of B. infantis. Further in vitro analysis confirmed that B. infantis produced significantly greater quantities of the ILA when grown on HMO versus lactose, suggesting a growth substrate relationship to ILA production. The direct effects of ILA were assessed in a macrophage cell line and intestinal epithelial cell lines. ILA (1-10 mM) significantly attenuated lipopolysaccharide (LPS)-induced activation of NF-kB in macrophages. ILA significantly attenuated TNF-α- and LPS-induced increase in the pro-inflammatory cytokine IL-8 in intestinal epithelial cells. ILA increased mRNA expression of the aryl hydrogen receptor (AhR)-target gene CYP1A1 and nuclear factor erythroid 2-related factor 2 (Nrf2)-targeted genes glutathione reductase 2 (GPX2), superoxide dismutase 2 (SOD2), and NAD(P) H dehydrogenase (NQO1). Pretreatment with either the AhR antagonist or Nrf-2 antagonist inhibited the response of ILA on downstream effectors.

CONCLUSIONS

These findings suggest that ILA, a predominant metabolite from B. infantis grown on HMO and elevated in infant stool high in B. infantis, and protects gut epithelial cells in culture via activation of the AhR and Nrf2 pathway.

Genetic mutations in RNA-binding proteins and their roles in ALS

Mutations in genes that encode RNA-binding proteins (RBPs) have emerged as critical determinants of neurological diseases, especially motor neuron disorders such as amyotrophic lateral sclerosis (ALS). RBPs are involved in all aspects of RNA processing, controlling the life cycle of RNAs from synthesis to degradation. Hallmark features of RBPs in neuron dysfunction include misregulation of RNA processing, mislocalization of RBPs to the cytoplasm, and abnormal aggregation of RBPs. Much progress has been made in understanding how ALS-associated mutations in RBPs drive pathogenesis. Here, we focus on several key RBPs involved in ALS—TDP-43, HNRNP A2/B1, HNRNP A1, FUS, EWSR1, and TAF15—and review our current understanding of how mutations in these proteins cause disease.

Alliance for clinical trials in oncology (ALLIANCE) trial A021501: preoperative extended chemotherapy vs. chemotherapy plus hypofractionated radiation therapy for borderline resectable adenocarcinoma of the head of the pancreas

BACKGROUND

Borderline resectable pancreatic cancers infiltrate into adjacent vascular structures to an extent that makes an R0 resection unlikely when pancreatectomy is performed de novo. In a pilot study, Alliance for Clinical Trials in Oncology Trial A021101, the median survival of patients who received chemotherapy and radiation prior to anticipated pancreatectomy was 22 months, and 64% of operations achieved an R0 resection. However, the individual contributions of preoperative chemotherapy and radiation therapy to therapeutic outcome remain poorly defined.

METHODS

In Alliance for Clinical Oncology Trial A021501, a recently activated randomized phase II trial, patients (N = 134) with a CT or MRI showing a biopsy-confirmed pancreatic ductal adenocarcinoma that meets centrally-reviewed anatomic criteria for borderline resectable disease will be randomized to receive either 8 cycles of modified FOLFIRINOX (oxaliplatin 85 mg/m2, irinotecan 180 mg/m2, leucovorin 400 mg/m2 and infusional 5-fluorouracil 2400 mg/m2 over 2 days for 4 cycles) or to 7 cycles of modified FOLFIRINOX followed by stereotactic body radiation therapy (33-40 Gy in 5 fractions). Patients without evidence of disease progression following preoperative therapy will undergo pancreatectomy and will subsequently receive 4 cycles of postoperative modified FOLFOX6 (oxaliplatin 85 mg/m2, leucovorin 400 mg/m2, bolus 5-fluorouracil 400 mg/m2, and infusional 5-fluorouracil 2400 mg/m2 over 2 days for 4 cycles). The primary endpoint is the 18-month overall survival rate of patients enrolled into each of the two treatment arms. An interim analysis of the R0 resection rate within each arm will be conducted to assess treatment futility after accrual of 30 patients. Secondary endpoints include rates of margin-negative resection and event-free survival. The primary analysis will compare the 18-month overall survival rate of each arm to a historical control rate of 50%. The trial is activated nationwide and eligible to be opened for accrual at any National Clinical Trials Network cooperative group member site.

DISCUSSION

This study will help define standard preoperative treatment regimens for borderline resectable pancreatic cancer and position the superior arm for further evaluation in future phase III trials.

TRIAL REGISTRATION

ClinicalTrials.gov : NCT02839343 , registered July 14, 2016.

Machine learning in cardiovascular magnetic resonance: basic concepts and applications

Machine learning (ML) is making a dramatic impact on cardiovascular magnetic resonance (CMR) in many ways. This review seeks to highlight the major areas in CMR where ML, and deep learning in particular, can assist clinicians and engineers in improving imaging efficiency, quality, image analysis and interpretation, as well as patient evaluation. We discuss recent developments in the field of ML relevant to CMR in the areas of image acquisition & reconstruction, image analysis, diagnostic evaluation and derivation of prognostic information. To date, the main impact of ML in CMR has been to significantly reduce the time required for image segmentation and analysis. Accurate and reproducible fully automated quantification of left and right ventricular mass and volume is now available in commercial products. Active research areas include reduction of image acquisition and reconstruction time, improving spatial and temporal resolution, and analysis of perfusion and myocardial mapping. Although large cohort studies are providing valuable data sets for ML training, care must be taken in extending applications to specific patient groups. Since ML algorithms can fail in unpredictable ways, it is important to mitigate this by open source publication of computational processes and datasets. Furthermore, controlled trials are needed to evaluate methods across multiple centers and patient groups.

Benzene formation in electronic cigarettes

BACKGROUND/OBJECTIVE

The heating of the fluids used in electronic cigarettes ("e-cigarettes") used to create "vaping" aerosols is capable of causing a wide range of degradation reaction products. We investigated formation of benzene (an important human carcinogen) from e-cigarette fluids containing propylene glycol (PG), glycerol (GL), benzoic acid, the flavor chemical benzaldehyde, and nicotine.

METHODS/MAIN RESULTS

Three e-cigarette devices were used: the JUULTM "pod" system (provides no user accessible settings other than flavor cartridge choice), and two refill tank systems that allowed a range of user accessible power settings. Benzene in the e-cigarette aerosols was determined by gas chromatography/mass spectrometry. Benzene formation was ND (not detected) in the JUUL system. In the two tank systems benzene was found to form from propylene glycol (PG) and glycerol (GL), and from the additives benzoic acid and benzaldehyde, especially at high power settings. With 50:50 PG+GL, for tank device 1 at 6W and 13W, the formed benzene concentrations were 1.9 and 750 μg/m3. For tank device 2, at 6W and 25W, the formed concentrations were ND and 1.8 μg/m3. With benzoic acid and benzaldehyde at ~10 mg/mL, for tank device 1, values at 13W were as high as 5000 μg/m3. For tank device 2 at 25W, all values were ≤~100 μg/m3. These values may be compared with what can be expected in a conventional (tobacco) cigarette, namely 200,000 μg/m3. Thus, the risks from benzene will be lower from e-cigarettes than from conventional cigarettes. However, ambient benzene air concentrations in the U.S. have typically been 1 μg/m3, so that benzene has been named the largest single known cancer-risk air toxic in the U.S. For non-smokers, chronically repeated exposure to benzene from e-cigarettes at levels such as 100 or higher μg/m3 will not be of negligible risk.

Radiation-induced lung toxicity - cellular and molecular mechanisms of pathogenesis, management, and literature review

Lung, breast, and esophageal cancer represent three common malignancies with high incidence and mortality worldwide. The management of these tumors critically relies on radiotherapy as a major part of multi-modality care, and treatment-related toxicities, such as radiation-induced pneumonitis and/or lung fibrosis, are important dose limiting factors with direct impact on patient outcomes and quality of life. In this review, we summarize the current understanding of radiation-induced pneumonitis and pulmonary fibrosis, present predictive factors as well as recent diagnostic and therapeutic advances. Novel candidates for molecularly targeted approaches to prevent and/or treat radiation-induced pneumonitis and pulmonary fibrosis are discussed.