Inferring correlation networks from genomic survey data.

In a majority of the solar/thermal studies to date, a utility economic methodology has been used to assess the potential of solar power systems. The utility sector is precluded from taking advantage of loan leveraging because the effective rate of return is artificially set. Utilities are regulated by public commissions and thus must finance new capital investments according to a prescribed set of rules on after tax cost of capital and fixed charge rates. Commercial ventures have no such externally imposed constraints and make decisions for capital expenditures which include the effect of loan leveraging. The relevant parameters for a commercial institution are interest rate on debt, a discount rate which accounts for risk, and the effect of favorable tax incentives. An expression is developed for a capital cost factor which contains these parameters. Results are shown for various downpayments and discount rates. It will be shown that the effect of loan leveraging can be substantial in affecting the penetration of solar process heat into the commercial energy market. In addition, the relation between loan leveraging and risk is investigated.

Meta'omic Analytic Techniques for Studying the Intestinal Microbiome

The agents we now know as bacteria have been known for centuries to play a key role in certain kinds of illnesses and ailments. But aside from infectious diseases, the communities of microbes we carry in specific parts of our bodies—our microbiomes—are a relatively new topic in human health. Now this field of study has taken an evolutionary leap forward with new research showing human microbiomes may play a far greater role in environ-mental health than ever imagined. The excitement around this field was obvious at a National Academy of Sciences (NAS) workshop on the interplay of the microbiomes, environ-mental agents, and human health held 27–28 April 2011,1 where talks by researchers working in this area inspired numerous “eureka!” moments. New findings about the ways in which human microbiomes transform arsenic and mercury—two of our most prevalent and well-defined external human health hazards—suggest the role of commensal bacteria may equal or exceed that of genetic polymorphisms that regulate metal transformations within the body, says Ellen Silbergeld, a professor of environmental health sciences at the Johns Hopkins University Bloomberg School of Public Health. The implications of these new insights are staggering. Environmental health scientists may need to expand the toxicokinetics of metals and other environmental agents, as well as associated biomarkers, to include the microbial component. “This is a huge thing that has never been thought of before in environmental health sciences,” Silbergeld told workshop attendees. Emerging findings also demand a re-examination of what it means to be exposed to environmental agents, Silbergeld says. To a toxicologist, she explains, a contaminant is only “in the body” once it has crossed from the external environment into circulating blood, or a cell, or an organ. But new findings suggest biologically relevant transformations may take place prior to absorption, when contaminants interact with the microbiome in the mouth, intestines, or other tissues. Because of the metabolic processes mediated by microbiomes, a great deal of what toxicologists attribute to human metabolism—such as methylation of arsenic—may actually take place at least in part before contaminants cross into the internal environment of our bodies.

In an effort to better understand the ancestral state of the human distal gut microbiome, we examine feces retrieved from archaeological contexts (coprolites). To accomplish this, we pyrosequenced the 16S rDNA V3 region from duplicate coprolite samples recovered from three archaeological sites, each representing a different depositional environment: Hinds Cave (∼8000 years B.P.) in the southern United States, Caserones (1600 years B.P.) in northern Chile, and Rio Zape in northern Mexico (1400 years B.P.). Clustering algorithms grouped samples from the same site. Phyletic representation was more similar within sites than between them. A Bayesian approach to source-tracking was used to compare the coprolite data to published data from known sources that include, soil, compost, human gut from rural African children, human gut, oral and skin from US cosmopolitan adults and non-human primate gut. The data from the Hinds Cave samples largely represented unknown sources. The Caserones samples, retrieved directly from natural mummies, matched compost in high proportion. A substantial and robust proportion of Rio Zape data was predicted to match the gut microbiome found in traditional rural communities, with more minor matches to other sources. One of the Rio Zape samples had taxonomic representation consistent with a child. To provide an idealized scenario for sample preservation, we also applied source tracking to previously published data for Ötzi the Iceman and a soldier frozen for 93 years on a glacier. Overall these studies reveal that human microbiome data has been preserved in some coprolites, and these preserved human microbiomes match more closely to those from the rural communities than to those from cosmopolitan communities. These results suggest that the modern cosmopolitan lifestyle resulted in a dramatic change to the human gut microbiome.

The Intestinal Metabolome: An Intersection Between Microbiota and Host

The human gut microbiome: current knowledge, challenges, and future directions

Introduction. The study addresses the challenge of utilizing human gut microbiome data for the early detection of colorectal cancer (CRC). The research emphasizes the potential of using machine learning techniques to analyze complex microbiome datasets, providing a non-invasive approach to identifying CRC-related microbial markers.Hypothesis/Gap Statement. The primary hypothesis is that a robust machine learning-based analysis of 16S rRNA microbiome data can identify specific microbial features that serve as effective biomarkers for CRC detection, overcoming the limitations of classical statistical models in high-dimensional settings.Aim. The primary objective of this study is to explore and validate the potential of the human microbiome, specifically in the colon, as a valuable source of biomarkers for colorectal cancer (CRC) detection and progression. The focus is on developing a classifier that effectively predicts the presence of CRC and normal samples based on the analysis of three previously published faecal 16S rRNA sequencing datasets.Methodology. To achieve the aim, various machine learning techniques are employed, including random forest (RF), recursive feature elimination (RFE) and a robust correlation-based technique known as the fuzzy forest (FF). The study utilizes these methods to analyse the three datasets, comparing their performance in predicting CRC and normal samples. The emphasis is on identifying the most relevant microbial features (taxa) associated with CRC development via partial dependence plots, i.e. a machine learning tool focused on explainability, visualizing how a feature influences the predicted outcome.Results. The analysis of the three faecal 16S rRNA sequencing datasets reveals the consistent and superior predictive performance of the FF compared to the RF and RFE. Notably, FF proves effective in addressing the correlation problem when assessing the importance of microbial taxa in explaining the development of CRC. The results highlight the potential of the human microbiome as a non-invasive means to detect CRC and underscore the significance of employing FF for improved predictive accuracy.Conclusion. In conclusion, this study underscores the limitations of classical statistical techniques in handling high-dimensional information such as human microbiome data. The research demonstrates the potential of the human microbiome, specifically in the colon, as a valuable source of biomarkers for CRC detection. Applying machine learning techniques, particularly the FF, is a promising approach for building a classifier to predict CRC and normal samples. The findings advocate for integrating FF to overcome the challenges associated with correlation when identifying crucial microbial features linked to CRC development.

The human microbiome is an emerging research area of interest with a series of reports on associations with various diseases and health, mainly due to advances in comprehensive analysis with the advent of next-generation sequencers. Fecal microbiota transplantation, a powerful method for modulating gut microbiota, has been reported to be surprisingly effective for certain disease populations that cannot be treated with conventional drugs. These results provide the possibility of microbiome modulation as a new approach for drug discovery. In the United States, the Human Microbiome Project was initiated in 2010 and many human microbiome data have been acquired, archived, and published. This has led to the emergence of a number of biopharmaceutical companies with clinical stage pipelines. Some of the most advanced pipelines are in the late clinical stage. However, issues such as the variability of data generated by different assays/protocols and the lack of data from healthy individuals as a reliable reference dataset should be addressed to further facilitate microbiome research and drug discovery based on human microbiome research. In Japan, in particular, established pharmaceutical companies tend to be reluctant to use these new approaches, as seen in human genome-based drug discovery. This may result in increased barriers to entry into emerging markets and a potential loss of opportunity. Since human microbiome research, especially gut microbiome research, has long been addressed by various research groups in Japan, microbe expertise and technology is high mainly in academia. It is important for Japanese industry to utilize the existing expertise of academic research groups. To accelerate the industrial use, Japan Microbiome Consortium (JMBC) was established in 2017, and its member companies include 33 pharmaceutical, chemical, food, and testing companies in Japan, including major companies and bio-venture companies. In this lecture, I will introduce the background of the establishment of JMBC and share the activities of the project for establishment of the measurement infrastructure and the project for construction of the database of healthy persons, which have been common goals. I would like to summarize the recent global trends and activities on drug discovery based on the human microbiome, as well as issues for obtaining approval and launching the drug. We would also like to discuss how we can activate drug discovery activities as one of JMBC's activities and lead to the creation of innovative new drugs.

The NIH recently announced the first recipients of funding for its Human Microbiome Project. The number of microorganisms that live inside or on the body of a healthy adult human is thought to be least 10 times greater than the number of human somatic and germ cells (Figure ​(Figure1).1). Some of these microorganisms are beneficial to the human host (e.g., some bacteria in the intestines break down carbohydrates that humans would otherwise not be able to digest), and some can cause illness (e.g., Streptococcus pneumoniae, which can be found in the nasopharynx of healthy individuals but causes disease if it colonizes other parts of the body, such as the lungs, where it causes pneumonia). However, the effects on human health of most of these microorganisms are not known; as Martin Blaser, Professor of Medicine and Microbiology at New York University School of Medicine, told the JCI, “We need to know who these microorganisms are and what they do.” In an attempt to answer these questions and increase our understanding of how these microorganisms influence human health and disease, the NIH launched the Human Microbiome Project at the end of 2007 (1). Figure 1 The genome of Lactobacillus acidophilus is part of the human microbiome, as L. acidophilus can be found in the mouth, intestines, and vagina. The human microbiome consists of all the genomes of all the microorganisms that live inside or on the body of a healthy adult human. The idea behind the Human Microbiome Project is to use metagenomic and traditional genome sequencing approaches, which enable the analysis of all the DNA of all the microbes recovered in an environmental sample and of single clonal microbial cultures, respectively, to determine whether individuals share a core human microbiome and to understand whether changes in the human microbiome correlate in any way with changes in human health. Other goals of the project include developing new technological and bioinformatic tools to address the central issues and determining the ethical, legal, and social effects of research into the human microbiome. The initial goal is to sequence 600 genomes from both cultured and uncultured bacteria as well as several nonbacterial microorganisms, focusing on the genomes of microorganisms in the digestive tract, mouth, skin, nose, and female urogenital tract. The aim of this goal is to provide researchers the background information to investigate the relationship between the microorganisms that contribute to the microbiome and human disease. Upon completion, the total number of microbial genomes sequenced will be raised to around 1,000, as some have already been completed, some are underway, and some will be sequenced as part of other projects that are already planned. An estimated $115 million will be distributed to researchers as part of the Human Microbiome Project over 5 years. Most of the recently announced initial round of funding, which will provide approximately $21.2 million, is to support researchers developing innovative technologies — specifically technologies that should help improve and refine the way in which researchers identify the microorganisms that contribute to the microbiome — and computational tools. According to Alan Krensky, director of the Office of Portfolio Analysis and Strategic Initiatives, which oversees the NIH Roadmap for Medical Research, the NIH targeted initial funding in this area because “The development of new tools and technologies is central to our ability to meet the goals of the Human Microbiome Project. An exceptional amount of information will be generated by this project and we need robust technologies and analytical tools that are equal to the task” (2). The remainder of the money awarded in this round of funding is going toward establishing the Human Microbiome Project Data Analysis and Coordination Center and to researchers examining ethical, legal, and social implications of human microbiome research. The Data Analysis and Coordination Center will act as a repository for all the data generated by the Human Microbiome Project, which will also be deposited in other public databases, including those supported by the National Center for Biotechnology Information (3). The importance of the Human Microbiome Project was highlighted to the JCI by Blaser, who thinks that understanding how the microorganisms that contribute to the microbiome affect human health and disease is an area of research as exciting as stem cell research, with as much therapeutic potential. He continued that the microbiome is likely much more diverse than the human genome and that it changes over time as the microorganisms that colonize the human body change. By understanding these changes and taking a census of the microorganisms present under different conditions of health and disease, Blaser hopes that the Human Microbiome Project will lead to the identification of new drugs from microorganisms — just as drugs have been developed using botulinum toxin and cyclosporine A from the bacterium Clostridium botulinum and the fungus Tolypocladium inflatum Gams, respectively — and the use of approaches to manipulate the populating microorganisms to benefit human health; there is a “lot of potential” there, he says.

AimsThe DNA marker HF183 is a partial 16S rRNA gene sequence highly specific to human‐associated Bacteroides including Bacteroides dorei. While HF183 is used to assess human faecal contamination in aquatic environments worldwide, little is known about the existence of HF183 and B. dorei in human microbiomes outside of the human gastrointestinal tract and faeces.Methods and ResultsPreviously published human skin and urine microbiome data sets from five independent human body skin studies, the Human Microbiome Project (HMP) and three independent human urine studies were analysed. The HF183 gene sequence was detected in all skin data sets, with the ratios of positive samples ranging from 0.5% to 36.3%. Popliteal fossa (knee), volar forearm and inguinal (groin) creases were identified as hot spots. HF183 was detected in two of three urine data sets, with ratios of positive samples ranging from 0% to 37.5%. All HF183‐containing sequences from these data sets were classified as associated with B. dorei.ConclusionsHF183 is widespread on human skin and present in urine.Significance and Impact of StudySkin and urine microbiomes could be sources of HF183 to environmental waters. Such non‐faecal sources of HF183 might explain low concentrations of HF183 in recreational waters when swimmers are present.

Navigating a Two-Way Street: Metal Toxicity and the Human Gut Microbiome.

The human gut carries a vast and diverse microbial community that is essential for human health. Understanding the structure of this complex community is a crucial step toward comprehending human-microbiome interactions. Traditional co-occurrence and correlation analyses typically focus on pairwise relationships and ignore higher-order relationships. Association rule mining (ARM) is a well-developed technique in data mining and has been applied to human microbiome data to identify higher-order relationships. Yet, existing attempts suffer from small sample sizes and low taxonomic resolution. We developed an advanced ARM framework and systematically investigated the interactions between microbial species using a public large-scale uniformly processed human microbiome data from the curatedMetagenomicData (CMD) together with ARM. First, we inferred association rules in the gut microbiome samples of healthy individuals (n=2,815) in CMD. Then we compared those rules with those inferred from the individuals with different diseases: inflammatory bowel disease (IBD, n=768), colorectal cancer (CRC, n=368), impaired glucose tolerance (IGT, n=199), and type 2 diabetes (T2D, n=164). Finally, we demonstrated that ARM is an efficient feature selection tool that can improve the performance of microbiome-based disease classification. Together, this study illustrates the higher-order microbial relationships in the human gut microbiome and highlights the critical importance of incorporating association rules in microbiome-based disease classification.

Welcome to the third Pediatric Obesity column for JPSN! This column's missions are to provide information and resources to the pediatric surgical nurse caring for children affected by obesity and to share focused assessment and intervention strategies that can be incorporated into the practitioner's daily practice. POOP, BUGS, AND OBESITY Beverly B. Haynes, MSN, RN, CPN The prevalence of obesity remains elevated, affecting at least 170 million children around the world. More than one third of children and one of every eight preschoolers in the United States are affected. Many theories are proposed, and much research is being conducted to find causes and treatments for pediatric obesity; still, there are no definite answers. Recently, there has been a surge in interest in stool microbiome as a possible culprit. The following is a brief introduction to this interesting and promising research. The microbiome is defined as the collective genomes of microbes (composed of bacteria, bacteriophages, fungi, protozoa, and viruses) that live in and on the human body (Yang, 2012). There are estimated to be about 100 million microbes per human body or about 10 times the number of human cells (Qin et al., 2010). Bacteria in the gut, especially in the lower gut, appear to influence digestion and energy extraction from food, catabolism of dietary toxins and carcinogens, synthesis of micronutrients, and fermentation of indigestible food substances and assist in the absorption of electrolytes and minerals (Payne et al., 2011). They also influence certain intestinal cell growth and play a role in the immune system. Differences in metabolic activities of gut bacteria may be responsible for the tendency of certain individuals to gain weight more easily or to experience diabetes, metabolic syndrome, and other diseases (Devaraj, Hemarajata, & Versalovic, 2013). There are two main phyla in the human gut, bacteroidetes (gram-negative rods) and firmicutes (gram-negative and gram-positive cocci). These phyla are known to be involved in energy harvest and are implicated in obesity. There are many subdivisions of these phyla found in the healthy human gut, creating a great diversity of microbes. Diversity is defined as the number and abundance distribution of distinct organism types. Low diversity in the gut is linked to obesity and bowel diseases (Human Microbiome Project Consortium, 2012). Studies of lean and obese mice and human twins suggest that individual differences in microbiomes influence metabolism leading to differences in resorption of energy from food substances, energy utilization, and energy storage. Obesity is noted to be more prevalent when the bacterial phyla are altered and diversity is decreased (Bervoets et al., 2013; Turnbaugh et al., 2009). The human intestine contains most bacteria (as opposed to other areas of the body) and is necessary for life because of bacterial influence on physiology and nutrient harvest. As part of the Human Microbiome Project, bacteria in the body are cataloged, and the impact these bacteria have on humans is studied (Qin et al., 2010). Various methods of identifying the microbiome are used, but the most useful involve DNA testing and metagenomics. In the past, bacteria were identified using culture techniques. This only allowed differentiation between gram-positive and gram-negative bacteria. Many more bacteria remained unidentified; by using DNA methods, scientists are able to identify many more bacteria. In addition, researchers are able to determine how the bacteria function, allowing them to study bacterial influences on the body. Knowing the bacterial influence of bacteria within the body has given clinicians the ability to change the course of disease. For example, by studying Clostridium difficile, researchers were able to develop a treatment for Clostridium difficile infections unresponsive to antibiotic therapy. In these cases, a stool transplant using purified stool from a healthy donor is introduced via colonoscopy into several points in the colon. Recipients resumed healthy bowel patterns within 2–3 days of the transplant with no recurrence during the subsequent 6 months (Petrof et al., 2013). In summary, humans are host to vast bacterial populations that regulate our health. Changes in bacterial populations appear to have significant influence on the body, including obesity. Research continues to identify bacteria in the body and various influences that change bacterial populations or their function. In the area of obesity, there is hope that knowledge gained from these studies will allow new practices with regard to feeding routines at various ages for children worldwide resulting in the control of obesity. Understanding how bacteria function in other diseases and the treatment derived from that research may allow routine use of similar treatments, such as fecal transplantation, of obesity.

The human microbiota comprises all microorganisms living within the human body and microbiome is their collective genome. They include eukaryotes, archaea, bacteria and viruses. Most microbes live in the gut, particularly in the large intestine. The number of genes contributed to by the microbes is 100 times the number of genes in the human genome. The sheer microbial abundance suggests that the human body is a collection of human and microbial cells and their genes resulting in a blend of human and microbial traits. The microbiome is essential for maintenance of human life. The microbes that live in and on us are mostly beneficial colonizers and have many important functions such as assisting in digestion, regulating immune system, producing essential vitamins and protecting against bacteria that cause diseases. Research has demonstrated that dysbioses in the human microbiome correlate with numerous disease states, including inflammatory bowel disease, malignancy and autoimmune diseases. The metagenome of the human microbiome, the total DNA content of microbes inhabiting our bodies shows a large degree of interpersonal diversity. With the advent of culture-independent techniques such as high throughput next generation sequencing of DNA and advances in computational biology, research on human microbiome has reached new heights. A breakthrough in research was the development of Human Microbiome Project (HMP), which was established in 2008, which is also known as the second human genome project, with the objective of generating a metagenomic reference database for “normal” individuals to serve as a resource for researchers. The introduction of new technologies has permitted the study of the functional component of the microbe–host interactions through metabolomics. The resultant biomarkers have been used to predict and diagnose diseases early. Future research should focus on understanding mechanisms responsible for pathology and the causality role of the microbiome to use them as therapeutic modalities .

If you are not an astronomer (amateur or otherwise), looking at the stars can be an exercise in frustration. Where are the constellations? The planets? A telescope doesn’t necessarily help: you can see things more clearly, but without knowing where to look and what to look for, you can’t tell what you are seeing.