Showing papers by "Rob Knight published in 2007"
TL;DR: A strategy to understand the microbial components of the human genetic and metabolic landscape and how they contribute to normal physiology and predisposition to disease.
Abstract: A strategy to understand the microbial components of the human genetic and metabolic landscape and how they contribute to normal physiology and predisposition to disease.
4,730 citations
TL;DR: It is shown that applying qualitative and quantitative measures to the same data set can lead to dramatically different conclusions about the main factors that structure microbial diversity and can provide insight into the nature of community differences.
Abstract: The assessment of microbial diversity and distribution is a major concern in environmental microbiology. There are two general approaches for measuring community diversity: quantitative measures, which use the abundance of each taxon, and qualitative measures, which use only the presence/absence of data. Quantitative measures are ideally suited to revealing community differences that are due to changes in relative taxon abundance (e.g., when a particular set of taxa flourish because a limiting nutrient source becomes abundant). Qualitative measures are most informative when communities differ primarily by what can live in them (e.g., at high temperatures), in part because abundance information can obscure significant patterns of variation in which taxa are present. We illustrate these principles using two 16S rRNA-based surveys of microbial populations and two phylogenetic measures of community β diversity: unweighted UniFrac, a qualitative measure, and weighted UniFrac, a new quantitative measure, which we have added to the UniFrac website (http://bmf.colorado.edu/unifrac). These studies considered the relative influences of mineral chemistry, temperature, and geography on microbial community composition in acidic thermal springs in Yellowstone National Park and the influences of obesity and kinship on microbial community composition in the mouse gut. We show that applying qualitative and quantitative measures to the same data set can lead to dramatically different conclusions about the main factors that structure microbial diversity and can provide insight into the nature of community differences. We also demonstrate that both weighted and unweighted UniFrac measurements are robust to the methods used to build the underlying phylogeny.
1,927 citations
TL;DR: The most comprehensive analysis of the environmental distribution of bacteria to date, based on 21,752 16S rRNA sequences compiled from 111 studies of diverse physical environments, is reported in this article.
Abstract: Microbes are difficult to culture. Consequently, the primary source of information about a fundamental evolutionary topic, life's diversity, is the environmental distribution of gene sequences. We report the most comprehensive analysis of the environmental distribution of bacteria to date, based on 21,752 16S rRNA sequences compiled from 111 studies of diverse physical environments. We clustered the samples based on similarities in the phylogenetic lineages that they contain and found that, surprisingly, the major environmental determinant of microbial community composition is salinity rather than extremes of temperature, pH, or other physical and chemical factors represented in our samples. We find that sediments are more phylogenetically diverse than any other environment type. Surprisingly, soil, which has high species-level diversity, has below-average phylogenetic diversity. This work provides a framework for understanding the impact of environmental factors on bacterial evolution and for the direction of future sequencing efforts to discover new lineages.
1,440 citations
TL;DR: The results show that sequencing effort is best focused on gathering more short sequences rather than fewer longer ones, provided that the primers are chosen wisely, and that community comparison methods such as UniFrac are surprisingly robust to variation in the region sequenced.
Abstract: Pyrosequencing technology allows us to characterize microbial communities using 16S ribosomal RNA (rRNA) sequences orders of magnitude faster and more cheaply than has previously been possible. However, results from different studies using pyrosequencing and traditional sequencing are often difficult to compare, because amplicons covering different regions of the rRNA might yield different conclusions. We used sequences from over 200 globally dispersed environments to test whether studies that used similar primers clustered together mistakenly, without regard to environment. We then tested whether primer choice affects sequence-based community analyses using UniFrac, our recently-developed method for comparing microbial communities. We performed three tests of primer effects. We tested whether different simulated amplicons generated the same UniFrac clustering results as near-full-length sequences for three recent large-scale studies of microbial communities in the mouse and human gut, and the Guerrero Negro microbial mat. We then repeated this analysis for short sequences (100-, 150-, 200- and 250-base reads) resembling those produced by pyrosequencing. The results show that sequencing effort is best focused on gathering more short sequences rather than fewer longer ones, provided that the primers are chosen wisely, and that community comparison methods such as UniFrac are surprisingly robust to variation in the region sequenced.
674 citations
TL;DR: In this first study to comprehensively survey viral communities using a metagenomic approach, it is found that soil viruses are taxonomically diverse and distinct from the communities of viruses found in other environments that have been surveyed using a similar approach.
Abstract: Recent studies have highlighted the surprising richness of soil bacterial communities; however, bacteria are not the only microorganisms found in soil. To our knowledge, no study has compared the diversities of the four major microbial taxa, i.e., bacteria, archaea, fungi, and viruses, from an individual soil sample. We used metagenomic and small-subunit RNA-based sequence analysis techniques to compare the estimated richness and evenness of these groups in prairie, desert, and rainforest soils. By grouping sequences at the 97% sequence similarity level (an operational taxonomic unit [OTU]), we found that the archaeal and fungal communities were consistently less even than the bacterial communities. Although total richness levels are difficult to estimate with a high degree of certainty, the estimated number of unique archaeal or fungal OTUs appears to rival or exceed the number of unique bacterial OTUs in each of the collected soils. In this first study to comprehensively survey viral communities using a metagenomic approach, we found that soil viruses are taxonomically diverse and distinct from the communities of viruses found in other environments that have been surveyed using a similar approach. Within each of the four microbial groups, we observed minimal taxonomic overlap between sites, suggesting that soil archaea, bacteria, fungi, and viruses are globally as well as locally diverse.
505 citations
TL;DR: Oral MPR therapy is a promising first-line treatment for elderly myeloma patients and aspirin appears to provide adequate antithrombosis prophylaxis.
Abstract: Purpose Lenalidomide has shown significant antimyeloma activity in clinical studies. Oral melphalan, prednisone, and thalidomide have been regarded as the standard of care in elderly multiple myeloma patients. We assessed dosing, efficacy, and safety of melphalan, prednisone, and lenalidomide (MPR) in newly diagnosed elderly myeloma patients. Patients and Methods Oral melphalan was administered in doses ranging from 0.18 to 0.25 mg/kg on days 1 to 4, prednisone at a 2-mg/kg dose on days 1 to 4, and lenalidomide at doses ranging from 5 to 10 mg on days 1 to 21, every 28 days for nine cycles, followed by maintenance therapy with lenalidomide alone. Aspirin was given as a prophylaxis for thrombosis. Results Fifty-four patients were enrolled and evaluated after completing the assigned treatment schedule. The maximum tolerated dose was defined as 0.18 mg/kg melphalan and 10 mg lenalidomide. With these doses, 81% of patients achieved at least a partial response, 47.6% achieved a very good partial response, and ...
312 citations
TL;DR: The results suggest that common regions of a dorsal frontoparietal network and the ACC are engaged in the flexible control of a wide range of executive processes, and that response anticipation modulates overall activity in the executive control network but does not interact with response conflict processing.
Abstract: Response anticipation and response conflict processes are supported by executive control. However, few neuroimaging studies have attempted to study the relationship between these two processes in the same experimental session. In this study, we isolated brain activity associated with response anticipation (after a cue to prepare vs relax) and with response conflict (responding to a target with incongruent vs congruent flankers) and examined the independence and interaction of brain networks supporting these processes using event-related potentials (ERPs) and functional magnetic resonance imaging. Response anticipation generated a contingent negative variation ERP that correlated with shorter reaction times, and was associated with activation of a thalamo-cortico-striatal network, as well as increased gamma band power in frontal and parietal regions, and decreased spectral power in theta, alpha, and beta bands in most regions. Response conflict was associated with increased activation in the anterior cingulate cortex (ACC) and prefrontal cortex of the executive control network, with an overlap in activation with response anticipation in regions including the middle frontal gyrus, ACC, and superior parietal lobule. Although the executive control network showed increased activation in response to unanticipated versus anticipated targets, the response conflict effect was not altered by response anticipation. These results suggest that common regions of a dorsal frontoparietal network and the ACC are engaged in the flexible control of a wide range of executive processes, and that response anticipation modulates overall activity in the executive control network but does not interact with response conflict processing.
219 citations
TL;DR: The COmparative GENomic Toolkit is implemented in Python, a fully integrated and thoroughly tested framework for novel probabilistic analyses of biological sequences, devising workflows, and generating publication quality graphics.
Abstract: We have implemented in Python the COmparative GENomic Toolkit, a fully integrated and thoroughly tested framework for novel probabilistic analyses of biological sequences, devising workflows, and generating publication quality graphics. PyCogent includes connectors to remote databases, built-in generalized probabilistic techniques for working with biological sequences, and controllers for third-party applications. The toolkit takes advantage of parallel architectures and runs on a range of hardware and operating systems, and is available under the general public license from http://sourceforge.net/projects/pycogent.
214 citations
01 Dec 2007
TL;DR: This work reports the most comprehensive analysis of the environmental distribution of bacteria to date, based on 21,752 16S rRNA sequences compiled from 111 studies of diverse physical environments, and finds that sediments are more phylogenetically diverse than any other environment type.
Abstract: Microbes are difficult to culture. Consequently, the primary source of information about a fundamental evolutionary topic, life's diversity, is the environmental distribution of gene sequences. We report the most comprehensive analysis of the environmental distribution of bacteria to date, based on 21,752 16S rRNA sequences compiled from 111 studies of diverse physical environments. We clustered the samples based on similarities in the phylogenetic lineages that they contain and found that, surprisingly, the major environmental determinant of microbial community composition is salinity rather than extremes of temperature, pH, or other physical and chemical factors represented in our samples. We find that sediments are more phylogenetically diverse than any other environment type. Surprisingly, soil, which has high species-level diversity, has below-average phylogenetic diversity. This work provides a framework for understanding the impact of environmental factors on bacterial evolution and for the direction of future sequencing efforts to discover new lineages.
184 citations
TL;DR: It is shown that considerable heterogeneity exists in the relative rates of evolution of different secondary structure categories within the rRNA, and that in eukaryotes, loops actually evolve much faster than stems, suggesting that phylogenetically and structurally specific models will improve evolutionary and structural predictions.
Abstract: Understanding patterns of rRNA evolution is critical for a number of fields, including structure prediction and phylogeny. The standard model of RNA evolution is that compensatory mutations in stems make up the bulk of the changes between homologous sequences, while unpaired regions are relatively homogeneous. We show that considerable heterogeneity exists in the relative rates of evolution of different secondary structure categories (stems, loops, bulges, etc.) within the rRNA, and that in eukaryotes, loops actually evolve much faster than stems. Both rates of evolution and abundance of different structural categories vary with distance from functionally important parts of the ribosome such as the tRNA path and the peptidyl transferase center. For example, fast-evolving residues are mainly found at the surface; stems are enriched at the subunit interface, and junctions near the peptidyl transferase center. However, different secondary structure categories evolve at different rates even when these effects are accounted for. The results demonstrate that relative rates and patterns of evolution are lineage specific, suggesting that phylogenetically and structurally specific models will improve evolutionary and structural predictions.
113 citations
TL;DR: It is shown that a zebrafish tfap2c is expressed in the nonneural ectoderm during early development and functions redundantly with TFap2a in NC specification, and cell transplantation experiments indicate that tfap 2c functions cell-autonomously in NC specifications.
Abstract: Transcription factor AP2 (Tfap2) genes play essential roles in development of the epidermis and migratory cells of the neural crest (NC) in vertebrate embryos. These transcriptional activators are among the earliest genes expressed in the ectoderm and specify fates within the epidermis/crest through both direct and indirect mechanisms. The Tfap2 family arose from a single ancestral gene in a chordate ancestor that underwent gene duplication to give up to five family members in living vertebrates. This coincided with the acquisition of important roles in NC development by Tfap2 genes suggesting that this gene family was important in ectodermal evolution and possibly in the origin of NC. Here, we show that a zebrafish tfap2c is expressed in the nonneural ectoderm during early development and functions redundantly with tfap2a in NC specification. In zebrafish embryos depleted of both tfap2a and tfap2c, NC cells are virtually eliminated. Cell transplantation experiments indicate that tfap2c functions cell-autonomously in NC specification. Cells of the enveloping layer, which forms a temporary skin layer surrounding the ectoderm, also fail to differentiate or to express appropriate keratins in tfap2c deficient embryos. The role of Tfap2 genes in epidermal and NC development is considered here in the broader context of ectodermal evolution. Distinct, tissue-specific functions for Tfap2 genes in different vertebrates may reflect subfunctionalisation of an ancestral gene that consequently led to the gain of novel roles for different subfamily members in patterning the epidermis and NC.
TL;DR: The results provide electrophysiological and behavioral evidence that unpleasant, emotionally arousing stimuli interfere with the right hemisphere-dependent attention capacity.
Abstract: Rapid interaction of the emotional and attentional networks is critical for adaptive behavior. Here, we examined the effects of emotional stimulation on hemifield attention allocation using event-related potential and behavioral measures. Participants performed a visual-discrimination task on nonemotional targets presented randomly in the left or right hemifield. A brief task-irrelevant emotional (pleasant or unpleasant; 150-ms duration) or neutral picture was presented centrally 350 ms before the next target (150-ms duration). Unpleasant stimuli interfered with the left visual field attention capacity, slowing behavioral responses to attended left field stimuli. In keeping with the behavioral data, event-related potential responses to nonemotional attended left field stimuli were reduced over the right parietal regions when preceded by an unpleasant event. The results provide electrophysiological and behavioral evidence that unpleasant, emotionally arousing stimuli interfere with the right hemisphere-dependent attention capacity.
TL;DR: It is shown that, during motor learning, the BOLD response of unimodal motor cortical areas precedes the response in higher-order multimodal association areas, including posterior parietal cortex.
TL;DR: In this paper, the concept of Markov chain embedding is used to analyze patterns in random strings produced by a memoryless source, together with the capability of automata to recognize complicated patterns, allows a systematic analysis of problems related to the occurrence and frequency of patterns.
Abstract: RNA motifs typically consist of short, modular patterns that include base pairs formed within and between modules. Estimating the abundance of these patterns is of fundamental importance for assessing the statistical significance of matches in genomewide searches, and for predicting whether a given function has evolved many times in different species or arose from a single common ancestor. In this manuscript, we review in an integrated and self-contained manner some basic concepts of automata theory, generating functions and transfer matrix methods that are relevant to pattern analysis in biological sequences. We formalize, in a general framework, the concept of Markov chain embedding to analyze patterns in random strings produced by a memoryless source. This conceptualization, together with the capability of automata to recognize complicated patterns, allows a systematic analysis of problems related to the occurrence and frequency of patterns in random strings. The applications we present focus on the concept of synchronization of automata, as well as automata used to search for a finite number of keywords (including sets of patterns generated according to base pairing rules) in a general text.
01 Jan 2007
TL;DR: Among the words that have migrated from the laboratory to the high street is ‘antioxidant’ as mentioned in this paper. But will the man in the street be familiar with the word "antioxidants"?
Abstract: Among the words that have migrated from the laboratory to the high street is ‘antioxidant’. Even more than the man in the street, will his wife be familiar with the word ‘antioxidant’. Antioxidants rule, OK! Nutraceuticals, functional foods and cosmetics may include the antioxidant word in their promotion and antioxidant materials in their substance. Manufacturers of these products, aware of the current and growing interest in antioxidants, should now be prepared to justify any claims they make for their product from the results of rigorous analysis.
30 Apr 2007
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TL;DR: In this paper, the concept of Markov chain embedding is used to analyze patterns in random strings produced by a memoryless source, together with the capability of automata to recognize complicated patterns, allows a systematic analysis of problems related to the occurrence and frequency of patterns.
Abstract: RNA motifs typically consist of short, modular patterns that include base pairs formed within and between modules. Estimating the abundance of these patterns is of fundamental importance for assessing the statistical significance of matches in genomewide searches, and for predicting whether a given function has evolved many times in different species or arose from a single common ancestor. In this manuscript, we review in an integrated and self-contained manner some basic concepts of automata theory, generating functions and transfer matrix methods that are relevant to pattern analysis in biological sequences. We formalize, in a general framework, the concept of Markov chain embedding to analyze patterns in random strings produced by a memoryless source. This conceptualization, together with the capability of automata to recognize complicated patterns, allows a systematic analysis of problems related to the occurrence and frequency of patterns in random strings. The applications we present focus on the concept of synchronization of automata, as well as automata used to search for a finite number of keywords (including sets of patterns generated according to base pairing rules) in a general text.