Biology subjects

Hess, M.

Publications and source records attributed to Hess, M..

11 recordsLinked to original sources

bioRxiv2018

Prevotella copri, a potential indicator for high feed efficiency in western steers

There has been a great interest to identify a microbial marker that can be used to predict feed efficiency of beef cattle. Such a marker, specifically one that would allow an early identification of animals with high feed efficiency for future breeding efforts, would facilitate increasing the profitability of cattle operations and simultaneously render them more sustainable by reducing their methane footprint. The work presented here suggests that Prevotella copri might be an ideal microbial marker for identifying beef cattle with high feed efficiency early in their life span and in the production cycle. Developing more refined quantification techniques that allow correlation of P. copri to feed efficiency of beef cattle that can be applied by lay people in the field holds great promise to improve the economy of cattle operations while simultaneously reducing their environmental impact by mitigating methane production from enteric fermentation.

microbiology↗

bioRxiv2018

Methane Reduction Potential of Two Pacific Coast Macroalgae During in-vitro Ruminant Fermentation.

With increasing interest in feed based methane mitigation strategies, fueled by local legal directives aimed at methane production from the agricultural sector in California, identifying local sources of biological feed additives will be critical in keeping the implementation of these strategies affordable. In a recent study, the red alga Asparagopsis taxiformis stood out as the most effective species of seaweed to reduce methane production from enteric fermentation. Due to the potential differences in effectiveness based on the location from where A. taxiformis is collected and the financial burden of collection and transport, we tested the potential of A. taxiformis, as well as the brown seaweed Zonaria farlowii collected in the nearshore waters off Santa Catalina Island, CA, USA, for their ability to mitigate methane production during in-vitro rumen fermentation. At a dose rate of 5% dry matter (DM), A. taxiformis reduced methane production by 74% (p [≤] 0.01) and Z. farlowii reduced methane production by 11% (p [≤] 0.04) after 48 hours and 24 hours of in-vitro rumen fermentation respectively. The methane reducing effect of A. taxiformis and Z. farlowii described here make these local macroalgae promising candidates for biotic methane mitigation strategies in the largest milk producing state in the US. To determine their real potential as methane mitigating feed supplements in the dairy industry, their effect in-vivo requires investigation.

systems biology↗

bioRxiv2018

Effect of the macroalgae Asparagopsis taxiformis on methane production and the rumen microbiome assemblage

BackgroundRecent studies using batch-fermentation suggest that the red macroalgae Asparagopsis taxiformis might reduce methane (CH4) emission from beef cattle by up to ~99% when added to rhodes grass hay, a common feed in the Australian beef industry. These experiments have shown significant reductions in methane without compromising other fermentation parameters (i.e. volatile fatty acid production) with A. taxiformis organic matter (OM) inclusion rates of up to 5%. In the study presented here, A. taxiformis was evaluated for its ability to reduce methane production from dairy cattle fed a mixed ration widely utilized in California; the largest milk producer in the US.\n\nResultsFermentation in a semi-continuous in-vitro rumen system suggests that A. taxiformis can reduce methane production from enteric fermentation in dairy cattle by 95% when added at a 5% OM inclusion rate without any obvious negative impacts on volatile fatty acid production. High-throughput 16S ribosomal RNA (rRNA) gene amplicon sequencing showed that seaweed amendment effects rumen microbiome communities consistent with the Anna Karenina hypothesis, with increased beta-diversity, over time scales of approximately three days. The relative abundance of methanogens in the fermentation vessels amended with A. taxiformis decreased significantly compared to control vessels, but this reduction in methanogen abundance was only significant when averaged over the course of the experiment. Alternatively, significant reductions of methane in the A. taxiformis amended vessels was measured in the early stages of the experiment. This suggests that A. taxiformis has an immediate effect on the metabolic functionality of rumen methanogens whereas its impact on microbiome assemblage, specifically methanogen abundance, is delayed.\n\nConclusionsThe methane reducing effect of A. taxiformis during rumen fermentation makes this macroalgae a promising candidate as a biotic methane mitigation strategy in the largest milk producing state in the US. But its effect in-vivo (i.e. in dairy cattle) remains to be investigated in animal trials. Furthermore, to obtain a holistic understanding of the biochemistry responsible for the significant reduction of methane, gene expression profiles of the rumen microbiome and the host animal are warranted.

systems biology↗

bioRxiv2018

Community structure of phototrophic co-cultures from extreme environments

Cyanobacteria are found in most illuminated environments and are key players in global carbon and nitrogen cycling. Although significant efforts have been made to advance our understanding of this important phylum, still little is known about how members of the cyanobacteria affect and respond to changes in complex biological systems. This lack of knowledge is in part due to our dependence on pure cultures when determining the metabolism and function of a microorganism. In the work presented here we took advantage of the Culture Collection of Microorganisms from Extreme Environments (CCMEE), a collection of more than 1,000 publicly available photosynthetic co-cultures now maintained at the Pacific Northwest National Laboratory. To highlight some of their scientific potential, we selected 26 of these photosynthetic co-cultures from the CCMEE for 16S rRNA gene sequencing. We assessed if samples readily available from the CCMEE could be used to generate new insights into the role of microbial communities in global and local carbon and nitrogen cycling. Results from this work support the existing notion that culture depositories in general hold the potential to advance fundamental and applied research. If collections of co-cultures can be used to infer roles of the individual organisms remains to be seen and requires further investigation.

microbiology↗

bioRxiv2018

A deep learning approach for uncovering lung cancer immunome patterns

Tumor immune cell infiltration is a well known factor related to survival of cancer patients. This has led to deconvolution approaches that can quantify immune cell proportions for each individual. What is missing, is an approach for modeling joint patterns of different immune cell types. We adapt a deep learning approach, deep Boltzmann machines (DBMs), for modeling immune cell gene expression patterns in lung adenocarcinoma. Specifically, a partially partitioned training approach for dealing with a relatively large number of genes. We also propose a sampling-based approach that smooths the original data according to a trained DBM and can be used for visualization and clustering. The identified clusters can subsequently be judged with respect to association with clinical characteristics, such as tumor stage, providing an external criterion for selecting DBM network architecture and tuning parameters for training. We show that the hidden nodes of the trained networks cannot only be linked to clinical characteristics but also to specific genes, which are the visible nodes of the network. We find that hidden nodes that are linked to tumor stage and survival represent expression of T-cell and mast cell genes among others, probably reflecting specific immune cell infiltration patterns. Thus, DBMs, trained and selected by the proposed approach, might provide a useful tool for extracting immune cell gene expression patterns. In the case of lung adenocarcinomas, these patterns are linked to survival as well as other patient characteristics, which could be useful for uncovering the underlying biology.

bioinformatics↗

bioRxiv2018

Conceptual content in images triggers rapid shifts of covert attention

The visual system can use conceptual information to search for targets even in the absence of clear featural signifiers1, and visual saccades are often directed at target objects defined by conceptual content2. These abilities are a core component of our facility with the visual world. Here, we evaluate whether contingent mechanisms of visual attention, known to trigger in response to target features such as motion, color or luminance3, are also triggered by visual patterns that match conceptually specified categories. These pre-registered experiments provide convergent behavioral and electrophysiological support that covert spatial attention is rapidly triggered by natural image exemplars from superordinate conceptually described target sets such as dinner food or four-legged animal, even when each target was viewed only once. In the behavioral experiment when two targets were presented with onsets separated by only 167ms, subjects reported the second target more often when it was in the same spatial location as the first. In the EEG experiment, images elicited clear N2pc and P3 components only when they matched the conceptually specified target set. The latency of the N2pc peaked at roughly 250ms, which is comparable to that commonly found in other N2pc studies for simpler stimulus types. These results suggest that vision quickly decodes conceptual information from natural images and selectively deploys spatial attention to locations containing information that matches current search goals.

neuroscience↗

bioRxiv2017

The evolutionary history of the current global Ramularia collo-cygni epidemic

Ramularia Leaf Spot (RLS) has emerged as a threat for barley production in many regions of the world. Late appearance of unspecific symptoms caused that Ramularia collo-cygni could only by molecular diagnostics be detected as the causal agent of RLS. Although recent research has shed more light on the biology and genomics of the pathogen, the cause of the recent global spread remains unclear.\n\nTo address urgent questions, especially on the emergence to a major disease, life-cycle, transmission, and quick adaptation to control measures, we de-novo sequenced the genome of R. collo-cygni (urug2 isolate). Additionally, we sequenced fungal RNA from 6 different conditions, which allowed for an improved genome annotation. This resulted in a high quality draft assembly of about 32 Mb, with only 78 scaffolds with an N50 of 2.1 Mb. The overall annotation enabled the prediction of 12.346 high confidence genes. Genomic comparison revealed that R. collo-cygni has significantly diverged from related Dothidiomycetes, including gain and loss of putative effectors, however without obtaining species-specific genome features.\n\nTo evaluate the species-wide genetic diversity, we sequenced the genomes of 19 R. collo-cygni isolates from multiple geographic locations and diverse hosts and mapped sequences to our reference genome. Admixture analyses show that R. collo-cygni is world-wide genetically uniform and that samples do not show a strong clustering on either geographical location or host species. To date, the teleomorph of R. collo-cygni has not been observed. Analysis of linkage disequilibrium shows that in the world-wide sample set there are clear signals of recombination and thus sexual reproduction, however these signals largely disappear when excluding three outliers samples, suggesting that the main global expansion of R. collo-cygni comes from mixed or clonally propagating populations. We further analysed the historic population size (Ne) of R. collo-cygni using Bayesian simulations.\n\nWe discuss how our genomic data and population genetics analysis can help understand the current R. collo-cygni epidemic and provide different hypothesis that are supported by our data. We specifically highlight how recombination, clonal spreading and lack of host-specificity could further support global epidemics of this increasingly recognized plant disease and suggest specific approaches to combat this pathogen.

plant biology↗

bioRxiv2017

The rumen microbiome: an underexplored resource for novel antimicrobial discovery

Antimicrobial peptides (AMPs) are promising drug candidates to target multi-drug resistant bacteria. The rumen microbiome presents an underexplored resource for the discovery of novel microbial enzymes and metabolites, including AMPs. Using functional screening and computational approaches, we identified 181 potentially novel AMPs from a rumen bacterial metagenome. Here, we show that three of the selected AMPs (Lynronne-1, 2 and 3) were effective against numerous bacterial pathogens, including methicillin resistant Staphylococcus aureus (MRSA). No decrease in MRSA susceptibility was observed after 25 days of sub-lethal exposure to these AMPs. The AMPs bound preferentially to bacterial membrane lipids and induced membrane permeability leading to cytoplasmic leakage. Topical administration of Lynronne-1 (10% w/v) to a mouse model of MRSA wound infection elicited a significant reduction in bacterial counts, which was comparable to treatment with 2% mupirocin ointment. Our findings indicate that the rumen microbiome may provide viable alternative antimicrobials for future therapeutic application.

microbiology↗

bioRxiv2017

Cerebral ischemia-induced genes are increased in acute schizophrenia: An opportunity for clinical translation of genomic research findings

Schizophrenia is a brain disorder of unknown etiology. Brain imaging studies have revealed evidence for hypoperfusion of the frontal cortex (hypofrontality) and progressive brain volume reduction in schizophrenic patients. Mild cerebral ischemia (oligemia) has been postulated as a cause of the disorder. If the ischemia hypothesis for the adult brain is correct, genes induced by cerebral ischemia should be increased in the frontal cortex of schizophrenic patients during acute psychosis. Here, we show for the first time through a combined analysis of gene expression data from all the studies of the Stanley Brain Collection covering the Brodmann area 46 of the frontal cortex and employing the well-established Affymetrix HGU133a microarray platform that genes upregulated by cerebral ischemia are significantly overexpressed (4.5-fold) in the frontal cortex of acute schizophrenic patients (representation factor (RF) 4.5, p < 0.0002) and to a lesser degree in chronic patients (RF 3.9, p < 0.008) in comparison to normal controls. Neurodevelopmental-, repair-, inflammation- and synapse-related genes showed no significant change. The difference between acute and chronic schizophrenic patients regarding cerebral ischemia-induced genes was highly significant (RF 2.8, p < 0.00007). The results reported here are in line with evidence from biochemical, cellular, electroencephalographic, brain imaging, cerebral near-infrared spectroscopy, vascular, and genetic association studies. In summary, our genomic analysis revealed a clear ischemic signature in the frontal cortex of schizophrenia patients, confirming the prediction of the adult ischemia hypothesis for this disorder. This finding suggests new possibilities for the treatment and prevention of schizophrenia.

neuroscience↗

bioRxiv2017

Recovery of genomes from metagenomes via a dereplication, aggregation, and scoring strategy

Microbial communities are critical to ecosystem function. A key objective of metagenomic studies is to analyse organism-specific metabolic pathways and reconstruct community interaction networks. This requires accurate assignment of assembled genome fragments to genomes. Existing binning methods often fail to reconstruct a reasonable number of genomes and report many bins of low quality and completeness. Furthermore, the performance of existing algorithms varies between samples and biotopes. Here, we present a dereplication, aggregation and scoring strategy, DAS Tool, that combines the strengths of a flexible set of established binning algorithms. DAS Tool applied to a constructed community generated more accurate bins than any automated method. Further, when applied to environmental and host-associated samples of different complexity, DAS Tool recovered substantially more near-complete genomes, including novel lineages, than any single binning method alone. The ability to reconstruct many near-complete genomes from metagenomics data will greatly advance genome-centric analyses of ecosystems.

microbiology↗

bioRxiv2016

Partitioned learning of deep Boltzmann machines for SNP data

Learning the joint distributions of measurements, and in particular identification of an appropriate low-dimensional manifold, has been found to be a powerful ingredient of deep leaning approaches. Yet, such approaches have hardly been applied to single nucleotide polymorphism (SNP) data, probably due to the high number of features typically exceeding the number of studied individuals. After a brief overview of how deep Boltzmann machines (DBMs), a deep learning approach, can be adapted to SNP data in principle, we specifically present a way to alleviate the dimensionality problem by partitioned learning. We propose a sparse regression approach to coarsely screen the joint distribution of SNPs, followed by training several DBMs on SNP partitions that were identified by the screening. Aggregate features representing SNP patterns and the corresponding SNPs are extracted from the DBMs by a combination of statistical tests and sparse regression. In simulated case-control data, we show how this can uncover complex SNP patterns and augment results from univariate approaches, while maintaining type 1 error control. Time-to-event endpoints are considered in an application with acute myeloid lymphoma patients, where SNP patterns are modeled after a pre-screening based on gene expression data. The proposed approach identified three SNPs that seem to jointly influence survival in a validation data set. This indicates the added value of jointly investigating SNPs compared to standard univariate analyses and makes partitioned learning of DBMs an interesting complementary approach when analyzing SNP data.

bioinformatics↗