Abstract
Motivation
Estimating microbial association networks from high-throughput sequencing data is a common exploratory data analysis approach aiming at understanding the complex interplay of ...microbial communities in their natural habitat. Statistical network estimation workflows comprise several analysis steps, including methods for zero handling, data normalization and computing microbial associations. Since microbial interactions are likely to change between conditions, e.g. between healthy individuals and patients, identifying network differences between groups is often an integral secondary analysis step. Thus far, however, no unifying computational tool is available that facilitates the whole analysis workflow of constructing, analysing and comparing microbial association networks from high-throughput sequencing data.
Results
Here, we introduce NetCoMi (Network Construction and comparison for Microbiome data), an R package that integrates existing methods for each analysis step in a single reproducible computational workflow. The package offers functionality for constructing and analysing single microbial association networks as well as quantifying network differences. This enables insights into whether single taxa, groups of taxa or the overall network structure change between groups. NetCoMi also contains functionality for constructing differential networks, thus allowing to assess whether single pairs of taxa are differentially associated between two groups. Furthermore, NetCoMi facilitates the construction and analysis of dissimilarity networks of microbiome samples, enabling a high-level graphical summary of the heterogeneity of an entire microbiome sample collection. We illustrate NetCoMi’s wide applicability using data sets from the GABRIELA study to compare microbial associations in settled dust from children’s rooms between samples from two study centers (Ulm and Munich).
Availability
R scripts used for producing the examples shown in this manuscript are provided as supplementary data. The NetCoMi package, together with a tutorial, is available at https://github.com/stefpeschel/NetCoMi.
Contact
Tel:+49 89 3187 43258; stefanie.peschel@mail.de
Supplementary information
Supplementary data are available at Briefings in Bioinformatics online.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Growing up on a farm is associated with an asthma-protective effect, but the mechanisms underlying this effect are largely unknown. In the Protection against Allergy: Study in Rural Environments ...(PASTURE) birth cohort, we modeled maturation using 16S rRNA sequence data of the human gut microbiome in infants from 2 to 12 months of age. The estimated microbiome age (EMA) in 12-month-old infants was associated with previous farm exposure (β = 0.27 (0.12-0.43), P = 0.001, n = 618) and reduced risk of asthma at school age (odds ratio (OR) = 0.72 (0.56-0.93), P = 0.011). EMA mediated the protective farm effect by 19%. In a nested case-control sample (n = 138), we found inverse associations of asthma with the measured level of fecal butyrate (OR = 0.28 (0.09-0.91), P = 0.034), bacterial taxa that predict butyrate production (OR = 0.38 (0.17-0.84), P = 0.017) and the relative abundance of the gene encoding butyryl-coenzyme A (CoA):acetate-CoA-transferase, a major enzyme in butyrate metabolism (OR = 0.43 (0.19-0.97), P = 0.042). The gut microbiome may contribute to asthma protection through metabolites, supporting the concept of a gut-lung axis in humans.
In recent years, unsupervised analysis of microbiome data, such as microbial network analysis and clustering, has increased in popularity. Many new statistical and computational methods have been ...proposed for these tasks. This multiplicity of analysis strategies poses a challenge for researchers, who are often unsure which method(s) to use and might be tempted to try different methods on their dataset to look for the "best" ones. However, if only the best results are selectively reported, this may cause over-optimism: the "best" method is overly fitted to the specific dataset, and the results might be non-replicable on validation data. Such effects will ultimately hinder research progress. Yet so far, these topics have been given little attention in the context of unsupervised microbiome analysis. In our illustrative study, we aim to quantify over-optimism effects in this context. We model the approach of a hypothetical microbiome researcher who undertakes four unsupervised research tasks: clustering of bacterial genera, hub detection in microbial networks, differential microbial network analysis, and clustering of samples. While these tasks are unsupervised, the researcher might still have certain expectations as to what constitutes interesting results. We translate these expectations into concrete evaluation criteria that the hypothetical researcher might want to optimize. We then randomly split an exemplary dataset from the American Gut Project into discovery and validation sets multiple times. For each research task, multiple method combinations (e.g., methods for data normalization, network generation, and/or clustering) are tried on the discovery data, and the combination that yields the best result according to the evaluation criterion is chosen. While the hypothetical researcher might only report this result, we also apply the "best" method combination to the validation dataset. The results are then compared between discovery and validation data. In all four research tasks, there are notable over-optimism effects; the results on the validation data set are worse compared to the discovery data, averaged over multiple random splits into discovery/validation data. Our study thus highlights the importance of validation and replication in microbiome analysis to obtain reliable results and demonstrates that the issue of over-optimism goes beyond the context of statistical testing and fishing for significance.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The number, distribution, size, and function of stomata and wettability of the sweet cherry (
Prunus avium L.) fruit surface were investigated. The number of stomata per fruit differed significantly ...among sweet cherry cultivars, ranging from 143±26 per fruit in ‘Adriana’ to 2124±142 per fruit in ‘Hedelfinger’. The number of stomata per fruit was not affected by fruit mass (‘Burlat’). For a given cultivar, the stylar scar region had the highest stomatal density, followed by ventral suture or cheek. The stem cavity region was essentially astomatous. Stomatal density decreased as distance from the scar increased. Cross-sectional areas of stomatal pores had a log-normal distribution and differed among cultivars, with medians ranging from 39.0 to 105.2
μm
2 for ‘Van’ and ‘Sam’, respectively. The length/width ratio of stomatal pores increased in the course of a day in early stage II, but not in mature stage III fruit. Treating exocarp segments with ABA (0.1
mM) or sucrose (1
M) decreased length/width ratios of stomatal pores in early stage II fruit, but not in the mature stage III, suggesting that stomata were non-functional at maturity. Contact angles of 1
μl water droplets (71
mN
m
−1) with the sweet cherry fruit surface averaged 92.4±0.6° (
n=164) across years and cultivars and did not differ between regions (cheek, suture vs. stylar end). The critical surface tension of the sweet cherry fruit was not affected by developmental stage (stage II vs. mature stage III ‘Burlat’ fruit) or cultivar, and averaged 24.9
mN
m
−1 making Poiseuille-flow of water through open stomata unlikely.
The cuticular membrane (CM) represents the primary barrier to water uptake into sweet cherry ( Prunus avium L.) fruit and thus has a central role in rain-induced cracking. The objective was to ...quantify CM properties potentially relevant to cracking and to estimate variance components and broad-sense heritabilities for these traits in selected sweet cherry cultivars. Within the scion cultivars, CM mass per area ranged from 0.85 g·m −2 in ‘Rainier’ to 1.61 g·m −2 in ‘Kordia’. Wax mass accounted for one-fourth of CM mass and ranged from 0.21 g·m −2 in ‘Burlat’ to 0.42 g·m −2 in ‘Zeppelin’. Biaxial elastic strain of the CM averaged 76.7% across cultivars and ranged from 56.6% in ‘Namosa’ to 97.0% in ‘Oktavia’. Strain was a linear function of fruit mass ( r 2 = 0.33, P < 0.0001). Partitioning total variance into variance components revealed that fruit mass, CM, and wax mass and strain of the CM had a high genotypic variance and a low residual error variance. Stomatal density ranged from 0.12 stomata/mm 2 in ‘Adriana’ to 2.13 stomata/mm 2 in ‘Namosa’. The heritability of stomatal density was 67.5%. Across cultivars and years, mean densities of microcracks were of similar orders of magnitude as those of stomata, but ranges were larger and the heritabilities of microcrack density lower. Permeability for transpiration was lowest in ‘Flamingo Srim’ and highest in ‘Nadino’; that for osmotic water uptake was lowest in ‘Adriana’ and highest in ‘Hedelfinger’. Heritability estimates for permeabilities were low. Based on these data, breeding strategies for less cracking susceptible fruit should focus on genotypes that maintain an intact CM throughout development. This may be achieved by selecting for low CM strain and high CM thickness because thicker CM have more “reserve” for thinning. Finally, genotypes that deposit cutin and wax also during Stage III would be most interesting but were not found among the cultivars investigated.
Water conductance of the cuticular membrane (CM) of mature sweet cherry fruit (Prunus avium L. cv. Sam) was investigated by monitoring water loss from segments of the outer pericarp excised from the ...cheek of the fruit. Segments consisted of epidermis, hypodermis and several cell layers of the mesocarp. Segments were mounted in stainless-steel diffusion cells with the mesocarp surface in contact with water, while the outer cuticular surface was exposed to dry silica (22 ± 1 °C). Conductance was calculated by dividing the amount of water transpired per unit area and time by the difference in water vapour concentration across the segment. Conductance values had a log normal distribution with a median of 1.15 × 10-4 m s-1 (n = 357). Transpiration increased linearly with time. Conductance remained constant and was not affected by metabolic inhibitors (1 mM NaN3 or 0.1 mM carbonylcyanide m-chlorophenylhydrazone) or thickness of segments (range 0.8—2.8 mm). Storing fruit (up to 42 d, 1 °C) used as a source of segments had no consistent effect on conductance. Conductance of the CM increased from cheek (1.16 ± 0.10 × 10-4 m s-1) to ventral suture (1.32 ± 0.07 × 10-4 m s-1) and to stylar end (2.53 ± 0.17 × 10-4 m s-1). There was a positive relationship (r2 = 0.066**; n = 108) between conductance and stomatal density. From this relationship the cuticular conductance of a hypothetical astomatous CM was estimated to be 0.97 ± 0.09 × 10-4 m s-1. Removal of epicuticular wax by stripping with cellulose acetate or extracting epicuticular plus cuticular wax by dipping in CHCl3/methanol increased conductance 3.6- and 48.6-fold, respectively. Water fluxes increased with increasing temperature (range 10—39 °C) and energies of activation, calculated for the temperature range from 10 to 30 °C, were 64.8 ± 5.8 and 22.2 ± 5.0 kJ mol-1 for flux and vapour-concentration-based conductance, respectively.
Water conductance of the cuticular membrane (CM) of sweet cherry (Prunus avium L. cv. Sam) fruit during stages II and III (31—78 days after full bloom, DAFB) was investigated by gravimetrically ...monitoring water loss through segments of the exocarp. Segments were mounted in stainless-steel diffusion cells, filled with 0.5 ml of deionized water and incubated for 8 h at 25 ± 2 °C over dry silica. Conductance was calculated by dividing the amount of water transpired per unit surface area and time by the difference in water vapor concentration across the segment (23.07 g m-3 at 25 °C). Fruit mass and fruit surface area increased 4.9- and 2.8-fold between 31 and 78 DAFB, respectively. However, CM mass per unit area decreased from 3.9 to 1.5 g m-2, and percentage of total wax content remained constant at about 31%. Stomatal density decreased from 0.8 to 0.2 mm-2 (31—78 DAFB). Total conductance of the CM on the fruit cheek (gtot.) remained constant during stage II of development (approx. 1.38×10-4 m s-1 from 31 to 37 DAFB), increased to 1.73×10-4 m s-1 during early stage III of fruit growth (43—64 DAFB) then decreased to 0.95×10-4 m s-1 at maturity (78 DAFB). Partitioning gtot. into cuticular (gcut.) and stomatal conductance (gsto.) revealed that the relative contribution of gcut. to gtot. increased linearly from 30% to 87% of gtot. between 31 and 78 DAFB, respectively. On a whole-fruit basis, gtot. and gcut. consistently increased up to 64 DAFB, and decreased thereafter. A significant negative linear relationship was obtained between gcut. and CM thickness, but not between the permeability coefficient (p) and CM thickness. Further, p was positively related to strain rate, suggesting that strain associated with expansion of the fruit surface increased p.
Rain cracking of sweet cherry (
Prunus avium
L.) fruit is commonly thought to result from excessive net water uptake. This excess increases flesh turgor, which then strains and eventually ruptures ...the skin at the weakest point. This idea—the critical turgor hypothesis—assumes the fruit comprises a semifluid flesh, held under pressure by a taut skin. The objectives of this study were to test the validity of this popular hypothesis. We investigated the effects of 1) the different pathways of water uptake and 2) the fruit’s water balance on cracking. Incubating fruit of 19 cultivars in water resulted in rapid fruit cracking. The time to 50% cracking (T
50
) averaged 7.5 ± 1.3 hours with considerable variability between cultivars (T
50
range from 1.5 to 18.6 hours). The amount of water taken up at 50% cracking (WU
50
) averaged 96.5 ± 17.6 mg (WU
50
range from 17.7 to 331.5 mg). There was no correlation between either the T
50
or the WU
50
, and the rate of water uptake. Also, there was no correlation between the values of T
50
(
r
= 0.58) and only a weak correlation between the values of WU
50
(
r
= 0.80*) determined in different years. Comparing the value of WU
50
under incubation vs. under perfusion revealed a 3.9- to 38-fold higher WU
50
under perfusion (397.6 to 1840 mg) than under incubation (48.8 to 102.6 mg). This marked dissimilarity remained, regardless of pretreatments with isotonic polyethylene glycol (PEG) 6000 to induce microcracking or by manipulation of skin wetness during perfusion. Sealing the pedicel/fruit junction markedly decreased the rate of water uptake under incubation. It had no effect on the T
50
, and it markedly decreased the WU
50
. Similarly, manually induced skin defects greatly increased the rate of water uptake but, with few exceptions, had no effect on the T
50,
whereas, the WU
50
had increased. The location on the fruit surface of the resulting cracks was not related to the region of the skin in which the manual defect was induced. Allowing the fruit to transpire increased both, the T
50
and the WU
50
. Interestingly, the amount of water lost by transpiration exceeded the amount that was subsequently required to cause cracking up to 5-fold. Incubating fruit with their stylar ends immersed in water, whereas their remaining surfaces were in air of 0%, 28%, 75%, or 100% relative humidity (RH) resulted in net losses of water of up to 5.9 ± 0.7 mg·h
−1
, nevertheless their stylar ends still cracked. All our results indicate rain cracking in sweet cherries is a localized phenomenon that is not related to the net fruit water balance (the critical turgor hypothesis) but is the result of more local exposure of the fruit skin to liquid-phase water (the zipper hypothesis).
Sweet cherry fruit wax consists of triterpenes, alkanes and alcohols, the cutin fraction is dominated by midchain-oxygenated hydroxyacids. Marked changes in composition of wax and cutin occurred ...during fruit development indicating that deposition of major constituents ceased during early development resulting in a thinning of the cuticle on the expanding fruit surface.
The composition of wax and cutin from developing sweet cherry (
Prunus avium) fruit was studied by GC–MS between 22 and 85 days after full bloom (DAFB). In this and our previous study, fruit mass and surface area increased in a sigmoidal pattern with time, but mass of the cuticular membrane (CM) per unit fruit surface area decreased. On a whole fruit basis, mass of CM increased up to 36 DAFB and remained constant thereafter. At maturity, triterpenes, alkanes and alcohols accounted for 75.6%, 19.1% and 1.2% of total wax, respectively. The most abundant constituents were the triterpenes ursolic (60.0%) and oleanolic acid (7.5%), the alkanes nonacosane (13.0%) and heptacosane (3.0%), and the secondary alcohol nonacosan-10-ol (1.1%). In developing fruit triterpenes per unit area decreased, but alkanes and alcohols remained essentially constant. The cutin fraction of mature fruit consisted of mostly C16 (69.5%) and, to a lower extent, C18 monomers (19.4%) comprising alkanoic, ω-hydroxyacids, α,ω-dicarboxylic and midchain hydroxylated acids. The most abundant constituents were 9(10),16-dihydroxy-hexadecanoic acid (53.6%) and 9,10,18-trihydroxy-octadecanoic acid (7.8%). Amounts of C16 and C18 monomers per unit area decreased in developing fruit, but remained approximately constant on a whole fruit basis. Within both classes of monomers, opposing changes occurred. Amounts of hexadecandioic, 16-hydroxy-hexadecanoic, 9(10)-hydroxy-hexadecane-1,16-dioic and 9,10-epoxy-octadecane-1,18-dioic acids increased, but 9,10,18-trihydroxy-octadecanoic and 9,10,18-trihydroxy-octadecenoic acids decreased. There were no qualitative and minor quantitative differences in wax and cutin composition between cultivars at maturity. Our data indicate that deposition of some constituents of wax and cutin ceased during early fruit development.
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