Exploring microbial community structure and metabolic gene clusters during silage fermentation of paper mulberry, a high-protein woody plant

There is a need to standardize the NDF procedure. Procedures have varied because of the use of different amylases in attempts to remove starch interference. The original Bacillus subtilis enzyme Type IIIA (XIA) no longer is available and has been replaced by a less effective enzyme. For fiber work, a new enzyme has received AOAC approval and is rapidly displacing other amylases in analytical work. This enzyme is available from Sigma (Number A3306; Sigma Chemical Co., St. Louis, MO). The original publications for NDF and ADF (43, 53) and the Agricultural Handbook 379 (14) are obsolete and of historical interest only. Up to date procedures should be followed. Triethylene glycol has replaced 2-ethoxyethanol because of reported toxicity. Considerable development in regard to fiber methods has occurred over the past 5 yr because of a redefinition of dietary fiber for man and monogastric animals that includes lignin and all polysaccharides resistant to mammalian digestive enzymes. In addition to NDF, new improved methods for total dietary fiber and nonstarch polysaccharides including pectin and beta-glucans now are available. The latter are also of interest in rumen fermentation. Unlike starch, their fermentations are like that of cellulose but faster and yield no lactic acid. Physical and biological properties of carbohydrate fractions are more important than their intrinsic composition.

Profiling phylogenetic marker genes, such as the 16S rRNA gene, is a key tool for studies of microbial communities but does not provide direct evidence of a community’s functional capabilities. Here we describe PICRUSt (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States), a computational approach to predict the functional composition of a metagenome using marker gene data and a database of reference genomes. PICRUSt uses an extended ancestral-state reconstruction algorithm to predict which gene families are present and then combines gene families to estimate the composite metagenome. Using 16S information, PICRUSt recaptures key findings from the Human Microbiome Project and accurately predicts the abundance of gene families in host-associated and environmental communities, with quantifiable uncertainty. Our results demonstrate that phylogeny and function are sufficiently linked that this ‘predictive metagenomic’ approach should provide useful insights into the thousands of uncultivated microbial communities for which only marker gene surveys are currently available.

Gene families are growing rapidly, but standard methods for inferring phylogenies do not scale to alignments with over 10,000 sequences. We present FastTree, a method for constructing large phylogenies and for estimating their reliability. Instead of storing a distance matrix, FastTree stores sequence profiles of internal nodes in the tree. FastTree uses these profiles to implement Neighbor-Joining and uses heuristics to quickly identify candidate joins. FastTree then uses nearest neighbor interchanges to reduce the length of the tree. For an alignment with N sequences, L sites, and a different characters, a distance matrix requires O(N 2) space and O(N 2 L) time, but FastTree requires just O(NLa + N ) memory and O(N log (N)La) time. To estimate the tree's reliability, FastTree uses local bootstrapping, which gives another 100-fold speedup over a distance matrix. For example, FastTree computed a tree and support values for 158,022 distinct 16S ribosomal RNAs in 17 h and 2.4 GB of memory. Just computing pairwise Jukes–Cantor distances and storing them, without inferring a tree or bootstrapping, would require 17 h and 50 GB of memory. In simulations, FastTree was slightly more accurate than Neighbor-Joining, BIONJ, or FastME; on genuine alignments, FastTree's topologies had higher likelihoods. FastTree is available at http://microbesonline.org/fasttree.