Mass spectrometry is an analytical technique for the characterization of biological samples and is increasingly used in omics studies because of its targeted, nontargeted, and high throughput abilities. However, due to the large datasets generated, it requires informatics approaches such as machine learning techniques to analyze and interpret relevant data. Machine learning can be applied to MS-derived proteomics data in two ways. First, directly to mass spectral peaks and second, to proteins identified by sequence database searching, although relative protein quantification is required for the latter. Machine learning has been applied to mass spectrometry data from different biological disciplines, particularly for various cancers. The aims of such investigations have been to identify biomarkers and to aid in diagnosis, prognosis, and treatment of specific diseases. This review describes how machine learning has been applied to proteomics tandem mass spectrometry data. This includes how it can be used to identify proteins suitable for use as biomarkers of disease and for classification of samples into disease or treatment groups, which may be applicable for diagnostics. It also includes the challenges faced by such investigations, such as prediction of proteins present, protein quantification, planning for the use of machine learning, and small sample sizes.

N₂‐fixation by Rhizobium‐legume symbionts is of major ecological and agricultural importance, responsible for producing a substantial fraction of the biosphere's nitrogen. On the basis of ¹⁵N‐labelling studies, it had been generally accepted that ammonium is the sole secretion product of N₂‐fixation by the bacteroid and that the plant is responsible for assimilating it into amino acids. However, this paradigm has been challenged in a recent ¹⁵N‐labelling study showing that soybean bacteroids only secrete alanine. Hitherto, nitrogen secretion has only been assessed from in vitro¹⁵N‐labelling studies of isolated bacteroids. We show that both ammonium and alanine are secreted by pea bacteroids. The in vitro partitioning between them will depend on whether the system is open or closed, as well as the ammonium concentration and bacteroid density. To overcome these limitations we identified and mutated the gene for alanine dehydrogenase (aldA) and demonstrate that AldA is the primary route for alanine synthesis in isolated bacteroids. Bacteroids of the aldA mutant fix nitrogen but only secrete ammonium at a significant rate, resulting in lower total nitrogen secretion. Peas inoculated with the aldA mutant are green and healthy, demonstrating that ammonium secretion by bacteroids can provide sufficient nitrogen for plant growth. However, plants inoculated with the mutant are reduced in biomass compared with those inoculated with the wild type. The labelling and plant growth studies suggest that alanine synthesis and secretion contributes to the efficiency of N₂‐fixation and therefore biomass accumulation.

Amino acid uptake byRhizobium leguminosarumis dominated by two ABC transporters, the general amino acid permease (Aap) and the branched-chain amino acid permease (Bra_Rl). Characterization of the solute specificity of Bra_Rlshows it to be the second general amino acid permease ofR. leguminosarum. Although Bra_Rlhas high sequence identity to members of the family of hydrophobic amino acid transporters (HAAT), it transports a broad range of solutes, including acidic and basic polar amino acids (l-glutamate,l-arginine, andl-histidine), in addition to neutral amino acids (l-alanine andl-leucine). While amino and carboxyl groups are required for transport, solutes do not have to be α-amino acids. Consistent with this, Bra_Rlis the first ABC transporter to be shown to transport γ-aminobutyric acid (GABA). All previously identified bacterial GABA transporters are secondary carriers of the amino acid-polyamine-organocation (APC) superfamily. Also, transport by Bra_Rldoes not appear to be stereospecific asdamino acids cause significant inhibition of uptake ofl-glutamate andl-leucine. Unlike all other solutes tested,l-alanine uptake is not dependent on solute binding protein BraC_Rl. Therefore, a second, unidentified solute binding protein may interact with the BraDEFG_Rlmembrane complex duringl-alanine uptake. Overall, the data indicate that Bra_Rlis a general amino acid permease of the HAAT family. Furthermore, Bra_Rlhas the broadest solute specificity of any characterized bacterial amino acid transporter.

Companion animals provide an excellent model for studies of the gut microbiome because potential confounders such as diet and environment can be more readily controlled for than in humans. Additionally, domestic cats and dogs are typically neutered early in life, enabling an investigation into the potential effect of sex hormones on the microbiome. In a longitudinal study to investigate the potential effects of neutering, neutering age and gender on the gut microbiome during growth, the faeces of kittens (16 male, 14 female) were sampled at 18, 30 and 42 weeks of age. DNA was shotgun sequenced on the Illumina platform and sequence reads were annotated for taxonomy and function by comparison to a database of protein coding genes. In a statistical analysis of diversity, taxonomy and functional potential of the microbiomes, age was identified as the only factor with significant associations. No significant effects were detected for gender, neutering, or age when neutered (19 or 31 weeks). At 18 weeks of age the microbiome was dominated by the genera Lactobacillus and Bifidobacterium (35% and 20% average abundance). Structural and functional diversity was significantly increased by week 30 but there was no further significant increase. At 42 weeks of age the most abundant genera were Bacteroides (16%), Prevotella (14%) and Megasphaera (8%). Significant differences in functional potential included an enrichment for genes in energy metabolism (carbon metabolism and oxidative phosphorylation) and depletion in cell motility (flagella and chemotaxis). We conclude that the feline faecal microbiome is predominantly determined by age when diet and environment are controlled for. We suggest this finding may also be informative for studies of the human microbiome, where control over such factors is usually limited.