RESUMO
Quantitative measuring the population-level diversity-scaling of human microbiomes is different from conventional approach to traditional individual-level diversity analysis, and it is of obvious significance. For example, it is well known that individuals are of significant heterogeneity with their microbiome diversities, and the population-level analysis can effectively capture such kind of individual differences. Here we reanalyze a dozen datasets of 2,115 human breast milk microbiome (BMM) samples with diversity-area relationship (DAR) to tackle the previous questions. Our focus on BMM is aimed to offer insights for supplementing the gut microbiome research from nutritional perspective. DAR is an extension to classic species-area relationship, which was discovered in the 19th century and established as one of a handful fundamental laws in community ecology. Our DAR modeling revealed the following numbers, all approximately: (i) The population-level potential diversity of BMM is 1,108 in terms of species richness (number of total species), and 67 in terms of typical species. (ii) On average, an individual carry 17% of population-level diversity in terms of species richness, and 61% in terms of typical species. (iii) The similarity (overlap) between individuals according to pair-wise diversity overlap (PDO) should be approximately 76% in terms of total species, and 92% in terms of typical species, which symbolizes the inter-individual heterogeneity. (iv) The average individual (alpha-) diversity of BMM is approximately 188 (total-species) and 37 (typical-species). (v) To deal with the potential difference among 12 BMM datasets, we conducted DAR modeling separately for each dataset, and then performed permutation tests for DAR parameters. It was found that the DAR scaling parameter that measures inter-individual heterogeneity in diversity is invariant (constant), but the population potential diversity is different among 30% of the pair-wise comparison between 12 BMM datasets. These results offer comprehensive biodiversity analyses of the BMM from host individual, inter-individual, and population level perspectives.
RESUMO
Background: Hereditary spastic paraplegia (HSP) is a rare group of genetically heterogeneous, neurodegenerative disorders. The aim of this study was to identify pathological candidate genes and variants in a large pedigree cohort of 11 purely HSP patients in Yunnan Province. Methods: Whole-exome sequencing (WES) was applied to 2 HSP patients and 1 control patient to screen out the candidate gene variants. Then, filtration and verification of these pathological variants were performed by Sanger sequencing. Results: After the raw data were filtered, two genes with novel variations (SPAST: c.1510 C>T, p.Gln504X, RefSeq.NM_199436; DNAJC16: c.718 C>T, p.Q240X, Ref Seq NM_015291) were identified. The accession numbers of the genes in the ClinVar database were SCV001573094 and SCV001573804, respectively. One gene with a reported single nucleotide polymorphism (CPT1C: rs150853576) was filtered as a candidate variant. Using Sanger sequencing, the novel SPAST gene (protein: Spastin) variant leading to a predicted premature termination and an 18% deletion of the SPAST/spastic paraplegia type 4 (SPG4) protein was confirmed to exist only in affected individuals. The candidate CPT1C and DNAJC16 variants were verified in almost all HSP patients, with one exception. Conclusions: Considering that the clinical symptoms and time of onset of HSP are highly heterogeneous, the SPAST as a genotype-phenotype cosegregated variant might be the causative gene of this pedigree, and the other two variants might present cumulative risks to the occurrence and progression of HSP. These three candidate genes with or without novel variants may be potential contributors to disease onset, and therefore useful diagnostic and therapeutic biomarkers. Further research is required to confirm the functions of these genes.
