Semen Microbiome Biogeography: An Analysis Based on a Chinese Population Study

Body fluids should be identified before estimating the time since deposition (TsD) in microbiome-based stain analyses for forensics

Identification and the time since deposition (TsD) estimation of body fluid stains from a crime scene could provide valuable information for solving the cases and are always difficult for forensics. Microbial characteristics were considered as a promising biomarker to address the issues. However, changes in the microbiota may damage the specific characteristics of body fluids. Correspondingly, incorrect body fluid identification may result in inaccurate TsD estimation. The mutual influence is not well understood and limited the codetection. In the current study, saliva, semen, vaginal secretion, and menstrual blood samples were exposed to indoor conditions and collected at eight time points (from fresh to 30 days). High-throughput sequencing based on the 16S rRNA gene was performed to characterize the microbial communities. The results showed that a longer TsD could decrease the discrimination of different body fluid stains. However, the accuracies of identification still reached a quite high value even without knowing the TsD. Correspondingly, the mean absolute error (MAE) of TsD estimation significantly increased without distinguishing the types of body fluids. The predictive TsD of menstrual blood reached a quite low MAE (1.54 ± 0.39 d). In comparison, those of saliva (6.57 ± 1.17 d), semen (6.48 ± 1.33 d), and vaginal secretion (5.35 ± 1.11 d) needed to be further improved. The great effect of individual differences on these stains limited the TsD estimation accuracy. Overall, microbial characteristics allow for codetection of body fluid identification and TsD estimation, and body fluids should be identified before estimating TsD in microbiome-based stain analyses.IMPORTANCEEmerged evidences suggest microbial characteristics could be considered a promising tool for identification and time since deposition (TsD) estimation of body fluid stains. However, the two issues should be studied together due to a potential mutual influence. The current study provides the first evidence to understand the mutual influence and determines an optimal process for codetection of identification and TsD estimation for unknown stains for forensics. In addition, we involved aged stains into our study for identification of body fluid stains, rather than only using fresh stains like previous studies. This increased the predictive accuracy. We have preliminary verified that individual differences in microbiotas limited the predictive accuracy of TsD estimation for saliva, semen, and vaginal secretion. Microbial characteristics could provide an accurate TsD estimation for menstrual blood. Our study benefits the comprehensive understanding of microbiome-based stain analyses as an essential addition to previous studies.

Possibilities and limitations of using low biomass samples for urologic disease and microbiome research

With the dogma of sterile urine no longer held as truth, numerous studies have implicated distinct changes in microbial diversity and composition to diseased subgroups in both benign and malignant urological diseases, ranging from overactive bladder to bladder and prostate cancer. Further facilitated by novel and effective techniques of urine culture and sequencing, analysis of the genitourinary microbiome holds high potential to identify biomarkers for disease and prognosis. However, the low biomass of samples included in microbiome studies of the urinary tract challenge researchers to draw definitive conclusions, confounded by technical and procedural considerations that must be addressed. Lack of samples and adequate true negative controls can lead to overestimation of microbial influence with clinical relevance. As such, results from currently available studies and assessment of their limitations required a thorough understanding. The purpose of this narrative review was to summarize notable microbiome studies in the field of urology with a focus on significant findings and limitations of study design. Methodological considerations in future research are also discussed.

Diversity-scaling analysis of human breast milk microbiomes from population perspective

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.

Semen dysbiosis—just a male problem?

Seminal microflora is crucial to male fertility. Dysbiosis—disturbance of quantitative ratios of individual bacteria or appearance of pathogenic species—rarely results in symptomatic disease. Inflammation results in decreased sperm production, lower motility, or morphological changes and, in the long term, can cause ejaculatory duct obstruction, leading to infertility. Moreover, it may cause infection of the partner’s female genital tract. Dysbiosis in both partners results in fertility problems, disorders in embryo implantation, or miscarriages. In addition, chronic inflammation of the male genitourinary system may accelerate the appearance of antisperm antibodies. A comprehensive examination of seminal microflora can clarify the causes of infertility or prevent pathological conditions that affect seminal parameters. Seminal microflora as a direct impact on fertility problems as well as a decrease in the effectiveness of assisted reproduction methods, insemination, or in vitro procedures.

Diagnosis of bacteriospermia and its impact on spermogram parameters in adolescents with varicocele

Infectious etiology is the cause of about 15% of cases of male infertility. And if sexually transmitted infections are easily diagnosed, the role of asymptomatic bacteriospermia in the formation of infertility in men, and especially in adolescents against the background of the existing pathology of the reproductive sphere (varicocele), remains insufficiently studied. A microbiological study in the ejaculate of adolescents revealed the following types of bacteria: Escherichia coli, Enterococcus faecalis, Corynebacterium glucuronolyticum, Corynebacterium minitissimum, Streptococcus anginosus, Staphylococcus epidermidis, Staphylococcus haemolyticus. Bacteria in the ejaculate were also detected during semen analysis and electron microscopic examination of spermatozoa. With abundant growth of microorganisms in a monoculture or an association of two microorganisms present in a moderate amount, in all cases, violations of sperm motility, an increase in the viscosity of the ejaculate, the presence of leukocytes in the seminal fluid were detected, and damage to the chromatin, acrosome and mitochondria was recorded at the ultrastructural level, which may indicate active infection. When bacterial flora was detected in a small and moderate amount (<10 CFU/ml), no pathological changes in the ejaculate were observed. The microflora of the ejaculate of the examined adolescents is represented by gram-positive microflora. Simultaneous study of the ejaculate sample by bacteriological seeding, the performance of spermogram and EMIS allowed to increase the detection of bacteriospermia. Opportunistic pathogens with abundant growth or their various combinations can serve as a factor in the development of pathospermia. It is possible to distinguish an active infection from commensal microflora or sample contamination not only by the presence of bacteria in the ejaculate and their quantitative accounting, but also by the degree of damage to the function of spermatozoa and pathological changes in the parameters of the ejaculate, by combining diagnostic methods. Most often, in the presence of bacteria in the ejaculate, asthenozoospermia is diagnosed.

Shifts in rhizosphere microbial communities in Oplopanax elatus Nakai are related to soil chemical properties under different growth conditions

Plant growth environment plays an important role in shaping soil microbial communities. To understand the response of soil rhizosphere microbial communities in Oplopanax elatus Nakai plant to a changed growth conditions from natural habitation to cultivation after transplant. Here, a comparative study of soil chemical properties and microbial community using high-throughput sequencing was conducted under cultivated conditions (CT) and natural conditions (WT), in Changbai Mountain, Northeast of China. The results showed that rhizosphere soil in CT had higher pH and lower content of soil organic matter (SOM) and available nitrogen compared to WT. These changes influenced rhizosphere soil microbial communities, resulting in higher soil bacterial and fungi richness and diversity in CT soil, and increased the relative abundance of bacterial phyla Acidobacteria, Chloroflexi, Gemmatimonadetes, Firmicutes and Patescibacteria, and the fungi phyla Mortierellomycota and Zoopagomycota, while decreased bacterial phyla Actinobacteria, WPS-2, Gemmatimonadetes, and Verrucomicrobia, and the fungi phyla Ascomycota, and Basidiomycota. Redundancy analysis analysis indicated soil pH and SOM were the primarily environmental drivers in shaping the rhizosphere soil microbial community in O. elatus under varied growth conditions. Therefore, more attention on soil nutrition management especially organic fertilizer inputs should be paid in O. elatus cultivation.

Seminal and testicular microbiome and male fertility: A systematic review

Supplemental Digital Content is available in the text

Microbiome is of upmost importance for the well-being of the human body. Based on culture and PCR methods, seminal flora has been pointed as a potential cause for some of the unexplained male infertility.

This is a systematic review about the effect of seminal microbiota studied by Next Generation Sequencing techniques on sperm quality and male fertility, performed according to PRISMA statement.

Nine articles were included. Results of different studies are diverse. It seems that microbiota may a play a role in seminal quality and further male fertility, but the way this effect is modulated is still to be unknown. Lactobacillus spp seemed to play a beneficial role in semen quality, but the role of the remaining bacteria is unclear.

Due to the lack of research and the incongruence of the results so far, the effect of microbiota on seminal quality is still unclear.

The search for aliens within us: a review of evidence and theory regarding the foetal microbiome

The microbiome is believed to be established during the birthing process through exposure to the maternal microbiome and immediate external environment. The absence of a microbiome prior to birth is based on the sterile womb hypothesis, which was formulated at the beginning of the 20th century and is supported primarily by the culture-based approach in microbiological studies.Findings of bacterial presence in products of fertilization such as the placenta, amniotic fluid, foetal membranes, and umbilical cord blood in studies using next-generation DNA sequencing technologies began to challenge the sterile nature of the intrauterine environment during gestation. These studies have been mainly criticized by their approach to contamination and inconclusive evidence of viability. The implications of bacterial presence in utero are far reaching in medicine and basic sciences. If commensal bacteria exist in the foetus, antibiotic therapies in pregnancy particularly for asymptomatic cases will need to be re-evaluated. Experimental studies utilizing gnotobiology may also be impacted by a realignment of theory.This review of existing literature aims to provide insight into the existence of bacteria in utero, specifically the foetal microbiome through analysis of experimental evidence and theoretical concepts, and to suggest approaches that may further provide clarity into this inquiry.

Truffle species strongly shape their surrounding soil mycobiota in a Pinus armandii forest

Truffles contribute to crucial soil systems dynamics, being involved in plentiful ecological functions important for ecosystems. Despite this, the interactions between truffles and their surrounding mycobiome remain unknown. Here, we investigate soil mycobiome differences between two truffle species, Tuber indicum (Ti) and Tuber pseudohimalayense (Tp), and their relative influence on surrounding soil mycobiota. Using traditional chemical analysis and ITS Illumina sequencing, we compared soil nutrients and the mycobiota, respectively, in soil, gleba, and peridium of the two truffle species inhabiting the same Pinus armandii forest in southwestern China. Tp soil was more acidic (pH 6.42) and had a higher nutrient content (total C, N content) than Ti soil (pH 6.62). Fungal richness and diversity of fruiting bodies (ascomata) and surrounding soils were significantly higher in Tp than in Ti. Truffle species recruited unique soil mycobiota around their ascomata: in Ti soil, fungal taxa, including Suillus, Alternaria, Phacidium, Mycosphaerella, Halokirschsteiniothelia, and Pseudogymnoascus, were abundant, while in Tp soil species of Melanophyllum, Inocybe, Rhizopogon, Rhacidium, and Lecanicillium showed higher abundances. Three dissimilarity tests, including adonis, anosim, and MRPP, showed that differences in fungal community structure between the two truffle species and their surrounding soils were stronger in Tp than in Ti, and these differences extended to truffle tissues (peridium and gleba). Redundancy analysis (RDA) further demonstrated that correlations between soil fungal taxa and soil properties changed from negative (Tp) to positive (Ti) and shifted from a moisture-driven (Tp) to a total N-driven (Ti) relationship. Overall, our results shed light on the influence that truffles have on their surrounding soil mycobiome. However, further studies are required on a broader range of truffle species in different soil conditions in order to determine causal relationships between truffles and their soil mycobiome.

Diversity Scaling of Human Digestive Tract (DT) Microbiomes: The Intra-DT and Inter-individual Patterns

The human gut microbiome has been extensively studied, but its diversity scaling (changes or heterogeneities) along the digestive tract (DT) as well as their inter-individual heterogeneities have not been adequately addressed to the best of our knowledge. Here we fill the gap by applying the diversity-area relationship (DAR), a recent extension to the classic species-area relationship (SAR) in biogeography, by reanalyzing a dataset of over 2000 16s-rRNA microbiome samples obtained from 10 DT sites of over 200 individuals. We sketched out the biogeography “maps” for each of the 10 DT sites by cross-individual DAR analysis, and the intra-DT distribution pattern by cross-DT-site DAR analysis. Regarding the inter-individual biogeography, it was found that all DT sites have the invariant (constant) scaling parameter—all sites possessing the same diversity change rate across individuals, but most sites have different potential diversities, which include the portions of diversity that may be absent locally but present regionally. In the case of this study, the potential diversity of each DT site covers the total diversity of the respective site from all individuals in the cohort. In terms of the genus richness, an average individual hosts approximately 20% of the population-level genus richness (total bacterial genus of a human population). In contrast, in terms of community biodiversity, the percentages of individual over population may exceed 90%. This suggests that the differences between individuals in their DT microbiomes are predominantly in the composition of bacterial species, rather than how their abundances are distributed (i.e., biodiversity). Regarding the intra-DT patterns, the scaling parameter (z) is larger—suggesting that the intra-DT biodiversity changes are larger than inter-individual changes. The higher intra-DT heterogeneity of bacteria diversity, as suggested by larger intra-DT z than the inter-individual heterogeneity, should be expected since the intra-DT heterogeneity reflects the functional differentiations of the DT tract, while the inter-individual heterogeneity (z) reflects the difference of the same DT site across individuals. On average, each DT site contains 21–36% of the genus diversity of the whole DT, and the percentages are even higher in terms of higher taxon levels.

Population-level diversity-disease relationship (p-DDR) in the human microbiome associated diseases

Diversity-disease relationship (DDR) is a de facto standard analysis in the studies of human microbiome associated diseases (MADs). For example, the species richness or Shannon entropy are routinely compared between the healthy and diseased groups. Nevertheless, the basic scale of the standard diversity analysis is individual subject rather than a cohort or population because the diversity is computed for individual samples, not for the group. Here we aim to expand the current DDR study from individual focus to population level, which can offer important insights for understanding the epidemiology of MADs. We analyzed the diversity-disease relationship at cohort scale based on a collection of 23 datasets covering the major human MADs. Methodologically, we harness the power of a recent extension to the classic species-area relationship (SAR), i.e., the diversity-area relationship (DAR), to achieve the expansion from individual DDR to inter-subject diversity scaling analysis. Specifically, we apply the DAR analysis to estimate and compare the potentially maximal accrual diversities of the healthy and diseases groups, as well as the inter-subject diversity scaling parameters and the individual-to-population diversity ratios. It was shown that, except for the potential diversity (D_max) at the cohort level in approximately 5.4% cases of MADs, DAR parameters displayed no significant differences between healthy and diseased treatments. That is, the DAR parameters are rather resilient against MADs, except for the potential diversity in some diseases. We compared our population-level DDR with the existing individual-level DDR patterns and proposed a hypothesis to interpret their differences.

The seminal microbiome in health and disease

Owing to the fact that there are more microbial than human cells in our body and that humans contain more microbial than human genes, the microbiome has huge potential to influence human physiology, both in health and in disease. The use of next-generation sequencing technologies has helped to elucidate functional, quantitative and mechanistic aspects of the complex microorganism-host interactions that underlie human physiology and pathophysiology. The microbiome of semen is a field of increasing scientific interest, although this microbial niche is currently understudied compared with other areas of microbiome research. However, emerging evidence is beginning to indicate that the seminal microbiome has important implications for the reproductive health of men, the health of the couple and even the health of offspring, owing to transfer of microorganisms to the partner and offspring. As this field expands, further carefully designed and well-powered studies are required to unravel the true nature and role of the seminal microbiome.

Global Microbiome Diversity Scaling in Hot Springs With DAR (Diversity-Area Relationship) Profiles

The spatial distribution of biodiversity (i.e., the biogeography) of the hot-spring microbiome is critical for understanding the microbial ecosystems in hot springs. We investigated the microbiome diversity scaling (changes) over space by analyzing the diversity-area relationship (DAR), which is an extension to classic SAR (species-area relationship) law in biogeography. We built DAR models for archaea and bacteria with 16S-rRNA sequencing datasets from 165 hot springs globally. From the DAR models, we sketch out the biogeographic maps of hot-spring microbiomes by constructing: (i) DAR profile-measuring the archaea or bacteria diversity scaling over space (areas); (ii) PDO (pair-wise diversity overlap or similarity) profile-estimating the PDO between two hot springs; (iii) MAD (maximal accrual diversity) profile-predicting the global MAD; (iv) LRD/LGD (ratio of local diversity to regional or global diversity) profile. We further investigated the differences between archaea and bacteria in their biogeographic maps. For example, the comparison of DAR-profile maps revealed that the archaea diversity is more heterogeneous (i.e., more diverse) or scaling faster than the bacterial diversity does in terms of species numbers (species richness), but is less heterogeneous (i.e., less diverse) or scaling slower than bacteria when the diversity (Hill numbers) were weighted in favor of more abundant dominant species. When the diversity is weighted equally in terms of species abundances, archaea, and bacteria are equally heterogeneous over space or scaling at the same rate. Finally, unified DAR models (maps) were built with the combined datasets of archaea and bacteria.