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Staphylococcus aureus infection of bone is challenging to treat because it colonizes the osteocyte lacuno-canalicular network (OLCN) of cortical bone. To elucidate factors involved in OLCN invasion and identify novel drug targets, we completed a hypothesis-driven screen of 24 S. aureus transposon insertion mutant strains for their ability to propagate through 0.5 μm-sized pores in the Microfluidic Silicon Membrane Canalicular Arrays (μSiM-CA), developed to model S. aureus invasion of the OLCN. This screen identified the uncanonical S. aureus transpeptidase, penicillin binding protein 4 (PBP4), as a necessary gene for S. aureus deformation and propagation through nanopores. In vivo studies revealed that Δpbp4 infected tibiae treated with vancomycin showed a significant 12-fold reduction in bacterial load compared to WT infected tibiae treated with vancomycin (p<0.05). Additionally, Δpbp4 infected tibiae displayed a remarkable decrease in pathogenic bone-loss at the implant site with and without vancomycin therapy. Most importantly, Δpbp4 S. aureus failed to invade and colonize the OLCN despite high bacterial loads on the implant and in adjacent tissues. Together, these results demonstrate that PBP4 is required for S. aureus colonization of the OLCN and suggest that inhibitors may be synergistic with standard of care antibiotics ineffective against bacteria within the OLCN.

In many omics data, including microbiome sequencing data, we are only able to measure relative information. Various computational or statistical methods have been proposed to extract absolute (or biologically relevant) information from this relative information; however, these methods are under rather strong assumptions that may not be suitable for multigroup (more than two groups) and/or longitudinal outcome data. In this article, we first introduce the minimal assumption required to extract absolute from relative information. This assumption is less stringent than those imposed in existing methods, thus being applicable to multigroup and/or longitudinal outcome data. We then propose the first normalization method that works under this minimal assumption. The optimality and validity of the proposed method and its beneficial effects on downstream analysis are demonstrated in extensive simulation studies, where existing methods fail to produce consistent performance under the minimal assumption. We also demonstrate its application to real microbiome datasets to determine biologically relevant microbes to a specific disease/condition.

Osteomyelitis is a devastating disease caused by microbial infection of bone. While the frequency of infection following elective orthopedic surgery is low, rates of reinfection are disturbingly high. is responsible for the majority of chronic osteomyelitis cases and is often considered to be incurable due to bacterial persistence deep within bone. Unfortunately, there is no consensus on clinical classifications of osteomyelitis and the ensuing treatment algorithm. Given the high patient morbidity, mortality, and economic burden caused by osteomyelitis, it is important to elucidate mechanisms of bone infection to inform novel strategies for prevention and curative treatment. Recent discoveries in this field have identified three distinct reservoirs of bacterial biofilm including: abscess communities in the local soft tissue and bone marrow, glycocalyx formation on implant hardware and necrotic tissue, and colonization of the osteocyte-lacuno canalicular network (OLCN) of cortical bone. In contrast, intracellular persistence in bone cells has not been substantiated in vivo, which challenges this mode of chronic osteomyelitis. There have also been major advances in our understanding of the immune proteome against , from clinical studies of serum antibodies and media enriched for newly synthesized antibodies (MENSA), which may provide new opportunities for osteomyelitis diagnosis, prognosis, and vaccine development. Finally, novel therapies such as antimicrobial implant coatings and antibiotic impregnated 3D-printed scaffolds represent promising strategies for preventing and managing this devastating disease. Here, we review these recent advances and highlight translational opportunities towards a cure.

Candida albicans has been detected together with Streptococcus mutans in high numbers in plaque-biofilm from children with early childhood caries (ECC). The goal of this study was to examine the C. albicans carriage in children with severe early childhood caries (S-ECC) and the maternal relatedness. Subjects in this pilot cross-sectional study were recruited based on a convenient sample. DMFT(S)/dmft(s) caries and plaque scores were assessed during a comprehensive oral exam. Social-demographic and related background information was collected through a questionnaire. Saliva and plaque sample from all children and mother subjects were collected. C. albicans were isolated by BBL™ CHROMagar™ and also identified using germ tube test. S. mutans was isolated using Mitis Salivarius with Bacitracin selective medium and identified by colony morphology. Genetic relatedness was examined using restriction endonuclease analysis of the C. albicans genome using BssHII (REAG-B). Multilocus sequence typing was used to examine the clustering information of isolated C. albicans. Spot assay was performed to examine the C. albicans Caspofungin susceptibility between S-ECC children and their mothers. All statistical analyses (power analysis for sample size, Spearman's correlation coefficient and multiple regression analyses) were implemented with SAS 9.4. A total of 18 S-ECC child-mother pairs and 17 caries free child-mother pairs were enrolled in the study. Results indicated high C. albicans carriage rate in the oral cavity (saliva and plaque) of both S-ECC children and their mothers (>80%). Spearman's correlation coefficient also indicated a significant correlation between salivary and plaque C. albicans and S. mutans carriage (p<0.01) and caries severity (p<0.05). The levels of C. albicans in the prepared saliva and plaque sample (1ml resuspension) of S-ECC children were 1.3 ± 4.5 x104 cfu/ml and 1.2 ± 3.5 x104 cfu/ml (~3-log higher vs. caries-free children). Among 18 child-mother pairs, >60% of them demonstrated identical C. albicans REAG-B pattern. C. albicans isolated from >65% of child-mother pairs demonstrated similar susceptibility to caspofungin in spot assay, while no caspofungin resistant strains were seen when compared with C. albicans wild-type strain SC5314. Interestingly, the regression analysis showed that factors such as antibiotic usage, birth weight, inhaler use, brushing frequency, and daycare attendance had no significant effect on the oral carriage of C. albicans in the S-ECC children. Our results reveal that both the child with S-ECC and the mother were highly infected with C. albicans, while most of the strains were genetically related, suggesting that the mother might be a source for C. albicans acquisition in the oral cavity of children affected by the disease.

The composition of the oral microbiota from 10 individuals with healthy oral tissues was determined using culture-independent techniques. From each individual, 26 specimens, each from different oral sites at a single point in time, were collected and pooled. An 11th pool was constructed using portions of the subgingival specimens from all 10 individuals. The 16S ribosomal RNA gene was amplified using broad-range bacterial primers, and clone libraries from the individual and subgingival pools were constructed. From a total of 11 368 high-quality, nonchimeric, near full-length sequences, 247 species-level phylotypes (using a 99% sequence identity threshold) and 9 bacterial phyla were identified. At least 15 bacterial genera were conserved among all 10 individuals, with significant interindividual differences at the species and strain level. Comparisons of these oral bacterial sequences with near full-length sequences found previously in the large intestines and feces of other healthy individuals suggest that the mouth and intestinal tract harbor distinct sets of bacteria. Co-occurrence analysis showed significant segregation of taxa when community membership was examined at the level of genus, but not at the level of species, suggesting that ecologically significant, competitive interactions are more apparent at a broader taxonomic level than species. This study is one of the more comprehensive, high-resolution analyses of bacterial diversity within the healthy human mouth to date, and highlights the value of tools from macroecology for enhancing our understanding of bacterial ecology in human health.

Background Identification of factors that influence the neonatal gut microbiome is urgently needed to guide clinical practices that support growth of healthy preterm infants. Here, we examined the influence of nutrition and common practices on the gut microbiota and growth in a cohort of preterm infants. Results With weekly gut microbiota samples spanning postmenstrual age (PMA) 24 to 46 weeks, we developed two models to test associations between the microbiota, nutrition and growth: a categorical model with three successive microbiota phases (P1, P2, and P3) and a model with two periods (early and late PMA) defined by microbiota composition and PMA, respectively. The more significant associations with phase led us to use a phase-based framework for the majority of our analyses. Phase transitions were characterized by rapid shifts in the microbiota, with transition out of P1 occurring nearly simultaneously with the change from meconium to normal stool. The rate of phase progression was positively associated with gestational age at birth, and delayed transition to a P3 microbiota was associated with growth failure. We found distinct bacterial metabolic functions in P1–3 and significant associations between nutrition, microbiota phase, and infant growth. Conclusion The phase - dependent impact of nutrition on infant growth along with phase - specific metabolic functions suggests a pioneering potential for improving growth outcomes by tailoring nutrient intake to microbiota phase.

The human intestinal microbiota is composed of 10¹³ to 10¹⁴ microorganisms whose collective genome (\"microbiome\") contains at least 100 times as many genes as our own genome. We analyzed ~78 million base pairs of unique DNA sequence and 2062 polymerase chain reaction-amplified 16S ribosomal DNA sequences obtained from the fecal DNAs of two healthy adults. Using metabolic function analyses of identified genes, we compared our human genome with the average content of previously sequenced microbial genomes. Our microbiome has significantly enriched metabolism of glycans, amino acids, and xenobiotics; methanogenesis; and 2-methyl-D-erythritol 4-phosphate pathway-mediated biosynthesis of vitamins and isoprenoids. Thus, humans are superorganisms whose metabolism represents an amalgamation of microbial and human attributes.

Background Postnatal development of early life microbiota influences immunity, metabolism, neurodevelopment, and infant health. Microbiome development occurs at multiple body sites, with distinct community compositions and functions. Associations between microbiota at multiple sites represent an unexplored influence on the infant microbiome. Here, we examined co-occurrence patterns of gut and respiratory microbiota in pre- and full-term infants over the first year of life, a period critical to neonatal development. Results Gut and respiratory microbiota collected as longitudinal rectal, throat, and nasal samples from 38 pre-term and 44 full-term infants were first clustered into community state types (CSTs) on the basis of their compositional profiles. Multiple methods were used to relate the occurrence of CSTs to temporal microbiota development and measures of infant maturity, including gestational age (GA) at birth, week of life (WOL), and post-menstrual age (PMA). Manifestation of CSTs followed one of three patterns with respect to infant maturity: (1) chronological , with CST occurrence frequency solely a function of post-natal age (WOL), (2) idiosyncratic to maturity at birth , with the interval of CST occurrence dependent on infant post-natal age but the frequency of occurrence dependent on GA at birth, and (3) convergent , in which CSTs appear first in infants of greater maturity at birth, with occurrence frequency in pre-terms converging after a post-natal interval proportional to pre-maturity. The composition of CSTs was highly dissimilar between different body sites, but the CST of any one body site was highly predictive of the CSTs at other body sites. There were significant associations between the abundance of individual taxa at each body site and the CSTs of the other body sites, which persisted after stringent control for the non-linear effects of infant maturity. Canonical correlations exist between the microbiota composition at each pair of body sites, with the strongest correlations between proximal locations. Conclusion These findings suggest that early microbiota is shaped by neonatal innate and adaptive developmental responses. Temporal progression of CST occurrence is influenced by infant maturity at birth and post-natal age. Significant associations of microbiota across body sites reveal distal connections and coordinated development of the infant microbial ecosystem.

The microbiota of the human stomach and the influence of Helicobacter pylori colonization on its composition remain largely unknown. We characterized bacterial diversity within the human gastric mucosa by using a small subunit 16S rDNA clone library approach and analyzed 1,833 sequences generated by broad-range bacterial PCR from 23 gastric endoscopic biopsy samples. A diverse community of 128 phylotypes was identified, featuring diversity at this site greater than previously described. The majority of sequences were assigned to the Proteobacteria, Firmicutes, Actinobacteria, Bacteroidetes, and Fusobacteria phyla. Ten percent of the phylotypes were previously uncharacterized, including a Deinococcus-related organism, relatives of which have been found in extreme environments but not reported before in humans. The gastric clone libraries from 19 subjects contained H. pylori rDNA; however, only 12 of these subjects tested positive for H. pylori by conventional laboratory methods. Statistical analysis revealed a large degree of intersubject variability of the gastric ecosystem. The presence of H. pylori did not affect the composition of the gastric community. This gastric bacterial rDNA data set was significantly different from sequence collections of the human mouth and esophagus described in other studies, indicating that the human stomach may be home to a distinct microbial ecosystem. The gastric microbiota may play important, as-yet-undiscovered roles in human health and disease.

Lower respiratory tract infection (LRTI) commonly causes hospitalization in adults. Because bacterial diagnostic tests are not accurate, antibiotics are frequently prescribed. Peripheral blood gene expression to identify subjects with bacterial infection is a promising strategy. We evaluated whole blood profiling using RNASeq to discriminate infectious agents in adults with microbiologically defined LRTI. Hospitalized adults with LRTI symptoms were recruited. Clinical data and blood was collected, and comprehensive microbiologic testing performed. Gene expression was measured using RNASeq and qPCR. Genes discriminatory for bacterial infection were identified using the Bonferroni-corrected Wilcoxon test. Constrained logistic models to predict bacterial infection were fit using screened LASSO. We enrolled 94 subjects who were microbiologically classified; 53 as “non-bacterial” and 41 as “bacterial”. RNAseq and qPCR confirmed significant differences in mean expression for 10 genes previously identified as discriminatory for bacterial LRTI. A novel dimension reduction strategy selected three pathways (lymphocyte, α-linoleic acid metabolism, IGF regulation) including eleven genes as optimal markers for discriminating bacterial infection (naïve AUC = 0.94; nested CV-AUC = 0.86). Using these genes, we constructed a classifier for bacterial LRTI with 90% (79% CV) sensitivity and 83% (76% CV) specificity. This novel, pathway-based gene set displays promise as a method to distinguish bacterial from nonbacterial LRTI.