Vishal N. Koparde

Identification of a gene in Mycoplasma hominis associated with preterm birth and microbial burden in intraamniotic infection

OBJECTIVE Microbial invasion of the amniotic cavity is associated with spontaneous preterm labor and adverse pregnancy outcome, and Mycoplasma hominis often is present. However, the pathogenic process by which M hominis invades the amniotic cavity and gestational tissues, often resulting in chorioamnionitis and preterm birth, remains unknown. We hypothesized that strains of M hominis vary genetically with regards to their potential to invade and colonize the amniotic cavity and placenta. STUDY DESIGN We sequenced the entire genomes of 2 amniotic fluid isolates and a placental isolate of M hominis from pregnancies that resulted in preterm births and compared them with the previously sequenced genome of the type strain PG21. We identified genes that were specific to the amniotic fluid/placental isolates. We then determined the microbial burden and the presence of these genes in another set of subjects from whom samples of amniotic fluid had been collected and were positive for M hominis. RESULTS We identified 2 genes that encode surface-located membrane proteins (Lmp1 and Lmp-like) in the sequenced amniotic fluid/placental isolates that were truncated severely in PG21. We also identified, for the first time, a microbial gene of unknown function that is referred to in this study as gene of interest C that was associated significantly with bacterial burden in amniotic fluid and the risk of preterm delivery in patients with preterm labor. CONCLUSION A gene in M hominis was identified that is associated significantly with colonization and/or infection of the upper reproductive tract during pregnancy and with preterm birth.

Whole exome sequencing to estimate alloreactivity potential between donors and recipients in stem cell transplantation

Whole exome sequencing (WES) was performed on stem cell transplant donor‐recipient (D‐R) pairs to determine the extent of potential antigenic variation at a molecular level. In a small cohort of D‐R pairs, a high frequency of sequence variation was observed between the donor and recipient exomes independent of human leucocyte antigen (HLA) matching. Nonsynonymous, nonconservative single nucleotide polymorphisms were approximately twice as frequent in HLA‐matched unrelated, compared with related D‐R pairs. When mapped to individual chromosomes, these polymorphic nucleotides were uniformly distributed across the entire exome. In conclusion, WES reveals extensive nucleotide sequence variation in the exomes of HLA‐matched donors and recipients.

In silico Derivation of HLA-Specific Alloreactivity Potential from Whole Exome Sequencing of Stem-Cell Transplant Donors and Recipients: Understanding the Quantitative Immunobiology of Allogeneic Transplantation

Donor T-cell mediated graft versus host (GVH) effects may result from the aggregate alloreactivity to minor histocompatibility antigens (mHA) presented by the human leukocyte antigen (HLA) molecules in each donor–recipient pair undergoing stem-cell transplantation (SCT). Whole exome sequencing has previously demonstrated a large number of non-synonymous single nucleotide polymorphisms (SNP) present in HLA-matched recipients of SCT donors (GVH direction). The nucleotide sequence flanking each of these SNPs was obtained and the amino acid sequence determined. All the possible nonameric peptides incorporating the variant amino acid resulting from these SNPs were interrogated in silico for their likelihood to be presented by the HLA class I molecules using the Immune Epitope Database stabilized matrix method (SMM) and NetMHCpan algorithms. The SMM algorithm predicted that a median of 18,396 peptides weakly bound HLA class I molecules in individual SCT recipients, and 2,254 peptides displayed strong binding. A similar library of presented peptides was identified when the data were interrogated using the NetMHCpan algorithm. The bioinformatic algorithm presented here demonstrates that there may be a high level of mHA variation in HLA-matched individuals, constituting a HLA-specific alloreactivity potential.

Stem cell transplantation as a dynamical system: are clinical outcomes deterministic?

Outcomes in stem cell transplantation (SCT) are modeled using probability theory. However, the clinical course following SCT appears to demonstrate many characteristics of dynamical systems, especially when outcomes are considered in the context of immune reconstitution. Dynamical systems tend to evolve over time according to mathematically determined rules. Characteristically, the future states of the system are predicated on the states preceding them, and there is sensitivity to initial conditions. In SCT, the interaction between donor T cells and the recipient may be considered as such a system in which, graft source, conditioning, and early immunosuppression profoundly influence immune reconstitution over time. This eventually determines clinical outcomes, either the emergence of tolerance or the development of graft versus host disease. In this paper, parallels between SCT and dynamical systems are explored and a conceptual framework for developing mathematical models to understand disparate transplant outcomes is proposed.

Dynamical System Modeling to Simulate Donor T Cell Response to Whole Exome Sequencing-Derived Recipient Peptides Demonstrates Different Alloreactivity Potential in HLA-Matched and -Mismatched Donor–Recipient Pairs

Immune reconstitution kinetics and subsequent clinical outcomes in HLA-matched recipients of allogeneic stem cell transplantation (SCT) are variable and difficult to predict. Considering SCT as a dynamical system may allow sequence differences across the exomes of the transplant donors and recipients to be used to simulate an alloreactive T cell response, which may allow better clinical outcome prediction. To accomplish this, whole exome sequencing was performed on 34 HLA-matched SCT donor-recipient pairs (DRPs) and the nucleotide sequence differences translated to peptides. The binding affinity of the peptides to the relevant HLA in each DRP was determined. The resulting array of peptide-HLA binding affinity values in each patient was considered as an operator modifying a hypothetical T cell repertoire vector, in which each T cell clone proliferates in accordance with the logistic equation of growth. Using an iterating system of matrices, each simulated T cell clones growth was calculated with the steady-state population being proportional to the magnitude of the binding affinity of the driving HLA-peptide complex. Incorporating competition between T cell clones responding to different HLA-peptide complexes reproduces a number of features of clinically observed T cell clonal repertoire in the simulated repertoire, including sigmoidal growth kinetics of individual T cell clones and overall repertoire, Power Law clonal frequency distribution, increase in repertoire complexity over time with increasing clonal diversity, and alteration of clonal dominance when a different antigen array is encountered, such as in SCT. The simulated, alloreactive T cell repertoire was markedly different in HLA-matched DRPs. The patterns were differentiated by rate of growth and steady-state magnitude of the simulated T cell repertoire and demonstrate a possible correlation with survival. In conclusion, exome wide sequence differences in DRPs may allow simulation of donor alloreactive T cell response to recipient antigens and may provide a quantitative basis for refining donor selection and titration of immunosuppression after SCT.

Applying an empirical hydropathic forcefield in refinement may improve low-resolution protein X-ray crystal structures.

Background The quality of X-ray crystallographic models for biomacromolecules refined from data obtained at high-resolution is assured by the data itself. However, at low-resolution, >3.0 Å, additional information is supplied by a forcefield coupled with an associated refinement protocol. These resulting structures are often of lower quality and thus unsuitable for downstream activities like structure-based drug discovery. Methodology An X-ray crystallography refinement protocol that enhances standard methodology by incorporating energy terms from the HINT (Hydropathic INTeractions) empirical forcefield is described. This protocol was tested by refining synthetic low-resolution structural data derived from 25 diverse high-resolution structures, and referencing the resulting models to these structures. The models were also evaluated with global structural quality metrics, e.g., Ramachandran score and MolProbity clashscore. Three additional structures, for which only low-resolution data are available, were also re-refined with this methodology. Results The enhanced refinement protocol is most beneficial for reflection data at resolutions of 3.0 Å or worse. At the low-resolution limit, ≥4.0 Å, the new protocol generated models with Cα positions that have RMSDs that are 0.18 Å more similar to the reference high-resolution structure, Ramachandran scores improved by 13%, and clashscores improved by 51%, all in comparison to models generated with the standard refinement protocol. The hydropathic forcefield terms are at least as effective as Coulombic electrostatic terms in maintaining polar interaction networks, and significantly more effective in maintaining hydrophobic networks, as synthetic resolution is decremented. Even at resolutions ≥4.0 Å, these latter networks are generally native-like, as measured with a hydropathic interactions scoring tool.

MeFiT: merging and filtering tool for illumina paired-end reads for 16S rRNA amplicon sequencing

BackgroundRecent advances in next-generation sequencing have revolutionized genomic research. 16S rRNA amplicon sequencing using paired-end sequencing on the MiSeq platform from Illumina, Inc., is being used to characterize the composition and dynamics of extremely complex/diverse microbial communities. For this analysis on the Illumina platform, merging and quality filtering of paired-end reads are essential first steps in data analysis to ensure the accuracy and reliability of downstream analysis.ResultsWe have developed the Merging and Filtering Tool (MeFiT) to combine these pre-processing steps into one simple, intuitive pipeline. MeFiT invokes CASPER (context-aware scheme for paired-end reads) for merging paired-end reads and provides users the option to quality filter the reads using the traditional average Q-score metric or using a maximum expected error cut-off threshold.ConclusionsMeFiT provides an open-source solution that permits users to merge and filter paired end illumina reads. The tool has been implemented in python and the source-code is freely available at https://github.com/nisheth/MeFiT.

Dynamical system modeling to simulate donor T cell response to whole exome sequencing-derived recipient peptides: Understanding randomness in alloreactivity incidence following stem cell transplantation

Quantitative relationship between the magnitude of variation in minor histocompatibility antigens (mHA) and graft versus host disease (GVHD) pathophysiology in stem cell transplant (SCT) donor-recipient pairs (DRP) is not established. In order to elucidate this relationship, whole exome sequencing (WES) was performed on 27 HLA matched related (MRD), & 50 unrelated donors (URD), to identify nonsynonymous single nucleotide polymorphisms (SNPs). An average 2,463 SNPs were identified in MRD, and 4,287 in URD DRP (p<0.01); resulting peptide antigens that may be presented on HLA class I molecules in each DRP were derived in silico (NetMHCpan ver2.0) and the tissue expression of proteins these were derived from determined (GTex). MRD DRP had an average 3,670 HLA-binding-alloreactive peptides, putative mHA (pmHA) with an IC50 of <500 nM, and URD, had 5,386 (p<0.01). To simulate an alloreactive donor cytotoxic T cell response, the array of pmHA in each patient was considered as an operator matrix modifying a hypothetical cytotoxic T cell clonal vector matrix; each responding T cell clone’s proliferation was determined by the logistic equation of growth, accounting for HLA binding affinity and tissue expression of each alloreactive peptide. The resulting simulated organ-specific alloreactive T cell clonal growth revealed marked variability, with the T cell count differences spanning orders of magnitude between different DRP. Despite an estimated, uniform set of constants used in the model for all DRP, and a heterogeneously treated group of patients, higher total and organ-specific T cell counts were associated with cumulative incidence of moderate to severe GVHD in recipients. In conclusion, exome wide sequence differences and the variable alloreactive peptide binding to HLA in each DRP yields a large range of possible alloreactive donor T cell responses. Our findings also help understand the apparent randomness observed in the development of alloimmune responses.

Comparison of Lactobacillus crispatus isolates from Lactobacillus-dominated vaginal microbiomes with isolates from microbiomes containing bacterial vaginosis-associated bacteria.

Vaginal lactobacilli can inhibit colonization by and growth of other bacteria, thereby preventing development of bacterial vaginosis (BV). Amongst the lactobacilli, Lactobacillus crispatus appears to be particularly effective at inhibiting growth of BV-associated bacteria. Nonetheless, some women who are colonized with this species can still develop clinical BV. Therefore, we sought to determine whether strains of L. crispatus that colonize women with lactobacilli-dominated vaginal microbiomes are distinct from strains that colonize women who develop BV. The genomes of L. crispatus isolates from four women with lactobacilli-dominated vaginal microbiomes ( <1% 16S rRNA reads above threshold from genera other than Lactobacillus) and four women with microbiomes containing BV-associated bacteria (>12% 16S rRNA reads from bacterial taxa associated with BV) were sequenced and compared. Lactic acid production by the different strains was quantified. Phage induction in the strains was also analysed. There was considerable genetic diversity between strains, and several genes were exclusive to either the strains from Lactobacillus-dominated microbiomes or those containing BV-associated bacteria. Overall, strains from microbiomes dominated by lactobacilli did not differ from strains from microbiomes containing BV-associated bacteria with respect to lactic acid production. All of the strains contained multiple phage, but there was no clear distinction between the presence or absence of BV-associated bacteria with respect to phage-induced lysis. Genes found to be exclusive to the Lactobacillus-dominated versus BV-associated bacteria-containing microbiomes could play a role in the maintenance of vaginal health and the development of BV, respectively.

Determining the Quantitative Principles of T Cell Response to Antigenic Disparity: The Case of Stem Cell Transplant Donor-Recipient Exome Wide Mismatching and HLA Bound Alloreactive Peptides

Alloreactivity compromising clinical outcomes in stem cell transplantation is observed despite HLA matching of donors and recipients. This has its origin in the variation between the exomes of the two, which provides the basis for minor histocompatibility antigens (mHA). The mHA presented on the HLA class I and II molecules and the ensuing T cell response to these antigens results in graft versus host disease. In this paper, results of a whole exome sequencing study are presented, with resulting alloreactive polymorphic peptides and their HLA class I and HLA class II (DRB1) binding affinity quantified. Large libraries of potentially alloreactive recipient peptides binding both sets of molecules were identified, with HLA-DRB1 presenting an order of magnitude greater number of peptides. These results are used to develop a quantitative framework to understand the immunobiology of transplantation. A tensor-based approach is used to derive the equations needed to determine the alloreactive donor T cell response from the mHA-HLA binding affinity and protein expression data. This approach may be used in future studies to simulate the magnitude of expected donor T cell response and risk for alloreactive complications in HLA matched or mismatched hematopoietic cell and solid organ transplantation.