A network of immune and microbial modifications underlies viral persistence in the gastrointestinal tract.

Many pathogens subvert intestinal immunity to persist within the gastrointestinal tract (GIT); yet, the underlying mechanisms that enable sanctuary specifically in this reservoir are unclear. Using mass cytometry and network analysis, we demonstrate that chronic LCMV infection of the GIT leads to dysregulated microbial composition, a cascade of metabolic alterations, increased susceptibility to GI disease, and a system-wide recalibration of immune composition that defines viral persistence. Chronic infection led to outgrowth of activated Tbet-expressing T reg cell populations unique to the GIT and the rapid erosion of pathogen-specific CD8 tissue-resident memory T cells. Mechanistically, T reg cells and coinhibitory receptors maintained long-term viral sanctuary within the GIT, and their targeting reactivated T cells and eliminated this viral reservoir. Thus, our data provide a high-dimensional definition of the mechanisms of immune regulation that chronic viruses implement to exploit the unique microenvironment of the GIT and identify T reg cells as key modulators of viral persistence in the intestinal tract.

Validation of CyTOF Against Flow Cytometry for Immunological Studies and Monitoring of Human Cancer Clinical Trials.

Flow cytometry is a widely applied approach for exploratory immune profiling and biomarker discovery in cancer and other diseases. However, flow cytometry is limited by the number of parameters that can be simultaneously analyzed, severely restricting its utility. Recently, the advent of mass cytometry (CyTOF) has enabled high dimensional and unbiased examination of the immune system, allowing simultaneous interrogation of a large number of parameters. This is important for deep interrogation of immune responses and particularly when sample sizes are limited (such as in tumors). Our goal was to compare the accuracy and reproducibility of CyTOF against flow cytometry as a reliable analytic tool for human PBMC and tumor tissues for cancer clinical trials. We developed a 40+ parameter CyTOF panel and demonstrate that compared to flow cytometry, CyTOF yields analogous quantification of cell lineages in conjunction with markers of cell differentiation, function, activation, and exhaustion for use with fresh and viably frozen PBMC or tumor tissues. Further, we provide a protocol that enables reliable quantification by CyTOF down to low numbers of input human cells, an approach that is particularly important when cell numbers are limiting. Thus, we validate CyTOF as an accurate approach to perform high dimensional analysis in human tumor tissue and to utilize low cell numbers for subsequent immunologic studies and cancer clinical trials.

CD8+ T Cell Priming in Established Chronic Viral Infection Preferentially Directs Differentiation of Memory-like Cells for Sustained Immunity.

CD8+ T cell exhaustion impedes control of chronic viral infection; yet how new T cell responses are mounted during chronic infection is unclear. Unlike T cells primed at the onset of infection that rapidly differentiate into effectors and exhaust, we demonstrate that virus-specific CD8+ T cells primed after establishment of chronic LCMV infection preferentially generate memory-like transcription factor TCF1+ cells that were transcriptionally and proteomically distinct, less exhausted, and more responsive to immunotherapy. Mechanistically, adaptations of antigen-presenting cells and diminished T cell signaling intensity promoted differentiation of the memory-like subset at the expense of rapid effector cell differentiation, which was now highly dependent on IL-21-mediated CD4+ T cell help for its functional generation. Chronic viral infection similarly redirected de novo differentiation of tumor-specific CD8+ T cells, ultimately preventing cancer control. Thus, targeting these T cell stimulatory pathways could enable strategies to control chronic infection, tumors, and enhance immunotherapeutic efficacy.

Control of catalytic efficiency by a coevolving network of catalytic and noncatalytic residues.

The active sites of enzymes consist of residues necessary for catalysis and structurally important noncatalytic residues that together maintain the architecture and function of the active site. Examples of evolutionary interactions between catalytic and noncatalytic residues have been difficult to define and experimentally validate due to a general intolerance of these residues to substitution. Here, using computational methods to predict coevolving residues, we identify a network of positions consisting of two catalytic metal-binding residues and two adjacent noncatalytic residues in LAGLIDADG homing endonucleases (LHEs). Distinct combinations of the four residues in the network map to distinct LHE subfamilies, with a striking distribution of the metal-binding Asp (D) and Glu (E) residues. Mutation of these four positions in three LHEs--I-LtrI, I-OnuI, and I-HjeMI--indicate that the combinations of residues tolerated are specific to each enzyme. Kinetic analyses under single-turnover conditions revealed that I-LtrI activity could be modulated over an ∼100-fold range by mutation of residues in the coevolving network. I-LtrI catalytic site variants with low activity could be rescued by compensatory mutations at adjacent noncatalytic sites that restore an optimal coevolving network and vice versa. Our results demonstrate that LHE activity is constrained by an evolutionary barrier of residues with strong context-dependent effects. Creation of optimal coevolving active-site networks is therefore an important consideration in engineering of LHEs and other enzymes.

Bioinformatics identification of coevolving residues.

Positions in a protein are thought to coevolve to maintain important structural and functional interactions over evolutionary time. The detection of putative coevolving positions can provide important new insights into a protein family in the same way that knowledge is gained by recognizing evolutionarily conserved characters and characteristics. Putatively coevolving positions can be detected with statistical methods that identify covarying positions. However, positions in protein alignments can covary for many other reasons than coevolution; thus, it is crucial to create high-quality multiple sequence alignments for coevolution inference. Furthermore, it is important to understand common signs and sources of error. When confounding factors are accounted for, coevolution is a rich resource for protein engineering information.

Protein sequence alignment analysis by local covariation: coevolution statistics detect benchmark alignment errors.

The use of sequence alignments to understand protein families is ubiquitous in molecular biology. High quality alignments are difficult to build and protein alignment remains one of the largest open problems in computational biology. Misalignments can lead to inferential errors about protein structure, folding, function, phylogeny, and residue importance. Identifying alignment errors is difficult because alignments are built and validated on the same primary criteria: sequence conservation. Local covariation identifies systematic misalignments and is independent of conservation. We demonstrate an alignment curation tool, LoCo, that integrates local covariation scores with the Jalview alignment editor. Using LoCo, we illustrate how local covariation is capable of identifying alignment errors due to the reduction of positional independence in the region of misalignment. We highlight three alignments from the benchmark database, BAliBASE 3, that contain regions of high local covariation, and investigate the causes to illustrate these types of scenarios. Two alignments contain sequential and structural shifts that cause elevated local covariation. Realignment of these misaligned segments reduces local covariation; these alternative alignments are supported with structural evidence. We also show that local covariation identifies active site residues in a validated alignment of paralogous structures. Loco is available at https://sourceforge.net/projects/locoprotein/files/.

Tapping natural reservoirs of homing endonucleases for targeted gene modification.

Homing endonucleases mobilize their own genes by generating double-strand breaks at individual target sites within potential host DNA. Because of their high specificity, these proteins are used for "genome editing" in higher eukaryotes. However, alteration of homing endonuclease specificity is quite challenging. Here we describe the identification and phylogenetic analysis of over 200 naturally occurring LAGLIDADG homing endonucleases (LHEs). Biochemical and structural characterization of endonucleases from one clade within the phylogenetic tree demonstrates strong conservation of protein structure contrasted against highly diverged DNA target sites and indicates that a significant fraction of these proteins are sufficiently stable and active to serve as engineering scaffolds. This information was exploited to create a targeting enzyme to disrupt the endogenous monoamine oxidase B gene in human cells. The ubiquitous presence and diversity of LHEs described in this study may facilitate the creation of many tailored nucleases for genome editing.

Microbiome profiling by illumina sequencing of combinatorial sequence-tagged PCR products.

We developed a low-cost, high-throughput microbiome profiling method that uses combinatorial sequence tags attached to PCR primers that amplify the rRNA V6 region. Amplified PCR products are sequenced using an Illumina paired-end protocol to generate millions of overlapping reads. Combinatorial sequence tagging can be used to examine hundreds of samples with far fewer primers than is required when sequence tags are incorporated at only a single end. The number of reads generated permitted saturating or near-saturating analysis of samples of the vaginal microbiome. The large number of reads allowed an in-depth analysis of errors, and we found that PCR-induced errors composed the vast majority of non-organism derived species variants, an observation that has significant implications for sequence clustering of similar high-throughput data. We show that the short reads are sufficient to assign organisms to the genus or species level in most cases. We suggest that this method will be useful for the deep sequencing of any short nucleotide region that is taxonomically informative; these include the V3, V5 regions of the bacterial 16S rRNA genes and the eukaryotic V9 region that is gaining popularity for sampling protist diversity.

Deep sequencing of the vaginal microbiota of women with HIV.

BACKGROUND: Women living with HIV and co-infected with bacterial vaginosis (BV) are at higher risk for transmitting HIV to a partner or newborn. It is poorly understood which bacterial communities constitute BV or the normal vaginal microbiota among this population and how the microbiota associated with BV responds to antibiotic treatment. METHODS AND FINDINGS: The vaginal microbiota of 132 HIV positive Tanzanian women, including 39 who received metronidazole treatment for BV, were profiled using Illumina to sequence the V6 region of the 16S rRNA gene. Of note, Gardnerella vaginalis and Lactobacillus iners were detected in each sample constituting core members of the vaginal microbiota. Eight major clusters were detected with relatively uniform microbiota compositions. Two clusters dominated by L. iners or L. crispatus were strongly associated with a normal microbiota. The L. crispatus dominated microbiota were associated with low pH, but when L. crispatus was not present, a large fraction of L. iners was required to predict a low pH. Four clusters were strongly associated with BV, and were dominated by Prevotella bivia, Lachnospiraceae, or a mixture of different species. Metronidazole treatment reduced the microbial diversity and perturbed the BV-associated microbiota, but rarely resulted in the establishment of a lactobacilli-dominated microbiota. CONCLUSIONS: Illumina based microbial profiling enabled high though-put analyses of microbial samples at a high phylogenetic resolution. The vaginal microbiota among women living with HIV in Sub-Saharan Africa constitutes several profiles associated with a normal microbiota or BV. Recurrence of BV frequently constitutes a different BV-associated profile than before antibiotic treatment.

Identifying and seeing beyond multiple sequence alignment errors using intra-molecular protein covariation.

BACKGROUND: There is currently no way to verify the quality of a multiple sequence alignment that is independent of the assumptions used to build it. Sequence alignments are typically evaluated by a number of established criteria: sequence conservation, the number of aligned residues, the frequency of gaps, and the probable correct gap placement. Covariation analysis is used to find putatively important residue pairs in a sequence alignment. Different alignments of the same protein family give different results demonstrating that covariation depends on the quality of the sequence alignment. We thus hypothesized that current criteria are insufficient to build alignments for use with covariation analyses. METHODOLOGY/PRINCIPAL FINDINGS: We show that current criteria are insufficient to build alignments for use with covariation analyses as systematic sequence alignment errors are present even in hand-curated structure-based alignment datasets like those from the Conserved Domain Database. We show that current non-parametric covariation statistics are sensitive to sequence misalignments and that this sensitivity can be used to identify systematic alignment errors. We demonstrate that removing alignment errors due to 1) improper structure alignment, 2) the presence of paralogous sequences, and 3) partial or otherwise erroneous sequences, improves contact prediction by covariation analysis. Finally we describe two non-parametric covariation statistics that are less sensitive to sequence alignment errors than those described previously in the literature. CONCLUSIONS/SIGNIFICANCE: Protein alignments with errors lead to false positive and false negative conclusions (incorrect assignment of covariation and conservation, respectively). Covariation analysis can provide a verification step, independent of traditional criteria, to identify systematic misalignments in protein alignments. Two non-parametric statistics are shown to be somewhat insensitive to misalignment errors, providing increased confidence in contact prediction when analyzing alignments with erroneous regions because of an emphasis on they emphasize pairwise covariation over group covariation.

Functionally relevant protein motions: extracting basin-specific collective coordinates from molecular dynamics trajectories.

Functionally relevant motion of proteins has been associated with a number of atoms moving in a concerted fashion along so-called "collective coordinates." We present an approach to extract collective coordinates from conformations obtained from molecular dynamics simulations. The power of this technique for differentiating local structural fluctuations between classes of conformers obtained by clustering is illustrated by analyzing nanosecond-long trajectories for the response regulator protein Spo0F of Bacillus subtilis, generated both in vacuo and using an implicit-solvent representation. Conformational clustering is performed using automated histogram filtering of the inter-C(alpha) distances. Orthogonal (varimax) rotation of the vectors obtained by principal component analysis of these interresidue distances for the members of individual clusters is key to the interpretation of collective coordinates dominating each conformational class. The rotated loadings plots isolate significant variation in interresidue distances, and these are associated with entire mobile secondary structure elements. From this we infer concerted motions of these structural elements. For the Spo0F simulations employing an implicit-solvent representation, collective coordinates obtained in this fashion are consistent with the location of the protein's known active sites and experimentally determined mobile regions.