Approximating lattice similarity.

A method is proposed for choosing unit cells for a group of crystals so that they all appear as nearly similar as possible to a selected cell. Related unit cells with varying cell parameters or indexed with different lattice centering can be accommodated.

An invertible seven-dimensional Dirichlet cell characterization of lattices.

Characterization of crystallographic lattices is an important tool in structure solution, crystallographic database searches and clustering of diffraction images in serial crystallography. Characterization of lattices by Niggli-reduced cells (based on the three shortest non-coplanar lattice vectors) or by Delaunay-reduced cells (based on four non-coplanar vectors summing to zero and all meeting at obtuse or right angles) is commonly performed. The Niggli cell derives from Minkowski reduction. The Delaunay cell derives from Selling reduction. All are related to the Wigner-Seitz (or Dirichlet, or Voronoi) cell of the lattice, which consists of the points at least as close to a chosen lattice point as they are to any other lattice point. The three non-coplanar lattice vectors chosen are here called the Niggli-reduced cell edges. Starting from a Niggli-reduced cell, the Dirichlet cell is characterized by the planes determined by 13 lattice half-edges: the midpoints of the three Niggli cell edges, the six Niggli cell face-diagonals and the four body-diagonals, but seven of the lengths are sufficient: three edge lengths, the three shorter of each pair of face-diagonal lengths, and the shortest body-diagonal length. These seven are sufficient to recover the Niggli-reduced cell.

Generating random unit cells.

Methods of generating random unit-cell data for testing software are discussed. Working within the space S 6 appears to be the most expeditious.

Serial crystallography with multi-stage merging of thousands of images.

KAMO and BLEND provide particularly effective tools to automatically manage the merging of large numbers of data sets from serial crystallography. The requirement for manual intervention in the process can be reduced by extending BLEND to support additional clustering options such as the use of more accurate cell distance metrics and the use of reflection-intensity correlation coefficients to infer `distances' among sets of reflections. This increases the sensitivity to differences in unit-cell parameters and allows clustering to assemble nearly complete data sets on the basis of intensity or amplitude differences. If the data sets are already sufficiently complete to permit it, one applies KAMO once and clusters the data using intensities only. When starting from incomplete data sets, one applies KAMO twice, first using unit-cell parameters. In this step, either the simple cell vector distance of the original BLEND or the more sensitive NCDist is used. This step tends to find clusters of sufficient size such that, when merged, each cluster is sufficiently complete to allow reflection intensities or amplitudes to be compared. One then uses KAMO again using the correlation between reflections with a common hkl to merge clusters in a way that is sensitive to structural differences that may not have perturbed the unit-cell parameters sufficiently to make meaningful clusters. Many groups have developed effective clustering algorithms that use a measurable physical parameter from each diffraction still or wedge to cluster the data into categories which then can be merged, one hopes, to yield the electron density from a single protein form. Since these physical parameters are often largely independent of one another, it should be possible to greatly improve the efficacy of data-clustering software by using a multi-stage partitioning strategy. Here, one possible approach to multi-stage data clustering is demonstrated. The strategy is to use unit-cell clustering until the merged data are sufficiently complete and then to use intensity-based clustering. Using this strategy, it is demonstrated that it is possible to accurately cluster data sets from crystals that have subtle differences.

Molecular Pathways and Mechanisms of LAG3 in Cancer Therapy.

Immunotherapy targeting coinhibitory receptors has been highly successful in treating a wide variety of malignancies; however, only a subset of patients exhibits durable responses. The first FDA-approved immunotherapeutics targeting coinhibitory receptors PD1 and CTLA4, alone or in combination, significantly improved survival but were also accompanied by substantial toxicity in combination. The third FDA-approved immune checkpoint inhibitor targets LAG3, a coinhibitory receptor expressed on activated CD4+ and CD8+ T cells, especially in settings of long-term antigenic stimulation, such as chronic viral infection or cancer. Mechanistically, LAG3 expression limits both the expansion of activated T cells and the size of the memory pool, suggesting that LAG3 may be a promising target for immunotherapy. Importantly, the mechanism(s) by which LAG3 contributes to CD8+ T-cell exhaustion may be distinct from those governed by PD1, indicating that the combination of anti-LAG3 and anti-PD1 may synergistically enhance antitumor immunity. Clinical studies evaluating the role of anti-LAG3 in combination with anti-PD1 are underway, and recent phase III trial results in metastatic melanoma demonstrate both the efficacy and safety of this combination. Further ongoing clinical trials are evaluating this combination across multiple tumor types and the adjuvant setting, with accompanying translational and biomarker-focused studies designed to elucidate the molecular pathways that lead to improved antitumor T-cell responses following dual blockade of PD1 and LAG3. Overall, LAG3 plays an important role in limiting T-cell activation and has now become part of the repertoire of combinatorial immunotherapeutics available for the treatment of metastatic melanoma.

A simple technique to classify diffraction data from dynamic proteins according to individual polymorphs.

One often observes small but measurable differences in the diffraction data measured from different crystals of a single protein. These differences might reflect structural differences in the protein and may reveal the natural dynamism of the molecule in solution. Partitioning these mixed-state data into single-state clusters is a critical step that could extract information about the dynamic behavior of proteins from hundreds or thousands of single-crystal data sets. Mixed-state data can be obtained deliberately (through intentional perturbation) or inadvertently (while attempting to measure highly redundant single-crystal data). To the extent that different states adopt different molecular structures, one expects to observe differences in the crystals; each of the polystates will create a polymorph of the crystals. After mixed-state diffraction data have been measured, deliberately or inadvertently, the challenge is to sort the data into clusters that may represent relevant biological polystates. Here, this problem is addressed using a simple multi-factor clustering approach that classifies each data set using independent observables, thereby assigning each data set to the correct location in conformational space. This procedure is illustrated using two independent observables, unit-cell parameters and intensities, to cluster mixed-state data from chymotrypsinogen (ChTg) crystals. It is observed that the data populate an arc of the reaction trajectory as ChTg is converted into chymotrypsin.

People critically ill with COVID-19 exhibit peripheral immune profiles predictive of mortality and reflective of SARS-CoV-2 lung viral burden.

Despite extensive analyses, there remains an urgent need to delineate immune cell states that contribute to mortality in people critically ill with COVID-19. Here, we present high-dimensional profiling of blood and respiratory samples from people with severe COVID-19 to examine the association between cell-linked molecular features and mortality outcomes. Peripheral transcriptional profiles by single-cell RNA sequencing (RNA-seq)-based deconvolution of immune states are associated with COVID-19 mortality. Further, persistently high levels of an interferon signaling module in monocytes over time lead to subsequent concerted upregulation of inflammatory cytokines. SARS-CoV-2-infected myeloid cells in the lower respiratory tract upregulate CXCL10, leading to a higher risk of death. Our analysis suggests a pivotal role for viral-infected myeloid cells and protracted interferon signaling in severe COVID-19.

A Cre-driven allele-conditioning line to interrogate CD4+ conventional T cells.

CD4+ T cells share common developmental pathways with CD8+ T cells, and upon maturation, CD4+ T conventional T (Tconv) cells lack phenotypic markers that distinguish these cells from FoxP3+ T regulatory cells. We developed a tamoxifen-inducible ThPOKCreERT2.hCD2 line with Frt sites inserted on either side of the CreERT2-hCD2 cassette, and a Foxp3Ametrine-FlpO strain, expressing Ametrine and FlpO in Foxp3+ cells. Breeding these mice resulted in a CD4conviCreERT2-hCD2 line that allows for the specific manipulation of a gene in CD4+ Tconv cells. As FlpO removes the CreERT2-hCD2 cassette, CD4+ Treg cells are spared from Cre activity, which we refer to as allele conditioning. Comparison with an E8IiCreERT2.GFP mouse that enables inducible targeting of CD8+ T cells, and deletion of two inhibitory receptors, PD-1 and LAG-3, in a melanoma model, support the fidelity of these lines. These engineered mouse strains present a resource for the temporal manipulation of genes in CD4+ T cells and CD4+ Tconv cells.

Bifurcated monocyte states are predictive of mortality in severe COVID-19.

Coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 infection presents with varied clinical manifestations1, ranging from mild symptoms to acute respiratory distress syndrome (ARDS) with high mortality2,3. Despite extensive analyses, there remains an urgent need to delineate immune cell states that contribute to mortality in severe COVID-19. We performed high-dimensional cellular and molecular profiling of blood and respiratory samples from critically ill COVID-19 patients to define immune cell genomic states that are predictive of outcome in severe COVID-19 disease. Critically ill patients admitted to the intensive care unit (ICU) manifested increased frequencies of inflammatory monocytes and plasmablasts that were also associated with ARDS not due to COVID-19. Single-cell RNAseq (scRNAseq)-based deconvolution of genomic states of peripheral immune cells revealed distinct gene modules that were associated with COVID-19 outcome. Notably, monocytes exhibited bifurcated genomic states, with expression of a cytokine gene module exemplified by CCL4 (MIP-1ß) associated with survival and an interferon signaling module associated with death. These gene modules were correlated with higher levels of MIP-1ß and CXCL10 levels in plasma, respectively. Monocytes expressing genes reflective of these divergent modules were also detectable in endotracheal aspirates. Machine learning algorithms identified the distinctive monocyte modules as part of a multivariate peripheral immune system state that was predictive of COVID-19 mortality. Follow-up analysis of the monocyte modules on ICU day 5 was consistent with bifurcated states that correlated with distinct inflammatory cytokines. Our data suggests a pivotal role for monocytes and their specific inflammatory genomic states in contributing to mortality in life-threatening COVID-19 disease and may facilitate discovery of new diagnostics and therapeutics.

Resistance to PD1 blockade in the absence of metalloprotease-mediated LAG3 shedding.

Mechanisms of resistance to cancer immunotherapy remain poorly understood. Lymphocyte activation gene-3 (LAG3) signaling is regulated by a disintegrin and metalloprotease domain-containing protein-10 (ADAM10)- and ADAM17-mediated cell surface shedding. Here, we show that mice expressing a metalloprotease-resistant, noncleavable LAG3 mutant (LAG3NC) are resistant to PD1 blockade and fail to mount an effective antitumor immune response. Expression of LAG3NC intrinsically perturbs CD4+ T conventional cells (Tconvs), limiting their capacity to provide CD8+ T cell help. Furthermore, the translational relevance for these observations is highlighted with an inverse correlation between high LAG3 and low ADAM10 expression on CD4+ Tconvs in the peripheral blood of patients with head and neck squamous cell carcinoma, which corresponded with poor prognosis. This correlation was also observed in a cohort of patients with skin cancers and was associated with increased disease progression after standard-of-care immunotherapy. These data suggest that subtle changes in LAG3 inhibitory receptor signaling can act as a resistance mechanism with a substantive effect on patient responsiveness to immunotherapy.

Best practices for high data-rate macromolecular crystallography (HDRMX).

In macromolecular crystallography, higher flux, smaller beams, and faster detectors open the door to experiments with very large numbers of very small samples that can reveal polymorphs and dynamics but require re-engineering of approaches to the clustering of images both at synchrotrons and XFELs (X-ray free electron lasers). The need for the management of orders of magnitude more images and limitations of file systems favor a transition from simple one-file-per-image systems such as CBF to image container systems such as HDF5. This further increases the load on computers and networks and requires a re-examination of the presentation of metadata. In this paper, we discuss three important components of this problem-improved approaches to the clustering of images to better support experiments on polymorphs and dynamics, recent and upcoming changes in metadata for Eiger images, and software to rapidly validate images in the revised Eiger format.

Converting three-space matrices to equivalent six-space matrices for Delone scalars in S6.

The transformations from the primitive cells of the centered Bravais lattices to the corresponding centered cells have conventionally been listed as three-by-three matrices that transform three-space lattice vectors. Using those three-by-three matrices when working in the six-dimensional space of lattices represented as Selling scalars as used in Delone (Delaunay) reduction, one could transform to the three-space representation, apply the three-by-three matrices and then back-transform to the six-space representation, but it is much simpler to have the equivalent six-by-six matrices and apply them directly. The general form of the transformation from the three-space matrix to the corresponding matrix operating on Selling scalars (expressed in space S6) is derived, and the particular S6matrices for the centered Delone types are listed. (Note: in his later publications, Boris Delaunay used the Russian version of his surname, Delone.).

Inhibitory receptors and ligands beyond PD-1, PD-L1 and CTLA-4: breakthroughs or backups.

Although immunotherapeutics targeting the inhibitory receptors (IRs) CTLA-4, PD-1 or PD-L1 have made substantial clinical progress in cancer, a considerable proportion of patients remain unresponsive to treatment. Targeting novel IR-ligand pathways in combination with current immunotherapies may improve clinical outcomes. New clinical immunotherapeutics target T cell-expressed IRs (LAG-3, TIM-3 and TIGIT) as well as inhibitory ligands in the B7 family (B7-H3, B7-H4 and B7-H5), although many of these targets have complex biologies and unclear mechanisms of action. With only modest clinical success in targeting these IRs, current immunotherapeutic design may not be optimal. This Review covers the biology of targeting novel IR-ligand pathways and the current clinical status of their immunotherapeutics, either as monotherapy or in combination with antibody to PD-1 or to its ligand PD-L1. Further understanding of the basic biology of these targets is imperative to the development of effective cancer immunotherapies.

A space for lattice representation and clustering.

Algorithms for quantifying the differences between two lattices are used for Bravais lattice determination, database lookup for unit cells to select candidates for molecular replacement, and recently for clustering to group together images from serial crystallography. It is particularly desirable for the differences between lattices to be computed as a perturbation-stable metric, i.e. as distances that satisfy the triangle inequality, so that standard tree-based nearest-neighbor algorithms can be used, and for which small changes in the lattices involved produce small changes in the distances computed. A perturbation-stable metric space related to the reduction algorithm of Selling and to the Bravais lattice determination methods of Delone is described. Two ways of representing the space, as six-dimensional real vectors or equivalently as three-dimensional complex vectors, are presented and applications of these metrics are discussed. (Note: in his later publications, Boris Delaunay used the Russian version of his surname, Delone.).

Intratumoral regulatory T cells: markers, subsets and their impact on anti-tumor immunity.

Regulatory T (Treg) cells play a crucial role in maintaining self-tolerance and resolution of immune responses by employing multifaceted immunoregulatory mechanisms. However, Treg cells readily infiltrate into the tumor microenvironment (TME) and dampen anti-tumor immune responses, thereby becoming a barrier to effective cancer immunotherapy. There has been a substantial expansion in the development of novel immunotherapies targeting various inhibitory receptors (IRs), such as CTLA4, PD1 and LAG3, but these approaches have mechanistically focused on the elicitation of anti-tumor responses. However, enhanced inflammation in the TME could also play a detrimental role by facilitating the recruitment, stability and function of Treg cells by up-regulating chemokines that promote Treg cell migration, and/or increasing inhibitory cytokine production. Furthermore, IR blockade may enhance Treg cell function and survival, thereby serving as a resistance mechanism against effective immunotherapy. Given that Treg cells are comprised of functionally and phenotypically heterogeneous sub-populations that may alter their characteristics in a context-dependent manner, it is critical to identify unique molecular pathways that are preferentially used by intratumoral Treg cells. In this review, we discuss markers that serve to identify certain Treg cell subsets, distinguished by chemokine receptors, IRs and cytokines that facilitate their migration, stability and function in the TME. We also discuss how these Treg cell subsets correlate with the clinical outcome of patients with various types of cancer and how they may serve as potential TME-specific targets for novel cancer immunotherapies.

The past and future roles of competition and habitat in the range-wide occupancy dynamics of Northern Spotted Owls.

Slow ecological processes challenge conservation. Short-term variability can obscure the importance of slower processes that may ultimately determine the state of a system. Furthermore, management actions with slow responses can be hard to justify. One response to slow processes is to explicitly concentrate analysis on state dynamics. Here, we focus on identifying drivers of Northern Spotted Owl (Strix occidentalis caurina) territorial occupancy dynamics across 11 study areas spanning their geographic range and forecasting response to potential management actions. Competition with Barred Owls (Strix varia) has increased Spotted Owl territory extinction probabilities across all study areas and driven recent declines in Spotted Owl populations. Without management intervention, the Northern Spotted Owl subspecies will be extirpated from parts of its current range within decades. In the short term, Barred Owl removal can be effective. Over longer time spans, however, maintaining or improving habitat conditions can help promote the persistence of northern spotted owl populations. In most study areas, habitat effects on expected Northern Spotted Owl territorial occupancy are actually greater than the effects of competition from Barred Owls. This study suggests how intensive management actions (removal of a competitor) with rapid results can complement a slower management action (i.e., promoting forest succession).

Selling reduction versus Niggli reduction for crystallographic lattices.

The unit-cell reduction described by Selling and used by Delone (whose early publications were under the spelling Delaunay) is explained in a simple form. The transformations needed to implement the reduction are listed. The simplicity of this reduction contrasts with the complexity of Niggli reduction.

Treg-recruiting microspheres prevent inflammation in a murine model of dry eye disease.

Dry eye disease (DED) is a common ocular disorder affecting millions of individuals worldwide. The pathology of DED involves the infiltration of CD4+ lymphocytes, leading to tear film instability and destructive inflammation. In the healthy steady state, a population of immunosuppressive T-cells called regulatory T-cells (Treg) regulates proliferation of immune cells that would otherwise lead to a disruption of immunological homeostasis. For this reason, it has been suggested that Tregs could restore the immunological imbalance in DED. To this end, one possible approach would be to recruit the body's own, endogenous Tregs in order to enrich them at the site of inflammation and tissue destruction. Previously, we have demonstrated a reduction of inflammation and disease symptoms in models of periodontitis corresponding to recruitment of endogenous Tregs, which was accomplished by local placement of controlled release systems that sustain a gradient of the chemokine CCL22, referred to here as Treg-recruiting microspheres. Given that DED is characterized by a pro-inflammatory environment resulting in local tissue destruction, we hypothesized that the controlled release of CCL22 could also recruit Tregs to the ocular surface potentially mediating inflammation and symptoms of DED. Indeed, data suggest that Treg-recruiting microspheres are capable of overcoming the immunological imbalance of Tregs and CD4+ IFN-γ+ cells in the lacrimal gland. Administration of Treg-recruiting microspheres effectively mitigated the symptoms of DED as measured through a number of outcomes such as tear clearance, goblet cells density and corneal epithelial integrity, suggesting that recruitment of endogenous Treg can mitigate inflammation associated with DED.

LAG3 (CD223) as a cancer immunotherapy target.

Despite the impressive impact of CTLA4 and PD1-PDL1-targeted cancer immunotherapy, a large proportion of patients with many tumor types fail to respond. Consequently, the focus has shifted to targeting alternative inhibitory receptors (IRs) and suppressive mechanisms within the tumor microenvironment. Lymphocyte activation gene-3 (LAG3) (CD223) is the third IR to be targeted in the clinic, consequently garnering considerable interest and scrutiny. LAG3 upregulation is required to control overt activation and prevent the onset of autoimmunity. However, persistent antigen exposure in the tumor microenvironment results in sustained LAG3 expression, contributing to a state of exhaustion manifest in impaired proliferation and cytokine production. The exact signaling mechanisms downstream of LAG3 and interplay with other IRs remain largely unknown. However, the striking synergy between LAG3 and PD1 observed in multiple settings, coupled with the contrasting intracellular cytoplasmic domain of LAG3 as compared with other IRs, highlights the potential uniqueness of LAG3. There are now four LAG3-targeted therapies in the clinic with many more in preclinical development, emphasizing the broad interest in this IR. Given the translational relevance of LAG3 and the heightened interest in the impact of dual LAG3/PD1 targeting in the clinic, the outcome of these trials could serve as a nexus; significantly increasing or dampening enthusiasm for subsequent targets in the cancer immunotherapeutic pipeline.

NearTree, a data structure and a software toolkit for the nearest-neighbor problem.

Many problems in crystallography and other fields can be treated as nearest-neighbor problems. The neartree data structure provides a flexible way to organize and retrieve metric data. In some cases, it can provide near-optimal performance. NearTree is a software tool that constructs neartrees and provides a number of different query tools.