Interactions between immune cell types facilitate the evolution of immune traits.

An essential prerequisite for evolution by natural selection is variation among individuals in traits that affect fitness1. The ability of a system to produce selectable variation, known as evolvability2, thus greatly affects the rate of evolution. The immune system belongs to the fastest evolving components in mammals3, yet the sources of variation in immune traits remain largely unknown4,5. Here, we show that an important determinant of the immune system's evolvability is its organisation into interacting modules represented by different immune cell types. By profiling immune cell variation in bone marrow of 54 genetically diverse mouse strains from the Collaborative Cross6, we found that variation in immune cell frequencies is polygenic and that many associated genes are involved in homeostatic balance through cell-intrinsic functions of proliferation, migration and cell death. However, we also found genes associated with the frequency of a particular cell type, which are expressed in a different cell type, exerting their effect in what we term cyto-trans. Vertebrate evolutionary record shows that genes associated in cyto-trans have faced weaker negative selection, thus increasing the robustness and hence evolvability2,7,8 of the immune system. This phenomenon is similarly observable in human blood. Our findings suggest that interactions between different components of the immune system provide a phenotypic space where mutations can produce variation without much detriment, underscoring the role of modularity in the evolution of complex systems9.

A personalized network framework reveals predictive axis of anti-TNF response across diseases.

Personalized treatment of complex diseases has been mostly predicated on biomarker identification of one drug-disease combination at a time. Here, we use a computational approach termed Disruption Networks to generate a data type, contextualized by cell-centered individual-level networks, that captures biology otherwise overlooked when performing standard statistics. This data type extends beyond the "feature level space", to the "relations space", by quantifying individual-level breaking or rewiring of cross-feature relations. Applying Disruption Networks to dissect high-dimensional blood data, we discover and validate that the RAC1-PAK1 axis is predictive of anti-TNF response in inflammatory bowel disease. Intermediate monocytes, which correlate with the inflammatory state, play a key role in the RAC1-PAK1 responses, supporting their modulation as a therapeutic target. This axis also predicts response in rheumatoid arthritis, validated in three public cohorts. Our findings support blood-based drug response diagnostics across immune-mediated diseases, implicating common mechanisms of non-response.

Reconstructing disease dynamics for mechanistic insights and clinical benefit.

Diseases change over time, both phenotypically and in their underlying molecular processes. Though understanding disease progression dynamics is critical for diagnostics and treatment, capturing these dynamics is difficult due to their complexity and the high heterogeneity in disease development between individuals. We present TimeAx, an algorithm which builds a comparative framework for capturing disease dynamics using high-dimensional, short time-series data. We demonstrate the utility of TimeAx by studying disease progression dynamics for multiple diseases and data types. Notably, for urothelial bladder cancer tumorigenesis, we identify a stromal pro-invasion point on the disease progression axis, characterized by massive immune cell infiltration to the tumor microenvironment and increased mortality. Moreover, the continuous TimeAx model differentiates between early and late tumors within the same tumor subtype, uncovering molecular transitions and potential targetable pathways. Overall, we present a powerful approach for studying disease progression dynamics-providing improved molecular interpretability and clinical benefits for patient stratification and outcome prediction.

Alignment of single-cell trajectories by tuMap enables high-resolution quantitative comparison of cancer samples.

Single-cell technologies allow characterization of cancer samples as continuous developmental trajectories. Yet, the obtained temporal resolution cannot be leveraged for a comparative analysis due to the large phenotypic heterogeneity existing between patients. Here, we present the tuMap algorithm that exploits high-dimensional single-cell data of cancer samples exhibiting an underlying developmental structure to align them with the healthy development, yielding the tuMap pseudotime axis that allows their systematic, meaningful comparison. We applied tuMap on single-cell mass cytometry data of acute lymphoblastic and myeloid leukemia to reveal associations between the tuMap pseudotime axis and clinics that outperform cellular assignment into developmental populations. Application of the tuMap algorithm on single-cell RNA sequencing data further identified gene signatures of stem cells residing at the very-early parts of the cancer trajectories. The quantitative framework provided by tuMap allows generation of metrics for cancer patients evaluation.

An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging.

While many diseases of aging have been linked to the immunological system, immune metrics capable of identifying the most at-risk individuals are lacking. From the blood immunome of 1,001 individuals aged 8-96 years, we developed a deep-learning method based on patterns of systemic age-related inflammation. The resulting inflammatory clock of aging (iAge) tracked with multimorbidity, immunosenescence, frailty and cardiovascular aging, and is also associated with exceptional longevity in centenarians. The strongest contributor to iAge was the chemokine CXCL9, which was involved in cardiac aging, adverse cardiac remodeling and poor vascular function. Furthermore, aging endothelial cells in human and mice show loss of function, cellular senescence and hallmark phenotypes of arterial stiffness, all of which are reversed by silencing CXCL9. In conclusion, we identify a key role of CXCL9 in age-related chronic inflammation and derive a metric for multimorbidity that can be utilized for the early detection of age-related clinical phenotypes.

Effect of the Lymphocyte Activation Gene 3 Polymorphism rs951818 on Mortality and Disease Progression in Patients with Sepsis-A Prospective Genetic Association Study.

(1) Background: Sepsis is a leading cause of death and a global public health problem. Accordingly, deciphering the underlying molecular mechanisms of this disease and the determinants of its morbidity and mortality is pivotal. This study examined the effect of the rs951818 SNP of the negative costimulatory lymphocyte-activation gene 3 (LAG-3) on sepsis mortality and disease severity. (2) Methods: 707 consecutive patients with sepsis were prospectively enrolled into the present study from three surgical ICUs at University Medical Center Goettingen. Both 28- and 90-day mortality were analyzed as the primary outcome, while parameters of disease severity served as secondary endpoints. (3) Results: In the Kaplan-Meier analysis LAG-3 rs951818 AA-homozygote patients showed a significantly lower 28-day mortality (17.3%) compared to carriers of the C-allele (23.7%, p = 0.0476). In addition, these patients more often received invasive mechanical ventilation (96%) during the course of disease than C-allele carriers (92%, p = 0.0466). (4) Conclusions: Genetic profiling of LAG-3 genetic variants alone or in combination with other genetic biomarkers may represent a promising approach for risk stratification of patients with sepsis. Patient-individual therapeutic targeting of immune checkpoints, such as LAG-3, may be a future component of sepsis therapy. Further detailed investigations in clinically relevant sepsis models are necessary.

TIM-3 Genetic Variants Are Associated with Altered Clinical Outcome and Susceptibility to Gram-Positive Infections in Patients with Sepsis.

Background: Previous studies have reported the fundamental role of immunoregulatory proteins in the clinical phenotype and outcome of sepsis. This study investigated two functional single nucleotide polymorphisms (SNPs) of T cell immunoglobulin and mucin domain-containing protein 3 (TIM-3), which has a negative stimulatory function in the T cell immune response. Methods: Patients with sepsis (n = 712) were prospectively enrolled from three intensive care units (ICUs) at the University Medical Center Goettingen since 2012. All patients were genotyped for the TIM-3 SNPs rs1036199 and rs10515746. The primary outcome was 28-day mortality. Disease severity and microbiological findings were secondary endpoints. Results: Kaplan-Meier survival analysis demonstrated a significantly lower 28-day mortality for TIM-3 rs1036199 AA homozygous patients compared to C-allele carriers (18% vs. 27%, p = 0.0099) and TIM-3 rs10515746 CC homozygous patients compared to A-allele carriers (18% vs. 26%, p = 0.0202). The TIM-3 rs1036199 AA genotype and rs10515746 CC genotype remained significant predictors for 28-day mortality in the multivariate Cox regression analysis after adjustment for relevant confounders (adjusted hazard ratios: 0.67 and 0.70). Additionally, patients carrying the rs1036199 AA genotype presented more Gram-positive and Staphylococcus epidermidis infections, and rs10515746 CC homozygotes presented more Staphylococcus epidermidis infections. Conclusion: The studied TIM-3 genetic variants are associated with altered 28-day mortality and susceptibility to Gram-positive infections in sepsis.

A clinically meaningful metric of immune age derived from high-dimensional longitudinal monitoring.

Immune responses generally decline with age. However, the dynamics of this process at the individual level have not been characterized, hindering quantification of an individual's immune age. Here, we use multiple 'omics' technologies to capture population- and individual-level changes in the human immune system of 135 healthy adult individuals of different ages sampled longitudinally over a nine-year period. We observed high inter-individual variability in the rates of change of cellular frequencies that was dictated by their baseline values, allowing identification of steady-state levels toward which a cell subset converged and the ordered convergence of multiple cell subsets toward an older adult homeostasis. These data form a high-dimensional trajectory of immune aging (IMM-AGE) that describes a person's immune status better than chronological age. We show that the IMM-AGE score predicted all-cause mortality beyond well-established risk factors in the Framingham Heart Study, establishing its potential use in clinics for identification of patients at risk.

CTLA-4 Genetic Variants Predict Survival in Patients with Sepsis.

Cytotoxic T lymphocyte-associated protein 4 (CTLA-4) is a coinhibitory checkpoint protein expressed on the surface of T cells. A recent study by our working group revealed that the rs231775 single nucleotide polymorphism (SNP) in the CTLA-4 gene was associated with the survival of patients with sepsis and served as an independent prognostic variable. To further investigate the impact of CTLA-4 genetic variants on sepsis survival, we examined the effect of two functional SNPs, CTLA-4 rs733618 and CTLA-4 rs3087243, and inferred haplotypes, on the survival of 644 prospectively enrolled septic patients. Kaplan⁻Meier survival analysis revealed significantly lower 90-day mortality for rs3087243 G allele carriers (n = 502) than for AA-homozygous (n = 142) patients (27.3% vs. 40.8%, p = 0.0024). Likewise, lower 90-day mortality was observed for TAA haplotype-negative patients (n = 197; compound rs733618 T/rs231775 A/rs3087243 A) than for patients carrying the TAA haplotype (n = 447; 24.4% vs. 32.9%, p = 0.0265). Carrying the rs3087243 G allele hazard ratio (HR): 0.667; 95% confidence interval (CI): 0.489⁻0.909; p = 0.0103) or not carrying the TAA haplotype (HR: 0.685; 95% CI: 0.491⁻0.956; p = 0.0262) remained significant covariates for 90-day survival in the multivariate Cox regression analysis and thus served as independent prognostic variables. In conclusion, our findings underscore the significance of CTLA-4 genetic variants as predictors of survival of patients with sepsis.

UV-Protection Timer Controls Linkage between Stress and Pigmentation Skin Protection Systems.

Skin sun exposure induces two protection programs: stress responses and pigmentation, the former within minutes and the latter only hours afterward. Although serving the same physiological purpose, it is not known whether and how these programs are coordinated. Here, we report that UVB exposure every other day induces significantly more skin pigmentation than the higher frequency of daily exposure, without an associated increase in stress responses. Using mathematical modeling and empirical studies, we show that the melanocyte master regulator, MITF, serves to synchronize stress responses and pigmentation and, furthermore, functions as a UV-protection timer via damped oscillatory dynamics, thereby conferring a trade-off between the two programs. MITF oscillations are controlled by multiple negative regulatory loops, one at the transcriptional level involving HIF1α and another post-transcriptional loop involving microRNA-148a. These findings support trait linkage between the two skin protection programs, which, we speculate, arose during furless skin evolution to minimize skin damage.

The CTLA-4 rs231775 GG genotype is associated with favorable 90-day survival in Caucasian patients with sepsis.

Cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) is a surface protein on T cells, that has an inhibitory effect on the host immune reaction and prevents overreaction of the immune system. Because the functional single-nucleotide polymorphism (SNP) rs231775 of the CTLA-4 gene is associated with autoimmune diseases and because of the critical role of the immune reaction in sepsis, we intended to examine the effect of this polymorphism on survival in patients with sepsis. 644 septic adult Caucasian patients were prospectively enrolled in this study. Patients were followed up for 90 days. Mortality risk within this period was defined as primary outcome parameter. Kaplan-Meier survival analysis revealed a significantly lower 90-day mortality risk among GG homozygous patients (n = 101) than among A allele carriers (n = 543; 22% and 32%, respectively; p = 0.03565). Furthermore, the CTLA-4 rs231775 GG genotype remained a significant covariate for 90-day mortality risk after controlling for confounders in the multivariate Cox regression analysis (hazard ratio: 0.624; 95% CI: 0.399-0.975; p = 0.03858). In conclusion, our study provides the first evidence for CTLA-4 rs231775 as a prognostic variable for the survival of patients with sepsis and emphasizes the need for further research to reveal potential functional associations between CTLA-4 and the immune pathophysiology of sepsis.

Alignment of single-cell trajectories to compare cellular expression dynamics.

Single-cell RNA sequencing and high-dimensional cytometry can be used to generate detailed trajectories of dynamic biological processes such as differentiation or development. Here we present cellAlign, a quantitative framework for comparing expression dynamics within and between single-cell trajectories. By applying cellAlign to mouse and human embryonic developmental trajectories, we systematically delineate differences in the temporal regulation of gene expression programs that would otherwise be masked.