Identifying disease-causing mutations in genomes of single patients by computational approaches

Understanding molecular signaling cascades in neural disease using multi-resolution models

If the genome defines the program for the operations of a cell, signaling networks execute it. These cascades of chemical, cell-biological, structural, and trafficking events span milliseconds (e.g., synaptic release) to potentially a lifetime (e.g., stabilization of dendritic spines). In principle almost every aspect of neuronal function, particularly at the synapse, depends on signaling. Thus dysfunction of these cascades, whether through mutations, local dysregulation, or infection, leads to disease. The sheer complexity of these pathways is matched by the range of diseases and the diversity of their phenotypes. In this review, we discuss how to build computational models, how these models are essential to tackle this complexity, and the benefits of using families of models at different levels of detail to understand signaling in health and disease.

Approach to genetic diagnosis of inborn errors of immunity through next-generation sequencing

Patients with inborn errors of immunity (IEI) present with a heterogeneous clinical and immunological phenotype, therefore a correct molecular diagnosis is crucial for the classification and subsequent therapeutic management. On the other hand, IEI are a group of rare congenital diseases with highly diverse features and, in most cases, an as yet unknown genetic etiology. Next generation sequencing has facilitated genetic examinations of rare inherited disorders during the recent years, thus allowing a suitable molecular diagnosis in the IEI patients. This review aimed to investigate the current findings about these techniques in the field of IEI, suggesting an efficient stepwise approach to molecular diagnosis of inborn errors of immunity.

A computational approach for detecting physiological homogeneity in the midst of genetic heterogeneity

The human genetic dissection of clinical phenotypes is complicated by genetic heterogeneity. Gene burden approaches that detect genetic signals in case-control studies are underpowered in genetically heterogeneous cohorts. We therefore developed a genome-wide computational method, network-based heterogeneity clustering (NHC), to detect physiological homogeneity in the midst of genetic heterogeneity. Simulation studies showed our method to be capable of systematically converging genes in biological proximity on the background biological interaction network, and capturing gene clusters harboring presumably deleterious variants, in an efficient and unbiased manner. We applied NHC to whole-exome sequencing data from a cohort of 122 individuals with herpes simplex encephalitis (HSE), including 13 individuals with previously published monogenic inborn errors of TLR3-dependent IFN-α/β immunity. The top gene cluster identified by our approach successfully detected and prioritized all causal variants of five TLR3 pathway genes in the 13 previously reported individuals. This approach also suggested candidate variants of three reported genes and four candidate genes from the same pathway in another ten previously unstudied individuals. TLR3 responsiveness was impaired in dermal fibroblasts from four of the five individuals tested, suggesting that the variants detected were causal for HSE. NHC is, therefore, an effective and unbiased approach for unraveling genetic heterogeneity by detecting physiological homogeneity.

Missense3D-DB web catalogue: an atom-based analysis and repository of 4M human protein-coding genetic variants

The interpretation of human genetic variation is one of the greatest challenges of modern genetics. New approaches are urgently needed to prioritize variants, especially those that are rare or lack a definitive clinical interpretation. We examined 10,136,597 human missense genetic variants from GnomAD, ClinVar and UniProt. We were able to perform large-scale atom-based mapping and phenotype interpretation of 3,960,015 of these variants onto 18,874 experimental and 84,818 in house predicted three-dimensional coordinates of the human proteome. We demonstrate that 14% of amino acid substitutions from the GnomAD database that could be structurally analysed are predicted to affect protein structure (n = 568,548, of which 566,439 rare or extremely rare) and may, therefore, have a yet unknown disease-causing effect. The same is true for 19.0% (n = 6266) of variants of unknown clinical significance or conflicting interpretation reported in the ClinVar database. The results of the structural analysis are available in the dedicated web catalogue Missense3D-DB ( http://missense3d.bc.ic.ac.uk/ ). For each of the 4 M variants, the results of the structural analysis are presented in a friendly concise format that can be included in clinical genetic reports. A detailed report of the structural analysis is also available for the non-experts in structural biology. Population frequency and predictions from SIFT and PolyPhen are included for a more comprehensive variant interpretation. This is the first large-scale atom-based structural interpretation of human genetic variation and offers geneticists and the biomedical community a new approach to genetic variant interpretation.

Rare Diseases of the Oral Cavity, Neck, and Pharynx

Diseases occurring with an incidence of less than 1-10 cases per 10 000 individuals are considered as rare. Currently, between 5 000 and 8 000 rare or orphan diseases are known, every year about 250 rare diseases are newly described. Many of those pathologies concern the head and neck area. In many cases, a long time is required to diagnose an orphan disease. The lives of patients who are affected by those diseases are often determined by medical consultations and inpatient stays. Most orphan diseases are of genetic origin and cannot be cured despite medical progress. However, during the last years, the perception of and the knowledge about rare diseases has increased also due to the fact that publicly available databases have been created and self-help groups have been established which foster the autonomy of affected people. Only recently, innovative technical progress in the field of biogenetics allows individually characterizing the genetic origin of rare diseases in single patients. Based on this, it should be possible in the near future to elaborate tailored treatment concepts for patients suffering from rare diseases in the sense of translational and personalized medicine. This article deals with orphan diseases of the lip, oral cavity, pharynx, and cervical soft tissues depicting these developments. The readers will be provided with a compact overview about selected diseases of these anatomical regions. References to further information for medical staff and affected patients support deeper knowledge and lead to the current state of knowledge in this highly dynamic field.

Artificial intelligence and the hunt for immunological disorders

PURPOSE OF REVIEW

Artificial intelligence has pervasively transformed many industries and is beginning to shape medical practice. New use cases are being identified in subspecialty domains of medicine and, in particular, application of artificial intelligence has found its way to the practice of allergy-immunology. Here, we summarize recent developments, emerging applications and obstacles to realizing full potential.

RECENT FINDINGS

Artificial/augmented intelligence and machine learning are being used to reduce dimensional complexity, understand cellular interactions and advance vaccine work in the basic sciences. In genomics, bioinformatic methods are critical for variant calling and classification. For clinical work, artificial intelligence is enabling disease detection, risk profiling and decision support. These approaches are just beginning to have impact upon the field of clinical immunology and much opportunity exists for further advancement.

SUMMARY

This review highlights use of computational methods for analysis of large datasets across the spectrum of research and clinical care for patients with immunological disorders. Here, we discuss how big data methods are presently being used across the field clinical immunology.

The human genetic determinism of life-threatening infectious diseases: genetic heterogeneity and physiological homogeneity?

Multicellular eukaryotes emerged late in evolution from an ocean of viruses, bacteria, archaea, and unicellular eukaryotes. These macroorganisms are exposed to and infected by a tremendous diversity of microorganisms. Those that are large enough can even be infected by multicellular fungi and parasites. Each interaction is unique, if only because it operates between two unique living organisms, in an infinite diversity of circumstances. This is neatly illustrated by the extraordinarily high level of interindividual clinical variability in human infections, even for a given pathogen, ranging from a total absence of clinical manifestations to death. We discuss here the idea that the determinism of human life-threatening infectious diseases can be governed by single-gene inborn errors of immunity, which are rarely Mendelian and frequently display incomplete penetrance. We briefly review the evidence in support of this notion obtained over the last two decades, referring to a number of focused and thorough reviews published by eminent colleagues in this issue of Human Genetics. It seems that almost any life-threatening infectious disease can be driven by at least one, and, perhaps, a great many diverse monogenic inborn errors, which may nonetheless be immunologically related. While the proportions of monogenic cases remain unknown, a picture in which genetic heterogeneity is combined with physiological homogeneity is emerging from these studies. A preliminary sketch of the human genetic architecture of severe infectious diseases is perhaps in sight.