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Historical review of rare immune pathway analysis

28 May 2021

Itan & Casanova contributions

We are tackling this problem of protein pathway analysis from the viewpoint of rare immune disease and infection. Historically, several topics in bioinformatic and functional analysis have been required before we could achieve our current position of statistically-robust genetic discovery for rare disease:

  1. Candidate variant select for individual genomes
  2. Compiling reliable cohorts of patients with shared phenotypes
  3. Protein-protein interactions
  4. Variant collapse
  5. Protein pathway analysis
  6. Functional validation

Therefore, a historical review of the timeline is beneficial to illustrate the technical successes that allow us to reliably produce candidate variants by genome sequencing and to validate statistically-driven results by “traditional” functional validation. These steps [1, 2 and 6 in our list] are exemplified by the following historical review. The complete list of steps 1-6 are touched on, but full validation of each step is the culmination of what we are currently working on and will be explicitly reviewed when we have completed our study.

As one of the leaders in this field, Casanova lab has provided a lot of insider history to the story in a great twitter thread https://twitter.com/casanova_lab/status/1397539593608695808. The literature for discussion is first listed here to facilitate downloading but sources are referenced as usual throughout.

2013 PNAS. The human gene connectome as a map of short cuts for morbid allele discovery. (Itan et al., 2013) https://pubmed.ncbi.nlm.nih.gov/23509278/

2014 BMC Gen. HGCS: an online tool for prioritizing disease-causing gene variants by biological distance. (Itan et al., 2014) https://pubmed.ncbi.nlm.nih.gov/24694260/

2015 Front. Novel primary immunodeficiency candidate genes predicted by the human gene connectome. (Itan & Casanova, 2015) https://pubmed.ncbi.nlm.nih.gov/25883595/,

2015 PNAS. The human gene damage index as a gene-level approach to prioritizing exome variants. (Itan et al., 2015) https://pubmed.ncbi.nlm.nih.gov/26483451/

2016 NatMet. The mutation significance cutoff: gene-level thresholds for variant predictions. (Itan et al., 2016) https://pubmed.ncbi.nlm.nih.gov/26820543/

2015 PNAS. Can the impact of human genetic variations be predicted? (Itan & Casanova, 2015) https://pubmed.ncbi.nlm.nih.gov/26351682/

2018 Bioinf. PopViz: a webserver for visualizing minor allele frequencies and damage prediction scores of human genetic variations. (Zhang et al., 2018) https://pubmed.ncbi.nlm.nih.gov/30535305/

2019 PNAS. Blacklisting variants common in private cohorts but not in public databases optimizes human exome analysis. (Maffucci et al., 2019) https://pubmed.ncbi.nlm.nih.gov/30591557/

2019 NAR. SeqTailor: a user-friendly webserver for the extraction of DNA or protein sequences from next-generation sequencing data. (Zhang et al., 2019) https://pubmed.ncbi.nlm.nih.gov/31045209/.

2020 Hum Gen. The human genetic determinism of life-threatening infectious diseases: genetic heterogeneity and physiological homogeneity? (Casanova & Abel, 2020) https://pubmed.ncbi.nlm.nih.gov/32462426/

2021 JCI. Herpes simplex encephalitis in a patient with a distinctive form of inherited IFNAR1 deficiency. (Bastard et al., 2021) https://pubmed.ncbi.nlm.nih.gov/32960813/

2021 JCI. TLR3 controls constitutive IFN-β antiviral immunity in human fibroblasts and cortical neurons. (Gao et al., 2021) https://pubmed.ncbi.nlm.nih.gov/33393505/

2021 AJHG. A computational approach for detecting physiological homogeneity in the midst of genetic heterogeneity (Zhang et al., 2021) https://pubmed.ncbi.nlm.nih.gov/34015270/

To date, the main paper that implements protein pathway analysis for rare immune disease is that by Peng Zhang and Yuval Itan (Zhang et al., 2021).

The history begins with determining methods for candidate variant selection - the main challenge in human genomics, especially for individual patients who can benefit from precision medicine. Over the last decade, we have reached a point where we can now reasonably discern individual candidate-causal variants from the background noise of genomic variability.

From the authors’ perspective, the project began around 2011 and its first step was concluded in 2013 with Yuval Itan’s first “Human Gene Connectome” paper while he was a post-doc with Casanova lab. (Itan et al., 2013). This software connected genes like streets in a map, based on their physiological relatedness. It was soon followed by methodological development (Itan et al., 2014) and application to inborn errors of immunity (Itan & Casanova, 2015) or both (Itan et al., 2015), and a couple of necessary detours (Itan et al., 2016) and (Maffucci et al., 2019). A review was also written by two of the main authors during the same period (Itan & Casanova, 2015).

When Peng Zhang joined the Casanova lab as post-doc, Yuval Itan had started his own lab. However, the pair worked together to continue producing the papers on variant interpretation and data processing (Zhang et al., 2018) and (Zhang et al., 2019).

After completing this period of work, they renamed “Human Gene Connectome II” the “Network-based Heterogeneity Clustering”. At this point, their aims were defined as being generally indistinguishable from ours. That is, “the detection of physiological homogeneity in a cohort of patients sharing a clinical phenotype but with high genetic heterogeneity - a hallmark of severe infectious diseases” (Casanova via twitter), as presented in their next paper on this topic (Casanova & Abel, 2020).

Shen-Ying Zhang came on board as senior author on the next two papers. With an excellent database of immune disorders and infections, the team could gradually build their software. Exomes from patients with HSV-1 encephalitis were used for testing successive versions in (Bastard et al., 2021) and (Gao et al., 2021).

Quoting Casanova “When they were capable of detecting the known TLR3-IFN needles in the HSE stack, they installed camp 1, rested a bit and reported to me on the radio, while I was watching them from the basecamp with binoculars. I encouraged them to push for the final ascent and they did.”

With the same goal as our own - producing unbiased methods for detection of biologically-connected causal genetic variation - they found new gene variants that interact via the TLR3-IFN protein pathway, in individual patients. Shen-Ying Zhang found them to be biochemically deleterious, an important factor for validation of genetic-first aproaches. In this case, Zhang et al get as close to the “gold-standard” as anyone to date.

The functional validation of candidate variants in disease then provided a proof-of-principle indication that they could detect physiological homogeneity in the midst of genetic heterogeneity (Zhang et al., 2021).

Quoting Casanova “A computational approach for detecting physiological homogeneity in the midst of genetic heterogeity. That was terrific!”.

An aside on what I call the “gold-standard” for our field should be:

  1. Unbiased statistical detection of a genetic phenomenon.
  2. Validation by functional models under systematic control.

Part [1] Depends on patient cohorts that are large enough to detect the effect based on the phenotype strength - difficult for rare disease.
Part [2] Depends on independently testing biological mechanisms.

This second step generally consists of two hurdles:

  • If the same researchers perform (1) genetic stats and (2) functional work, there is a bias that is difficult to avoid when trying to functionally validate statistically positive results.
  • If the statistical genetic associations happen to contain a false positive for something like severe rare immune disease, the sensitive functional models may detect a damaging response. One might find a truly damaging biological mechanism, but if the statistical genetic association is not correct then this biological mechanism should not be deemed causal; back-tracking at this stage would be very difficult due to self-imposed bias.

Ideally, in the future we hope to see a separation of the two steps (stat genetics and wet-lab) such that each are performed independently. The wet-lab would also ideally focus their routines on a particular protein pathway/system with SOPs that improve accuracy and precision (e.g. clinical diagnostics labs, clinical trials) rather than setting up models for each new study.

Returning to our historical review, we have been producing our protocols similarly in parallel. With patient cohorts of comparable sizes and phenotypes we will soon have a complementary validation of protocols. However, great care is also being taken to test and select the most reliable statistical methods for association testing - an improvement to the fine work by (Zhang et al., 2021).

Best practices in candidate variant selection protocols are basically standardised as of 2021, so the main remaining task is standardisation of the protein-pathway annotation and association testing methods - steps which we will soon be ready to publish after peer-review.

References

  1. Itan, Y., Zhang, S.-Y., Vogt, G., Abhyankar, A., Herman, M., Nitschke, P., Fried, D., Quintana-Murci, L., Abel, L., & Casanova, J.-L. (2013). The human gene connectome as a map of short cuts for morbid allele discovery. Proceedings of the National Academy of Sciences, 110(14), 5558–5563.
  2. Itan, Y., Mazel, M., Mazel, B., Abhyankar, A., Nitschke, P., Quintana-Murci, L., Boisson-Dupuis, S., Boisson, B., Abel, L., Zhang, S.-Y., & others. (2014). HGCS: an online tool for prioritizing disease-causing gene variants by biological distance. BMC Genomics, 15(1), 1–8.
  3. Itan, Y., & Casanova, J.-L. (2015). Novel primary immunodeficiency candidate genes predicted by the human gene connectome. Frontiers in Immunology, 6, 142.
  4. Itan, Y., Shang, L., Boisson, B., Patin, E., Bolze, A., Moncada-Vélez, M., Scott, E., Ciancanelli, M. J., Lafaille, F. G., Markle, J. G., & others. (2015). The human gene damage index as a gene-level approach to prioritizing exome variants. Proceedings of the National Academy of Sciences, 112(44), 13615–13620.
  5. Itan, Y., Shang, L., Boisson, B., Ciancanelli, M. J., Markle, J. G., Martinez-Barricarte, R., Scott, E., Shah, I., Stenson, P. D., Gleeson, J., & others. (2016). The mutation significance cutoff: gene-level thresholds for variant predictions. Nature Methods, 13(2), 109–110.
  6. Itan, Y., & Casanova, J.-L. (2015). Can the impact of human genetic variations be predicted? Proceedings of the National Academy of Sciences, 112(37), 11426–11427.
  7. Zhang, P., Bigio, B., Rapaport, F., Zhang, S.-Y., Casanova, J.-L., Abel, L., Boisson, B., & Itan, Y. (2018). PopViz: a webserver for visualizing minor allele frequencies and damage prediction scores of human genetic variations. Bioinformatics, 34(24), 4307–4309.
  8. Maffucci, P., Bigio, B., Rapaport, F., Cobat, A., Borghesi, A., Lopez, M., Patin, E., Bolze, A., Shang, L., Bendavid, M., & others. (2019). Blacklisting variants common in private cohorts but not in public databases optimizes human exome analysis. Proceedings of the National Academy of Sciences, 116(3), 950–959.
  9. Zhang, P., Boisson, B., Stenson, P. D., Cooper, D. N., Casanova, J.-L., Abel, L., & Itan, Y. (2019). SeqTailor: a user-friendly webserver for the extraction of DNA or protein sequences from next-generation sequencing data. Nucleic Acids Research, 47(W1), W623–W631.
  10. Casanova, J.-L., & Abel, L. (2020). The human genetic determinism of life-threatening infectious diseases: genetic heterogeneity and physiological homogeneity? Human Genetics, 139, 681–694.
  11. Bastard, P., Manry, J., Chen, J., Rosain, J., Seeleuthner, Y., AbuZaitun, O., Lorenzo, L., Khan, T., Hasek, M., Hernandez, N., & others. (2021). Herpes simplex encephalitis in a patient with a distinctive form of inherited IFNAR1 deficiency. The Journal of Clinical Investigation, 131(1).
  12. Gao, D., Ciancanelli, M. J., Zhang, P., Harschnitz, O., Bondet, V., Hasek, M., Chen, J., Mu, X., Itan, Y., Cobat, A., & others. (2021). TLR3 controls constitutive IFN-βantiviral immunity in human fibroblasts and cortical neurons. The Journal of Clinical Investigation, 131(1).
  13. Zhang, P., Cobat, A., Lee, Y.-S., Wu, Y., Bayrak, C. S., Boccon-Gibod, C., Matuozzo, D., Lorenzo, L., Jain, A., Boucherit, S., & others. (2021). A computational approach for detecting physiological homogeneity in the midst of genetic heterogeneity. The American Journal of Human Genetics.
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Maintained by Dylan Lawless. Scientist at EPFL. Resume and Google scholar. RSS feed