Prioritizing genes of potential relevance to diseases affected by sex hormones: an example of myasthenia gravis

HLA-DRB1*03:01 is associated with female sex and younger age of anti-LGI1 encephalitis

OBJECTIVE

To investigate the association between human leukocyte antigen (HLA) genotype and clinical characteristics of anti-LGI1 encephalitis.

METHODS

HLA genotyping was performed in 34 patients with anti-LGI1 encephalitis admitted to West China Hospital between April 2014 and May 2021, as well as in 305 healthy controls. The on-line tool NetMHCIIpan 4.0 and AutoDock Vina software were used to predict binding between LGI1 peptide and HLA Class II molecules.

RESULTS

Risk of anti-LGI1 encephalitis was strongly associated with the DRB1*03:01 allele (OR 4.31, 95% CI 1.96-9.25, Pc = 2.75×10^-4 ) and the DRB1*03:01-DQB1*02:01 haplotype (OR 4.45, 95% CI 2.02-9.59, Pc = 2.94×10^-4 ). Compared to carriers of the DRB1*07:01 allele, those with the DRB1*03:01 allele were more likely to be female (93.3% vs 33.3%; P = 0.004) and to be younger (median age, 38 vs 65 years; P < 0.001). DRB1*03:01 carriers showed stronger response to immunotherapy than carriers of the DRB1*07:01 allele, based on median score decrease on the modified Rankin scale [2 (interquartile range, 1-2) vs. 1 (interquartile range, 0-1); P = 0.03] at 4 weeks after immunotherapy. Prediction and docking algorithms suggested that the LGI1 peptide can bind to the DRB1*03:01 molecule strongly.

CONCLUSIONS

The strong association between anti-LGI1 encephalitis and certain HLA class II alleles supports a primary autoimmune origin for the disease. Carriers of the DRB1*03:01 allele in Chinese patients with anti-LGI1 encephalitis are more likely to be female, to suffer earlier disease onset and to respond better to immunotherapy.

Clinical and Prognostic Value of Immunogenetic Characteristics in Anti-LGI1 Encephalitis

Objective

Antibodies against leucine-rich glioma-inactivated 1 (LGI1-Abs) characterize a limbic encephalitis (LE) strongly associated with HLA-DRB1*07:01, although some patients lack LGI1-Abs in CSF or do not carry this allele. Whether they represent a different subtype of disease or have different prognoses is unclear.

Methods

Retrospective analysis of clinical features, IgG isotypes, and outcome according to LGI1-Ab CSF positivity and DRB1*07:01 in a cohort of anti-LGI1 LE patients.

Results

Patients with LGI1-Abs detected in both CSF and serum (105/134, 78%) were compared with those who were CSF negative (29/134, 22%). Both groups had similar clinical features and serum levels, but CSF-positive patients had shorter diagnostic delay, more frequently hyponatremia, inflammatory CSF, and abnormal MRI (p < 0.05). Human leukocyte antigen (HLA) genotyping was performed in 72/134 (54%) patients and 63/72 (88%) carried DRB1*07:01. Noncarriers (9/72, 12%) were younger, more commonly women, and had less frequently psychiatric and frontal symptoms (p < 0.05). No difference in IgG isotypes according to CSF positivity or HLA was found (p > 0.05). HLA and IgG isotypes were not associated with poor outcome (mRS >2 at last follow-up) in univariate analyses; CSF positivity was only identified as a poor outcome predictor in the multivariate analysis including the complete follow-up, whereas age and female sex also remained when just the first year was considered.

Conclusions

LE without CSF LGI1-Abs is clinically indistinguishable and likely reflects just a lesser LGI1-Ab production. HLA association is sex and age biased and presents clinical particularities, suggesting subtle differences in the immune response. Long-term outcome depends mostly on demographic characteristics and the intensity of the intrathecal synthesis.

VAV1 and BAFF, via NFκB pathway, are genetic risk factors for myasthenia gravis

Objective

To identify novel genetic loci that predispose to early-onset myasthenia gravis (EOMG) applying a two-stage association study, exploration, and replication strategy.

Methods

Thirty-four loci and one confirmation loci, human leukocyte antigen (HLA)-DRA, were selected as candidate genes by team members of groups involved in different research aspects of MG. In the exploration step, these candidate genes were genotyped in 384 EOMG and 384 matched controls and significant difference in allele frequency were found in eight genes. In the replication step, eight candidate genes and one confirmation loci were genotyped in 1177 EOMG patients and 814 controls, from nine European centres.

Results

Allele frequency differences were found in four novel loci: CD86, AKAP12, VAV1, B-cell activating factor (BAFF), and tumor necrosis factor-alpha (TNF-α), and these differences were consistent in all nine cohorts. Haplotype trend test supported the differences in allele frequencies between cases and controls. In addition, allele frequency difference in female versus male patients at HLA-DRA and TNF-α loci were observed.

Interpretation

The genetic associations to EOMG outside the HLA complex are novel and of interest as VAV1 is a key signal transducer essential for T- and B-cell activation, and BAFF is a cytokine that plays important roles in the proliferation and differentiation of B-cells. Moreover, we noted striking epistasis between the predisposing VAV1 and BAFF haplotypes; they conferred a greater risk in combination than alone. These, and CD86, share the same signaling pathway, namely nuclear factor-kappaB (NFκB), thus implicating dysregulation of proinflammatory signaling in predisposition to EOMG.

The role of gender and organ specific autoimmunity

Autoimmunity is influenced by multiple factors including gender and sex hormones. A definite female predominance is found in many autoimmune diseases. Gender is also associated with differences in clinical presentation, onset, progression and outcome of autoimmune diseases. Sex hormones might influence the target organ's vulnerability to an autoimmune response. Gender differences also exist in organ specific autoimmune diseases such as multiple sclerosis, Guillain-Barré syndrome, Crohn's disease and celiac disease. Nevertheless, other organ specific autoimmune diseases (i.e. ulcerative colitis) are seemingly characterized with similar prevalence in both males and females. The reason for gender differences in certain autoimmune diseases remains unknown, but may be attributed to sex hormone influence, fetal microchimerism, X chromosome inactivation, and X chromosome abnormalities. Sex hormones have been found to have immune modulating properties, as well as providing cellular protection following tissue damage in certain circumstances. Sex hormones also influence innate and adaptive immune cells, number of B and T cells, antigen presentation and cytokine secretion. Herein, we review the influence of gender on organ-specific autoimmune diseases affecting the heart, blood vessels, central nervous system and gastrointestinal tract. It appears that sex hormones may have a therapeutic potential in several autoimmune conditions, although further research is required before therapeutic recommendations can be made.

In Silico discovery of transcription factors as potential diagnostic biomarkers of ovarian cancer

BACKGROUND

Our study focuses on identifying potential biomarkers for diagnosis and early detection of ovarian cancer (OC) through the study of transcription regulation of genes affected by estrogen hormone.

RESULTS

The results are based on a set of 323 experimentally validated OC-associated genes compiled from several databases, and their subset controlled by estrogen. For these two gene sets we computationally determined transcription factors (TFs) that putatively regulate transcription initiation. We ranked these TFs based on the number of genes they are likely to control. In this way, we selected 17 top-ranked TFs as potential key regulators and thus possible biomarkers for a set of 323 OC-associated genes. For 77 estrogen controlled genes from this set we identified three unique TFs as potential biomarkers.

CONCLUSIONS

We introduced a new methodology to identify potential diagnostic biomarkers for OC. This report is the first bioinformatics study that explores multiple transcriptional regulators of OC-associated genes as potential diagnostic biomarkers in connection with estrogen responsiveness. We show that 64% of TF biomarkers identified in our study are validated based on real-time data from microarray expression studies. As an illustration, our method could identify CP2 that in combination with CA125 has been reported to be sensitive in diagnosing ovarian tumors.

A computational approach to candidate gene prioritization for X-linked mental retardation using annotation-based binary filtering and motif-based linear discriminatory analysis

Background

Several computational candidate gene selection and prioritization methods have recently been developed. These in silico selection and prioritization techniques are usually based on two central approaches - the examination of similarities to known disease genes and/or the evaluation of functional annotation of genes. Each of these approaches has its own caveats. Here we employ a previously described method of candidate gene prioritization based mainly on gene annotation, in accompaniment with a technique based on the evaluation of pertinent sequence motifs or signatures, in an attempt to refine the gene prioritization approach. We apply this approach to X-linked mental retardation (XLMR), a group of heterogeneous disorders for which some of the underlying genetics is known.

Results

The gene annotation-based binary filtering method yielded a ranked list of putative XLMR candidate genes with good plausibility of being associated with the development of mental retardation. In parallel, a motif finding approach based on linear discriminatory analysis (LDA) was employed to identify short sequence patterns that may discriminate XLMR from non-XLMR genes. High rates (>80%) of correct classification was achieved, suggesting that the identification of these motifs effectively captures genomic signals associated with XLMR vs. non-XLMR genes. The computational tools developed for the motif-based LDA is integrated into the freely available genomic analysis portal Galaxy (http://main.g2.bx.psu.edu/). Nine genes (APLN, ZC4H2, MAGED4, MAGED4B, RAP2C, FAM156A, FAM156B, TBL1X, and UXT) were highlighted as highly-ranked XLMR methods.

Conclusions

The combination of gene annotation information and sequence motif-orientated computational candidate gene prediction methods highlight an added benefit in generating a list of plausible candidate genes, as has been demonstrated for XLMR.

Reviewers: This article was reviewed by Dr Barbara Bardoni (nominated by Prof Juergen Brosius); Prof Neil Smalheiser and Dr Dustin Holloway (nominated by Prof Charles DeLisi).