Since antibiotic development lags, we search for potential drug targets through directed evolution experiments. A challenge is that many resistance genes hide in a noisy mutational background as mutator clones emerge in the adaptive population. Here, to overcome this noise, we quantify the impact of mutations through evolutionary action (EA). After sequencing ciprofloxacin or colistin resistance strains grown under different mutational regimes, we find that an elevated sum of the evolutionary action of mutations in a gene identifies known resistance drivers. This EA integration approach also suggests new antibiotic resistance genes which are then shown to provide a fitness advantage in competition experiments. Moreover, EA integration analysis of clinical and environmental isolates of antibiotic resistant of E. coli identifies gene drivers of resistance where a standard approach fails. Together these results inform the genetic basis of de novo colistin resistance and support the robust discovery of phenotype-driving genes via the evolutionary action of genetic perturbations in fitness landscapes.

Abs are very efficient drugs, ∼70 of them are already approved for medical use, over 500 are in clinical development, and many more are in preclinical development. One important step in the characterization and protection of a therapeutic Ab is the determination of its cognate epitope. The gold standard is the three-dimensional structure of the Ab/Ag complex by crystallography or nuclear magnetic resonance spectroscopy. However, it remains a tedious task, and its outcome is uncertain. We have developed MAbTope, a docking-based prediction method of the epitope associated with straightforward experimental validation procedures. We show that MAbTope predicts the correct epitope for each of 129 tested examples of Ab/Ag complexes of known structure. We further validated this method through the successful determination, and experimental validation (using human embryonic kidney cells 293), of the epitopes recognized by two therapeutic Abs targeting TNF-α: certolizumab and golimumab.

Targeting transferrin receptor 1 (TfR1) with monoclonal antibodies is a promising therapeutic strategy in cancer as tumor cells often overexpress TfR1 and show increased iron needs. We have re-engineered six anti-human TfR1 single-chain variable fragment (scFv) antibodies into fully human scFv₂-Fcγ1 and IgG1 antibodies. We selected the more promising candidate (H7), based on its ability to inhibit TfR1-mediated iron-loaded transferrin internalization in Raji cells (B-cell lymphoma). The H7 antibody displayed nanomolar affinity for its target in both formats (scFv₂-Fcγ1 and IgG1), but cross-reacted with mouse TfR1 only in the scFv₂-Fc format. H7 reduced the intracellular labile iron pool and, contrary to what has been observed with previously described anti-TfR1 antibodies, upregulated TfR1 level in Raji cells. H7 scFv₂-Fc format elimination half-life was similar in FcRn knock-out and wild type mice, suggesting that TfR1 recycling contributes to prevent H7 elimination in vivo. In vitro, H7 inhibited the growth of erythroleukemia and B-cell lymphoma cell lines (IC₅₀ 0.1 µg/mL) and induced their apoptosis. Moreover, the Im9 B-cell lymphoma cell line, which is resistant to apoptosis induced by rituximab (anti-CD20 antibody), was sensitive to H7. In vivo, tumor regression was observed in nude mice bearing ERY-1 erythroleukemia cell xenografts treated with H7 through a mechanism that involved iron deprivation and antibody-dependent cytotoxic effector functions. Therefore, targeting TfR1 using the fully human anti-TfR1 H7 is a promising tool for the treatment of leukemia and lymphoma.

Background: Coronary artery disease is a primary cause of death around the world, with both genetic and environmental risk factors. Although genome‐wide association studies have linked >100 unique loci to its genetic basis, these only explain a fraction of disease heritability. Methods and Results: To find additional gene drivers of coronary artery disease, we applied machine learning to quantitative evolutionary information on the impact of coding variants in whole exomes from the Myocardial Infarction Genetics Consortium. Using ensemble‐based supervised learning, the Evolutionary Action–Machine Learning framework ranked each gene's ability to classify case and control samples and identified 79 significant associations. These were connected to known risk loci; enriched in cardiovascular processes like lipid metabolism, blood clotting, and inflammation; and enriched for cardiovascular phenotypes in knockout mouse models. Among them, INPP5F and MST1R are examples of potentially novel coronary artery disease risk genes that modulate immune signaling in response to cardiac stress. Conclusions: We concluded that machine learning on the functional impact of coding variants, based on a massive amount of evolutionary information, has the power to suggest novel coronary artery disease risk genes for mechanistic and therapeutic discoveries in cardiovascular biology, and should also apply in other complex polygenic diseases.

The COVID-19 pandemic is caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) which enters the body principally through the nasal and larynx mucosa and progress to the lungs through the respiratory tract. SARS-CoV-2 replicates efficiently in respiratory epithelial cells motivating the development of alternative and rapidly scalable vaccine inducing mucosal protective and long-lasting immunity. We have previously developed an immunologically optimized multi-neoepitopes-based peptide vaccine platform which has already demonstrated tolerance and efficacy in hundreds of lung cancer patients. Here, we present a multi-target CD8 T cell peptide COVID-19 vaccine design targeting several structural (S, M, N) and non-structural (NSPs) SARS-CoV-2 proteins with selected epitopes in conserved regions of the SARS-CoV-2 genome. We observed that a single subcutaneous injection of a serie of epitopes induces a robust immunogenicity in-vivo as measured by IFNγ ELIspot. Upon tetramer characterization we found that this serie of epitopes induces a strong proportion of virus-specific CD8 T cells expressing CD103, CD44, CXCR3 and CD49a, the specific phenotype of tissue-resident memory T lymphocytes (Trm). Finally, we observed broad cellular responses, as characterized by IFNγ production, upon restimulation with structural and non-structural protein-derived epitopes using blood T cells isolated from convalescent asymptomatic, moderate and severe COVID-19 patients. These data provide insights for further development of a second generation of COVID-19 vaccine focused on inducing lasting Th1-biased memory CD8 T cell sentinels protection using immunodominant epitopes naturally observed after SARS-CoV-2 infection resolution. Statement of Significance: Humoral and cellular adaptive immunity are different and complementary immune defenses engaged by the body to clear viral infection. While neutralizing antibodies have the capacity to block virus binding to its entry receptor expressed on human cells, memory T lymphocytes have the capacity to eliminate infected cells and are required for viral clearance. However, viruses evolve quickly, and their antigens are prone to mutations to avoid recognition by the antibodies (phenomenon named ‘antigenic drift’). This limitation of the antibody-mediated immunity could be addressed by the T-cell mediated immunity, which is able to recognize conserved viral peptides from any viral proteins presented by virus-infected cells. Thus, by targeting several proteins and conserved regions on the genome of a virus, T-cell epitope-based vaccines are less subjected to mutations and may work effectively on different strains of the virus. We designed a multi-target T cell-based vaccine containing epitope regions optimized for CD8+ T cell stimulation that would drive long-lasting cellular immunity with high specificity, avoiding undesired effects such as antibody-dependent enhancement (ADE) and antibody-induced macrophages hyperinflammation that could be observed in subjects with severe COVID-19. Our in-vivo results showed that a single injection of selected CD8 T cell epitopes induces memory viral-specific T-cell responses with a phenotype of tissue-resident memory T cells (Trm). Trm has attracted a growing interest for developing vaccination strategies since they act as immune sentinels in barrier tissue such as the respiratory tract and the lung. Because of their localization in tissues, they are able to immediately recognize infected cells and, because of their memory phenotypes, they rapidly respond to viral infection by orchestrating local protective immune responses to eliminate pathogens. Lastly, such multiepitope-based vaccination platform uses robust and well-validated synthetic peptide production technologies that can be rapidly manufactured in a distributed manner.

Developing a therapeutic antibody is a long, tedious, and expensive process. Many obstacles need to be overcome, such as biophysical properties (issues of solubility, stability, weak production yields, etc.), as well as cross-reactivity and subsequent toxicity, which are major issues. No in silico method exists today to solve such issues. We hypothesized that if we were able to properly measure the similarity between the CDRs of antibodies (Ab) by considering not only their evolutionary proximity (sequence identity) but also their structural features, we would be able to identify families of Ab recognizing similar epitopes. As a consequence, Ab within the family would share the property to recognize their targets, which would allow (i) to identify off-targets and forecast the cross-reactions, and (ii) to identify new Ab specific for a given target. Testing our method on 238D2, an antagonistic anti-CXCR4 nanobody, we were able to find new nanobodies against CXCR4 and to identify influenza hemagglutinin as an off-target of 238D2.