MotivationMicrobial communities play important roles in the function and maintenance of various biosystems, ranging from the human body to the environment. A major challenge in microbiome research is the classification of microbial communities of different environments or host phenotypes. The most common and cost-effective approach for such studies to date is 16S rRNA gene sequencing. Recent falls in sequencing costs have increased the demand for simple, efficient and accurate methods for rapid detection or diagnosis with proved applications in medicine, agriculture and forensic science. We describe a reference- and alignment-free approach for predicting environments and host phenotypes from 16S rRNA gene sequencing based on k-mer representations that benefits from a bootstrapping framework for investigating the sufficiency of shallow sub-samples. Deep learning methods as well as classical approaches were explored for predicting environments and host phenotypes.ResultsA k-mer distribution of shallow sub-samples outperformed Operational Taxonomic Unit (OTU) features in the tasks of body-site identification and Crohn’s disease prediction. Aside from being more accurate, using k-mer features in shallow sub-samples allows (i) skipping computationally costly sequence alignments required in OTU-picking and (ii) provided a proof of concept for the sufficiency of shallow and short-length 16S rRNA sequencing for phenotype prediction. In addition, k-mer features predicted representative 16S rRNA gene sequences of 18 ecological environments, and 5 organismal environments with high macro-F1 scores of 0.88 and 0.87. For large datasets, deep learning outperformed classical methods such as Random Forest and Support Vector Machine.Availability and implementationThe software and datasets are available at https://llp.berkeley.edu/micropheno.Supplementary information Supplementary data are available at Bioinformatics online.

Bacterial adhesion to collagen, the most abundant protein in humans, is a critical step in the initiation and persistence of numerous bacterial infections. In this study, we explore the collagen binding mechanism of the multi-modular cell wall anchored collagen adhesin (CNA) in Staphylococcus aureus and examine how applied mechanical forces can modulate adhesion ability. The common structural-functional elements and domain organization of CNA are present across over 50 genera of bacteria. Through the use of molecular dynamics models and normal mode analysis, we shed light on the CNA’s structural and conformational dynamics and its interactions with collagen that lead to collagen binding. Our results suggest that the linker region, CNA165-173, acts as a hinge exhibiting bending, extensional, and torsional modes of structural flexibility and its residues are key in the interaction of the CNA-collagen complex. Steered molecular dynamics simulations were conducted with umbrella sampling. During the course of these simulations, the ‘locking’ latch from the CNA N2 domain was dissociated from its groove in the CNA N1 domain, implying the importance of the latch for effective ligand binding. Finally, we observed that the binding efficiency of the CNA N1-N2 domains to collagen decreases greatly with increasing tensile force application to the collagen peptides. Thus, CNA and similar adhesins might preferentially bind to sites in which collagen fibers are cleaved, such as in wounded, injured, or inflamed tissues, or in which the collagenous tissue is less mature. As alternative techniques for control of bacterial infection are in-demand due to the rise of bacterial antibiotic resistance, results from our computational studies with respect to the mechanoregulation of the collagen binding site may inspire new therapeutics and engineering solutions by mechanically preventing colonization and/or further pathogenesis.

α-Actinin is an essential actin cross-linker involved in cytoskeletal organization and dynamics. The molecular conformation of α-actinin's actin-binding domain (ABD) regulates its association with actin and thus mutations in this domain can lead to severe pathogenic conditions. A point mutation at lysine 255 in human α-actinin-4 to glutamate increases the binding affinity resulting in stiffer cytoskeletal structures. The role of different ABD conformations and the effect of K255E mutation on ABD conformations remain elusive. To evaluate the impact of K255E mutation on ABD binding to actin we use all-atom molecular dynamics and free energy calculation methods and study the molecular mechanism of actin association in both wild-type α-actinin and in the K225E mutant. Our models illustrate that the strength of actin association is indeed sensitive to the ABD conformation, predict the effect of K255E mutation--based on simulations with the K237E mutant chicken α-actinin--and evaluate the mechanism of α-actinin binding to actin. Furthermore, our simulations showed that the calmodulin domain binding to the linker region was important for regulating the distance between actin and ABD. Our results provide valuable insights into the molecular details of this critical cellular phenomenon and further contribute to an understanding of cytoskeletal dynamics in health and disease.

Chimeric antigen receptors (CARs) repurpose natural signaling components to retarget T cells to refractory cancers but have shown limited efficacy in persistent, recurrent malignancies. Here, we introduce “CAR Pooling,” a multiplexed approach to rapidly identify CAR designs with clinical potential. Forty CARs with signaling domains derived from a range of immune cell lineages were evaluated in pooled assays for their ability to stimulate critical T cell effector functions during repetitive stimulation that mimics long-term tumor antigen exposure. Several domains were identified from the tumor necrosis factor (TNF) receptor family that have been primarily associated with B cells. CD40 enhanced proliferation, whereas B cell–activating factor receptor (BAFF-R) and transmembrane activator and CAML interactor (TACI) promoted cytotoxicity. These functions were enhanced relative to clinical benchmarks after prolonged antigen stimulation, and CAR T cell signaling through these domains fell into distinct states of memory, cytotoxicity, and metabolism. BAFF-R CAR T cells were enriched for a highly cytotoxic transcriptional signature previously associated with positive clinical outcomes. We also observed that replacing the 4-1BB intracellular signaling domain with the BAFF-R signaling domain in a clinically validated B cell maturation antigen (BCMA)–specific CAR resulted in enhanced activity in a xenotransplant model of multiple myeloma. Together, these results show that CAR Pooling is a general approach for rapid exploration of CAR architecture and activity to improve the efficacy of CAR T cell therapies.