Stiffness of the human foot and evolution of the transverse arch

The stiff human foot enables an efficient push-off when walking or running, and was critical for the evolution of bipedalism(1-6). The uniquely arched morphology of the human midfoot is thought to stiffen it(5-9), whereas other primates have flat feet that bend severely in the midfoot(7,10,11). However, the relationship between midfoot geometry and stiffness remains debated in foot biomechanics(12,13), podiatry(14,15) and palaeontology(4-6). These debates centre on the medial longitudinal arch(5,6) and have not considered whether stiffness is affected by the second, transverse tarsal arch of the human foot(16). Here we show that the transverse tarsal arch, acting through the inter-metatarsal tissues, is responsible for more than 40% of the longitudinal stiffness of the foot. The underlying principle resembles a floppy currency note that stiffens considerably when it curls transversally. We derive a dimensionless curvature parameter that governs the stiffness contribution of the transverse tarsal arch, demonstrate its predictive power using mechanical models of the foot and find its skeletal correlate in hominin feet. In the foot, the material properties of the inter-metatarsal tissues and the mobility of the metatarsals may additionally influence the longitudinal stiffness of the foot and thus the curvature-stiffness relationship of the transverse tarsal arch. By analysing fossils, we track the evolution of the curvature parameter among extinct hominins and show that a human-like transverse arch was a key step in the evolution of human bipedalism that predates the genus Homo by at least 1.5 million years. This renewed understanding of the foot may improve the clinical treatment of flatfoot disorders, the design of robotic feet and the study of foot function in locomotion

Novel targets of the CbrAB/Crc carbon catabolite control system revealed by transcript abundance in Pseudomonas aeruginosa.

The opportunistic human pathogen Pseudomonas aeruginosa is able to utilize a wide range of carbon and nitrogen compounds, allowing it to grow in vastly different environments. The uptake and catabolism of growth substrates are organized hierarchically by a mechanism termed catabolite repression control (Crc) whereby the Crc protein establishes translational repression of target mRNAs at CA (catabolite activity) motifs present in target mRNAs near ribosome binding sites. Poor carbon sources lead to activation of the CbrAB two-component system, which induces transcription of the small RNA (sRNA) CrcZ. This sRNA relieves Crc-mediated repression of target mRNAs. In this study, we have identified novel targets of the CbrAB/Crc system in P. aeruginosa using transcriptome analysis in combination with a search for CA motifs. We characterized four target genes involved in the uptake and utilization of less preferred carbon sources: estA (secreted esterase), acsA (acetyl-CoA synthetase), bkdR (regulator of branched-chain amino acid catabolism) and aroP2 (aromatic amino acid uptake protein). Evidence for regulation by CbrAB, CrcZ and Crc was obtained in vivo using appropriate reporter fusions, in which mutation of the CA motif resulted in loss of catabolite repression. CbrB and CrcZ were important for growth of P. aeruginosa in cystic fibrosis (CF) sputum medium, suggesting that the CbrAB/Crc system may act as an important regulator during chronic infection of the CF lung

Protein Structure Modeling with MODELLER.

Genome sequencing projects have resulted in a rapid increase in the number of known protein sequences. In contrast, only about one-hundredth of these sequences have been characterized at atomic resolution using experimental structure determination methods. Computational protein structure modeling techniques have the potential to bridge this sequence-structure gap. In the following chapter, we present an example that illustrates the use of MODELLER to construct a comparative model for a protein with unknown structure. Automation of a similar protocol has resulted in models of useful accuracy for domains in more than half of all known protein sequences